From 36b992cdd66f42393b29acc2645a3405871ca128 Mon Sep 17 00:00:00 2001 From: Aniket Date: Mon, 15 Jan 2024 21:17:21 +0530 Subject: [PATCH 1/3] added model --- .../Dataset/house_rent_mumbai.csv | 1001 +++ .../Images/Screenshot 2024-01-14 203212.png | Bin 0 -> 30285 bytes .../Images/Screenshot 2024-01-14 203348.png | Bin 0 -> 40059 bytes .../Images/Screenshot 2024-01-15 154053.png | Bin 0 -> 115559 bytes .../Images/Screenshot 2024-01-15 154155.png | Bin 0 -> 40854 bytes .../Images/boxplot for area.png | Bin 0 -> 67723 bytes .../Images/boxplot for price.png | Bin 0 -> 76728 bytes .../Images/boxplot for size.png | Bin 0 -> 52700 bytes .../Images/scores of model.png | Bin 0 -> 33362 bytes ...t _Analysis_and_Prediction_of_Mumbai.ipynb | 5529 +++++++++++++++++ .../Model/ReadMe.MD | 55 + .../requirements.txt | 6 + 12 files changed, 6591 insertions(+) create mode 100644 House-rent-analysis-and-prediction-Bombay/Dataset/house_rent_mumbai.csv create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-14 203212.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-14 203348.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-15 154053.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-15 154155.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/boxplot for area.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/boxplot for price.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/boxplot for size.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Images/scores of model.png create mode 100644 House-rent-analysis-and-prediction-Bombay/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb create mode 100644 House-rent-analysis-and-prediction-Bombay/Model/ReadMe.MD create mode 100644 House-rent-analysis-and-prediction-Bombay/requirements.txt diff --git a/House-rent-analysis-and-prediction-Bombay/Dataset/house_rent_mumbai.csv b/House-rent-analysis-and-prediction-Bombay/Dataset/house_rent_mumbai.csv new file mode 100644 index 000000000..8ca665720 --- /dev/null +++ b/House-rent-analysis-and-prediction-Bombay/Dataset/house_rent_mumbai.csv @@ -0,0 +1,1001 @@ +,seller_name,seller_type,size,type_,type_of_house,name,location,city,price,area,area_type,status,deposit,no_bathroom,facing +0,Kasturi Developers,BUILDER,2,BHK,Apartment,Shagun White Woods,Ulwe,Mumbai,"17,000",1180,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +1,Kasturi Developers,BUILDER,3,BHK,Apartment,Surana Tulsi Gaurav,Ulwe,Mumbai,"22,000",1720,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +2,Kasturi Developers,BUILDER,2,BHK,Apartment,Tricity Enclave,Ulwe,Mumbai,"12,500",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +3,seller,VERIFIED OWNER,2,BHK,Apartment,Godrej Prime,Chembur,Mumbai,"55,000",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +4,seller,VERIFIED OWNER,2,BHK,Apartment,Tanvi Eminence Phase 2,Mira Road East,Mumbai,"18,500",1165,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +5,seller,VERIFIED OWNER,1,RK,Studio Apartment,,Ville Parle East,Mumbai,"17,000",200,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +6,seller,VERIFIED OWNER,2,BHK,Apartment,Atul Blue Arch,Kandivali West,Mumbai,"28,500",750,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +7,seller,VERIFIED OWNER,2,BHK,Apartment,Reputed Builder Genevieve,Nilje Gaon,Mumbai,"9,000",634,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +8,seller,VERIFIED OWNER,2,BHK,Apartment,DSS Tivon Park,Ghatkopar West,Mumbai,"35,000",1089,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +9,seller,VERIFIED OWNER,3,BHK,Apartment,Neelkanth Lotus Court,Fort,Mumbai,1.85 L,1450,Area in sq ft,Furnished,No Deposit,3 bathrooms, +10,seller,VERIFIED OWNER,1,BHK,Apartment,Atlanta Enclave,Shil Phata,Mumbai,"18,500",680,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +11,seller,VERIFIED OWNER,1,BHK,Apartment,Sree Krishna Builders Shreeji Dham Neral,Neral,Mumbai,"5,500",490,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +12,seller,VERIFIED OWNER,2,BHK,Apartment,Reputed Builder Suprema Casa Bella,Usarghar Gaon,Mumbai,"12,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +13,seller,VERIFIED OWNER,2,BHK,Apartment,Reputed Builder palm islend 3,Goregaon East,Mumbai,"26,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +14,seller,VERIFIED OWNER,1,BHK,Apartment,K R Godrej Vihaa,Badlapur East,Mumbai,"7,000",654,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +15,seller,VERIFIED OWNER,1,RK,Studio Apartment,,Worli,Mumbai,"20,000",260,Area in sq ft,Furnished,No Deposit,1 bathrooms, +16,seller,VERIFIED OWNER,1,RK,Studio Apartment,,Vikroli East,Mumbai,"14,500",300,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +17,seller,VERIFIED OWNER,3,BHK,Apartment,,Dombivali East,Mumbai,"40,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +18,seller,VERIFIED OWNER,2,BHK,Apartment,Reputed Builder Satya Darshan,Andheri East,Mumbai,"50,000",850,Area in sq ft,Furnished,No Deposit,2 bathrooms, +19,seller,VERIFIED OWNER,2,BHK,Apartment,Agami Emerald,Boisar,Mumbai,"11,000",1500,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +20,seller,VERIFIED OWNER,3,BHK,Apartment,Charisma Navdurga,Chembur,Mumbai,"90,000",1311,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +21,seller,VERIFIED OWNER,2,BHK,Apartment,,Malad East,Mumbai,"32,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +22,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +23,seller,VERIFIED OWNER,3,BHK,Apartment,,Madh,Mumbai,"50,000",1950,Area in sq ft,Unfurnished,No Deposit,4 bathrooms,East facing +24,seller,VERIFIED OWNER,1,BHK,Apartment,Lodha Casa Rio,Dombivali,Mumbai,"9,500",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +25,seller,VERIFIED OWNER,1,BHK,Apartment,Swaraj Homes Little Flower Apartment,Bandra West,Mumbai,"55,000",600,Area in sq ft,Furnished,No Deposit,2 bathrooms, +26,Neha,AGENT,1,BHK,Apartment,,Dombivali East,Mumbai,"11,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +27,EstatesHUB,AGENT,2,BHK,Apartment,,Chembur,Mumbai,"50,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +28,Earth Property Consultant,AGENT,2,BHK,Apartment,,Andheri East,Mumbai,"50,000",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +29,First home,AGENT,2,BHK,Apartment,Shree Prathamesh Vasudev Sky High,Mira Road East,Mumbai,"25,000",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthWest facing +30,Rutu Real estate,AGENT,1,BHK,Apartment,Reputed Builder Rutu Park,Thane West,Mumbai,"22,000",535,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +31,Aditya Properties,AGENT,3,BHK,Apartment,Reputed Builder Tagore Park,Malad West,Mumbai,"23,000",1800,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +32,Blue Diamond Realtors,AGENT,2,BHK,Apartment,,Santosh Nagar,Mumbai,"10,000",900,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +33,RAS PROPERTIES,AGENT,2,BHK,Apartment,RNA Regency Park,Kandivali West,Mumbai,"50,000",1050,Area in sq ft,Furnished,No Deposit,2 bathrooms,North facing +34,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Anant Infra Residency,Kalamboli,Mumbai,"10,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +35,Kuber property,AGENT,2,BHK,Apartment,,Kurla East,Mumbai,"60,000",1150,Area in sq ft,Furnished,No Deposit,2 bathrooms, +36,Cordeiro Real Estate,AGENT,4,BHK,Apartment,Bombay ICC,Wadala,Mumbai,2.9 L,2346,Area in sq ft,Unfurnished,No Deposit,4 bathrooms,East facing +37,sahdev chaudhari,AGENT,1,BHK,Apartment,,Airoli,Mumbai,"20,000",575,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +38,Rahul yadav,AGENT,2,BHK,Apartment,Sai Baba Complex,Goregaon East,Mumbai,"55,000",800,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +39,Kushvin Properties,AGENT,4,BHK,Apartment,Gajra Bhoomi Heights,Kharghar,Mumbai,"64,000",2500,Area in sq ft,Furnished,No Deposit,4 bathrooms, +40,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"78,000",900,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +41,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"42,000",1049,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +42,VibrantKey,AGENT,5,BHK,Apartment,,Malabar Hill,Mumbai,4.9 L,3450,Area in sq ft,Furnished,No Deposit,5 bathrooms,East facing +43,ADITYA PROPERTY,AGENT,3,BHK,Apartment,Swaraj Homes Retreat Apartment,Santacruz West,Mumbai,"35,000",1500,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +44,Aashiyana property consultant,AGENT,1,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"25,101",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +45,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,,Virar West,Mumbai,"6,500",590,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthWest facing +46,Satyam Enterprises,AGENT,2,BHK,Apartment,,Kamothe,Mumbai,"14,500",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +47,Shree Homes Enterprises,AGENT,1,BHK,Apartment,,Adaigaon,Mumbai,"6,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +48,Om Sai Siddhi Properties,AGENT,2,BHK,Apartment,Lodha Casa Bella,Dombivali,Mumbai,"12,500",747,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +49,Bajrangi Realtors,AGENT,1,BHK,Apartment,,Kurla East,Mumbai,"25,000",540,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +50,Khalsa Propera,AGENT,2,BHK,Apartment,Varsha Balaji Heritage,Kharghar,Mumbai,"25,000",1275,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +51,Om sai estate,AGENT,1,BHK,Apartment,Seasons Orchid,Kalyan West,Mumbai,"15,000",715,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +52,Krishna Estate,AGENT,2,BHK,Apartment,Veena Saaz,Kandivali East,Mumbai,"43,000",1200,Area in sq ft,Furnished,No Deposit,2 bathrooms, +53,Shri Sidhanath Estate Consultant,AGENT,2,BHK,Apartment,Gee Cee Aspira 206,Panvel,Mumbai,"15,000",500,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +54,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,BSEL Infrastructure Realty Ltd Kasturi Villa,Kharghar,Mumbai,"25,000",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +55,Noronha Estate Agency,AGENT,2,BHK,Apartment,,Vasai West,Mumbai,"13,000",1035,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +56,Royal Real Estate Agency,AGENT,1,BHK,Apartment,,Dombivali East,Mumbai,"10,000",525,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +57,Swastik Reality,AGENT,5,BHK,Apartment,Lodha World One,Lower Parel,Mumbai,4.5 L,7000,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,SouthEast facing +58,Aadhar enterprises,AGENT,3,BHK,Apartment,,Thakurli,Mumbai,"25,000",1250,Area in sq ft,Unfurnished,No Deposit,3 bathrooms, +59,Trishul property,AGENT,2,BHK,Apartment,,Santacruz East,Mumbai,"55,000",1000,Area in sq ft,Furnished,No Deposit,2 bathrooms, +60,Takshak Properties,AGENT,2,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"13,000",1220,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +61,Shetty?s Realty,AGENT,1,BHK,Apartment,Reputed Builder Anand Nagar Barkha CHS,Bhandup West,Mumbai,"23,000",500,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +62,My Vastu Realtors,AGENT,1,BHK,Apartment,,Ulwe,Mumbai,"10,000",745,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +63,Horizon Real Estate,AGENT,3,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"32,000",1785,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +64,Perfect Housing Dwell,AGENT,1,BHK,Apartment,Neel Sidhi Orbit,Panvel,Mumbai,"15,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +65,Omkar Patil,AGENT,2,BHK,Apartment,Maithili Pride,Thane West,Mumbai,"30,000",750,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +66,Jyoti Enterprise,AGENT,3,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"24,500",1500,Area in sq ft,Furnished,No Deposit,3 bathrooms, +67,KD Real Estate,AGENT,1,BHK,Apartment,,Kalamboli,Mumbai,"8,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +68,Samadhan Real Estate Consultant,AGENT,2,BHK,Apartment,Paradise Sai Pride,Sanpada,Mumbai,"50,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +69,Laabh Properties,AGENT,1,BHK,Apartment,,Bandra West,Mumbai,"65,000",600,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +70,PREMIUM PROPERTIES,AGENT,1,BHK,Apartment,K Raheja Eastate,Borivali East,Mumbai,"30,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +71,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,,Andheri West,Mumbai,"65,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +72,Individual Agent,AGENT,3,BHK,Apartment,Platinum The Springs,Kalamboli,Mumbai,"30,000",1985,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +73,Urban Investment Property Solutions,AGENT,3,BHK,Apartment,Reputed Builder Nibbana Apartments,Bandra West,Mumbai,3 L,1600,Area in sq ft,Furnished,No Deposit,3 bathrooms,West facing +74,India Direct Homecom,AGENT,2,BHK,Apartment,Labh Aspire,Karanjade,Mumbai,"15,000",1550,Area in sq ft,Furnished,No Deposit,2 bathrooms, +75,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"10,995",625,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +76,City Home,AGENT,1,BHK,Apartment,,kasaradavali thane west,Mumbai,"13,000",650,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +77,SUNRISE REAL ESTATE,AGENT,3,BHK,Apartment,Reputed Builder Mount Alps A,Wadala,Mumbai,"72,000",1315,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +78,Sanjay,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"28,000",1200,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +79,Green Group Real Estate Consultants,AGENT,2,BHK,Apartment,Marathon Marathon Nexzone,Panvel,Mumbai,"14,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +80,Bricks Property Consultant,AGENT,1,BHK,Apartment,,Dombivali East,Mumbai,"8,000",729,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +81,Hari om realtors,AGENT,2,BHK,Apartment,Shailesh Riddhi Siddhi Residency,Ulwe,Mumbai,"16,000",1125,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthWest facing +82,Stilt Real Estate,AGENT,2,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,"98,000",980,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +83,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"45,000",1020,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +84,Right House Properties,AGENT,1,BHK,Apartment,,Chembur,Mumbai,"25,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthWest facing +85,Verma Real Estate,AGENT,2,BHK,Apartment,Reputed Builder Model Town,Mulund West,Mumbai,"18,000",1800,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +86,Liban Empire,AGENT,2,BHK,Apartment,Mangla Mayurs Nature Glory,Thane West,Mumbai,"28,000",1000,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +87,Rajesh Rasale,AGENT,2,BHK,Apartment,,Thane West,Mumbai,"30,000",640,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +88,Investor Floor,AGENT,1,BHK,Apartment,Space India Alliance,Panvel,Mumbai,"15,000",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +89,A A REAL ESTATE,AGENT,3,BHK,Apartment,Gundecha Gundecha Heights,Kanjurmarg,Mumbai,"85,000",1500,Area in sq ft,Furnished,No Deposit,3 bathrooms, +90,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Squarefeet Grand Square,Thane West,Mumbai,"13,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +91,Star Realtors,AGENT,2,BHK,Apartment,Lodha Elisium,Wadala,Mumbai,"89,000",955,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +92,Rightside Properties,AGENT,1,BHK,Apartment,Puneet Sanjivani Tower,Vikhroli,Mumbai,"27,000",410,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +93,Housing Guru,AGENT,3,BHK,Apartment,Nahar Amrit Shakti,Powai,Mumbai,"73,000",1250,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +94,Om Properties,AGENT,2,BHK,Apartment,,Manjarli,Mumbai,"25,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +95,H K homes,AGENT,1,BHK,Apartment,,Taloje,Mumbai,"6,000",460,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +96,Homehunt Realty,AGENT,1,BHK,Apartment,,Dronagiri,Mumbai,"5,500",625,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +97,Galaxy homes,AGENT,1,BHK,Apartment,Gami Viona,Kharghar,Mumbai,"20,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +98,Vijay Estate Agency,AGENT,4,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"38,000",2000,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,East facing +99,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"13,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +100,seller,VERIFIED OWNER,1,BHK,Apartment,Tanvi Eminence Phase 2,Mira Road East,Mumbai,"14,000",782,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +101,seller,VERIFIED OWNER,2,BHK,Apartment,ANA Avant Garde Phase 1,Mira Road East,Mumbai,"35,000",1056,Area in sq ft,Furnished,No Deposit,2 bathrooms, +102,seller,VERIFIED OWNER,2,BHK,Apartment,ANA Avant Garde Phase 1,Mira Road East,Mumbai,"21,000",865,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +103,seller,VERIFIED OWNER,2,BHK,Apartment,DB Ozone,Dahisar,Mumbai,"15,500",855,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +104,seller,VERIFIED OWNER,2,BHK,Apartment,Lodha Aqua,Mira Road East,Mumbai,"28,500",846,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +105,seller,VERIFIED OWNER,1,BHK,Apartment,Lodha Mira Road Project 1,Mira Road East,Mumbai,"25,000",658,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +106,seller,VERIFIED OWNER,3,BHK,Apartment,Lodha Aqua,Mira Road East,Mumbai,"36,000",1250,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +107,seller,VERIFIED OWNER,2,BHK,Apartment,Man Opus,Mira Road East,Mumbai,"15,000",670,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +108,seller,VERIFIED OWNER,2,BHK,Independent House,,Vikroli East,Mumbai,"32,000",550,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +109,seller,VERIFIED OWNER,3,BHK,Apartment,Godrej Prime,Chembur,Mumbai,"50,000",1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +110,seller,VERIFIED OWNER,2,BHK,Apartment,,Jogeshwari West,Mumbai,"62,000",1200,Area in sq ft,Furnished,No Deposit,2 bathrooms, +111,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +112,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +113,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +114,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +115,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +116,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +117,seller,VERIFIED OWNER,1,RK,Studio Apartment,Sagar Platinum Sagar Jewels,Badlapur East,Mumbai,"3,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +118,seller,VERIFIED OWNER,1,BHK,Apartment,Swaraj Homes Little Flower Apartment,Bandra West,Mumbai,"55,000",600,Area in sq ft,Furnished,No Deposit,2 bathrooms, +119,seller,VERIFIED OWNER,1,BHK,Apartment,Swaraj Homes Little Flower Apartment,Bandra West,Mumbai,"55,000",600,Area in sq ft,Furnished,No Deposit,2 bathrooms, +120,Santosh Magar,AGENT,1,BHK,Apartment,,Koper Khairane,Mumbai,"20,000",560,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthWest facing +121,First home,AGENT,4,BHK,Apartment,Reputed Builder Sagar Drishti,Mira Road East,Mumbai,"48,000",1800,Area in sq ft,Furnished,No Deposit,4 bathrooms, +122,SMP Group Real Estate,AGENT,1,BHK,Apartment,,Vashi,Mumbai,"19,000",300,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +123,Mumbai property solutions,AGENT,2,BHK,Apartment,Nahar Cayenne,Powai,Mumbai,"62,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +124,Mumbai property solutions,AGENT,2,BHK,Apartment,Nahar Cayenne,Powai,Mumbai,"62,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +125,First home,AGENT,2,BHK,Apartment,Reputed Builder Ashmita Garden,Mira Road East,Mumbai,"33,000",900,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthWest facing +126,First home,AGENT,3,BHK,Apartment,Unique Poonam Estate Cluster 2,Mira Road East,Mumbai,"33,000",1395,Area in sq ft,Unfurnished,No Deposit,3 bathrooms, +127,First home,AGENT,2,BHK,Apartment,RNA NG RNA N G Silver Spring,Mira Road East,Mumbai,"23,000",995,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +128,First home,AGENT,1,BHK,Apartment,Ravi Gaurav City,Mira Road East,Mumbai,"12,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +129,sahdev chaudhari,AGENT,1,RK,Studio Apartment,,Airoli,Mumbai,"16,000",500,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +130,Rutu Real estate,AGENT,1,BHK,Apartment,Reputed Builder Rutu Park,Thane West,Mumbai,"21,000",535,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +131,sahdev chaudhari,AGENT,1,RK,Studio Apartment,,Airoli,Mumbai,"12,000",350,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +132,Neha,AGENT,1,BHK,Apartment,,Santosh Nagar,Mumbai,"7,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +133,Dream solution Properties,AGENT,2,BHK,Apartment,,kasaradavali thane west,Mumbai,"23,000",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +134,Rutu Real estate,AGENT,1,BHK,Apartment,Reputed Builder Rutu Park,Thane West,Mumbai,"20,000",535,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +135,First home,AGENT,2,BHK,Apartment,Abhay Sheetal Complex Wing D E,Mira Road East,Mumbai,"22,000",920,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +136,Blue Diamond Realtors,AGENT,1,BHK,Apartment,,Santosh Nagar,Mumbai,"6,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +137,Blue Diamond Realtors,AGENT,1,BHK,Apartment,,Kalyan East,Mumbai,"7,500",560,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +138,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Neelsidhi Amarante,Kalamboli,Mumbai,"17,000",1089,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +139,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Ronak Residency,Kalamboli,Mumbai,"12,000",670,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +140,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Vision Phoenix Heights,Kalamboli,Mumbai,"17,500",1125,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +141,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Platinum Avior,Kalamboli,Mumbai,"20,000",1125,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +142,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Platinum Aura,Kalamboli,Mumbai,"16,500",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +143,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Akshar Valencia,Kalamboli,Mumbai,"11,500",710,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +144,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Sai Avaneesh,Kalamboli,Mumbai,"20,000",1139,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +145,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Mahaavir Heights,Kalamboli,Mumbai,"18,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +146,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Akshar Valencia,Kalamboli,Mumbai,"17,000",1120,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +147,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Giriraj Giriraj Tower,Kalamboli,Mumbai,"18,000",1175,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +148,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,AP Bianca,Kalamboli,Mumbai,"16,500",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +149,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,New Millenium Paradise,Kalamboli,Mumbai,"15,000",1010,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +150,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,RK Vaishnavi Heights,Kalamboli,Mumbai,"12,000",680,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +151,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Shreeji Phoenix Nest,Kalamboli,Mumbai,"12,000",690,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +152,Cordeiro Real Estate,AGENT,4,BHK,Apartment,,Colaba,Mumbai,1.9 L,1500,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +153,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Reputed Builder The Springs,Kalamboli,Mumbai,"22,000",1240,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +154,Hitech Realty Consultancy,AGENT,3,BHK,Apartment,Akshar Valencia,Kalamboli,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +155,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Paradise Sai Spring,Kharghar,Mumbai,"27,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +156,Cordeiro Real Estate,AGENT,1,BHK,Apartment,Reputed Builder Daulat Shirin,Cuffe Parade,Mumbai,"70,000",605,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +157,Cordeiro Real Estate,AGENT,1,BHK,Apartment,Reputed Builder Usha Sadan Apartment,Colaba,Mumbai,"60,000",600,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +158,sahdev chaudhari,AGENT,1,BHK,Apartment,,Airoli,Mumbai,"15,500",500,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +159,Cordeiro Real Estate,AGENT,4,BHK,Apartment,Bombay ICC,Wadala,Mumbai,2.8 L,2390,Area in sq ft,Furnished,No Deposit,4 bathrooms,East facing +160,Cordeiro Real Estate,AGENT,4,BHK,Apartment,Bombay ICC,Wadala,Mumbai,2.85 L,2346,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +161,Khalsa Propera,AGENT,2,BHK,Apartment,GHP Aston,Kharghar,Mumbai,"27,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +162,Cordeiro Real Estate,AGENT,2,BHK,Apartment,,Colaba,Mumbai,1.25 L,1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +163,Cordeiro Real Estate,AGENT,1,BHK,Apartment,,Colaba,Mumbai,"66,000",675,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +164,Cordeiro Real Estate,AGENT,1,BHK,Apartment,,Colaba,Mumbai,"55,000",500,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +165,Cordeiro Real Estate,AGENT,1,BHK,Apartment,Reputed Builder Sneh Sadan,Colaba,Mumbai,"60,000",650,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +166,Cordeiro Real Estate,AGENT,1,BHK,Apartment,Reputed Builder Usha Sadan Apartment,Colaba,Mumbai,"60,000",600,Area in sq ft,Furnished,No Deposit,1 bathrooms, +167,Cordeiro Real Estate,AGENT,3,BHK,Apartment,Peninsula Celestia Spaces,Sewri,Mumbai,1.45 L,2303,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +168,Rahul yadav,AGENT,2,BHK,Apartment,Omkar Ananta,Goregaon East,Mumbai,"58,000",1200,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +169,Rahul yadav,AGENT,2,BHK,Apartment,ACME Complex,Goregaon West,Mumbai,"60,000",1200,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +170,Rahul yadav,AGENT,1,BHK,Apartment,Reputed Builder Palm Island 7,Goregaon East,Mumbai,"23,000",545,Area in sq ft,Furnished,No Deposit,1 bathrooms,North facing +171,Rahul yadav,AGENT,2,RK,Studio Apartment,Royal Palms Piccadilly Condos,Goregaon East,Mumbai,"22,000",430,Area in sq ft,Furnished,No Deposit,2 bathrooms,North facing +172,Rahul yadav,AGENT,1,RK,Studio Apartment,Royal Palms Piccadilly Condos,Goregaon East,Mumbai,"16,000",330,Area in sq ft,Furnished,No Deposit,1 bathrooms,North facing +173,Rahul yadav,AGENT,1,RK,Studio Apartment,Royal Palms Piccadilly Condos,Goregaon East,Mumbai,"15,500",330,Area in sq ft,Furnished,No Deposit,1 bathrooms,North facing +174,Rahul yadav,AGENT,2,BHK,Apartment,Reputed Builder Emerald Isle 2,Goregaon East,Mumbai,"43,000",800,Area in sq ft,Furnished,No Deposit,2 bathrooms,North facing +175,Rahul yadav,AGENT,1,RK,Studio Apartment,Royal Palms Piccadilly Condos,Goregaon East,Mumbai,"16,000",330,Area in sq ft,Furnished,No Deposit,1 bathrooms,North facing +176,Rahul yadav,AGENT,1,BHK,Apartment,Royal Palms Golden Isle,Goregaon East,Mumbai,"24,000",545,Area in sq ft,Furnished,No Deposit,1 bathrooms,North facing +177,Rahul yadav,AGENT,1,BHK,Apartment,Royal Palms Ruby Isle,Goregaon East,Mumbai,"16,000",545,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +178,Rahul yadav,AGENT,1,BHK,Apartment,Royal Palms Ruby Isle,Goregaon East,Mumbai,"23,000",545,Area in sq ft,Furnished,No Deposit,1 bathrooms,North facing +179,Cordeiro Real Estate,AGENT,2,BHK,Apartment,Reputed Builder Sunita,Colaba,Mumbai,1.35 L,1150,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +180,Kushvin Properties,AGENT,3,BHK,Apartment,Tharwani Heritage,Kharghar,Mumbai,"35,500",1550,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +181,sahdev chaudhari,AGENT,1,BHK,Apartment,,Airoli,Mumbai,"20,000",575,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +182,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"90,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +183,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"55,000",605,Area in sq ft,Furnished,No Deposit,1 bathrooms,NorthEast facing +184,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Akshar Valencia,Kalamboli,Mumbai,"12,000",710,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +185,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Neelsidhi Amarante,Kalamboli,Mumbai,"20,500",1240,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +186,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"60,000",640,Area in sq ft,Furnished,No Deposit,1 bathrooms, +187,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Kanakia Zenworld Phase I,Kanjurmarg,Mumbai,"35,000",560,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +188,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Kanakia Zenworld Phase I,Kanjurmarg,Mumbai,"38,000",560,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +189,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej Platinum,Vikhroli,Mumbai,"75,000",1000,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +190,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,New Millenium Paradise,Kalamboli,Mumbai,"15,000",1010,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +191,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Gajra Bhoomi Gardenia II,Kalamboli,Mumbai,"20,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +192,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Siddhivinayak The Orien,Kalamboli,Mumbai,"25,500",1150,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +193,sahdev chaudhari,AGENT,2,BHK,Apartment,,Airoli,Mumbai,"25,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +194,sahdev chaudhari,AGENT,1,BHK,Apartment,,Airoli,Mumbai,"25,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +195,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Kanakia Zenworld Phase I,Kanjurmarg,Mumbai,"35,000",698,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +196,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"54,900",640,Area in sq ft,Furnished,No Deposit,1 bathrooms,West facing +197,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"72,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +198,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"76,100",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +199,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"18,020",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +200,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"17,000",417,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +201,Azuroin,AGENT,1,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"23,011",553,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +202,Azuroin,AGENT,1,BHK,Apartment,Lodha Amara Tower 24 And 25,Thane West,Mumbai,"24,000",553,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +203,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"32,000",740,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +204,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"46,000",1138,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +205,Azuroin,AGENT,3,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"70,011",1320,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +206,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"18,011",417,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +207,Kuber property,AGENT,2,BHK,Apartment,,Kurla East,Mumbai,"60,000",1050,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +208,sahdev chaudhari,AGENT,1,RK,Studio Apartment,,Airoli,Mumbai,"15,000",350,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +209,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Akshar Valencia,Kalamboli,Mumbai,"17,000",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +210,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Pratik Harmony,Kalamboli,Mumbai,"12,000",710,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +211,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Arihant Sharan,Kalamboli,Mumbai,"25,500",1020,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +212,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Sai Udanda,Kalamboli,Mumbai,"12,000",710,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +213,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Arihant Sharan,Kalamboli,Mumbai,"16,000",1080,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +214,Hitech Realty Consultancy,AGENT,2,BHK,Apartment,Sai Udanda,Kalamboli,Mumbai,"16,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +215,Hitech Realty Consultancy,AGENT,1,BHK,Apartment,Pratik Harmony,Kalamboli,Mumbai,"14,500",695,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +216,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"45,000",598,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +217,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"26,000",576,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +218,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"16,500",418,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +219,Cordeiro Real Estate,AGENT,1,BHK,Apartment,,Colaba,Mumbai,"80,000",650,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +220,Cordeiro Real Estate,AGENT,1,BHK,Apartment,Reputed Builder Sangam Bhavan,Colaba,Mumbai,"60,000",600,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +221,Cordeiro Real Estate,AGENT,1,BHK,Apartment,Reputed Builder Ashoka Apartment,Napeansea Road,Mumbai,"65,000",602,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +222,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"36,100",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +223,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"36,001",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +224,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"37,111",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +225,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"34,501",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +226,Aashiyana property consultant,AGENT,1,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"27,111",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,South facing +227,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"27,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthEast facing +228,Aashiyana property consultant,AGENT,1,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"27,200",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +229,Aashiyana property consultant,AGENT,1,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"26,500",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +230,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"26,101",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthEast facing +231,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"35,000",1008,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +232,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"37,000",1200,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +233,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"35,000",980,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +234,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"36,000",753,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +235,Aashiyana property consultant,AGENT,1,BHK,Apartment,Reputed Builder Spring Grove Uno Society,Kandivali East,Mumbai,"20,000",400,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +236,Aashiyana property consultant,AGENT,2,BHK,Apartment,Lokhandwala Living Essence,Kandivali East,Mumbai,"30,000",865,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +237,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,,Virar West,Mumbai,"10,000",1250,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +238,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,,Virar West,Mumbai,"9,500",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +239,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"67,000",1085,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthWest facing +240,Cordeiro Real Estate,AGENT,3,BHK,Apartment,Kalpataru Habitat,Parel,Mumbai,1.8 L,1781,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +241,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"73,000",1185,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +242,KNR Woods,AGENT,1,RK,Studio Apartment,Mohan Nano Estates I,Ambernath West,Mumbai,"6,000",430,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +243,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Kanakia Zenworld Phase II,Kanjurmarg,Mumbai,"34,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +244,Cordeiro Real Estate,AGENT,3,BHK,Apartment,Peninsula Celestia Spaces,Sewri,Mumbai,1.5 L,2303,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +245,Shree Homes Enterprises,AGENT,1,BHK,Apartment,,Adaigaon,Mumbai,"6,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +246,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,,Virar West,Mumbai,"6,500",600,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +247,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"92,000",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +248,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,,Virar West,Mumbai,"10,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +249,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,Dhartidhan Dharti 3,Virar,Mumbai,"8,300",615,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +250,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,Sunteck One World,Naigaon East,Mumbai,"14,000",920,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +251,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,Agarwal Paramount,Virar,Mumbai,"16,000",1150,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +252,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,Rustomjee Virar Avenue L1 L2 And L4 Wing I And J,Virar,Mumbai,"14,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +253,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,,Virar West,Mumbai,"10,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +254,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,M Baria Yashwant Vihar,Virar,Mumbai,"6,500",610,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthWest facing +255,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,Virar Virar Bolinj Yashwant Krupa CHSL,Virar,Mumbai,"8,200",680,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +256,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,Ekta Parksville Phase II,Virar,Mumbai,"8,000",670,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +257,Mhaskar real estate consultancy,AGENT,1,BHK,Apartment,,Virar West,Mumbai,"6,900",600,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +258,Mhaskar real estate consultancy,AGENT,2,BHK,Apartment,,Virar West,Mumbai,"11,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +259,sahdev chaudhari,AGENT,1,BHK,Apartment,,Airoli,Mumbai,"21,000",575,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +260,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"36,000",982,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +261,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"65,000",1085,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +262,Satyam Enterprises,AGENT,2,BHK,Apartment,Shree Ganesh Amrut Garden,Panvel,Mumbai,"19,500",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +263,Satyam Enterprises,AGENT,1,BHK,Apartment,,Kamothe,Mumbai,"17,000",680,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +264,Satyam Enterprises,AGENT,1,BHK,Apartment,Reputed Builder Suraj Complex,Kamothe,Mumbai,"12,000",680,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +265,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej Platinum,Vikhroli,Mumbai,"78,000",980,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +266,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"77,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +267,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"58,000",620,Area in sq ft,Furnished,No Deposit,1 bathrooms,West facing +268,Om sai estate,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"25,000",1000,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +269,Satyam Enterprises,AGENT,2,BHK,Apartment,Reputed Builder Mayur Park Building,Kamothe,Mumbai,"14,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +270,Satyam Enterprises,AGENT,2,BHK,Apartment,Reputed Builder Bhoomi Harmony,Kamothe,Mumbai,"22,000",1295,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +271,Satyam Enterprises,AGENT,2,BHK,Apartment,5P Bhagwati Heritage,Sector 21 Kamothe,Mumbai,"22,000",1235,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +272,Satyam Enterprises,AGENT,2,BHK,Apartment,Pooja White Flag,Kamothe,Mumbai,"21,000",1235,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +273,Satyam Enterprises,AGENT,1,BHK,Apartment,Marvels Nandan,Kamothe,Mumbai,"14,500",720,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +274,Satyam Enterprises,AGENT,2,BHK,Apartment,Shanti Hari Heritage,Kamothe,Mumbai,"22,000",1150,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +275,Satyam Enterprises,AGENT,2,BHK,Apartment,,Kamothe,Mumbai,"12,500",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +276,Takshak Properties,AGENT,1,BHK,Apartment,Nilkanth Bhaveshwar Hill View,Karanjade,Mumbai,"8,500",690,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +277,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"47,000",640,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +278,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"35,000",598,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +279,MANASVI PROPERTIES,AGENT,1,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,"46,000",640,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +280,Azuroin,AGENT,2,BHK,Apartment,,Thane West,Mumbai,"30,000",752,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +281,Satyam Enterprises,AGENT,1,BHK,Apartment,Om Shivam Residency,Kamothe,Mumbai,"13,500",750,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +282,EstatesHUB,AGENT,2,BHK,Apartment,Wadhwa Dukes Horizon,Chembur,Mumbai,"75,000",1000,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +283,Takshak Properties,AGENT,2,BHK,Apartment,Marathon Marathon Nexzone,Panvel,Mumbai,"14,000",1056,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +284,Takshak Properties,AGENT,1,BHK,Apartment,,Karanjade,Mumbai,"9,000",720,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +285,Takshak Properties,AGENT,2,BHK,Apartment,Dubey Gayatri Paradise,Panvel,Mumbai,"11,500",1090,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +286,Satyam Enterprises,AGENT,1,BHK,Apartment,,Kamothe,Mumbai,"15,500",700,Area in sq ft,Furnished,No Deposit,1 bathrooms, +287,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"49,002",1104,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +288,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"50,000",1104,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +289,Azuroin,AGENT,1,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"24,000",553,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthWest facing +290,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"30,000",740,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +291,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"24,000",575,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +292,Satyam Enterprises,AGENT,2,BHK,Apartment,Dharti Sai Archana,Kamothe,Mumbai,"17,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +293,Satyam Enterprises,AGENT,2,BHK,Apartment,Radiant Ravi Rachana,Kamothe,Mumbai,"18,500",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +294,Satyam Enterprises,AGENT,3,BHK,Apartment,Reputed Builder Sai Prasad Residency,Kamothe,Mumbai,"20,000",1500,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +295,Sarvam Properties,AGENT,3,BHK,Apartment,Reputed Builder Chetan Building,Ghatkopar East,Mumbai,1.3 L,1552,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +296,Sarvam Properties,AGENT,3,BHK,Apartment,,Ghatkopar East,Mumbai,1.1 L,1322,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +297,Vijay Estate Agency,AGENT,3,BHK,Apartment,,Mulund West,Mumbai,"52,000",1065,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +298,Kushvin Properties,AGENT,1,BHK,Apartment,Godrej City Woods Panvel Ph 1,Panvel,Mumbai,"11,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +299,Vijay Estate Agency,AGENT,4,BHK,Apartment,,Wadala,Mumbai,1.65 L,1900,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +300,Vijay Estate Agency,AGENT,4,BHK,Apartment,,Wadala,Mumbai,1.58 L,1900,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +301,Vijay Estate Agency,AGENT,4,BHK,Apartment,,Wadala,Mumbai,1.6 L,1900,Area in sq ft,Furnished,No Deposit,4 bathrooms,East facing +302,Swastik Reality,AGENT,2,BHK,Apartment,Reputed Builder Prefeb Shree Dutta Tower,Parel,Mumbai,"80,000",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +303,Swastik Reality,AGENT,3,BHK,Apartment,Reputed Builder Ashok Towers,Andheri East,Mumbai,1.5 L,2200,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +304,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Lily at Runwal Forest,Kanjurmarg,Mumbai,"46,000",771,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +305,Vijay Estate Agency,AGENT,4,BHK,Apartment,,Dadar West,Mumbai,3 L,3400,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,East facing +306,Vijay Estate Agency,AGENT,4,BHK,Apartment,,Dadar West,Mumbai,3.05 L,3400,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,East facing +307,Vijay Estate Agency,AGENT,4,BHK,Apartment,,Dadar West,Mumbai,3 L,3400,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms, +308,Swastik Reality,AGENT,3,BHK,Apartment,,Worli,Mumbai,4 L,2500,Area in sq ft,Furnished,No Deposit,3 bathrooms, +309,Swastik Reality,AGENT,4,BHK,Apartment,Lodha World Crest,Lower Parel,Mumbai,4.5 L,3000,Area in sq ft,Furnished,No Deposit,5 bathrooms,East facing +310,Swastik Reality,AGENT,4,BHK,Apartment,Omkar 1973,Worli,Mumbai,3 L,2850,Area in sq ft,Furnished,No Deposit,5 bathrooms,East facing +311,Swastik Reality,AGENT,1,BHK,Apartment,,Parel,Mumbai,"50,000",365,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +312,Swastik Reality,AGENT,3,BHK,Apartment,L And T Crescent Bay,Parel,Mumbai,1.25 L,1750,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +313,Swastik Reality,AGENT,4,BHK,Apartment,L And T Crescent Bay,Parel,Mumbai,2.6 L,3400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +314,Swastik Reality,AGENT,2,BHK,Apartment,L And T Crescent Bay,Parel,Mumbai,1.1 L,1200,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +315,Royal Real Estate Agency,AGENT,2,BHK,Apartment,,Dombivali East,Mumbai,"14,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +316,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"35,000",750,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +317,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"38,000",835,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +318,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"32,000",835,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +319,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"66,000",1085,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +320,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"68,000",1085,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthWest facing +321,Takshak Properties,AGENT,1,BHK,Apartment,Joshi Sai Anuraj,Karanjade,Mumbai,"8,500",660,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +322,PropertyPistol Realty Pvt Ltd,AGENT,1,BHK,Apartment,,Seawoods,Mumbai,"26,000",1000,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +323,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,Pyramid Aastha Alavio,Seawoods,Mumbai,"45,000",1500,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +324,Swastik Reality,AGENT,2,BHK,Apartment,Mittal Phoenix Towers,Lower Parel,Mumbai,1.25 L,1200,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,SouthEast facing +325,PropertyPistol Realty Pvt Ltd,AGENT,1,BHK,Apartment,Reputed Builder Balaji Bhavan,Seawoods,Mumbai,"16,000",580,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +326,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Seawoods,Mumbai,"58,000",1100,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +327,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Kalpataru Sunrise,Thane West,Mumbai,"35,000",1120,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +328,PropertyPistol Realty Pvt Ltd,AGENT,1,RK,Studio Apartment,,Seawoods,Mumbai,"8,500",480,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +329,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,HCBS Dheeraj Gaurav Heights 1,Andheri West,Mumbai,"95,000",1080,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +330,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"40,000",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +331,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"38,000",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +332,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"39,000",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +333,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"38,101",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +334,Trishul property,AGENT,5,BHK,Apartment,Dheeraj Realty Dheeraj Insignia,Santacruz East,Mumbai,1.3 L,1500,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms, +335,Trishul property,AGENT,2,BHK,Apartment,Reputed Builder 30 Union Park,Bandra West,Mumbai,2.2 L,1400,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +336,Trishul property,AGENT,1,BHK,Apartment,,Santacruz East,Mumbai,"30,000",550,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +337,Trishul property,AGENT,1,BHK,Apartment,,Santacruz East,Mumbai,"39,000",650,Area in sq ft,Furnished,No Deposit,2 bathrooms, +338,Trishul property,AGENT,1,RK,Studio Apartment,,Santacruz East,Mumbai,"19,000",550,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +339,Trishul property,AGENT,3,BHK,Apartment,Reputed Builder Fair Field,Santacruz West,Mumbai,1.3 L,1350,Area in sq ft,Furnished,No Deposit,3 bathrooms, +340,Trishul property,AGENT,1,BHK,Apartment,,Santacruz East,Mumbai,"30,000",500,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +341,Trishul property,AGENT,3,BHK,Apartment,,Santacruz East,Mumbai,"65,000",1100,Area in sq ft,Unfurnished,No Deposit,3 bathrooms, +342,Trishul property,AGENT,2,BHK,Apartment,,Santacruz East,Mumbai,"32,000",900,Area in sq ft,Unfurnished,No Deposit,3 bathrooms, +343,Trishul property,AGENT,2,BHK,Apartment,,Santacruz East,Mumbai,"32,000",950,Area in sq ft,Unfurnished,No Deposit,3 bathrooms, +344,Trishul property,AGENT,1,RK,Studio Apartment,,Santacruz East,Mumbai,"15,000",350,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +345,Trishul property,AGENT,1,BHK,Apartment,,Santacruz East,Mumbai,"30,000",500,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +346,Trishul property,AGENT,1,RK,Studio Apartment,,Santacruz East,Mumbai,"17,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +347,Trishul property,AGENT,2,BHK,Apartment,,Worli,Mumbai,2.5 L,1000,Area in sq ft,Furnished,No Deposit,3 bathrooms,West facing +348,Trishul property,AGENT,2,BHK,Apartment,,Santacruz West,Mumbai,"85,000",1050,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +349,Trishul property,AGENT,2,BHK,Apartment,,Santacruz East,Mumbai,"45,000",950,Area in sq ft,Furnished,No Deposit,2 bathrooms, +350,Trishul property,AGENT,2,BHK,Apartment,,Bandra West,Mumbai,1 L,900,Area in sq ft,Furnished,No Deposit,2 bathrooms, +351,Trishul property,AGENT,2,BHK,Apartment,,Santacruz East,Mumbai,"50,000",900,Area in sq ft,Furnished,No Deposit,2 bathrooms, +352,Laabh Properties,AGENT,1,BHK,Apartment,Reputed Builder Amiya,Khar West,Mumbai,"75,000",650,Area in sq ft,Furnished,No Deposit,2 bathrooms,SouthWest facing +353,Laabh Properties,AGENT,2,BHK,Apartment,,Bandra West,Mumbai,1.5 L,800,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +354,Laabh Properties,AGENT,1,BHK,Apartment,Silver Springs Apartment,Bandra West,Mumbai,"70,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthWest facing +355,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Juhi Greens,Seawoods,Mumbai,"58,000",975,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +356,PropertyPistol Realty Pvt Ltd,AGENT,1,BHK,Apartment,Reputed Builder Amar CHS,Seawoods,Mumbai,"26,000",790,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +357,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Juhi Greens,Seawoods,Mumbai,"57,000",980,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +358,Vijay Estate Agency,AGENT,2,BHK,Apartment,,Mahalaxmi,Mumbai,"80,000",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +359,Shree Homes Enterprises,AGENT,3,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"23,000",1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +360,VibrantKey,AGENT,3,BHK,Apartment,Darshan Pride,Tardeo,Mumbai,1.9 L,1750,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,South facing +361,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"13,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +362,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"13,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +363,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"13,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +364,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"13,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +365,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"13,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +366,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"13,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +367,Takshak Properties,AGENT,4,BHK,Apartment,Indiabulls Park,Panvel,Mumbai,"25,000",2150,Area in sq ft,Unfurnished,No Deposit,4 bathrooms,East facing +368,Satyam Enterprises,AGENT,1,BHK,Apartment,,Kamothe,Mumbai,"14,000",700,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +369,My Vastu Realtors,AGENT,2,BHK,Apartment,,Ulwe,Mumbai,"15,000",1175,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +370,My Vastu Realtors,AGENT,3,BHK,Apartment,,Ulwe,Mumbai,"23,000",1500,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +371,Takshak Properties,AGENT,2,BHK,Apartment,Krishna Amrut View,Karanjade,Mumbai,"13,000",1130,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +372,SMP Group Real Estate,AGENT,1,BHK,Apartment,,Vashi,Mumbai,"17,000",400,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +373,Swastik Reality,AGENT,4,BHK,Apartment,Lodha World Crest,Lower Parel,Mumbai,4.6 L,3000,Area in sq ft,Furnished,No Deposit,4 bathrooms,East facing +374,Takshak Properties,AGENT,1,BHK,Apartment,Shree Ambe Vinayak Ashray,Karanjade,Mumbai,"8,500",675,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +375,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Andheri West,Mumbai,"70,000",920,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +376,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Rustomjee Elita,Andheri West,Mumbai,1.4 L,1200,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +377,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,Samarth Aangan,Andheri West,Mumbai,1.35 L,1250,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +378,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Hiranandani Estates,Mumbai,"65,000",1120,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +379,Azuroin,AGENT,1,BHK,Apartment,Lodha Majiwada Tower 1,Thane West,Mumbai,"25,000",545,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +380,Azuroin,AGENT,2,BHK,Apartment,Lodha Luxuria,Thane West,Mumbai,"35,000",778,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +381,Jyoti Enterprise,AGENT,3,BHK,Apartment,Lodha Park,Lower Parel,Mumbai,1.15 L,1190,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +382,Jyoti Enterprise,AGENT,2,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"16,500",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +383,Jyoti Enterprise,AGENT,2,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"25,500",1100,Area in sq ft,Furnished,No Deposit,2 bathrooms, +384,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"16,000",1065,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +385,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"18,000",1055,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +386,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"15,500",1055,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +387,Jyoti Enterprise,AGENT,1,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"11,000",600,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +388,Jyoti Enterprise,AGENT,1,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"12,500",700,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +389,Neha,AGENT,1,BHK,Apartment,,Dombivali East,Mumbai,"12,500",700,Area in sq ft,Furnished,No Deposit,2 bathrooms, +390,Neha,AGENT,1,BHK,Apartment,,Dombivali East,Mumbai,"8,000",700,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +391,Neha,AGENT,2,BHK,Apartment,,Dombivali East,Mumbai,"11,000",560,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +392,Neha,AGENT,1,BHK,Apartment,,Kalyan East,Mumbai,"6,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +393,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,Varun Garden,Thane West,Mumbai,"45,000",1400,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +394,Swastik Reality,AGENT,4,BHK,Apartment,Reputed Builder Rajkamal Heights,Parel,Mumbai,2 L,2400,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,East facing +395,Swastik Reality,AGENT,3,BHK,Apartment,,Worli,Mumbai,3.5 L,1950,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +396,Swastik Reality,AGENT,4,BHK,Apartment,Indiabulls Blu Tower A,Worli,Mumbai,4.5 L,3200,Area in sq ft,Furnished,No Deposit,6 bathrooms,East facing +397,Swastik Reality,AGENT,3,BHK,Apartment,Lodha World One,Lower Parel,Mumbai,3.3 L,3400,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +398,Swastik Reality,AGENT,4,BHK,Apartment,Reputed Builder Beaumonde Towers,Prabhadevi,Mumbai,10 L,4500,Area in sq ft,Furnished,No Deposit,4 bathrooms, +399,Swastik Reality,AGENT,2,BHK,Apartment,,Lower Parel,Mumbai,1 L,850,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +400,Swastik Reality,AGENT,2,BHK,Apartment,,Prabhadevi,Mumbai,1 L,1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +401,Swastik Reality,AGENT,3,BHK,Apartment,,Worli,Mumbai,1.5 L,1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,SouthEast facing +402,Swastik Reality,AGENT,3,BHK,Apartment,Lodha World One,Lower Parel,Mumbai,3.5 L,2100,Area in sq ft,Furnished,No Deposit,4 bathrooms, +403,Swastik Reality,AGENT,5,BHK,Apartment,K Raheja Artesia Residential Wing Constructed On Part Of The Project Land,Worli,Mumbai,12 L,4900,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,East facing +404,Swastik Reality,AGENT,4,BHK,Apartment,Lodha Marquise,Worli,Mumbai,2.75 L,3000,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,SouthEast facing +405,Noronha Estate Agency,AGENT,2,BHK,Apartment,Agarwal Doshi Complex,Vasai,Mumbai,"16,000",950,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +406,Noronha Estate Agency,AGENT,2,BHK,Apartment,Rajhans Kshitij Aspen Wing C,Vasai,Mumbai,"13,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +407,Noronha Estate Agency,AGENT,1,BHK,Apartment,Reputed Builder Galaxy Villa,Vasai,Mumbai,"10,000",600,Area in sq ft,Semi-Furnished,No Deposit,, +408,Noronha Estate Agency,AGENT,2,BHK,Apartment,Reputed Builder Ram Rahim Tower,Vasai,Mumbai,"20,000",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +409,Noronha Estate Agency,AGENT,1,BHK,Apartment,Reputed Builder Galaxy Villa,Vasai,Mumbai,"10,000",550,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +410,Noronha Estate Agency,AGENT,2,BHK,Apartment,Rajhans Rajhans Kshitij Iris Wing E F G,Vasai,Mumbai,"12,000",990,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +411,Noronha Estate Agency,AGENT,2,BHK,Apartment,Kaul Kingston Tower,Vasai,Mumbai,"15,000",980,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +412,Noronha Estate Agency,AGENT,1,BHK,Apartment,,Vasai West,Mumbai,"10,000",600,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +413,Noronha Estate Agency,AGENT,2,BHK,Apartment,Manav Wisteria,Vasai,Mumbai,"18,000",850,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +414,Sarvam Properties,AGENT,2,BHK,Apartment,,Ghatkopar East,Mumbai,"45,000",753,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +415,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,Soham Crystal Spires,Thane West,Mumbai,"75,000",1324,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +416,SMP Group Real Estate,AGENT,2,BHK,Apartment,,Vashi,Mumbai,"17,000",550,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +417,Samadhan Real Estate Consultant,AGENT,2,BHK,Apartment,Cidco NRI Complex,Seawoods,Mumbai,"60,000",1250,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +418,Samadhan Real Estate Consultant,AGENT,3,BHK,Apartment,Cidco NRI Complex Phase 2,Seawoods,Mumbai,"75,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +419,Samadhan Real Estate Consultant,AGENT,2,BHK,Apartment,Akshar Shreeji Heights,Seawoods,Mumbai,"55,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +420,Samadhan Real Estate Consultant,AGENT,2,BHK,Apartment,Tharwani Tharwani Heights,Sanpada,Mumbai,"55,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +421,Samadhan Real Estate Consultant,AGENT,3,BHK,Apartment,Cidco NRI Complex Phase 2,Seawoods,Mumbai,"75,000",1650,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,North facing +422,Samadhan Real Estate Consultant,AGENT,2,BHK,Apartment,Sejal Suyash Pride,Ulwe,Mumbai,"15,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthWest facing +423,Samadhan Real Estate Consultant,AGENT,3,BHK,Apartment,Tharwani Tharwani Heights,Sanpada,Mumbai,"75,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +424,KD Real Estate,AGENT,1,RK,Studio Apartment,,Kalamboli,Mumbai,"8,000",400,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +425,KD Real Estate,AGENT,2,BHK,Apartment,Swaraj Homes Vaikunth CHS,Kamothe,Mumbai,"17,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +426,Aashiyana property consultant,AGENT,2,BHK,Apartment,Godrej Tranquil,Kandivali East,Mumbai,"35,000",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +427,PropertyPistol Realty Pvt Ltd,AGENT,1,BHK,Apartment,DSS Mahavir Kalpavruksha,Thane West,Mumbai,"24,000",620,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +428,Swastik Reality,AGENT,3,BHK,Apartment,,Worli,Mumbai,2.8 L,2200,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +429,SMP Group Real Estate,AGENT,3,BHK,Apartment,Reputed Builder Haridwar House,Vashi,Mumbai,"85,000",3000,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +430,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Lodha Bel Air,Jogeshwari West,Mumbai,"75,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +431,Reliance Estates - Since 1985,AGENT,3,BHK,Apartment,Lodha Bel Air,Jogeshwari West,Mumbai,1 L,1600,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +432,VibrantKey,AGENT,3,BHK,Apartment,,Malabar Hill,Mumbai,2.75 L,1380,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +433,VibrantKey,AGENT,2,BHK,Apartment,,Tardeo,Mumbai,1.25 L,950,Area in sq ft,Furnished,No Deposit,2 bathrooms,SouthWest facing +434,VibrantKey,AGENT,2,BHK,Apartment,,Tardeo,Mumbai,1.25 L,900,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +435,VibrantKey,AGENT,1,BHK,Apartment,,Tardeo,Mumbai,"70,000",460,Area in sq ft,Furnished,No Deposit,1 bathrooms,West facing +436,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Reputed Builder Garden Estate,Thane West,Mumbai,"28,000",700,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +437,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Paradise Sai Crystals,Kharghar,Mumbai,"32,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +438,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Seawoods,Mumbai,"26,000",1120,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +439,PropertyPistol Realty Pvt Ltd,AGENT,1,BHK,Apartment,Rutu Estate,Thane West,Mumbai,"24,000",450,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +440,Tejasvi Realty Pvt Ltd,AGENT,4,BHK,Apartment,,Juhu,Mumbai,3 L,2002,Area in sq ft,Furnished,No Deposit,4 bathrooms,West facing +441,Hari om realtors,AGENT,2,BHK,Apartment,Paradise Sai Sahil,Ulwe,Mumbai,"16,000",1125,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +442,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,DLH The Park Residences Phase 1,Andheri West,Mumbai,1.4 L,1100,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +443,Swastik Reality,AGENT,2,BHK,Apartment,,Lower Parel,Mumbai,1 L,1150,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +444,Swastik Reality,AGENT,3,BHK,Apartment,L And T Crescent Bay T4,Parel,Mumbai,1.2 L,1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +445,Azuroin,AGENT,2,BHK,Apartment,Vijay Orovia Phase 1,Thane West,Mumbai,"29,500",793,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +446,Azuroin,AGENT,2,BHK,Apartment,Dosti West County,Thane West,Mumbai,"30,000",853,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +447,Azuroin,AGENT,2,BHK,Apartment,Kalpataru Sunrise,Thane West,Mumbai,"28,000",635,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +448,Reliance Estates - Since 1985,AGENT,3,BHK,Apartment,Sunteck City Avenue 1,Goregaon West,Mumbai,"85,000",1750,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +449,Reliance Estates - Since 1985,AGENT,4,BHK,Apartment,Windsor Grande Residences,Andheri West,Mumbai,3.2 L,4300,Area in sq ft,Furnished,No Deposit,5 bathrooms,North facing +450,Reliance Estates - Since 1985,AGENT,3,BHK,Apartment,Sheth Auris Serenity,Malad West,Mumbai,1.2 L,1600,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +451,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Kabra Paradise,Andheri West,Mumbai,"60,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +452,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Supreme 19,Andheri West,Mumbai,"75,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +453,Reliance Estates - Since 1985,AGENT,3,BHK,Apartment,Rustomjee Elanza,Malad West,Mumbai,"80,000",1375,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +454,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Khandelwal Sai Iconic,Andheri West,Mumbai,"55,000",1080,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +455,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Kabra Metro One Wing A and B Of Pratap CHSL,Andheri West,Mumbai,"70,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +456,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Supreme 19,Andheri West,Mumbai,"67,500",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +457,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,,Andheri West,Mumbai,"60,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +458,Reliance Estates - Since 1985,AGENT,2,BHK,Apartment,Platinum Casa Millennia,Andheri West,Mumbai,"56,000",1200,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +459,Mahadev Properties,AGENT,1,BHK,Apartment,,kasaradavali thane west,Mumbai,"12,999",642,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthWest facing +460,PREMIUM PROPERTIES,AGENT,1,BHK,Apartment,Reputed Builder Bhoomi Green,Borivali East,Mumbai,"30,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +461,SUNRISE REAL ESTATE,AGENT,2,BHK,Apartment,Ajmera Julian Alps,Wadala,Mumbai,"55,600",955,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +462,Hari om realtors,AGENT,1,RK,Studio Apartment,,Ulwe,Mumbai,"8,500",655,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +463,Hari om realtors,AGENT,1,BHK,Apartment,,Ulwe,Mumbai,"10,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +464,Hari om realtors,AGENT,1,BHK,Apartment,,Ulwe,Mumbai,"9,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +465,SUNRISE REAL ESTATE,AGENT,2,BHK,Apartment,Lodha New Cuffe Parade Tower 11,Wadala,Mumbai,"79,000",840,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +466,SUNRISE REAL ESTATE,AGENT,2,BHK,Apartment,Lodha New Cuffe Parade Lodha Altia,Wadala,Mumbai,"80,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +467,Individual Agent,AGENT,2,BHK,Apartment,Goodwill Goodwill Gardens,Kharghar,Mumbai,"21,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +468,Laabh Properties,AGENT,2,BHK,Apartment,,Khar West,Mumbai,1 L,900,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +469,Laabh Properties,AGENT,3,BHK,Apartment,,Bandra West,Mumbai,2.5 L,1200,Area in sq ft,Furnished,No Deposit,3 bathrooms,West facing +470,Laabh Properties,AGENT,3,BHK,Apartment,L Nagpal Anupama Heights,Khar,Mumbai,1.6 L,1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +471,Laabh Properties,AGENT,1,BHK,Apartment,,Bandra West,Mumbai,"70,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +472,PropertyPistol Realty Pvt Ltd,AGENT,1,BHK,Apartment,Lodha Splendora,Thane West,Mumbai,"18,000",700,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +473,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Seawoods,Mumbai,"32,000",680,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +474,Om sai estate,AGENT,2,BHK,Apartment,Wadhwa Shiv Valley,Kalyan West,Mumbai,"13,000",900,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +475,Om sai estate,AGENT,2,BHK,Apartment,Raunak City,Kalyan West,Mumbai,"13,000",870,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +476,Om sai estate,AGENT,1,BHK,Apartment,Mehta Amrut Pearl,Kalyan West,Mumbai,"10,000",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +477,Om sai estate,AGENT,1,BHK,Apartment,Reputed Builder Nebula Darshan,Kalyan West,Mumbai,"9,500",575,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +478,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Bliss Wing B,Kanjurmarg,Mumbai,"55,000",850,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +479,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"49,000",800,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +480,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Kanakia Silicon Valley,Powai,Mumbai,"84,000",850,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +481,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"51,000",771,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +482,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,1.25 L,1250,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +483,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,,Seawoods,Mumbai,"45,000",1500,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +484,Swastik Reality,AGENT,2,BHK,Apartment,Darshan Rico,Lower Parel,Mumbai,1.5 L,1800,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +485,Swastik Reality,AGENT,2,BHK,Apartment,Lodha Park,Lower Parel,Mumbai,1.2 L,931,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +486,Swastik Reality,AGENT,1,BHK,Apartment,,Lower Parel,Mumbai,"65,000",700,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +487,Swastik Reality,AGENT,2,BHK,Apartment,Reputed Builder Shilpa Tower,Lower Parel,Mumbai,1.2 L,1100,Area in sq ft,Furnished,No Deposit,4 bathrooms,North facing +488,Swastik Reality,AGENT,2,BHK,Apartment,,Worli,Mumbai,"95,000",1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +489,Aadhar enterprises,AGENT,1,BHK,Apartment,,Thakurli,Mumbai,"12,000",700,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,North facing +490,VibrantKey,AGENT,1,BHK,Apartment,,Tardeo,Mumbai,"70,000",460,Area in sq ft,Furnished,No Deposit,1 bathrooms,West facing +491,Om Sai Siddhi Properties,AGENT,1,BHK,Apartment,Lodha Casa Rio,Dombivali,Mumbai,"8,000",693,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +492,Om Sai Siddhi Properties,AGENT,2,BHK,Apartment,Lodha Casa Rio,Dombivali,Mumbai,"12,400",909,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +493,MANASVI PROPERTIES,AGENT,4,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,1.95 L,1450,Area in sq ft,Furnished,No Deposit,3 bathrooms,West facing +494,Swastik Reality,AGENT,3,BHK,Apartment,K Raheja Modern Vivarea South Tower,Agripada,Mumbai,4.74 L,2250,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms, +495,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,Neelkanth Greens,Thane West,Mumbai,"36,000",1100,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +496,Sarvam Properties,AGENT,4,BHK,Apartment,,Ghatkopar East,Mumbai,1.45 L,1897,Area in sq ft,Furnished,No Deposit,4 bathrooms,East facing +497,Bajrangi Realtors,AGENT,2,BHK,Apartment,,Kurla East,Mumbai,"30,000",625,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +498,Bajrangi Realtors,AGENT,1,BHK,Apartment,Raghav One,Kurla,Mumbai,"41,000",425,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +499,Vijay Estate Agency,AGENT,2,BHK,Apartment,,Bhandup West,Mumbai,"48,000",800,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +500,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,,Andheri West,Mumbai,1.1 L,1200,Area in sq ft,Furnished,No Deposit,3 bathrooms,NorthEast facing +501,Shree associat,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"26,000",1200,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +502,Shree associat,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"25,000",920,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +503,Azuroin,AGENT,1,BHK,Apartment,,Thane West,Mumbai,"24,000",517,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +504,Azuroin,AGENT,1,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"27,000",545,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +505,Azuroin,AGENT,2,BHK,Apartment,,Thane West,Mumbai,"28,003",772,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +506,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Godrej The Trees,Vikhroli,Mumbai,1.3 L,1135,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +507,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"15,000",1065,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +508,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"13,000",800,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +509,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"19,000",1005,Area in sq ft,Furnished,No Deposit,2 bathrooms,South facing +510,Reliance Estates - Since 1985,AGENT,4,BHK,Apartment,Lodha Bel Air,Jogeshwari West,Mumbai,1.3 L,1900,Area in sq ft,Unfurnished,No Deposit,4 bathrooms,East facing +511,Eastern Coast Properties,AGENT,2,BHK,Apartment,Indiabulls Blu Tower A,Worli,Mumbai,2.45 L,827,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +512,SUNRISE REAL ESTATE,AGENT,3,BHK,Apartment,Rajhans Mount Everest,Wadala,Mumbai,"80,000",1215,Area in sq ft,Furnished,No Deposit,3 bathrooms, +513,Shree Homes Enterprises,AGENT,2,BHK,Apartment,Future Hill,Panvel,Mumbai,"11,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +514,Shree Homes Enterprises,AGENT,3,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"23,000",1560,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +515,Eastern Coast Properties,AGENT,4,BHK,Apartment,Lodha World Crest,Lower Parel,Mumbai,3.25 L,2357,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +516,Eastern Coast Properties,AGENT,4,BHK,Apartment,,Worli,Mumbai,3.9 L,1600,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +517,EstatesHUB,AGENT,3,BHK,Apartment,Bholenath Hresa Sainagar Apartment Pvt Ltd,Chembur,Mumbai,"82,000",980,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +518,Azuroin,AGENT,2,BHK,Apartment,Rustomjee Azziano Wing G,Thane West,Mumbai,"48,000",753,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +519,Green Group Real Estate Consultants,AGENT,2,BHK,Apartment,Moraj Riverside Park,Panvel,Mumbai,"14,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +520,Bricks Property Consultant,AGENT,3,BHK,Apartment,,Dombivali East,Mumbai,"14,000",1197,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +521,Bricks Property Consultant,AGENT,2,BHK,Apartment,,Dombivali East,Mumbai,"13,000",999,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthWest facing +522,Bricks Property Consultant,AGENT,2,BHK,Apartment,,Dombivali East,Mumbai,"12,000",999,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +523,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"18,000",417,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +524,Azuroin,AGENT,2,BHK,Apartment,,Thane West,Mumbai,"50,000",750,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +525,Azuroin,AGENT,3,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"40,000",1055,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +526,Azuroin,AGENT,2,BHK,Apartment,Runwal Garden City,Thane West,Mumbai,"37,000",744,Area in sq ft,Furnished,No Deposit,1 bathrooms, +527,Azuroin,AGENT,2,BHK,Apartment,Runwal Garden City,Thane West,Mumbai,"37,000",744,Area in sq ft,Furnished,No Deposit,1 bathrooms,NorthEast facing +528,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"16,000",417,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +529,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"45,001",1106,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +530,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"38,000",753,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +531,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"37,000",753,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +532,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"37,000",753,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +533,Azuroin,AGENT,2,BHK,Apartment,Swaraj Homes Nirlac Solitaire Society,Thane West,Mumbai,"30,000",1075,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +534,Individual Agent,AGENT,3,BHK,Apartment,Paradise Sai Solitaire,Kharghar,Mumbai,"40,000",1750,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +535,Laabh Properties,AGENT,2,BHK,Apartment,Bhuvnesh Westside,Bandra West,Mumbai,1.1 L,800,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +536,Hari om realtors,AGENT,1,BHK,Apartment,,Ulwe,Mumbai,"8,500",665,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthWest facing +537,Hari om realtors,AGENT,2,BHK,Apartment,Shreenathji Mayuresh Delta,Ulwe,Mumbai,"22,000",1350,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +538,Hari om realtors,AGENT,2,BHK,Apartment,Platinum Emporius,Ulwe,Mumbai,"24,000",1175,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +539,Hari om realtors,AGENT,3,BHK,Apartment,Reputed Builder City Heights,Ulwe,Mumbai,"17,000",1585,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +540,Hari om realtors,AGENT,1,BHK,Apartment,Shagun Paradise,Ulwe,Mumbai,"10,500",1085,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthWest facing +541,Hari om realtors,AGENT,1,RK,Studio Apartment,,Ulwe,Mumbai,"6,500",425,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +542,Individual Agent,AGENT,3,BHK,Apartment,Nisarg Hyde Park,Kharghar,Mumbai,"40,000",1570,Area in sq ft,Furnished,No Deposit,3 bathrooms,West facing +543,Jyoti Enterprise,AGENT,2,BHK,Apartment,Marathon Marathon Nexzone,Panvel,Mumbai,"12,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +544,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"15,000",1065,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +545,Jyoti Enterprise,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"32,000",1450,Area in sq ft,Furnished,No Deposit,3 bathrooms, +546,Jyoti Enterprise,AGENT,2,BHK,Apartment,Vishesh Balaji Symphony,Panvel,Mumbai,"16,000",1055,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +547,Bricks Property Consultant,AGENT,2,BHK,Apartment,Lodha Casa Rio Gold,Dombivali,Mumbai,"12,000",963,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +548,Bricks Property Consultant,AGENT,2,BHK,Apartment,Lodha Casa Rio Gold,Dombivali,Mumbai,"16,500",969,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +549,Noronha Estate Agency,AGENT,2,BHK,Apartment,,Vasai West,Mumbai,"13,000",850,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +550,Reliance Estates - Since 1985,AGENT,4,BHK,Apartment,HDIL Metropolis Residences,Andheri West,Mumbai,"95,000",2000,Area in sq ft,Unfurnished,No Deposit,4 bathrooms,East facing +551,Green Group Real Estate Consultants,AGENT,1,BHK,Apartment,Moraj Riverside Park,Panvel,Mumbai,"9,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +552,Green Group Real Estate Consultants,AGENT,1,BHK,Apartment,Moraj Riverside Park,Panvel,Mumbai,"10,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +553,Green Group Real Estate Consultants,AGENT,3,BHK,Apartment,Swaraj Homes Neelkanth Darshan CHS,Panvel,Mumbai,"21,000",1300,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +554,Green Group Real Estate Consultants,AGENT,1,BHK,Apartment,Moraj Riverside Park,Panvel,Mumbai,"12,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +555,Stilt Real Estate,AGENT,3,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1.05 L,1027,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +556,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"23,000",950,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +557,Kuber property,AGENT,3,BHK,Apartment,Ayodhya Construction Co Saffron Residency Phase 1,Kurla,Mumbai,"70,000",1100,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +558,PREMIUM PROPERTIES,AGENT,1,BHK,Apartment,K Raheja Eastate,Borivali East,Mumbai,"29,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +559,Hari om realtors,AGENT,2,BHK,Apartment,Ashtavinayak Aangan,Ulwe,Mumbai,"15,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +560,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Seawoods,Mumbai,"27,000",900,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +561,SUNRISE REAL ESTATE,AGENT,2,BHK,Apartment,Ajmera Girnar,Wadala,Mumbai,"67,000",716,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +562,SUNRISE REAL ESTATE,AGENT,3,BHK,Apartment,Rajhans Mount Everest,Wadala,Mumbai,"67,000",1315,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +563,Green Group Real Estate Consultants,AGENT,2,BHK,Apartment,National Plaza,Panvel,Mumbai,"17,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +564,Stilt Real Estate,AGENT,2,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,"95,000",980,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +565,Stilt Real Estate,AGENT,3,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1.25 L,1200,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +566,Stilt Real Estate,AGENT,3,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1.2 L,1150,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +567,Stilt Real Estate,AGENT,3,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1.05 L,1100,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +568,Stilt Real Estate,AGENT,3,BHK,Apartment,Kalpataru Sparkle,Bandra East,Mumbai,2.75 L,1600,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +569,Stilt Real Estate,AGENT,3,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1.35 L,1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +570,Stilt Real Estate,AGENT,2,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1 L,970,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +571,Stilt Real Estate,AGENT,3,BHK,Apartment,Kalpataru Sparkle,Bandra East,Mumbai,2 L,1580,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +572,Stilt Real Estate,AGENT,3,BHK,Apartment,Kalpataru Sparkle,Bandra East,Mumbai,2.2 L,1600,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +573,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"46,500",1020,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +574,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"47,000",1020,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +575,Prem Housing,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"68,000",1160,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +576,Prem Housing,AGENT,3,BHK,Apartment,Runwal Lily at Runwal Forest,Kanjurmarg,Mumbai,"75,000",1900,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +577,Right House Properties,AGENT,2,BHK,Apartment,,Chembur,Mumbai,"30,000",750,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +578,Right House Properties,AGENT,1,BHK,Apartment,,Chembur,Mumbai,"26,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +579,Right House Properties,AGENT,1,BHK,Apartment,,Chembur,Mumbai,"27,000",480,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +580,Housing Guru,AGENT,3,BHK,Apartment,Runwal Runwal Forests Tower 9 To 11,Kanjurmarg,Mumbai,"85,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +581,Right House Properties,AGENT,2,BHK,Apartment,,Chembur,Mumbai,"38,000",850,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthEast facing +582,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"47,500",1020,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +583,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"46,500",1020,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +584,Prem Housing,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"39,000",771,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +585,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"48,000",1020,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +586,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"13,500",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +587,Om Properties,AGENT,2,BHK,Apartment,,Kalwa,Mumbai,"21,000",800,Area in sq ft,Furnished,No Deposit,2 bathrooms, +588,India Direct Homecom,AGENT,2,BHK,Apartment,Marathon Marathon Nexzone,Panvel,Mumbai,"15,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +589,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Zodiac 1,Panvel,Mumbai,"14,000",995,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +590,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"14,000",995,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +591,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"14,000",995,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +592,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"15,500",995,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +593,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Acrux 1,Panvel,Mumbai,"15,500",995,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +594,Om Properties,AGENT,1,BHK,Apartment,,Kalwa,Mumbai,"18,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +595,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"28,000",574,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +596,Azuroin,AGENT,2,BHK,Apartment,Lodha Majiwada Tower 1,Thane West,Mumbai,"34,500",803,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +597,Azuroin,AGENT,1,BHK,Apartment,Lodha Majiwada Tower 1,Thane West,Mumbai,"25,000",545,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +598,Azuroin,AGENT,2,BHK,Apartment,Vijay Orovia Phase 1,Thane West,Mumbai,"29,500",793,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +599,Azuroin,AGENT,2,BHK,Apartment,Dosti West County,Thane West,Mumbai,"30,000",853,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +600,Azuroin,AGENT,2,BHK,Apartment,Kalpataru Sunrise,Thane West,Mumbai,"28,000",635,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +601,Azuroin,AGENT,3,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"70,000",1320,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +602,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"49,000",1104,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +603,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"46,000",1038,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +604,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"50,000",1104,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +605,Azuroin,AGENT,3,BHK,Apartment,Kalpataru Sunrise,Thane West,Mumbai,"45,000",1155,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +606,My Vastu Realtors,AGENT,1,BHK,Apartment,,Ulwe,Mumbai,"7,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +607,My Vastu Realtors,AGENT,1,BHK,Apartment,Krishna Heights,Dronagiri,Mumbai,"5,700",735,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +608,My Vastu Realtors,AGENT,1,BHK,Apartment,Radhe Krishna Heights,Ulwe,Mumbai,"7,500",690,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +609,SUNRISE REAL ESTATE,AGENT,3,BHK,Apartment,Lodha Enchante,Wadala,Mumbai,1.3 L,1450,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +610,SUNRISE REAL ESTATE,AGENT,1,BHK,Apartment,Lodha Enchante,Wadala,Mumbai,"65,000",550,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +611,SUNRISE REAL ESTATE,AGENT,1,BHK,Apartment,Lodha Enchante,Wadala,Mumbai,"60,002",570,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +612,SUNRISE REAL ESTATE,AGENT,1,BHK,Apartment,Lodha Enchante,Wadala,Mumbai,"60,000",570,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +613,SUNRISE REAL ESTATE,AGENT,3,BHK,Apartment,Lodha New Cuffe Parade Lodha Altia,Wadala,Mumbai,1.35 L,1750,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +614,Stilt Real Estate,AGENT,3,BHK,Apartment,Rustomjee Oriana,Bandra East,Mumbai,1.5 L,1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +615,Stilt Real Estate,AGENT,3,BHK,Apartment,Kalpataru Sparkle,Bandra East,Mumbai,2.25 L,1400,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +616,Swastik Reality,AGENT,4,BHK,Apartment,Lodha Marquise,Worli,Mumbai,3 L,3000,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,SouthEast facing +617,Green Group Real Estate Consultants,AGENT,1,BHK,Apartment,,Panvel,Mumbai,"14,000",750,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +618,Green Group Real Estate Consultants,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"18,000",1900,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +619,Green Group Real Estate Consultants,AGENT,2,BHK,Apartment,Moraj Riverside Park,Panvel,Mumbai,"22,000",1100,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +620,Green Group Real Estate Consultants,AGENT,2,BHK,Apartment,Moraj Riverside Park,Panvel,Mumbai,"12,000",1000,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +621,Green Group Real Estate Consultants,AGENT,1,RK,Studio Apartment,,Karanjade,Mumbai,"5,000",400,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +622,Green Group Real Estate Consultants,AGENT,2,BHK,Apartment,,Kamothe,Mumbai,"25,000",1100,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +623,SUNRISE REAL ESTATE,AGENT,2,BHK,Apartment,Lodha New Cuffe Parade Tower 11,Wadala,Mumbai,"79,000",790,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +624,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"55,000",681,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +625,Housing Guru,AGENT,2,BHK,Apartment,Hiranandani Zen Atlantis,Powai,Mumbai,"95,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +626,Housing Guru,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"85,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +627,Housing Guru,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"80,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +628,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"55,000",600,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +629,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"55,000",600,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +630,Housing Guru,AGENT,2,BHK,Apartment,Hiranandani Zen Atlantis,Powai,Mumbai,1.1 L,1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +631,Housing Guru,AGENT,3,BHK,Apartment,Runwal Runwal Forests Tower 9 To 11,Kanjurmarg,Mumbai,"85,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +632,Housing Guru,AGENT,3,BHK,Apartment,Runwal Runwal Forests Tower 9 To 11,Kanjurmarg,Mumbai,"86,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +633,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"54,000",600,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +634,Housing Guru,AGENT,3,BHK,Apartment,Runwal Runwal Forests Tower 9 To 11,Kanjurmarg,Mumbai,"85,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +635,Housing Guru,AGENT,3,BHK,Apartment,Runwal Runwal Forests Tower 9 To 11,Kanjurmarg,Mumbai,"87,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +636,Housing Guru,AGENT,3,BHK,Apartment,Runwal Runwal Forests Tower 9 To 11,Kanjurmarg,Mumbai,"87,000",1550,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +637,MANASVI PROPERTIES,AGENT,3,BHK,Apartment,Runwal Forest Tower 5 To 8,Kanjurmarg,Mumbai,"65,000",1085,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +638,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Borivali West,Mumbai,"40,000",920,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +639,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,Today Grande Vista,Ulwe,Mumbai,"27,000",1580,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +640,Housing Guru,AGENT,2,BHK,Apartment,Hiranandani Zen Atlantis,Powai,Mumbai,1.2 L,1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +641,Housing Guru,AGENT,2,BHK,Apartment,Hiranandani Zen Atlantis,Powai,Mumbai,1.2 L,1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +642,Kuber property,AGENT,2,BHK,Apartment,Hubtown Seasons,Chembur,Mumbai,"80,000",1200,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +643,Kuber property,AGENT,3,BHK,Apartment,Godrej Prime,Chembur,Mumbai,"80,000",1300,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +644,Kuber property,AGENT,2,BHK,Apartment,MICL Aaradhya One,Chembur,Mumbai,"75,000",1150,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +645,Prime property,AGENT,1,BHK,Apartment,Ram Pushpanjali Residency Phase III,Thane West,Mumbai,"14,000",630,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +646,Prime property,AGENT,2,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"15,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +647,MANASVI PROPERTIES,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"47,000",598,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +648,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"50,000",902,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +649,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"44,000",850,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +650,Om sai estate,AGENT,1,BHK,Apartment,Godrej Hill,Kalyan West,Mumbai,"10,000",600,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +651,Om sai estate,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"40,000",1550,Area in sq ft,Furnished,No Deposit,3 bathrooms,NorthEast facing +652,Om sai estate,AGENT,1,BHK,Apartment,Tharwani Rosalie,Kalyan West,Mumbai,"12,000",666,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +653,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"9,500",600,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +654,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"13,500",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +655,Prime property,AGENT,1,BHK,Apartment,Puraniks Tokyo Bay Phase 2C,Thane West,Mumbai,"13,000",600,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +656,Bricks Property Consultant,AGENT,3,BHK,Apartment,Lodha Casa Bella Gold,Dombivali,Mumbai,"18,000",1085,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +657,Bricks Property Consultant,AGENT,2,BHK,Apartment,Lodha Casa Bella Gold,Dombivali,Mumbai,"11,500",767,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +658,Bajrangi Realtors,AGENT,2,BHK,Apartment,,Kurla East,Mumbai,"34,440",615,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +659,Sanjay,AGENT,3,BHK,Apartment,Raunak City Sector IV D3,Kalyan West,Mumbai,"28,000",1200,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +660,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"14,000",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +661,Prime property,AGENT,1,BHK,Apartment,Raunak Heights,Thane West,Mumbai,"13,500",580,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +662,Right House Properties,AGENT,2,BHK,Apartment,,Ghatkopar East,Mumbai,"60,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthWest facing +663,Royal Real Estate Agency,AGENT,1,BHK,Apartment,Swaraj Homes Elora Complex CHS,Dombivali,Mumbai,"16,000",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +664,Royal Real Estate Agency,AGENT,1,BHK,Apartment,,Dombivli (West),Mumbai,"10,000",550,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +665,A A REAL ESTATE,AGENT,1,BHK,Apartment,Reputed Builder NG Complex,Andheri East,Mumbai,"36,000",615,Area in sq ft,Furnished,No Deposit,1 bathrooms, +666,A A REAL ESTATE,AGENT,3,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"89,000",1300,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +667,A A REAL ESTATE,AGENT,2,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"79,000",1050,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +668,A A REAL ESTATE,AGENT,2,BHK,Apartment,DSK Madhuban,Andheri East,Mumbai,"45,000",800,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +669,Prime property,AGENT,1,BHK,Apartment,Vihang Vermont,Thane West,Mumbai,"13,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +670,Prime property,AGENT,2,BHK,Apartment,Puraniks Tokyo Bay Phase 2C,Thane West,Mumbai,"19,500",970,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +671,Prime property,AGENT,1,BHK,Apartment,Khade KIPL Morya,Thane West,Mumbai,"12,500",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +672,DHARTI ESTATE CONSULTANT,AGENT,2,BHK,Apartment,Reputed Builder Akash Darshan,Santacruz East,Mumbai,"62,000",700,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +673,DHARTI ESTATE CONSULTANT,AGENT,1,RK,Studio Apartment,Reputed Builder Pride Of Kalina,Santacruz East,Mumbai,"24,000",300,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +674,DHARTI ESTATE CONSULTANT,AGENT,2,BHK,Apartment,,Santacruz East,Mumbai,"75,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +675,Disha Real Estate Consultant,AGENT,2,BHK,Apartment,Puraniks Hometown Phase 2,Thane West,Mumbai,"19,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +676,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Puraniks Rumah Bali,Thane West,Mumbai,"14,500",590,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +677,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Vihang Vermont,Thane West,Mumbai,"14,000",635,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +678,Disha Real Estate Consultant,AGENT,2,BHK,Apartment,Vihang Vermont,Thane West,Mumbai,"18,000",950,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +679,Disha Real Estate Consultant,AGENT,3,BHK,Apartment,Haware Citi,Thane West,Mumbai,"18,000",1170,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +680,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Vihang Vermont,Thane West,Mumbai,"16,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +681,Disha Real Estate Consultant,AGENT,2,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"16,000",850,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,South facing +682,Disha Real Estate Consultant,AGENT,2,BHK,Apartment,Monarch Cosmos Enclave Chestnut,Thane West,Mumbai,"20,000",850,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +683,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Maison Tarangan,Thane West,Mumbai,"14,000",600,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthWest facing +684,Bricks Property Consultant,AGENT,2,BHK,Apartment,,Dombivali East,Mumbai,"9,000",710,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +685,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"38,000",780,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +686,Prime property,AGENT,1,RK,Studio Apartment,Haware Haware Citi,Thane West,Mumbai,"8,500",324,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +687,Prime property,AGENT,2,BHK,Apartment,Godrej Emerald,Thane West,Mumbai,"22,000",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +688,Prime property,AGENT,1,BHK,Apartment,Khade KIPL Morya,Thane West,Mumbai,"13,500",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +689,Prime property,AGENT,1,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"13,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +690,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"15,500",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +691,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"17,500",950,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +692,Laabh Properties,AGENT,2,BHK,Apartment,Rizvi Nectar Apartment,Bandra West,Mumbai,1 L,800,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +693,Noronha Estate Agency,AGENT,2,BHK,Apartment,,Vasai West,Mumbai,"13,000",870,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +694,Noronha Estate Agency,AGENT,1,BHK,Apartment,Reputed Builder Galaxy Villa,Vasai,Mumbai,"10,000",600,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +695,Prime property,AGENT,2,BHK,Apartment,Puraniks City Phase 3,Thane West,Mumbai,"20,000",960,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +696,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"10,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +697,Laabh Properties,AGENT,2,BHK,Apartment,Swaraj Homes Hill Niketan Apartment,Bandra West,Mumbai,1.25 L,800,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +698,Om sai estate,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"16,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +699,Om sai estate,AGENT,1,BHK,Apartment,Gurukrupa Guru Atman,Kalyan West,Mumbai,"15,000",710,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +700,Om sai estate,AGENT,2,BHK,Apartment,Gurukrupa Guru Atman,Kalyan West,Mumbai,"21,500",967,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +701,Om sai estate,AGENT,1,BHK,Apartment,Ajmera New Era Yogidham Phase IV Tower C,Kalyan West,Mumbai,"13,500",715,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +702,Om sai estate,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"24,000",1120,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +703,Om sai estate,AGENT,1,BHK,Apartment,Raunak City,Kalyan West,Mumbai,"9,000",587,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +704,Om sai estate,AGENT,2,BHK,Apartment,Reputed Builder Vasant Valley,Kalyan West,Mumbai,"20,000",1120,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +705,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"42,000",1049,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +706,Azuroin,AGENT,1,BHK,Apartment,Lodha Casa Royale,Thane West,Mumbai,"21,000",685,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +707,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"40,000",982,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +708,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"23,000",553,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +709,Azuroin,AGENT,2,BHK,Apartment,Runwal Garden City,Thane West,Mumbai,"37,000",744,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +710,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"38,000",753,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +711,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"37,000",753,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +712,Azuroin,AGENT,2,BHK,Apartment,Swaraj Homes Nirlac Solitaire Society,Thane West,Mumbai,"31,000",1075,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +713,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"45,000",968,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +714,Azuroin,AGENT,2,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"42,000",982,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +715,Azuroin,AGENT,2,BHK,Apartment,Runwal Garden City,Thane West,Mumbai,"36,000",744,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +716,Star Realtors,AGENT,3,BHK,Apartment,Lodha Elisium,Wadala,Mumbai,1.1 L,1701,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +717,Star Realtors,AGENT,2,BHK,Apartment,Lodha Dioro,Wadala,Mumbai,"87,000",1377,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,SouthEast facing +718,Star Realtors,AGENT,3,BHK,Apartment,Lodha Dioro,Wadala,Mumbai,1.3 L,1701,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,East facing +719,Sanjay,AGENT,3,BHK,Apartment,Reputed Builder Vasant Valley,Kalyan West,Mumbai,"41,000",1300,Area in sq ft,Furnished,No Deposit,3 bathrooms,West facing +720,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"9,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +721,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"17,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +722,Prime property,AGENT,3,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"17,100",1105,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,North facing +723,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"10,990",625,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +724,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"13,999",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +725,Prime property,AGENT,1,RK,Studio Apartment,Haware Haware Citi,Thane West,Mumbai,"8,500",324,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +726,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"18,000",900,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +727,Sanjay,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"29,000",1200,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +728,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"16,000",650,Area in sq ft,Furnished,No Deposit,1 bathrooms,West facing +729,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"10,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +730,Sanjay,AGENT,2,BHK,Apartment,Ashapura Neelkanth Shrushti,Kalyan West,Mumbai,"19,000",950,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +731,Prime property,AGENT,1,BHK,Apartment,Puraniks Tokyo Bay Phase 1,Thane West,Mumbai,"15,000",650,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +732,Prime property,AGENT,1,BHK,Apartment,Puraniks Rumah Bali,Thane West,Mumbai,"14,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +733,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"19,000",650,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +734,Prem Housing,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"38,000",771,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +735,Prem Housing,AGENT,2,BHK,Apartment,Runwal Forest Tower 1 To 4,Kanjurmarg,Mumbai,"36,000",771,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthWest facing +736,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,Reputed Builder Innovative Heritage,Seawoods,Mumbai,"25,000",1000,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +737,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"13,900",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +738,Prime property,AGENT,1,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"12,000",630,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +739,Prime property,AGENT,1,BHK,Apartment,Vijay Yashraj Park,Thane West,Mumbai,"14,000",700,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +740,Prime property,AGENT,2,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"15,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +741,Prime property,AGENT,3,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"16,500",1105,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,North facing +742,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"20,000",900,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +743,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"14,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +744,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"14,000",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +745,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Terraform Everest Marigold,Thane West,Mumbai,"13,000",575,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +746,Disha Real Estate Consultant,AGENT,2,BHK,Apartment,Vihang Vermont,Thane West,Mumbai,"18,000",1050,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +747,Disha Real Estate Consultant,AGENT,1,BHK,Apartment,Puraniks Puranik City Sector 6,Thane West,Mumbai,"17,900",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +748,Disha Real Estate Consultant,AGENT,2,BHK,Apartment,Aakar Manas Residency,Thane West,Mumbai,"17,000",700,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +749,Right House Properties,AGENT,2,BHK,Apartment,,Chembur,Mumbai,"50,000",850,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +750,Right House Properties,AGENT,1,BHK,Apartment,,Chembur,Mumbai,"35,000",645,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +751,Right House Properties,AGENT,1,BHK,Apartment,,Chembur,Mumbai,"38,000",520,Area in sq ft,Furnished,No Deposit,1 bathrooms,West facing +752,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"14,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +753,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Seawoods,Mumbai,"34,000",1040,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +754,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,HDIL Metropolis Residences,Andheri West,Mumbai,1.1 L,1180,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +755,Star Realtors,AGENT,1,BHK,Apartment,Lodha Enchante,Wadala,Mumbai,"55,000",730,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,South facing +756,Star Realtors,AGENT,2,BHK,Apartment,Lodha Elisium,Wadala,Mumbai,"80,000",1701,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +757,Star Realtors,AGENT,2,BHK,Apartment,Lodha Dioro,Wadala,Mumbai,"85,000",925,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +758,Star Realtors,AGENT,3,BHK,Apartment,Lodha Elisium,Wadala,Mumbai,1.24 L,1701,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +759,Star Realtors,AGENT,2,BHK,Apartment,Lodha Dioro,Wadala,Mumbai,"57,000",1337,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,SouthWest facing +760,Star Realtors,AGENT,2,BHK,Apartment,Lodha Elisium,Wadala,Mumbai,"88,000",955,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,SouthWest facing +761,Star Realtors,AGENT,3,BHK,Apartment,Lodha Elisium,Wadala,Mumbai,1.25 L,1701,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,West facing +762,Star Realtors,AGENT,3,BHK,Apartment,Lodha Dioro,Wadala,Mumbai,1.2 L,1701,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,West facing +763,Star Realtors,AGENT,2,BHK,Apartment,Lodha New Cuffe Parade Lodha Altia,Wadala,Mumbai,"91,000",1500,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +764,Sarvam Properties,AGENT,3,BHK,Apartment,,Ghatkopar East,Mumbai,1.25 L,1437,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +765,Om sai estate,AGENT,2,BHK,Apartment,Mohan Heights,Kalyan West,Mumbai,"26,000",1060,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +766,Sanjay,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"26,000",1200,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +767,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"10,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +768,Bricks Property Consultant,AGENT,2,BHK,Apartment,Lodha Casa Bella Gold,Dombivali,Mumbai,"13,000",864,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +769,Bricks Property Consultant,AGENT,3,BHK,Apartment,,Dombivali East,Mumbai,"41,000",1445,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +770,SUNRISE REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Mount Alps A,Wadala,Mumbai,"49,000",925,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +771,Galaxy homes,AGENT,2,BHK,Apartment,Bhoomi Homes Maple Hills,Kharghar,Mumbai,"26,000",1085,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +772,Galaxy homes,AGENT,2,BHK,Apartment,Reputed Builder Dharti Aangan,Kharghar,Mumbai,"17,000",980,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +773,Galaxy homes,AGENT,2,BHK,Apartment,Prince Alisha Paradise,Kharghar,Mumbai,"18,500",1025,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +774,Galaxy homes,AGENT,1,BHK,Apartment,,Kharghar,Mumbai,"12,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +775,Galaxy homes,AGENT,1,BHK,Apartment,Gami Viona,Kharghar,Mumbai,"15,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +776,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"29,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +777,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"29,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +778,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"28,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +779,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"24,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +780,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +781,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"10,400",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +782,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"49,000",980,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +783,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"50,000",902,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +784,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"48,000",910,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +785,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"48,500",902,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +786,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder Raj Paradise,Andheri East,Mumbai,"49,000",1000,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +787,Stilt Real Estate,AGENT,3,BHK,Apartment,Kanakia Paris,Bandra Kurla Complex,Mumbai,1.1 L,1024,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +788,Om Properties,AGENT,1,BHK,Apartment,Ashar Aria,Thane West,Mumbai,"22,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +789,Om Properties,AGENT,1,RK,Studio Apartment,,Kalwa,Mumbai,"10,000",350,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +790,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 1,Thane West,Mumbai,"16,000",417,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +791,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"25,000",575,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +792,Azuroin,AGENT,2,BHK,Apartment,Lodha Crown Kolshet,Thane West,Mumbai,"24,000",574,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +793,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 1,Thane West,Mumbai,"17,000",417,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +794,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"28,000",575,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +795,Azuroin,AGENT,1,BHK,Apartment,Lodha Crown Kolshet,Thane West,Mumbai,"17,011",465,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +796,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 1,Thane West,Mumbai,"16,000",417,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +797,Azuroin,AGENT,2,BHK,Apartment,Lodha Crown Kolshet,Thane West,Mumbai,"28,020",574,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +798,Azuroin,AGENT,1,BHK,Apartment,Lodha Crown Kolshet,Thane West,Mumbai,"16,500",456,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +799,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"17,000",417,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +800,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"25,022",575,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +801,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home Tower 5,Thane West,Mumbai,"17,000",418,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +802,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"22,000",575,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +803,Azuroin,AGENT,1,BHK,Apartment,Lodha Crown Kolshet,Thane West,Mumbai,"18,000",456,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +804,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"28,000",574,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +805,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home Tower 2,Thane West,Mumbai,"25,000",575,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +806,Housing Guru,AGENT,3,BHK,Apartment,Nahar Amrit Shakti,Powai,Mumbai,"72,000",1250,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +807,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"56,000",610,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +808,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"56,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +809,Housing Guru,AGENT,1,BHK,Apartment,Hiranandani Regent Hill C D And E Wing,Powai,Mumbai,"56,000",600,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +810,Housing Guru,AGENT,2,BHK,Apartment,Nahar Olivia,Powai,Mumbai,"59,000",980,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +811,Kuber property,AGENT,3,BHK,Apartment,MM Spectra,Chembur,Mumbai,"65,000",1600,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthWest facing +812,Kuber property,AGENT,2,BHK,Apartment,MM Spectra,Chembur,Mumbai,"52,000",1200,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +813,Kuber property,AGENT,2,BHK,Apartment,MM Spectra,Chembur,Mumbai,"55,000",1200,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +814,Bajrangi Realtors,AGENT,2,BHK,Apartment,,Kurla East,Mumbai,"35,000",615,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +815,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"19,000",950,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +816,Rajesh Rasale,AGENT,1,BHK,Apartment,,Thane West,Mumbai,"23,000",500,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +817,Sanjay,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"26,000",1200,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +818,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"10,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +819,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,,Thane West,Mumbai,"35,000",720,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +820,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,A C Crystal Avenue,Ulwe,Mumbai,"15,000",1100,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +821,A A REAL ESTATE,AGENT,2,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"65,000",1000,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +822,A A REAL ESTATE,AGENT,1,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"40,000",650,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +823,A A REAL ESTATE,AGENT,2,BHK,Apartment,Reputed Builder NG Complex,Andheri East,Mumbai,"50,000",1010,Area in sq ft,Furnished,No Deposit,2 bathrooms, +824,A A REAL ESTATE,AGENT,1,BHK,Apartment,Reputed Builder Ashok Nagar Complex,Andheri East,Mumbai,"35,000",610,Area in sq ft,Furnished,No Deposit,2 bathrooms, +825,A A REAL ESTATE,AGENT,2,BHK,Apartment,Kanakia Rainforest,Andheri East,Mumbai,"57,000",750,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +826,Azuroin,AGENT,2,BHK,Apartment,Lodha Luxuria,Thane West,Mumbai,"35,000",887,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +827,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"30,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +828,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"26,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +829,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"24,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +830,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"24,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +831,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"30,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +832,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"30,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +833,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"30,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +834,Prime property,AGENT,2,BHK,Apartment,Vihang Vermont,Thane West,Mumbai,"18,000",750,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +835,Prime property,AGENT,1,BHK,Apartment,Puraniks Rumah Bali,Thane West,Mumbai,"14,000",640,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +836,Prime property,AGENT,1,BHK,Apartment,Khade KIPL Morya,Thane West,Mumbai,"13,000",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +837,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +838,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"30,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +839,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +840,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +841,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +842,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +843,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +844,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +845,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +846,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +847,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +848,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +849,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +850,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +851,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +852,G K GROUP,AGENT,3,BHK,Apartment,Paradise Sai World City Panvel,Panvel,Mumbai,"30,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +853,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Ion 1,Panvel,Mumbai,"12,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +854,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"24,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +855,A A REAL ESTATE,AGENT,3,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"92,000",1500,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +856,A A REAL ESTATE,AGENT,2,BHK,Apartment,Runwal Elina,Andheri East,Mumbai,"50,000",800,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +857,A A REAL ESTATE,AGENT,1,BHK,Apartment,Reputed Builder NG Complex,Andheri East,Mumbai,"35,000",610,Area in sq ft,Furnished,No Deposit,1 bathrooms, +858,A A REAL ESTATE,AGENT,2,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"80,000",1050,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +859,A A REAL ESTATE,AGENT,2,BHK,Apartment,DSK Madhuban,Andheri East,Mumbai,"55,000",800,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +860,A A REAL ESTATE,AGENT,3,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"85,000",1500,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +861,A A REAL ESTATE,AGENT,1,BHK,Apartment,Kanakia Rainforest,Andheri East,Mumbai,"52,000",550,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +862,A A REAL ESTATE,AGENT,2,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"78,000",1050,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +863,A A REAL ESTATE,AGENT,1,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"55,000",700,Area in sq ft,Furnished,No Deposit,2 bathrooms, +864,A A REAL ESTATE,AGENT,2,BHK,Apartment,Kanakia Kanakia Sevens,Andheri East,Mumbai,"56,000",1050,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +865,A A REAL ESTATE,AGENT,3,BHK,Apartment,Sheth Vasant Oasis,Andheri East,Mumbai,"85,000",1300,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +866,A A REAL ESTATE,AGENT,2,BHK,Apartment,DSK Madhuban,Andheri East,Mumbai,"57,000",800,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +867,Om Sai Siddhi Properties,AGENT,2,BHK,Apartment,Lodha Casa Rio,Dombivali,Mumbai,"12,500",909,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +868,Kushvin Properties,AGENT,2,BHK,Apartment,Balaji Delta Tower,Ulwe,Mumbai,"30,000",1275,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +869,Stilt Real Estate,AGENT,4,BHK,Apartment,Dheeraj Realty Dheeraj Insignia,Santacruz East,Mumbai,1.45 L,1450,Area in sq ft,Semi-Furnished,No Deposit,5 bathrooms,East facing +870,Jyoti Enterprise,AGENT,3,BHK,Apartment,Marathon Nexzone Triton 1,Panvel,Mumbai,"35,000",980,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +871,PropertyPistol Realty Pvt Ltd,AGENT,3,BHK,Apartment,DLH The Park Residences Phase 1,Andheri West,Mumbai,1.3 L,1210,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +872,PropertyPistol Realty Pvt Ltd,AGENT,2,BHK,Apartment,FSK Sukhkarta I CHSL,Seawoods,Mumbai,"35,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +873,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"20,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +874,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"22,000",1240,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +875,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Zodiac 1,Panvel,Mumbai,"13,100",960,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +876,G K GROUP,AGENT,3,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"19,500",1460,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +877,G K GROUP,AGENT,3,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"19,500",1460,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +878,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"20,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +879,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +880,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +881,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"22,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +882,H K homes,AGENT,1,BHK,Apartment,Gami Amar Harmony,Taloja,Mumbai,"10,000",690,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +883,H K homes,AGENT,1,BHK,Apartment,Sai Kaveesha,Taloja,Mumbai,"7,000",560,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +884,H K homes,AGENT,1,BHK,Apartment,Shiv Corner,Taloja,Mumbai,"8,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +885,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"20,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +886,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"20,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +887,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +888,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +889,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +890,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +891,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +892,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"23,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +893,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"20,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +894,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"22,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +895,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"22,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +896,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"20,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +897,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"22,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +898,Sanjay,AGENT,2,BHK,Apartment,,Kalyan West,Mumbai,"19,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +899,Om Real Estate Property Consultant,AGENT,2,BHK,Apartment,Varad Varad Heights,Chembur,Mumbai,"40,000",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms, +900,Azuroin,AGENT,3,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"70,000",1320,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +901,Azuroin,AGENT,2,BHK,Apartment,Piramal Vaikunth Thane,Thane West,Mumbai,"46,000",1138,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +902,Azuroin,AGENT,2,BHK,Apartment,Lodha Majiwada Tower 1,Thane West,Mumbai,"34,500",803,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +903,Om Sai Siddhi Properties,AGENT,2,BHK,Apartment,Lodha Casa Bella,Dombivali,Mumbai,"10,000",747,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +904,Om Sai Siddhi Properties,AGENT,2,BHK,Apartment,Lodha Casa Rio,Dombivali,Mumbai,"12,500",909,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +905,Sanjay,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"28,000",1200,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +906,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"26,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +907,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"26,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +908,Prime property,AGENT,3,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"21,000",1105,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,North facing +909,Galaxy homes,AGENT,1,BHK,Apartment,Reputed Builder Bella Vista,Kharghar,Mumbai,"12,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +910,Green Group Real Estate Consultants,AGENT,3,BHK,Apartment,National Harmony,Panvel,Mumbai,"25,000",1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +911,H K homes,AGENT,1,BHK,Apartment,Marwah Group Apartment,Taloja,Mumbai,"6,000",450,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +912,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +913,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,South facing +914,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +915,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +916,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +917,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +918,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"21,500",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +919,G K GROUP,AGENT,2,BHK,Apartment,Paradise Sai World City,Panvel,Mumbai,"22,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +920,Sanjay,AGENT,3,BHK,Apartment,,Kalyan West,Mumbai,"27,000",1200,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +921,G K GROUP,AGENT,3,BHK,Apartment,Indiabulls Greens,Panvel,Mumbai,"25,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +922,DHARTI ESTATE CONSULTANT,AGENT,3,BHK,Apartment,,Santacruz East,Mumbai,1.5 L,2500,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +923,Prem Housing,AGENT,2,BHK,Apartment,Mayfair The View,Vikhroli,Mumbai,"46,000",1020,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +924,Prime property,AGENT,1,RK,Studio Apartment,Haware Haware Citi,Thane West,Mumbai,"8,200",324,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +925,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"11,000",625,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +926,G K GROUP,AGENT,2,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"14,000",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +927,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"12,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +928,G K GROUP,AGENT,1,BHK,Apartment,Marathon Nexzone Aura 1,Panvel,Mumbai,"12,500",825,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,West facing +929,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"11,000",625,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +930,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"14,000",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +931,Prime property,AGENT,1,RK,Studio Apartment,Haware Haware Citi,Thane West,Mumbai,"8,500",324,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +932,Tejasvi Realty Pvt Ltd,AGENT,4,BHK,Apartment,,Juhu,Mumbai,4.5 L,2400,Area in sq ft,Furnished,No Deposit,4 bathrooms,West facing +933,Tejasvi Realty Pvt Ltd,AGENT,3,BHK,Apartment,,Juhu,Mumbai,1.7 L,1400,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +934,Prime property,AGENT,1,BHK,Apartment,Puraniks Tokyo Bay Phase 2C,Thane West,Mumbai,"12,900",630,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +935,Prime property,AGENT,1,BHK,Apartment,Khade KIPL Morya,Thane West,Mumbai,"13,000",630,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +936,Prime property,AGENT,3,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"16,800",1105,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,North facing +937,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"10,999",625,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +938,Prime property,AGENT,2,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"16,000",950,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +939,Prime property,AGENT,2,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"14,000",902,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +940,Prime property,AGENT,1,BHK,Apartment,Khade KIPL Morya,Thane West,Mumbai,"13,500",650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +941,Shree Homes Enterprises,AGENT,1,BHK,Apartment,Reputed Builder Shiv Kalptaru Apartment,Kamothe,Mumbai,"11,500",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms, +942,Green Group Real Estate Consultants,AGENT,3,BHK,Apartment,National Harmony,Panvel,Mumbai,"25,000",1400,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,East facing +943,Shree Riddhi Siddhi Estate Consultant,AGENT,1,BHK,Apartment,,Ghatkopar West,Mumbai,"22,000",430,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms, +944,Shree Riddhi Siddhi Estate Consultant,AGENT,1,BHK,Apartment,,Ghatkopar West,Mumbai,"21,000",410,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,East facing +945,Shree Riddhi Siddhi Estate Consultant,AGENT,1,BHK,Apartment,,Ghatkopar West,Mumbai,"24,000",400,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,South facing +946,Shree Riddhi Siddhi Estate Consultant,AGENT,1,BHK,Apartment,,Ghatkopar West,Mumbai,"21,500",400,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,South facing +947,Sanjay,AGENT,1,BHK,Apartment,Vasant Park,Kalyan West,Mumbai,"10,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +948,Sanjay,AGENT,2,BHK,Apartment,Reputed Builder Madhav Shruti,Kalyan West,Mumbai,"25,500",950,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,West facing +949,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"12,000",650,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,West facing +950,Sanjay,AGENT,3,BHK,Apartment,Godrej Hill,Kalyan West,Mumbai,"28,000",1300,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,West facing +951,Prime property,AGENT,1,BHK,Apartment,Puraniks Aarambh,Thane West,Mumbai,"12,000",700,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,North facing +952,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"11,999",625,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,North facing +953,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Giriraj Horizon,Kharghar,Mumbai,"32,000",1250,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +954,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,,Kharghar,Mumbai,"30,000",1180,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +955,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Kesar Exotica Phase I Basement Plus Ground Plus Upper 14 Floors,Kharghar,Mumbai,"46,000",1850,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +956,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Paradise Sai Mannat,Kharghar,Mumbai,"42,000",1650,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +957,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Regency Regency Gardens,Kharghar,Mumbai,"48,000",1450,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +958,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Mahaavir Heritage,Kharghar,Mumbai,"25,000",1300,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +959,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Paradise Sai Spring,Kharghar,Mumbai,"32,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,West facing +960,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Paradise Sai Mannat,Kharghar,Mumbai,"32,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +961,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Juhi Niharika Residency,Kharghar,Mumbai,"24,000",1300,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +962,Jai Shree Ganesh Realtors,AGENT,1,BHK,Apartment,CGEWHO Kendriya Vihar,Kharghar,Mumbai,"28,000",650,Area in sq ft,Furnished,No Deposit,1 bathrooms,East facing +963,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Regency Regency Gardens,Kharghar,Mumbai,"50,000",1500,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +964,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Reputed Builder Kesar Harmony,Kharghar,Mumbai,"55,000",1715,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +965,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Concrete Sai Saakshaat,Kharghar,Mumbai,"42,000",1650,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +966,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Reputed Builder Raghunath Vihar,Kharghar,Mumbai,"38,000",1600,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms, +967,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Seawood Seawoods Concept Unnathi,Kharghar,Mumbai,"25,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +968,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Bhagwati Greens 1,Kharghar,Mumbai,"65,000",1800,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +969,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Bhagwati Greens 1,Kharghar,Mumbai,"68,000",1800,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +970,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Regency Park,Kharghar,Mumbai,"42,000",1150,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +971,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Regency Regency Gardens,Kharghar,Mumbai,"42,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +972,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Devisha Hex Blox,Kharghar,Mumbai,"35,000",1230,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +973,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Kesar Exotica Phase I Basement Plus Ground Plus Upper 14 Floors,Kharghar,Mumbai,"46,000",1850,Area in sq ft,Unfurnished,No Deposit,3 bathrooms,NorthEast facing +974,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,CGEWHO Kendriya Vihar,Kharghar,Mumbai,"36,000",1500,Area in sq ft,Furnished,No Deposit,3 bathrooms, +975,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Sai Yashaskaram,Kharghar,Mumbai,"32,000",1300,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +976,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Keshav Winds,Kharghar,Mumbai,"25,000",1250,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +977,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Giriraj Horizon,Kharghar,Mumbai,"30,000",1300,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,East facing +978,Jai Shree Ganesh Realtors,AGENT,3,BHK,Apartment,Nisarg Hyde Park,Kharghar,Mumbai,"32,000",1700,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,East facing +979,Jai Shree Ganesh Realtors,AGENT,2,BHK,Apartment,Gajra Bhoomi Heights,Kharghar,Mumbai,"31,000",1250,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +980,Jai Shree Ganesh Realtors,AGENT,4,BHK,Apartment,B Chopda Oval Apartments,Kharghar,Mumbai,"36,000",2500,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,East facing +981,DHARTI ESTATE CONSULTANT,AGENT,2,BHK,Apartment,Vaibhav Paradise,Santacruz East,Mumbai,"70,000",1000,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +982,DHARTI ESTATE CONSULTANT,AGENT,2,BHK,Apartment,Reputed Builder Silver Avenue,Santacruz East,Mumbai,"75,000",1000,Area in sq ft,Furnished,No Deposit,2 bathrooms,East facing +983,DHARTI ESTATE CONSULTANT,AGENT,2,BHK,Apartment,Reputed Builder Silver Avenue,Santacruz East,Mumbai,1.1 L,1200,Area in sq ft,Furnished,No Deposit,3 bathrooms,East facing +984,DHARTI ESTATE CONSULTANT,AGENT,2,BHK,Apartment,Kamla Habitat,Santacruz East,Mumbai,"70,000",1200,Area in sq ft,Furnished,No Deposit,2 bathrooms,North facing +985,Azuroin,AGENT,1,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"26,000",486,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,NorthEast facing +986,Azuroin,AGENT,2,BHK,Apartment,Ashar Edge,Thane West,Mumbai,"40,000",753,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,NorthEast facing +987,Azuroin,AGENT,3,BHK,Apartment,Kalpataru Sunrise,Thane West,Mumbai,"45,000",1155,Area in sq ft,Semi-Furnished,No Deposit,3 bathrooms,NorthEast facing +988,Azuroin,AGENT,2,BHK,Apartment,Rustomjee Azziano Wing G,Thane West,Mumbai,"42,000",747,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +989,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"25,000",575,Area in sq ft,Furnished,No Deposit,2 bathrooms,NorthEast facing +990,Azuroin,AGENT,1,BHK,Apartment,Lodha Amara Tower 20 21,Thane West,Mumbai,"24,000",553,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +991,Azuroin,AGENT,2,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"26,000",575,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms,NorthEast facing +992,Azuroin,AGENT,1,BHK,Apartment,Lodha Quality Home,Thane West,Mumbai,"16,500",418,Area in sq ft,Semi-Furnished,No Deposit,1 bathrooms,NorthEast facing +993,Om Properties,AGENT,2,BHK,Apartment,,Kalwa,Mumbai,"21,000",925,Area in sq ft,Semi-Furnished,No Deposit,2 bathrooms, +994,Rightside Properties,AGENT,5,BHK,Apartment,Hiranandani Evita,Powai,Mumbai,4.25 L,4580,Area in sq ft,Semi-Furnished,No Deposit,4 bathrooms,NorthEast facing +995,Sanjay,AGENT,2,BHK,Apartment,Ajmera New Era Yogidham Phase IV Tower C,Kalyan West,Mumbai,"15,000",650,Area in sq ft,Furnished,No Deposit,2 bathrooms,West facing +996,Prime property,AGENT,1,BHK,Apartment,Haware Haware Citi,Thane West,Mumbai,"11,000",625,Area in sq ft,Unfurnished,No Deposit,2 bathrooms,East facing +997,Sanjay,AGENT,1,BHK,Apartment,,Kalyan West,Mumbai,"9,000",650,Area in sq ft,Unfurnished,No Deposit,1 bathrooms,West facing +998,Prime property,AGENT,1,BHK,Apartment,Raunak Heights,Thane West,Mumbai,"12,990",600,Area 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new file mode 100644 index 000000000..c9deea6df --- /dev/null +++ b/House-rent-analysis-and-prediction-Bombay/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb @@ -0,0 +1,5529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8878eaf0", + "metadata": {}, + "source": [ + "## House Rent Analysis and Prediction of Mumbai" + ] + }, + { + "cell_type": "markdown", + "id": "1a8027b6", + "metadata": {}, + "source": [ + "### 1.Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "f257fbc6", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "id": "d85e7712", + "metadata": {}, + "source": [ + "### 2. Load Dataset\n", + "Main Source: https://www.kaggle.com/datasets/lokeshgupta2020/house-rent-in-mumbai" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "0ca21a21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 16)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df= pd.read_csv(\"house_rent_mumbai.csv\")\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "9c536ee3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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Unnamed: 0seller_nameseller_typesizetype_type_of_housenamelocationcitypriceareaarea_typestatusdepositno_bathroomfacing
00Kasturi DevelopersBUILDER2BHKApartmentShagun White WoodsUlweMumbai17,0001180Area in sq ftUnfurnishedNo Deposit2 bathroomsNorthEast facing
11Kasturi DevelopersBUILDER3BHKApartmentSurana Tulsi GauravUlweMumbai22,0001720Area in sq ftUnfurnishedNo Deposit3 bathroomsNorthEast facing
22Kasturi DevelopersBUILDER2BHKApartmentTricity EnclaveUlweMumbai12,5001150Area in sq ftUnfurnishedNo Deposit2 bathroomsNorthEast facing
33sellerVERIFIED OWNER2BHKApartmentGodrej PrimeChemburMumbai55,0001050Area in sq ftSemi-FurnishedNo Deposit2 bathroomsNaN
44sellerVERIFIED OWNER2BHKApartmentTanvi Eminence Phase 2Mira Road EastMumbai18,5001165Area in sq ftSemi-FurnishedNo Deposit2 bathroomsEast facing
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 seller_name seller_type size type_ type_of_house \\\n", + "0 0 Kasturi Developers BUILDER 2 BHK Apartment \n", + "1 1 Kasturi Developers BUILDER 3 BHK Apartment \n", + "2 2 Kasturi Developers BUILDER 2 BHK Apartment \n", + "3 3 seller VERIFIED OWNER 2 BHK Apartment \n", + "4 4 seller VERIFIED OWNER 2 BHK Apartment \n", + "\n", + " name location city price area \\\n", + "0 Shagun White Woods Ulwe Mumbai 17,000 1180 \n", + "1 Surana Tulsi Gaurav Ulwe Mumbai 22,000 1720 \n", + "2 Tricity Enclave Ulwe Mumbai 12,500 1150 \n", + "3 Godrej Prime Chembur Mumbai 55,000 1050 \n", + "4 Tanvi Eminence Phase 2 Mira Road East Mumbai 18,500 1165 \n", + "\n", + " area_type status deposit no_bathroom facing \n", + "0 Area in sq ft Unfurnished No Deposit 2 bathrooms NorthEast facing \n", + "1 Area in sq ft Unfurnished No Deposit 3 bathrooms NorthEast facing \n", + "2 Area in sq ft Unfurnished No Deposit 2 bathrooms NorthEast facing \n", + "3 Area in sq ft Semi-Furnished No Deposit 2 bathrooms NaN \n", + "4 Area in sq ft Semi-Furnished No Deposit 2 bathrooms East facing " + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "434b842f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0seller_nameseller_typesizetype_type_of_housenamelocationcitypriceareaarea_typestatusdepositno_bathroomfacing
995995SanjayAGENT2BHKApartmentAjmera New Era Yogidham Phase IV Tower CKalyan WestMumbai15,000650Area in sq ftFurnishedNo Deposit2 bathroomsWest facing
996996Prime propertyAGENT1BHKApartmentHaware Haware CitiThane WestMumbai11,000625Area in sq ftUnfurnishedNo Deposit2 bathroomsEast facing
997997SanjayAGENT1BHKApartmentNaNKalyan WestMumbai9,000650Area in sq ftUnfurnishedNo Deposit1 bathroomsWest facing
998998Prime propertyAGENT1BHKApartmentRaunak HeightsThane WestMumbai12,990600Area in sq ftSemi-FurnishedNo Deposit2 bathroomsNorth facing
999999Prime propertyAGENT2BHKApartmentPuraniks Tokyo Bay Phase 2AThane WestMumbai20,0001050Area in sq ftSemi-FurnishedNo Deposit2 bathroomsNorth facing
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" + ], + "text/plain": [ + " Unnamed: 0 seller_name seller_type size type_ type_of_house \\\n", + "995 995 Sanjay AGENT 2 BHK Apartment \n", + "996 996 Prime property AGENT 1 BHK Apartment \n", + "997 997 Sanjay AGENT 1 BHK Apartment \n", + "998 998 Prime property AGENT 1 BHK Apartment \n", + "999 999 Prime property AGENT 2 BHK Apartment \n", + "\n", + " name location city price \\\n", + "995 Ajmera New Era Yogidham Phase IV Tower C Kalyan West Mumbai 15,000 \n", + "996 Haware Haware Citi Thane West Mumbai 11,000 \n", + "997 NaN Kalyan West Mumbai 9,000 \n", + "998 Raunak Heights Thane West Mumbai 12,990 \n", + "999 Puraniks Tokyo Bay Phase 2A Thane West Mumbai 20,000 \n", + "\n", + " area area_type status deposit no_bathroom \\\n", + "995 650 Area in sq ft Furnished No Deposit 2 bathrooms \n", + "996 625 Area in sq ft Unfurnished No Deposit 2 bathrooms \n", + "997 650 Area in sq ft Unfurnished No Deposit 1 bathrooms \n", + "998 600 Area in sq ft Semi-Furnished No Deposit 2 bathrooms \n", + "999 1050 Area in sq ft Semi-Furnished No Deposit 2 bathrooms \n", + "\n", + " facing \n", + "995 West facing \n", + "996 East facing \n", + "997 West facing \n", + "998 North facing \n", + "999 North facing " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "9fc0a681", + "metadata": {}, + "outputs": [], + "source": [ + "# We can see Column 'Unnamed: 0' is not necessary so we can drop it\n", + "df.drop('Unnamed: 0', axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4ef1c9d8", + "metadata": {}, + "source": [ + "### 3. Exploratory Data Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "90739620", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 seller_name 1000 non-null object\n", + " 1 seller_type 1000 non-null object\n", + " 2 size 1000 non-null int64 \n", + " 3 type_ 1000 non-null object\n", + " 4 type_of_house 1000 non-null object\n", + " 5 name 777 non-null object\n", + " 6 location 1000 non-null object\n", + " 7 city 1000 non-null object\n", + " 8 price 1000 non-null object\n", + " 9 area 1000 non-null int64 \n", + " 10 area_type 1000 non-null object\n", + " 11 status 1000 non-null object\n", + " 12 deposit 1000 non-null object\n", + " 13 no_bathroom 999 non-null object\n", + " 14 facing 853 non-null object\n", + "dtypes: int64(2), object(13)\n", + "memory usage: 117.3+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "a419b589", + "metadata": {}, + "outputs": [], + "source": [ + "# We have only 2 numerical features- size,area\n", + "# 13 Categorical features- seller_name,seller_type ,type_ ,type_of_house,\n", + "# name,location, city ,price ,area_type,status,deposit,\n", + "# no_bathroom,facing\n", + "# Target Feature ========>>> price >>>" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "c431d921", + "metadata": {}, + "outputs": [], + "source": [ + "# lets convert our target feature 'price' from object to integer\n", + "df['price']=df['price'].str.replace(',','')\n", + "df['price']=pd.to_numeric(df['price'], errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "15738279", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 seller_name 1000 non-null object \n", + " 1 seller_type 1000 non-null object \n", + " 2 size 1000 non-null int64 \n", + " 3 type_ 1000 non-null object \n", + " 4 type_of_house 1000 non-null object \n", + " 5 name 777 non-null object \n", + " 6 location 1000 non-null object \n", + " 7 city 1000 non-null object \n", + " 8 price 887 non-null float64\n", + " 9 area 1000 non-null int64 \n", + " 10 area_type 1000 non-null object \n", + " 11 status 1000 non-null object \n", + " 12 deposit 1000 non-null object \n", + " 13 no_bathroom 999 non-null object \n", + " 14 facing 853 non-null object \n", + "dtypes: float64(1), int64(2), object(12)\n", + "memory usage: 117.3+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "0a38a614", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizepricearea
count1000.000000887.0000001000.000000
mean1.96200031185.7406991025.603000
std0.81561121686.584537554.974035
min1.0000003000.000000200.000000
25%1.00000014500.000000650.000000
50%2.00000024000.000000950.000000
75%2.00000042500.0000001200.000000
max5.00000098000.0000007000.000000
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" + ], + "text/plain": [ + " size price area\n", + "count 1000.000000 887.000000 1000.000000\n", + "mean 1.962000 31185.740699 1025.603000\n", + "std 0.815611 21686.584537 554.974035\n", + "min 1.000000 3000.000000 200.000000\n", + "25% 1.000000 14500.000000 650.000000\n", + "50% 2.000000 24000.000000 950.000000\n", + "75% 2.000000 42500.000000 1200.000000\n", + "max 5.000000 98000.000000 7000.000000" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()\n", + "# we can observe that the price of 75% and max have huge differnce " + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "026290be", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ANIKET RAY\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n", + " self._figure.tight_layout(*args, **kwargs)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hsjlRa3HCbHdBp5JBo5DC6nDD5vLA5vBAq5JCIRVDIhLB4vTA6nBBn6GAw+OFuckFrVKGDLkEIgA2jwdKiQRWpwfmpubtySVw+XywOc9sL0MmgdXtQYPVhewMGdRyKRrt7uBr1HIJRPChye2Fzwd4fYDF7kamSopMhRQOtxemJhfUcinkUhFEPsDu8QI+wAfA5nQjSy2HIUMOi8MNs90Nq8ONDIUUIgBiMaBRyGB3eWBq3qdCKobZ7kZj83mQS8Qw213QKGUwZMihU8vDzpVWdWZ5ax2tt4Udb4ZcAqfXAzHEsLu9wTrIJGLUWRzIVMqgUUphc7phbvIfh1wCKCQSNIaea6UUF2arw/b1Y70N5pB9aZRSuNxeNDSFH0PLOqnkEtRZ7MhQypCpkKHJ6YGpyQm1QgqpWASFROx/n+1uaFVSZKvl6KZVttk56ixOmu1osDlhc7ihz1DA7fOhqbntZSqlUMslsLs9MDe5oVFKoZKIAbEINqcn+DlRSMRweLzw+gCrw41MpRRKmQQ2pxsWu7/NqWT+NioC0OT2+tuISgavD2FtXS4VQyOXwgug3uqMeH/jSfa952ck/XX4oHzkyJH46U9/ikceeSRseX19PSZMmIDNmzcntZ0bb7wx7PcnnngCL7/8Mr744gsG5UQU5lhDE2av34OtB2uhlkvwl/93OWQSEZ7/uArlVXXBcqUFBtx99SWYta4CSyYMwJ//dzBi/X2jLkEXjRJzN1QG16nlEiyfPBgvfVyFraHljQbMv7Ev5m/ci+dvH4TZ6/eEba/EqMfCm/rheIMdr279PmLdlOJ83LOuAjanB6UFBswcYYTN6cHrZd+H7XvFlCvw4uaDYfsuNuoxtTgf67YdwMQhvTBn/R4smTAAK8urw/YTKDfxtW0ovkSP+WP74uGNe7H1YG2wzPACA5ZMGIDcLFXS5/xQnRUPhZwjABjVuyvmj+2HeRsrURalDpNXbsegnlkRx3331Zdg+uodsDk9wXPzxPhC9NJnxNxXiVGP+WP74c6/7kCtxYk7ii/C/111UdRyC27shxMNdjy2dX/4eZ18BV78OPy8Bvb9+Kb9+N/XNed0jjqLw3VWPLyhEl+faMTaO4fiR1MTXmjR9gKf9znr9+CZXwxEbrYSj2zYm7CNRWsnj9zYF2IRMG3Vdjx2c3/YnJ6I/Y3uk4N5N/SN+CyWGPV4cnwhejZ/tloKvZYERHvvky1Hwiby+Xy+VFfifBKLxdDr9SguLsaaNWuQkeH/4J88eRK5ubnweDyt3qbH48Fbb72FyZMno6KiAn379k3qdWazGTqdDiaTCVqtNma5nNw8jFz4Zsz1mxfeippjR1pd7/MhUV0BYdWXOq9k29+5MtmcmLmuItg5zhxpxAU6JTZVHg/rpAOKjXpMK87HihbBa8C795Zg0X++Dls3c6QRFYfro5YvNeqx4KZ+WPjvfTHXX1/YA3M37I1al6Ke2Xhhc1Ww7HWFPfBQSNl4+w68vuJwfdxjCpQDEHNbwwsMeH5iUVJ3+o7W2yL+AEm2ri9sroo47pa/A/7gacmEARABeDDKvgJlphbnY/rqHdjyh6vD/pBqWW7mSCNue3VbUnUtMeoxsEV9gNado1Rrr/Z30mzHA2/uxtaqOiyfPBgnzXb8J0Hby1BI8PzmqqTbWGQ7MWDGyEuglkux90dT1P0len+fvnVgxB3zlteSUKHvfbLlSPg6/JhyAPjf//6HEydOYOjQofjhhx/OejuVlZXQaDRQKBT4zW9+gw0bNsQNyB0OB8xmc9gPEbWPVLW/WoszrHMsystCjlYZtTMGgPKqOuRoFTHX+yCKWFeUlxWz/NaqOrg9vrjrc2J8XV5eVYeivKywsi0DhXj7Drw+0TEFysXb1paDtai1OKOua6nR7o66nWTq2vLf0X4HgLKqOv8wlBj7CpTJ0SoAAFanJ265DEX4F9Xx6loWpT5A685Re0tV+6u3OoN3tnO0CnRLou1lKKStamOR7aQWGQoppBJxzP0len/rrZHvY8trSajQ9z7ZciR8nSIo79GjBz799FMUFhbiiiuuwCeffHJW27n00kuxe/dubNu2Db/97W8xefJk7N+/P2b5xYsXQ6fTBX/y8vLO8giIqLVS1f7MdlfY7w63Fw63N+5rLPbY39g1NrkiliXanjnKa5J9fct1iX6P9fp4xxQol2hbjfb4xxEQ63iTrWu0stFe22h3JTy3geNOttzZ1LVlnYQode3PHfy3xe5Jqu1F+6y29v2w2D1obHLFfF3CNhtS7zPL4r+3gfc+2XIkfB0+KA/kJVcoFFi7di3uvfdeXHvttXjppZdavS25XA6j0YjLL78cixcvxmWXXYbnnnsuZvm5c+fCZDIFf44c4RAOovaSqvanVcrCfldIxVBI419qNUpJzHWZKlnEskTb00Z5TbKvb7ku0e+xXh/vmALlEm0rUxn/OAJiHW+ydY1WNtprM5WyhOc2cNzJljuburaskxClrv2d+QZCo5Qk1faifVZb+35olBJkNj/YnEz5lkLrfWZZ/Pc28N4nW46Er8MH5S2HzM+bNw9r1qzB008/fc7b9nq9cDgcMdcrFIpgCsXADxG1j1S1P4NGjuEFhuDvFUcaUGN2oNioj1q+2KiPu14EH0parKs40hCzfKlRD6lEFPGa0PU1ZnvMulQcaQgre7JF2Xj7Drw+0TEFylUcaYhZz+EFBhg0yY2DzVRKo24n3vZDj7Xlcbf8HfCP+81USqGNsa9AmRqzv0/IkEvilrM6wu+MxjuvJVHqA7TuHLW3VLW/7Aw5So3+9ldjdqDGbE/Y9qwOd6vaWGQ7McDqcMPt8eJkjP3F+yyWGPXIzoh8H1teS0KFvvfJliPh6/BBeXV1NQyG8A/rhAkT8MUXX2DFihVJb2fu3LnYsmULfvjhB1RWVmLu3Ln45JNPMGnSpLauMhGlMZ1ajiUTBgQ7yRVl1bgwW4VZIwsiOmt/hpMCzF6/B1OL86OutznceHxc/7AOfUVZNWaOMKK0RUfsz77SDzP+vgvzx/aLCAJKjHo8clN/5GapIvYVeEhxRVn1mbqNLEAPXXjZFWXVmDWyIGLfgYwmXx8zYWpxfvCYWtahOGQ/3xw348nxhREBxfACA5ZOGJD0w2kXZqvxxPjCiH19fcyERVGWh9Yh6nGPKAj+Hjg3T4wvxIXZalwQY1+BrCqz1+8BAPz1sx/ilnO6fBHv6awRBcGAsuW+vzkePia7teeos+imVeKJ8f1RWmDA7PV7cPlFXTBzhDHm5332+j1wunxYeFO/6G0syvvR8vOy8KZ+yMlUYsaaXeihU0Xd34HjZiwaF/3z8OT4wqhpEVteSwJavvfJliPh6/DZV9rK9OnT8dFHH+H48ePQ6XQYMGAAZs+ejZ/97GdJb4PZV4hSp72yPwQEcgY3NucMbpmnPFMlhbJFnvIuGQo4m/OUZyr9uchb5ilvtPvXZbbIU56pkkLTnKfcZHNBp5YhozlPeeA1GbHylCulyFT685Sbm1xQySWQS8XR85Sr5DBooucpF4mBzOY85ebmfN2hecq1zbmfG+0uZChkMGjC85QH6hlY3lqBnOCB7Wji5Ck/bXUgQyFDZpw85cFzHSdPeWgZV3Oe99BjaFkntVyCOosDGQopMpWBPOUuqOUSSCUt8pQrpcjOCM9Tfq7nKFXau/0F85Q73dCrw/OUa5RSZCTIU65VyaBMkKc8UyWFukWe8ianG7oWecozm+ckiMhTHvL+xpPse5/unxHqoHnKb7nlFqxatQparRa33HJL3LL/+te/ktrm8uXL26JqRNRJ6NQdu0PUqeW4IMmyicq11blqGTjHc3FXzTnt64JsdVLHH61OvWLkpI6no3+e2lo3rTJhsJsqra1Xsu89PyPpr0MG5TqdLviAp06nS3FtiIiIiIji65BB+cqVK4P/fumll+D1eoOTBv3www/YuHEj+vTpgzFjxqSqikREREREQR3+Qc+bb74Zf/vb3wAADQ0NGDp0KJ5++mmMGzcOL7/8coprR0RERETUCYLyXbt2obS0FADwz3/+E926dcOhQ4fw17/+FcuWLUtx7YiIiIiIOkFQbrPZkJmZCQD44IMPcMstt0AsFmPo0KE4dOhQimtHRERERNQJgnKj0YiNGzfiyJEjeP/993HNNdcAAGpqajiZDxEREREJQocPyhcsWIAHHngAF110EYYMGYJhw4YB8N81LyoqSnHtiIiIiIg6aPaVUD//+c9RUlKC48eP47LLLgsuHzVqFMaPH5/CmhERERER+XX4oBwAunfvju7du4ctu/LKK1NUGyIiIiKicB1++AoRERERkdAxKCciIiIiSjEG5UREREREKcagnIiIiIgoxRiUExERERGlGINyIiIiIqIUY1BORERERJRiDMqJiIiIiFKMQTkRERERUYoxKCciIiIiSjEG5UREREREKcagnIiIiIgoxRiUExERERGlGINyIiIiIqIUY1BORERERJRi0lRXgKi9XDboChw/cSJumR7du+OrXdvbqUZEREREfgzKqdM4fuIERi58M26ZzQtvbafaEBEREZ3B4StERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQTkRERESUYgzKiYiIiIhSjEE5EREREVGKMSgnIiIiIkoxBuVERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQTkRERESUYtJUVyBdLF68GP/6179w4MABqFQqXHXVVVi6dCkuvfTSVFeNiNpZjdkOq8MNm8sDm8MDrUoKpVQMHwC72wu32w2dSgGrywP4AB8Aq8MNtVwClUwCEQCnzwufF4AIgA/w+vxlNAr/ZdkHH5QyCWQiEZq8HijEEthabE+rlEImEaPW4kBWhgxKiSRin4HtiURAhkwCj88Hi/NMvTNkEji8Hsiat9/kcEOvUcDq9MDc5EJWhgwZcika7W6Ym1zQqWTIkEsgBWB2eyAXi2F3e9Fod0GrlEEtlwBeH1w+wOX1QCGRRKxvsDmQoZTDkCGHTi3H0XobLA43RABkEjGaXOH1s3k8MNvcyFBIIQIgFgNquRQWhxsmm79OWqUUEIlganL566mWIVMhRZPLg3qbCxlyCTIVUojEorBj0Sil8Li9ON3kglYlgyFDjkaHO+J43V4PnB7A5wMsDjcym8/9aYsDGqV/Oxdmq4OfEZPNiTqrE26PF57m12iV/s+J0+ODx+cLvj9yqRgurxfw/webww2d+sz5AYBjDU1njk0lg7a5XrUWJ8z2M3UPlO8oTDYnahodkItdkIgVsERpA1KJGKetDmQqZVDL/J9jq8ONLLUcDrcXFrsbOpUUKpm/fZib/J9FhVQMh8f/GVWIRQCARpcH5iY3NEoJMuRS2F1emOz+z49KLoHIB9RandAopchWy9FNq0ztCaIOhUF5kj799FPMmDEDV1xxBdxuNx566CFcc8012L9/PzIyMlJdPSJqJ0fqrPixoQnPf1yF8qo6AIBaLsGKKVfgxc0HYWpyYdnEQXjknX24fUgvrCyvDpYDgGKjHrNGGNFFo8Cz//sGEy7Pi1pmanE+3th2GL+++hJ01cixMMb2So0G3PXTiyETi+PuM7C9O0rzMW31DticnuDrF43rj9e2fIef9e2B7llKzN1QifKqOqjlEiybWBSxvRKjHovGFUImFmHe23ujrOuPv3xahekll0RdP39sP/zyL59jaH4X/OHa3nj0nX34v6EXQSkT44WQ8xqo3/wb++I3f9+JWosTxUY9FozthwVv78XmA6fOnP/JV+DFjw9ia4t9TSnOxz3rKqCWS/DGXUPxyIZ9UY9l9j/3IEstxVM/H4iHN1ZGLfPUe/vwUfM+Q8/rr1Zux6CeWXhifCF66TNwrKEJC97ei9uu7Bl27tRyCZZPHoyXPq4Kq+eo3l3x4LV98Nim8LoNLzBgyYQBcHu8wfekZZ2mrPwSR+qbwsrnZqnQERxraMLsf+7Blb10uKkoDwvifL6nl+Sj0e7B62Xfo+JwA5ZNLMLTH34bcc4Cnweb0xP8LD71/gFMHpaPXJ0Ss9ZW4GhDU9TPfbFRj5kjjLC7vPjVii8xqGcWnhxfiJ56xgDUNkQ+n8+X6kqko1OnTiEnJweffvophg8fntRrzGYzdDodTCYTtFptzHI5uXkYufDNmOs3L7wVNceOtLrO50OiugLCqW861ZXaXrLtL56TZjs2f30SmyqPh3XWM0caUXG4HuVVdfjkgZ/i4Y17UdQzO7ispWKjHmMLeyBHq8SKFh1/aJmintn46kgDruvfHcdM9pjbKy0wJCwT2F7F4XoU9czGC5urzrzeqMfj4/rjhNmO5zefCYpDj6ulEqMej93cHyOf/jTqukdu6oeF/94X87VTi/MBACvKq1HUMxu5OiX+0+K8htZvSnE+pq/eEXz9wJBjiFfPwHEX5WXFPNeB+iok4ojgN7TMwBbnLXT7L2yuQolRjyUTBmDuvypxWV5WRJ1i1TNe/QPv7UMb9kat04PX9sZNL5QHlw0vMOD5iUWCu2Pe2vZnsjkxc20FtlbVYusfrsacDZVx21SpUY/rCnvgoQ17k/o8BN7HwGdxRXk1xhb2wOD8Lnh797G4r7+hsAeOmezB9/zpWwfyjjm1CY4pP0smkwkA0KVLl5hlHA4HzGZz2A8RtY/z0f7qrU7kaJURnXVRXlZwWZPLi/KqurBlLZVX1SFHq0SOVhG3TFFeFrYerEU3rTLu9pIpE1qnorys8NdX1aHJ5UWGQhr2+njbK6uqg8PtjbnO7fHFfW2OVhE8/qK8LHSLcl5D65ejVYS9PvQYkjnueOc6UF+r0xO3TMvzFrr9QJlGuxtbD9ZGrVOseibz3saqk1QS3o1vOViLWoszavn2dK7tr9bixNaqWgCApfl9iXuequqC5ymZz0NA4LMYaJNujy/h6wNtLfD6emvqzzd1DAzKz4LX68V9992H4uJi9O/fP2a5xYsXQ6fTBX/y8vLasZZEndv5aH9muztqIBq6zNzkilgWjX+sqydhmcD/k9leMmVi1a2xyRVRn0Tba2w+1tauAwCL3RPcXzJ1j1e3tjjXjc3jtRNtJ9HyxjjvfzKvb+36aOe50R7/ONrDubY/c8gxtKZNtaZcQOjnsLHJ1eq2Zra745YnShaD8rMwY8YM7N27F2+88UbccnPnzoXJZAr+HDnCYRFE7eV8tD+tUgqFNPKyGbpMq5JFLItGIRVDo5QkLBP4fzLbS6ZMrLplqmQR9Um0vczmY23tOgDQKCXB/SVT93h1a4tzndn88GSi7SRanhnn/U/m9a1dH+08ZyrjH0d7ONf2pw05hta0qdaUCwj9HGaqZK1ua1olH8+jtsGgvJVmzpyJTZs24eOPP8aFF14Yt6xCoYBWqw37IaL2cT7aX3aGHDWNDhQb9WHLK440BJepZGKUGPVhy1oqNupRY7ajxhy5rdAyFUcaUFpgQI3ZHnd7yZQpDqlTxZGG8Ncb9VDJxLA63CgJeX287ZUY9TGDlxKjHlKJKGxbLdfXmB2oMTuC5+qk2R77+JrLh74+9BiSOe7AvuLVN0MuiVum5XkL3X6gTKZSiuEFhqh1ilXPiiMNMfcbeG9j1cntCb+rO7zAAIMm9ePJz7X9GTRyDC8wAAA0ze9L3DZg1ONk83lK5vMQEPgsBtqkVCJK+PqTzW0t8PrsjNSfb+oYGJQnyefzYebMmdiwYQM2b96M/Pz8VFeJiNpZN60SJZfoMWtkQVinvaKsGrNGFqC0wIB71lVg0bhCfH3MhKnF+RGduz/7SgEG53fB+p1HYpaZWpyPA8fMuHdUAYZdoo+5vVKjAb8efnHcMqHbmzWyACvKqsNev2h8IZZv/R5Olw8LbuwXDBBXlFVjanF+RMBYYtTjifGFAHxR1y0aV4jXtnyH+WP7RV2/4MZ+mL1+DzbsOoonxvvPVQ+dCjNHGKMe3/zm8oFjWXBjP3x9zBR+/kcUoNRoiNjX1OJ8rCirxuz1e7Dwpuj1WTS+EDP+vgsPvLUbi8YVxjze0H2GntcVZdXBMhdmq7FkwgB8c9wc8V6sKKvGzBHGiHp+fcyE+WP7RRz78AIDnpowAMVGQ8zzPGPNrrDySycMENxDnmdDp5ZjyYQBKC0wYOOuI3giQZuaWpKPHjoVSoz64Oe2ZbnQz0Pg9wU39sM/dx7BrBEFKDYaMOPvu2J+7v3ZVwrQQ6cKvudPji/kQ57UZph9JUl333031q5di7fffjssN7lOp4NKlVz6KWZfSa10qiu1vbbIvhIQlqfc6Qnmnw7kKfe43dCeZZ7yQC7uZPKUZyqlkEvEqLM6oFXLoIqSpzywPYgATYs85ZkqKTQyCexeD+SBPOVON7pkKGBzetBob87TrfDnKW+0u5CplEETI095ptKfO9vn9cEdJU95YH2DzYkMhQwGTfw85YH6tcxTLhIDGc15ys1N/u3qQvKUB/alVfrzlDfYXFDLJciUSyGSiMKOJbM5T3l983YMmjN5ykOPNzRPudXhhiaQp9zqQIbCv53oecr9OckDuc1VgTzlXh+sTv8x+XOXewGfCF6fDzanBzrVmfMDnMlTHqiTLiRPeWBZaHmhOdv2lyhPeWbI+6BRyvx57VvmKW/OZ64OyVOeqZRBGStPud0NjeJMnnKz3RXWdmutTmgUUmRnME85tS0OhErSyy+/DAC4+uqrw5avXLkSU6ZMaf8KEVHK5AisI87vqknJfnuc5eta5nUODWbP1dnm6O4V8u/WBLYXxzj3OnXbBsi5WaqoxybUILytJHseY70P58NFXdttV9TJMChPEr9QICIiIqLzhWPKiYiIiIhSjEE5EREREVGKMSgnIiIiIkoxBuVERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQTkRERESUYgzKiYiIiIhSjEE5EREREVGKMSgnIiIiIkoxBuVERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQTkRERESUYgzKiYiIiIhSjEE5EREREVGKMSgnIiIiIkoxBuVERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQTkRERESUYgzKiYiIiIhSjEE5EREREVGKMSgnIiIiIkoxBuVERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQTkRERESUYgzKiYiIiIhSjEE5EREREVGKMSgnIiIiIkoxBuVERERERCnGoJyIiIiIKMUYlBMRERERpZg01RUgouguG3QFjp84EbdMj+7d8dWu7e1UIyIiIjpfGJS3wpYtW/DHP/4RO3fuxPHjx7FhwwaMGzcu1dWiDur4iRMYufDNuGU2L7y1nWrT8R2tt6HR7obF7oJBo4DL64XPB3h9gNXhRqZKCrVUArfXBx8Ah9sLi92NTKUUCqkY9TYn1AopFBIx7G4vrA43MuQSKGUSSMSACCLYXB6Ym/yvkUvFaGh+TYZMAo/XA6lYgkanB41NLmSqZNDIJfABsDYv06pkyJBLYHN50Ni8b6nYvx2NUgoRAC8A+ACRCMhQSHFhthoAYLI5UW91QiwWwer0wByyPZ/XB5lMgka7Cxa7G9lqOZze5mNQSCEWiSAVi6DPkEOnloedN5PNiVqLE6Ym/7G0LBtYb3G4kKWWw+n2wuJwQ6uSQaOQosnhPnM+m/cngr/+apkEZrsTarkMMqkYjXY3zE0u6FQyaJRnji3gx3obzCFl1HIJvF4fskPqHXif420nUGez3X+ODHGOO14ZSp7J5kSjzQWpCHABsHs9UIolsDg9wXamlIoBEWB3e9HY5EaGQgKVTAKZWASn1wNAjDqLA5lK//sqcXvR6PXB6/PB5nBDp5bDkOF/j/jekRAxKG8Fq9WKyy67DNOmTcMtt9yS6uoQURs5VGfFQxsqUXG4AcsmFuHlT7/D7UN6YWV5Ncqr6qCWS/Di7YOglosBiPDC5oPYWlUXfH2xUY87Si5GQ5MLy8v8rwld9+hN/bHkv/vwvwOnwpZPLc7H/1v+Ja7+iQGzr+2D2Rsqg69VyyVYMfkKvPhx+L5KjHpMKc7HPesqYHN6gtv5vxVf4vKe2ZhachH+/sUh3D6kF9ZtO4QFN/aDSiLGkvcPYNaoAswN2Udge4vGFWLRpn345NtaLJtYhGf+923EMUwtzsfi/36NR2/uj9wsFQDgWEMTZq/fg60HayPKPvPBN3jw2t54eONe7DxUj2UTi/DU+9+EbXdU766YN7Yv5m/ci7Io+1u37RDmje0Lj8+Heev3RNT7ifGF6KXPCHsPW5Z5fFx/PPrOPswZ0xtNHm/UMqHbiXZMwwsMWDJhQNzjblmGkne8oQnHTTbkZChg94nw0icHcffV/s9qWUh7WDX1Cjz/UWTbmznCiFydCi99chA/69cDk1dux6CeWVg0rhAvffwtNu4+HixfWmDAjBFGTFu1HTanBwDfOxIOjilvheuuuw6LFi3C+PHjU10VImojR+ttwUBtWkk+VpZXo2+uLhiQA8C0knwcNzXhu1NWPN8iIAeA8qo6HDc1YUWLgDywbuG/96J3ri5i+cryakwryceEy/Pw0MbwYHFaST6e/zhyX2Uhr2u5na1VtVhZdqb+fXJ1eHhDJSwuD6YU52Pexr0R9SurqsO8jZWYcHle8PijHcPK8mpc2kOLOev3wGRzwmRzRgSmoWXHD7oQczdUYuvB2pjb7ZOrw7wWAXnoNvrk6jB/41643N6o9X54QyWO1tvwY8h72LLM/I17MaU4HxaXJ2aZwHZiHdOWg7UJjzu0DCXPZHPik29PQSWXwukDHt5YiTuHX4KHN1aGfS6mleRHBOSA/7PywsdV+Oy7Wtwx/JJgWwh8rqeVXBxWfuvBWjy/+WCw/QB870g4GJSfRw6HA2azOeyHiNpHsu2v0e4OBmpFeVkor6oL/j+gKC8L3bRKdNMqIwLIgHjrypq32VJgXzlaRUSw2LIO0V4X7fetIfUvystCWVUdHG4vpBJxzO2VVdUhR6tIap9bDtai1uIfutEyMA0tG3pMsbabzP7KqurggyhmvRvtbphD3sNoZaQSMRxRAvuW24l3TMkcd6AMJd/+ai1O5GQq4IMo+B65Pb6o7aFlQB7g/7wpg68LtIXAex+tfMv2yPeOhIBB+Xm0ePFi6HS64E9eXl6qq0TUaSTb/sxNruC/HW5v2P9Dlwd+Yom3Lt56/9h0zzlvL/T3lsfR2ORCY8hxRmOxe5LeZ6PdBbM98fZi1TXR8oj9xal7o90V9h5GLdOURJkkjinZMtSK9md3weH2hr1H0d6rZD4rgc9JaNlYn51o2+N7R6nGoPw8mjt3LkwmU/DnyJEjqa4SUaeRbPvTqmTBfyuk4rD/hy4P/MQSb1289QqpGBql5Jy3F/p7y+PIVMmQGXKc0WiUkqT3mamUQatMvL1YdU20PGJ/ceqeqZSFvYdRy6iSKJPEMSVbhlrR/pQyKKTisPco2nuVzGcl8DkJLRvrsxNte3zvKNUYlJ9HCoUCWq027IeI2key7S9TKUWJUQ8AqDjSgGKjPvj/gIojDThptuOk2Y7SkOWh4q0rad5mS4F91ZgdwTqE7rM4xvaKW2wv9PfSkPpXHGlAiVEPhVQMt8cbsY/Q+tWYHUntc3iBAQaNHAaNHMMLDDHLhh5TrO0G6hdvfyVGPUTwxax3plIKbch7GK2M2+OFQiqOWyZTKY17TMkcd6AMJd/+DBo5ahodEMEXfI+kElHU9lBqjPd5s0MqEYW1hcB7H618y/bI946EgEE5EXVqF2ar8cT4QpQY9VhRVo2pxfnYf8yEqcX5wUByRVk1euhUuKRrBmaOLIgIDoqNevTQqTC1JD8imCg26rHwpv44cNwcsXxqcT5WlFVj/c4jwToErCirxqwRkfsqCXldy+2UGg2YWnKm/l8fM+GJ8YXQyCRYVV6Nx8f1j6hfiVGPReMLsX7nkeDxtwygA/v45rgZSycMgE7tTyG3ZMKAiAA1UHbDrqN4cnwhhhcYYm7362OmqHUKbOPrYyYsGlcIWZSAOpA15cJsNS4IeQ9blnl8XCFWlVcjUyaJWSawnVjHNLzAkPC4Q8tQ8nRqOa7+SVc0Od2Qi4BF4wrx+pbvsGhclPYwyojSKJ+3mSMKcJXRgNe3fBdsC4H3dWXZ92HlSwsMmDWyINh+AL53JBwin88X/RYERbBYLKiqqgIAFBUV4ZlnnsGIESPQpUsX9OzZM+HrzWYzdDodTCZT3LvmObl5cfNTb154K2qOCWMoTKK6AsKpbzrVFUi/+gpdovYXyF9tdbjQJUMBd4s85RqlP594WJ5yhxsahT9/crQ85Wq5P49yWJ5yu/81ihZ5yt1eD2SBPOV2lz/Xcmie8pBlVpc/d7NGKYVMLEZDk9OfTxxn8pRDBGji5CkPbC9anvIstRyuQJ5yuRQSsQiShHnKXVDLJRFlA+utDhd0KjmcHv92A7mkY+UphwjIkEnQaHdCFZKnPFDvzDh5ykOPLVae8njbCdQ5UMagiX3c8crQGYnaX6I85RqlFKoWecrVzXnK5SF5yk9bHchQ+N9XidsLi9cHj9cHm9MDnUoWvBvO946EiHnKW2HHjh0YMWJE8Pf7778fADB58mSsWrUqRbUiorbQMjBrrfzERVIqcJc3vtbnaU603eT2G09G0iUvyFbjggRlknmfk6nzuR8XhWqr83lxV03S+yMSGgblrXD11VeDXywQERERUVvjmHIiIiIiohTjnXIiSuiyQVfg+IkTMdf36N4dX+3a3o41IiIi6lgYlLejwNCXRDN7+nw+eNyRk4mErhfK7KCJ6hooI4T6plNdAWHV99jx47h6/hsx13/y+G1J1SMzMxMiUfTZGc+3ZNsfUUfF9keUOsm0P2ZfaUdHjx7lrJ7UqSXKPHQ+sf1RZ8f2R5Q6ybQ/BuXtyOv14tixYym9W9GWzGYz8vLycOTIEU6M1MY66rlN5Wc/tP01NjZ2yPMrVB318yxE8c61UNqfSCTqUJ8JHoswCe1Ykml/HL7SjsRiMS688MJUV6PNcbbS84fntu2Etr/AhZHnt33xfLcfoZ3rWP2f0Op5LngswpROx8LsK0REREREKcagnIiIiIgoxRiU01lTKBR45JFHoFAoUl2VDofn9vzi+W1fPN/tJ13OdbrUMxk8FmFKx2Phg55ERERERCnGO+VERERERCnGoJyIiIiIKMUYlBMRERERpRiDciIiIiKiFGNQ3o58Ph/MZjP4bC1R+2P7I0odtj+ixBiUt6PGxkbodDo0NjamuipEnQ7bH1HqsP0RJZbSoHzLli248cYbkZubC5FIhI0bN4at9/l8WLBgAXr06AGVSoXRo0fj4MGDYWVOnz6NSZMmQavVIisrC9OnT4fFYgkrs2fPHpSWlkKpVCIvLw9PPfVURF3eeust9O7dG0qlEoWFhfjvf//b6roQEREREZ2NlAblVqsVl112GV588cWo65966iksW7YMr7zyCrZt24aMjAyMGTMGdrs9WGbSpEnYt28fPvzwQ2zatAlbtmzBXXfdFVxvNptxzTXXoFevXti5cyf++Mc/YuHChXj11VeDZT777DNMnDgR06dPR0VFBcaNG4dx48Zh7969raoLEREREdFZ8QkEAN+GDRuCv3u9Xl/37t19f/zjH4PLGhoafAqFwrdu3Tqfz+fz7d+/3wfAt3379mCZd9991ycSiXw//vijz+fz+V566SVfdna2z+FwBMvMnj3bd+mllwZ/v/XWW3033HBDWH2GDBni+/Wvf510XZJhMpl8AHwmkynp1xBR22D7I0odtj+ixAQ7pry6uhonTpzA6NGjg8t0Oh2GDBmCzz//HADw+eefIysrC4MHDw6WGT16NMRiMbZt2xYsM3z4cMjl8mCZMWPG4JtvvkF9fX2wTOh+AmUC+0mmLkREREREZ0ua6grEcuLECQBAt27dwpZ369YtuO7EiRPIyckJWy+VStGlS5ewMvn5+RHbCKzLzs7GiRMnEu4nUV2icTgccDgcwd/NZnOcIyaitsT2R5Q6bH9ErSfYO+UdweLFi6HT6YI/eXl5qa5SmzHZnPiuxoKKw/X47pQFJpsz1VUiCtOR2x+R0KW6/bGPonQk2KC8e/fuAICTJ0+GLT958mRwXffu3VFTUxO23u124/Tp02Flom0jdB+xyoSuT1SXaObOnQuTyRT8OXLkSIKjTg/HGpowc10FRj3zKca/9BlGPf0pZq2rwLGGplRXjSioo7Y/onSQyvbHPorSlWCD8vz8fHTv3h0fffRRcJnZbMa2bdswbNgwAMCwYcPQ0NCAnTt3Bsts3rwZXq8XQ4YMCZbZsmULXC5XsMyHH36ISy+9FNnZ2cEyofsJlAnsJ5m6RKNQKKDVasN+0p3J5sTs9Xuw9WBt2PItB2sxZ/0e3o0gweiI7Y8oXaSq/bGPonSW0qDcYrFg9+7d2L17NwD/A5W7d+/G4cOHIRKJcN9992HRokX497//jcrKSvzqV79Cbm4uxo0bBwDo06cPrr32Wtx555348ssvUV5ejpkzZ+K2225Dbm4uAOD222+HXC7H9OnTsW/fPvzjH//Ac889h/vvvz9Yj3vvvRfvvfcenn76aRw4cAALFy7Ejh07MHPmTABIqi6dRa3FGXGxC9hysBa1Fl7wiIgoNdhHUTpL6YOeO3bswIgRI4K/BwLlyZMnY9WqVXjwwQdhtVpx1113oaGhASUlJXjvvfegVCqDr1mzZg1mzpyJUaNGQSwWY8KECVi2bFlwvU6nwwcffIAZM2bg8ssvh8FgwIIFC8JymV911VVYu3Yt5s2bh4ceeggFBQXYuHEj+vfvHyyTTF06A7PdFXd9Y4L1RERE5wv7KEpnIp/P50t1JToLs9kMnU4Hk8mUtl+lf1djwahnPo25/qP7f4pLcjTtWCOi5HSE9keUrtqr/bGPonQm2JSIJEwGjRy3FOViSnE+pBIxGptc0KpkcHm8WFVeDYNGnngj7cxkc6LW4oTZ7q+rIUMOnVp49QxIt/oSEQmFQSPHz/rk4NIeWgzqmQ2ZRASDRgGpRIQGqxNe+HDSbIfF7m6Tayyv19SWGJRTq+jUctw7+id4aEMlyqvqgstLjHo8Mb5QcBejYw1NEQ/9DC8wYMmEAcjNUqWwZtGlW32JiIREp5Zj/ti+eGhDJV7YXBVcXmzU446Si9HosGF5WXVY/3W211her6mtCTb7CgnTsYamiIAcAMqq6vDwhkpBpZxKt6fw062+RERCY7I58fDGvShr0UeVV9XhuKkJK1oE5MDZXWN5vabzgUE5tYqpyRVxQQsoq6qDqUk4D9Gk21P46VZfIiKhiXcd7aZVRgTrAa29xvJ6TecDg3JqFXOCoFtIT7an21P46VZfIiKhiXcddbi9cV/bmmssr9d0PjAop1bRqmRx12cq469vT9oEdRFSXYH0qy8RkdDEu44qpPFDntZcY3m9pvOBD3pSq+hUMpQY9VG/Aiwx6qFLELS3p9Cn8IvysuBwe6GUSbDrcD2+OW4WXKYYg0aO4QUGbInylejwAoPg6ktEJDTxrqMnzXaUGvXYGqX/+lmfHGiUUnxXY0kqkwqv13Q+ME95O+ooeZIP1Vnx8IbKsMA8kH2llz4jhTWLdKjOGjNTjNDqCvgfpJ2zfk/YhX54gQFLJwxADz7Nf046SvsjSkft2f6iXUcD2Vd88GFFWXVY//WzPjmYP7YvHt64t1WZVHi9prbGoLwddaSg4Md6G8x2N8zNecq1SikuyFanulphTprt+P2bu2Pe1X/61oHophXejKyBvLeNdhcylTIYNMx72xY6UvsjSjft3f5MNidqGh041ehAlloOmVSEeqsTUrEIDU0uqGQSKKQS6FQyaJRSPPDWV1Ef3BxeYMDzE4tiXoN5vaa2xOEr1GrHGpow51+Vgs/NWm91xnzSvqyqDvVWpyCDcp2aF3UionOhU8tRa3Hi9te3xSwTmN3zuxpLwkwqsa7JvF5TW+KDntQq6ZSb1Wx3n9N6IiJKLyabE9/VWFBxuB6nE/RH9TYnKg7Xoz5BOSGl+qWOjUE5tUo65WbVKuN/EZRoPRERpY9jDU2Yua4Co575FONf+ixhCl9TkwvjX/oMCln8UEgtl7RlNYliYlBOrWJqSp87CtrmTDHRlBj1CdM7EhFReoj2LW7FkQYUR+kD1HIJnhzfHzqVDC9NGgSpWBy1HOB/QFQiFp23ehOFYlBOraKWx7+7LKQ7Ck6XB4+P6x8RmJcY9Xh8XCGcLk+KakZERG0p2re4K8qqMbU4PyzgVsslWD55MN6tPI6fv/I57l6zCydN9ohygD8gn1qcz6Cc2g2/v6dWEYv8F6ryKA9QFhv1EIuEc/Fyen1Y8u7XGNgzG1OL8+Fwe6GQilFxpAGPb9qHudf1SXUViYioDUSbYdPm9OCedRWYVpKPOdf1hsvtQ5ZKhgX/3huWBGDH4XrsP2ZCUc9sTGvRV/zjy8P40y8ua89DoU6MQTm1ig/A9JJ8iIGwCRhKjXpMLcmHD8LJsOn1+rD5wClsPnAq6voHx/Ru5xoREdH5EJhhUy2XYFpJfsSEcXWNTuR1UcPp8UZk5VpRVo1lE4uwsrwaL2yuCi4P5BxndhVqLwzKqVXEYhEy5BJcV9gDU0LuKJw025Ehlwjqaz6bM352FZuTw1eIiDoCg0aO0X1ycNuVPSOC6xKjHqP7dIPV4YLbG3njKPSO+sPX94HT7WXOcUoJBuXUKnKJGC9sroo6TXGp0YBF4/unoFbR6VTxL6Y6PuhJRNQh6NRyLBjbF3NbzOAM+Oel8OEAnhxXGBaUR7urrpL5by7VWZ1web2wu72CnM+COiYG5dQqTrc3akAOAFurauF0e9u5RrEZNHKUFhiipnAsLTDAoOEdECKijsLh9kZ93gkAyqvq4PR4kZOpwPACA3Ycqo86ZKXUqMfk4nzcs64CNqcHJUY9nhxfiJ76jPY6DOrEmH2FWqUxysM04euFMyGPw+3F3VdfEvWJ+ruvNsIhoD8giIjo3Fgc8fsfq8MNnVqOJRMGYP7YvlhZXh0RxG+tqsPK8mpMK8kH4L/L/tCGSpw0289bvYkCGJRTq6RTSsTTViemr96Bop7ZWD55MF6aNAjLJw9GUc9sTF+9HaetwpnoiIiIzk3gYc9YMpvX52apMLhXdty76kV5WcHfy6rqUM/+gtoBh69Qq4jForgpEYX0oKfZ7oJaLkFRXhZytApY7B5kKqUoysuCWi6BWUB39YmI6NwYNHIMLzBgS4whi5qQWZx9AJZPHhwcS77naANEIqB/rg4Ot3+Yy8yRRqwoq4bN6YHZ7kLF4XpoVTIYMtr3AVCTzYlaixNmuysl+6f2w6CcWkUmFmHB2H54fNO+sLRSJUY9FtzYD1IBBeU6tQxr7hiKxzbtC/sjosSox5o7hgIi4aRvJCKic2NzenD3CCM8Pl/YNb/UaMDU4oswb0MlHru5P3wAHn9nX4u0vgbcPeISTF+9I5iZq9iox7KJRbhnXQUylTJc99xWAP5UiUsmDEBuluq8H9OxhqaImUrbc//UvkQ+n4+RSTsxm83Q6XQwmUzQarWprs5ZOdbQhHkbK9E3Vxd8Yj0wycLXx0x4fFyhYC4UR+qsmBPlSXzAH5gvHl+IPD6802l0hPZHlK7Od/s7abbjgTd3Y+fhhrCMKoGUvacsDvz5w4NYfEsh/rvnOLZWRd5NLzbqUdQzO+zBz2KjHmMLe6DYaMDwP34SXD68wIDnJxad1zvWJpsTM9dVRE1W0B77p/bHO+XUKma7K+6EPH+wu5ALYQTlFqcn5pjBsqo6WJinnIgo7R1raMIPtdbgne/QoDpg+eTBAICcTEXUgBzwjyWfVpwfsWz+DX3R5A7vL7YcrEWtxXleg+JaizNqQN5e+6f2xwc9qVUSZVcRUvYVc1OiTDHx1xMRkbCZbE7MXr8HDQmu94FsW4mybkVb/0OdDWZbZN92vvsQc8JsZ+zDOhoG5dQqGkX8L1cSrW9P2gSTA2UmeFKfiIiELXA3WSGNH84E1idbLpRMIgp7SDTgfPchyWaToY6DQTm1ikSEiLzfAcVGPSQi4TzomamUoiRGXUuMemRGucgSEVH6CNxNrjjSELNvKjUaAPhT9tY0OjC8wBC1XLFRj4ojDRHLasx2SCXhfdvwdpiALpBNJpr22D+1Pwbl1CpSiRhTi/OjTsgztTg/4sKVShdmq/HE+MKIwLzEqMcT4wtxYbY6RTUjIqK2ELibvKKsOmbfNLn4IqzZdggrplyBkT/piiUTBkQEu6UFBswaWYAVZdVhr505ogDFRgNm/H1XcPnwAgOWThhw3sdzByY6alnX9to/tT9mX2lHHSH7g8nmxANvfYVLe2gjsq98c9yMP/3iMsFcKEw2J57/6CB+ddVFsDo9aGxyIVMlQ4Zcgr9+9gNmjSoQTF3p/OsI7Y8oXZ2v9meyOTFrXQW2HKyFWi7B8xOLoJRJYGpyBfumQK7x0IwlgdzfjXYXMpUyGDRyONxe1NucMDe5oVZIoJJJoJSKkaGQRpRNRZ7yVO2f2g+/v6dW0anlePTm/pizfk/YE+5C/Mu91uLE6+U/4PXyH6Kunzikl6DqS0RErRO4mzxn/Z7gpEGTXt8WtWxoxpLAT0s5WmXM/aRKrLpSxyPooNzj8WDhwoX4+9//jhMnTiA3NxdTpkzBvHnzIGoeu+zz+fDII4/gtddeQ0NDA4qLi/Hyyy+joKAguJ3Tp09j1qxZeOeddyAWizFhwgQ899xz0Gg0wTJ79uzBjBkzsH37dnTt2hWzZs3Cgw8+GFaft956C/Pnz8cPP/yAgoICLF26FNdff32bHW+6zNqVm6XCk7cUotHuhrnJX9dMpRQ9BJKfPMBsd8GgkWPphAFhM3qeNNsxe/0ePrlORJSGAn2lxeFClloOl8eLeWP7wuvzwdzkz5KilkvC8pWrZBJ4fT44PR7s/OE0stVyOL1eWB1uaBRSiEQiyMQiqBVSWOxumO0u6FQyZIT8LuR+mToGQQflS5cuxcsvv4zVq1ejX79+2LFjB6ZOnQqdTod77rkHAPDUU09h2bJlWL16NfLz8zF//nyMGTMG+/fvh1Lp/4t30qRJOH78OD788EO4XC5MnToVd911F9auXQvA/7XaNddcg9GjR+OVV15BZWUlpk2bhqysLNx1110AgM8++wwTJ07E4sWLMXbsWKxduxbjxo3Drl270L9//3M+1nSatetQnRUPtZiUJzBOu5eAJuPJUsWf0VMuoPHvRESUWKCv3HmoHssmFuGp978Ju76vvWMI1HIJlk0swsry6rBvdEuMekwryYcIIjzzv2/DXlds1Ptnq/7XHmw+cCpsG6HlhNovU8cg6DHlY8eORbdu3bB8+fLgsgkTJkClUuHvf/87fD4fcnNz8fvf/x4PPPAAAMBkMqFbt25YtWoVbrvtNnz99dfo27cvtm/fjsGD/ZMHvPfee7j++utx9OhR5Obm4uWXX8bDDz+MEydOQC73/wU8Z84cbNy4EQcOHAAA/PKXv4TVasWmTZuCdRk6dCgGDhyIV155JanjiTWmLp1m7Tpab8Ps9XtizpK5ZMIAwTxAmU51pfOPY8qJUqct2l9oXzlzpBEVh+tR0WIGz25aJVweD57fXBX12l9q1OO6wh54aMPeiHUlRj0GNs/oGdh+tG0IrV+mjkPQ2VeuuuoqfPTRR/j2228BAF999RXKyspw3XXXAQCqq6tx4sQJjB49OvganU6HIUOG4PPPPwcAfP7558jKygoG5AAwevRoiMVibNu2LVhm+PDhwYAcAMaMGYNvvvkG9fX1wTKh+wmUCewnGofDAbPZHPYTTTKzdglFo90dd5ZMIU0elE51pbaXbPsjorZ3PtpfaF9ZlJeFisMNWDaxCBWH6zF99Q7cvWYXJrz8GXw+xLz2b62qQ3dd9LvcZVV1KMrLCm4/1jaE1i9TxyHo4Stz5syB2WxG7969IZFI4PF48MQTT2DSpEkAgBMnTgAAunXrFva6bt26BdedOHECOTk5YeulUim6dOkSViY/Pz9iG4F12dnZOHHiRNz9RLN48WI8+uijCY8znWbtamxyRYzVU8ok2HW4HivKqgVVV87o2bkl2/6IqO2dj/YX2lc63F5MK8mPGF4CAC6PL24/FS91b7Izf7L/oPNB0EH5m2++iTVr1mDt2rXo168fdu/ejfvuuw+5ubmYPHlyqquX0Ny5c3H//fcHfzebzcjLy4sol06zdmnVsqhj9YqNeiybWJRwFs32xBk9O7dk2x8Rtb3z0f5C+0qFVIyivKywfihAr4nfT+kzYl/7k535k/0HnQ+CHr7yhz/8AXPmzMFtt92GwsJC/N///R9+97vfYfHixQCA7t27AwBOnjwZ9rqTJ08G13Xv3h01NTVh691uN06fPh1WJto2QvcRq0xgfTQKhQJarTbsJ5p0mrVLLZNEvTNRXlWHVeXVUMskKapZJG2CGT21nNGzQ0u2/RFR2zsf7S+0r2w582YomSR+PyWXRu+nSkJm9Iw3Q6jQ+mXqOAQdlNtsNojF4VWUSCTwev1fK+Xn56N79+746KOPguvNZjO2bduGYcOGAQCGDRuGhoYG7Ny5M1hm8+bN8Hq9GDJkSLDMli1b4HKd+Trqww8/xKWXXors7OxgmdD9BMoE9nMu0mnWLqvTE3ecttXpaecaxXZBghk9L+BDnkREaSO0r1xRVo0crSJqOafbG7efcnt8UWf+XHBjP3x9zATgzAyhLfsPIfbL1HEI+lbhjTfeiCeeeAI9e/ZEv379UFFRgWeeeQbTpk0DAIhEItx3331YtGgRCgoKgikRc3NzMW7cOABAnz59cO211+LOO+/EK6+8ApfLhZkzZ+K2225Dbm4uAOD222/Ho48+iunTp2P27NnYu3cvnnvuOfz5z38O1uXee+/FT3/6Uzz99NO44YYb8MYbb2DHjh149dVX2+RYc7NUeH5ikeBn7bI64j8cmWh9e+ulz8DSCQNgtruD51WrlDIgJyJKQ6F9pdfnQ2mBISJRwmlr/IcwLQ43Hr+pfzBPeYZCColIBKlYhCdvGQBLc3+hVcnw9K0Dg78LtV+mjkPQQfnzzz+P+fPn4+6770ZNTQ1yc3Px61//GgsWLAiWefDBB2G1WnHXXXehoaEBJSUleO+994I5ygFgzZo1mDlzJkaNGhWcPGjZsmXB9TqdDh988AFmzJiByy+/HAaDAQsWLAjmKAf8mWDWrl2LefPm4aGHHkJBQQE2btzYJjnKg/VIg1m7MhMM+Ui0PhUuyFbjglRXgoiI2kRoX7l0woCIOT7iPcgJAF3Uclyco4m5vps2/u9E54ug85R3NB0hT3L1KQvmvb036leDxUY9Ft3cH/ldY1/siFKlI7Q/onR1PtufyebEcbMdDTZ/djCVXILH/r0PW5ljnNKM8G5rdmKBqYOFPJ3vaZsTM0dcgrEDcpGTqQimmjpptuMivQr1NifyE2+mXR2tt6HR7oa5yT9tskYp5aRBRERpKlpfaXW4MW3VdkwryceQi7rg8XH9cazBjoYmVzAd4oHjZsy5rg9ONNphanKhzuqAQipBhkKKLJUsor891tAEU5Mr2HdoVTLkZqnSoq+m9MSgXCACUweHfgUnxOl8dWoJZCIFXvr4u7C7EKVGPRaNK4TLJ6wx5YfqrHhoQ2XYnf3Ag5699BkprBkREbVWrL7ysZv64YXbi/D3Lw5hYF4WHt4Y/o1uqdGA34+5FLe9+jlqLU6UGPWYP7YfJr3+BS7tnolZIwvQq4saPZr722h9x6jeXbHgxn6Yt3Gv4PtqSk+Czr7SWZhszoiLDOCfNWzO+j0w2YQzc5hCLMVDGysjvhbcWlWHhzdWQiEWzt95R+ttERdVwP/0/cMbKnG03paimhERUWvF6ys/rz6NlWXV6Juri5oOcWtVLf74/gHcdmVPAP5+4PFN+7B0wgCUV9Xh+c0H8cm3p2CyOXGsoSlq39EnV4eHNlSmRV9N6YlBuQCETh3cktCm87UkSIloEVBKxEa7O25dG+3CuqtPRESxxesrczIV2FpVh6K8rJjX/fLm9QFlVXXBtIrlVXXIyVSg1uKEqckVdRvxti20vprSk3Bua3Zi5gTT9QppOt90mro+nepKRETxxesrHW5v2P8TlQuw2D1h6xrtrpjbSLRt9il0rhiUC4A2wXS9QprON52mrk+nuhIRUXzx+kqFVBz2/0TlAjRKSdi6TKUMCk/04DvRttmn0LliUC4AgamDt0T5Wk5o0/lmNk9dXxblK7wSo15QecozlVKM6t0VfXJ1KMrLCmaK2XW4Hl8fMwmqrkREFF+8vrKm0YHhBQZUHGlAsVEfM21v5Y8mzBxpDA5jEUGEmSON2H/MhJpGBwb3yobV6Ynaz1UcaYjZ/wmtr6b0xDzl7ShentZjDU2Ys35P2MUmMJ1vD4E90X2ozoqHN1SGXZiEmNHk+1NmiEUSPLwxevYVj9eDi7syX3VnwTzlRKnTVu0vVl/51IQBcHq8ePSdfZg4pFfEw54lRj2mleRDBBFeL/s+Yt3Cm/pDJgZ6GfzzbETr5wLZV+Zv3JsWfTWlHwbl7SjRRSmQ+1TI0/mabE48/9FB/Oqqi2B1etDY5EKmSoYMuQR//ewHzBpVIJg6HzhuxuP/2R/zjsn8G/qidw8GZ50Fg3Ki1GnL9hetrwSA37/1FXr30OKKXl1gyJRDJBLBYncjUymFVCKCWCTCwrf3Rp1UqNSox+xr+yCviyrYhwXylAf2o2uRp1zIfTWlJ35/LyChUwcLVa3FidfLf8Dr5T9EXT9xSC/BHIMPiPsUPv8aJSJKP9H6yu9qLPjf1zX439c1EeWXTx6M6at3YPnkwVEDcsCf1vdBkQ+1Fmdw27lZqqi5x9Ohr6b0xKBcQNJh5kmz3YWf5Gjw4v8bBLfHB3OTf0YzqUSEGX/fJainz60ON9RyCaaV5EeMKV9RVg2rgykRiYiE7KTZjnqrE2a7GzqVFBqFFFanGyabGxkKCdRyCbLVclgcruD1fnDPbGRnyCGTiGFx+O9mb/79T+F0e7F88mA4PV7kZCohk4hw3GSHTCLGrsP1aGxywePh7RpKHQblApEuM092Ucnw6q8GRx2n/eqvBkMkoNFQWpUUyyYWYWV5NV7YXBVcXmzUY9nEImhV/PgTEQnV4Tor5kbpF6cU5+OedRWwOT0oNuoxa2QBcrXK4Iyeg3pm4U/vHwjeFVfLJVg+eTBe+rgq7E55sVGPqcX5mLl2F4p6ZmFC0QUQtftREp3ByYMEIJ1mnpRIxREBOeCv67yNlZAkSBnVntRyadSZ3cqr6rCqvBpqOYNyIiIhOmm2RwTkgL+vWVlejWkl+QAQnI3zmNkenNFzRVl1WPA9rSQfL7QIyAOvDWyrvKoOC97eC5mA+jDqfPjpE4B0mnmSdSUiovOt3upMembO8qo6ZCgkwRk9W6YsTHaWz61VdTCzX6AU4q1CAUinmSfTra7xxpQLqa5ERHRGouA41syc0WbdbM0sn/H6hUDWFbPd/yyVIYMPfFLbYlAuAOk082Q61VWnliUYUy6cuhIR0RnaBJO7xZqZM9qsm62Z5TNWH3asoQmz1+/B1hb5yZdMGBA1QwvR2eDwFQEIzJIZjdBmydQmqGuiC2l70ijijynXKIRTVyIiOiM7Qx6zryk26rH3mH9mzuWTB2P55MEQi0RYfEshKn80obTF6wKzfMbaVsWRBgD+PkwX5WaNyeaMCMgBYMvBWsxZvwcmm/MsjpAoEoNyAbgwW40nxhdGXIAC2VeElBZRo5Bi0bj+Ueu6aFyhoAJdS4Ix5RaOHSQiEqRuWiWejNEv3lFyMQov0KHicD2mr96B6at34IZlZfhv5XFcdYke00svRqnREHzNirJqzBxhDFsGnMm+sqKsOtjfRrvrXWtxRgTkAVsO1qLWwqCc2oZwIqhOrpc+A0smDECj3R2cJSxTgHnKG2wuLH3vAKYW52P2db1hsXugUUpQY3Zg6XtfY861fQQzxi7R+Hczx5QTEQlWT30Gnr51YDBPuVYlRaZCCqfHiwUb90bcdNl6sBYiAItu7o95Y/vA4wUsDhca7W58+cNpXH5RNqYUXwSXx4cLspVQySSotzrxj18PRY3ZgafeO4AnxxdG9GGJ+go+n0RthUG5QBxraMLcf1UKfrya1enGu3tP4t29J6OunzWyoJ1rFFtmGo1/JyKiSN20SnTTKsOWfXPCHHNmzi0Ha+Hy+uADcP2yrcHZPGNZPnkwfvGXL4K/3/+zSyOCcm2CvoJ9CbUVDl8RgHQar2Z1euKutyVY356UUnHc8e9K5qMlIkorJpsTR+ub4pZptLugaL7+JzuePPS1LRk0cgwvMEQsB/w3zwwaYXw7TOmPd8oFoNbixA+1Vvx7ZjGkEjEam6eud3m8mLFmF2otTsEMCclSyeKmGYz2kEyqOL1ePHJjPzy2aX/YHzylBQYsGNsXTm/8NFlERCQcJpsTx812ZCplWDHlimC/0/JmkFYlA3zAnOv6wGx3YWxhDzg9XtRbXdh+6DRWlFWjqGcWpjbPDBoqcNc7NP2hTiXDk+ML8dCGSmxp8W320gkDBNM/U/pjUC4AdpcLf51+Jea1GCNXYtTjr9OvRJNDOOPVcjIVWDHlCjy/+WBEmsEVU65ATqYihbULJxMDEpEY1/XvjilXXQSH2wuFVIwasx1yiRgeH4NyIqJ0cKyhCbP/+VXYsJVAett71lUEA/PRfXIgl4gx9197IspOLc7HgeNmvD2jGP/dezzsdcCZu94t0x+q5RKsmnoFruvfA5ND+5JGRzsdPXUWDMoFQKuUY06M6YTnb9yLxeMLU1SzSFanBy9uPhg1zaAYIiz9+QDoBPJsqkwsiXpeAf8fPEI6r6E4QQUR0RkmmxOz/7knYhx54No+rSQfL2yuQrFRj99fc6n/+awYZYt6ZuOxTftxff8eEQH50gkDACBiOOm0knw891Fkvxd43fMTi5K+RvP6TvEwKBcAi9MTP3WfgMZpm5pcMR+w2VpVC1OTSzAPpqbTeQ3gBBVEROFqLU5srYqekrC8qg6zr+2NorwsVBxpwHGTPW7ZacX+AP7eUQX4772lsDncyFbLYdD4g+PvaiwRz3cV5WWFfTMcKpASMZnAmtd3SoRPuglAuk1dHw/revbS6YFfIqL2kigl4dH6JkxfvQMvbK6C3RX/ZovD7R+2WNPogMnmglQswiU5mmBQHW1fgdfEkkxfwus7JYNBuQCk09T1rOv5wwkqiIgiJUpJqAjJpKVIkFUrsF4hFUOjlET0A9H2lWibyfQlvL5TMhiUC0A6TV2vU8ni1lVI2VcyE5zXTAGdV4ATVBARRWPQyFHaIiWhWi7BzJFGrLljCGQSMVZMuQIzRxpx2uqMmb6wOCRFYo3ZDqlYHJHOMFr6w3hpFZNNicjrOyWDQbkAXJCtxhMxphN+YnwhLhDQrJ65Waq4dRXSuLgLE5xXoc2WygkqiIgi6dRyLJ0wIBiYq+USLJtYhIrD9Zj0+jb8asWXmLZqO3YfrsfQ/C5YElI2IJB9Zf8xE2aOKMCwSwx4bct3EXNv6NRyLJkwICwwX1FWjVkjCyK22ZqUiLy+UzJEPp/Pl+pKdBZmsxk6nQ4mkwlarTZi/Y/1NpjtbjTaXchUyqBVSgUVkAP+Oi54ey/65OqCecoVUjEqjjTg62MmPHZzf8HV+Wi9DY0h5zVTKRVcQA74xxzOWlcRlgc3oLVP+FOkRO2PiM6ftmh/JpsTJ80OeH0+PPGf/VGTDgwvMOBPv7gMHx2owaXdMuH2epGplEEEwOfzQS6V4Gi9DW9sP4xLcjKx50hD1GtrIEtKoN8I3A1vuaw1WVd4fadEhPX9fSd3QbYaF6S6EgmY7W58dOAUPjpwKur6B8a4BXcMQgzAowncoZmzfg8nqCAiakGnDsmQEiOz1paDtThtdWLuvyqxfPJgTF+9I+b2lk/Owwubq6JmTwnsK1odzrbuvL5TIoIPyn/88UfMnj0b7777Lmw2G4xGI1auXInBgwcD8P/l+8gjj+C1115DQ0MDiouL8fLLL6OgoCC4jdOnT2PWrFl45513IBaLMWHCBDz33HPQaDTBMnv27MGMGTOwfft2dO3aFbNmzcKDDz4YVpe33noL8+fPxw8//ICCggIsXboU119/fZsda+COrrnJP4OYRoB3dNMto0m6yc1S4fmJRWd9N4aIqKM6abbjtNWZsB8y291QyyXokiHH36ZeiW5ZSni8PpibZ8tWysSwuzxotHuwcsoV8LbTgAFe3ykRQQfl9fX1KC4uxogRI/Duu++ia9euOHjwILKzs4NlnnrqKSxbtgyrV69Gfn4+5s+fjzFjxmD//v1QKpUAgEmTJuH48eP48MMP4XK5MHXqVNx1111Yu3YtAP/Xatdccw1Gjx6NV155BZWVlZg2bRqysrJw1113AQA+++wzTJw4EYsXL8bYsWOxdu1ajBs3Drt27UL//v3P+VgP1VnxUIuJbgJjn3vpM855+21Fp44/7i1RxhNKLNYdGiKizupwnRVzm/vI5ZMHxy2rVUnx4u2D8PqW73DP6Evx6Dv7IvrW+WP74e41X6LW4kRp893q9ngmitd3ikfQY8rnzJmD8vJybN26Nep6n8+H3Nxc/P73v8cDDzwAADCZTOjWrRtWrVqF2267DV9//TX69u2L7du3B++uv/fee7j++utx9OhR5Obm4uWXX8bDDz+MEydOQC6XB/e9ceNGHDhwAADwy1/+ElarFZs2bQruf+jQoRg4cCBeeeWVpI4n1pi6o/U2zF6/J+bMk0smDBDMHfOj9TY80mJMuVImwa7D9fj6mAmP3txfMHUlCsUx5USpcy7t76TZjvvf3B3sI2eONOKrw/VRh7AUG/UYN/AC5HVRIUMhRY3ZAZFIhF2H67GirDo4i2dJ84OfgeEtHNdNQiDo7Cv//ve/MXjwYPziF79ATk4OioqK8NprrwXXV1dX48SJExg9enRwmU6nw5AhQ/D5558DAD7//HNkZWUFA3IAGD16NMRiMbZt2xYsM3z48GBADgBjxozBN998g/r6+mCZ0P0EygT2E43D4YDZbA77iabR7o4782Sj3R1zH+3N6nTjwWv7oOJwPaav3oG71+wKPvX+4LV9YHUKp67UuSXb/oio7bVl+6u3OsP6yBVl1Zg/tl9EmsJiox53lFwMvUaOFzdX4cbnyzF99Q5MW7UdFYfrsWxiEdRyCQB/35qjVQRfy1zhJASCDsq///774Pjw999/H7/97W9xzz33YPXq1QCAEydOAAC6desW9rpu3boF1504cQI5OTlh66VSKbp06RJWJto2QvcRq0xgfTSLFy+GTqcL/uTl5UUtl07jtH1e4LFN+yL+iCirqsNjm/bBF3/iM6J2k2z7I6K215btz9zixpTN6UF1nRVFPbOxfPJgvDRpEJZPHoyintn46mgDlpdVR9xFL6+qw8ryakwryQ8us9jD0yEKqa+lzknQY8q9Xi8GDx6MJ598EgBQVFSEvXv34pVXXsHkyZNTXLvE5s6di/vvvz/4u9lsjnphSqeZJ31AzLv65VV1EOxYKOp0km1/RNT22rL9RZtATyoW4YXNVRHLl08ejGf/dzDqdsqr6jCt+ExQrlFKwtYLqa+lzknQQXmPHj3Qt2/fsGV9+vTB+vXrAQDdu3cHAJw8eRI9evQIljl58iQGDhwYLFNTUxO2DbfbjdOnTwdf3717d5w8eTKsTOD3RGUC66NRKBRQKBQx1wcEZp4sizGmXEgzT1odbhg0/okccrQKWOweZCqlOGm2Y/b6PbA6OHyFhCHZ9kdEba8t2192hjyij6w40oCRvbvisrwsjLjU/224zemBVinFfaML8OqW74Pjx0M53P6vc0f17gqfzx/EO9xeZKv9Gc+IUknQn8Di4mJ88803Ycu+/fZb9OrVCwCQn5+P7t2746OPPgoG4WazGdu2bcNvf/tbAMCwYcPQ0NCAnTt34vLLLwcAbN68GV6vF0OGDAmWefjhh+FyuSCT+f9S/vDDD3HppZcGM70MGzYMH330Ee67775gXT788EMMGzbsnI/T5fZi/th+eHzTvrCLTolRjwU39oPLLZwxIVq1FGvuGBoxhKXEqMeaO4ZCJOa9ciIiajvdtEosGleIeRsrg33kG18exj9+PRQ1ZgeWvncgrD8qNRrwwu1FmLm2IiIwV0jFGNW7K+aN7Yt5G/eGvW54gQFL2ikLC1E0gs6+sn37dlx11VV49NFHceutt+LLL7/EnXfeiVdffRWTJk0CACxduhRLliwJS4m4Z8+esJSI1113HU6ePIlXXnklmBJx8ODBwZSIJpMJl156Ka655hrMnj0be/fuxbRp0/DnP/85LCXiT3/6UyxZsgQ33HAD3njjDTz55JOtSokY6+nzisP1eOTtvVg2sQhNLm8wl6pKJsY96yrw2M39MbBndpwtt58jzWmpYt3VXzy+EHkCSuFIFMDsK0Spc67tr/JoPcw2N/L0alidHojg7zs3VR6POqSy1KjHlOahKoEsYSfNdgzN7wKH2xt3RlBmYaFUEfSd8iuuuAIbNmzA3Llz8dhjjyE/Px/PPvtsMCAHgAcffBBWqxV33XUXGhoaUFJSgvfeey8YkAPAmjVrMHPmTIwaNSo4edCyZcuC63U6HT744APMmDEDl19+OQwGAxYsWBAMyAHgqquuwtq1azFv3jw89NBDKCgowMaNG9skR3m2SobnJhbh4RZ/tZcY9XhuYhFEAvqzyeL0RA3IAf/DnpYoXxcSERGdi0yFDBqlLCxXeY5WGfMZp61VdfjN1UZMen1bcFmp0YBuWgXEEMWdETTaDJ9E7eGs75T/7W9/wyuvvILq6mp8/vnn6NWrF5599lnk5+fj5ptvbut6dgix7hT8WG/D/Lf3om+M3N+P3dwfFwgk9/f26tP4xV9ip4F869fDcEV+l3asUWImmxO1FifMdv83EIYMTt7QGfFOOVHqnEv7M9n8s3jOCZlg7y//dzlUMgl+teLLmK97adIg3L1mV9iyUqMBD13fB8fN9ojc5QEb774q+O00+w9qT2d1p/zll1/GggULcN999+GJJ56Ax+P/QGdlZeHZZ59lUN5KjQ43bh/SCyvLq8OeJi9untygUUAPTyZ6EEZoD8oca2jC7PV7sPVgbXAZxw0SEaWHwDX8oev7BANytVyCfH0GTlkccV+rkEZmfd5aVYtjpiZMX70DxUY9lk0swj3rwseeB7KwsP+g9nZWecqff/55vPbaa3j44YchkZxJKTR48GBUVla2WeU6DR+wsrw64mu4QF5VIeUZFIsQMWFDQLFRD7GonSsUh8nmjLigAv6vJ+es3wOTjRNFEBEJVeg1PHQ+j2kl+Xhs0z58/n1d3P6o4khD1HWBDCzRcpcPLzDAoJGz/6CUOKugvLq6GkVFRRHLFQoFrFbrOVeqs0mn3N8+AFOL86POpDa1OF9Qda21OCMuqAGcvY2ISNhCr+Gh83kU5WWhrKoOK8qqo/ZHpQUGTC3Ox4qy6qjbDb2DXl5Vh6K8rODrlk4YAJ1azv6DUuKsxhrk5+dj9+7dwdSEAe+99x769OnTJhXrTBLl9hZU7m8fsHbbIRT1zMa04nw43F4opGJUHGnA2m2H8OCY3qmuYZA5wexsnL2NiEi4Qq/hGXJJMFd54E63zenBPesqMK0kP6w/yuuixpJ3v46apzzaHXSNQorlkwfjgiwVejQPS2H/QalwVkH5/fffjxkzZsBut8Pn8+HLL7/EunXrsHjxYrz++uttXccOL63GaYsQd/w7BDR8RZtgdjbO3kZEJFyh13Cf14dF4/pj3sa9YXe6bU5PxMyeq6ZcgduH9ILL7cPWqjN3uwP91D3rKsLKW50eTF+9A+/dWxp139Gw/6Dz4ayivTvuuAMqlQrz5s2DzWbD7bffjtzcXDz33HO47bbb2rqOHZ5aJok7o6daJonyqtTQKKRYF+NO+bpth/DozeeeIrKtGDRyDC8wYEuUryAD4waJiEiYQq/hVpcXv/7bDrw4aRA0CilKjYawgDug2KjHjsP12H/MhBkjL8G9owvgcHshAvDZ93URD3UWG/XYdbgeJUY9sjPkUffdEvsPOl/OefIgm80Gi8WCnJyctqpThxUrJdSeI6ehlMvw+Dv7wnKnlhr1mH9jP9idbgzIE8bkQQBwqM6Kh1tMIFRi1OOJ8YXoJbCJg441NGHO+j1hF9bhzeMGe/Dp+U6FKRGJUuds21/gGv6rqy7C7iMNKMrLgsfng7GrBo+8vTeszwzcCV+77RDuKr0EXbUKnDLbse2H07jswiws3/p9RB87tSQfa744hEdu7IeeLfov9h/U3s4qKK+urobb7UZBQUHY8oMHD0Imk+Giiy5qq/p1KLEuSt+fsuDIaRuONTQhR6sM3n2uMduRm6VCXhc1Lu6qSWHNw51oaMIPp23IUEhgsXugUUpgdXhwURc1ugvsQnW8oQmffHsKOZmKM+e10YGrf9KVF9VOhkE5Ueqca57y01Yn5r29Nywt4vwb+qCoZzbMdjcylVKIRYDX54NKJoFaLkWT043PvqtDD50KudlK7PyhPryPbXRgWH4XqBVS5GiVMfdda3Gi0e5CplIGg4Z5yun8OavhK1OmTMG0adMigvJt27bh9ddfxyeffNIWdes0MhRSvLr1+6gZWEqMejx968D2r1QMJpsTf4iSJgoQ3vTEJpsTD6ZJXYmIKLYFb+8L6yNtTg/mbtiLYqMeYwfk4vJe2ZCIRcHJfUw2J37/1j5sPViLmSONqCirj9rHBvqCWHRqBuHUfs4qJWJFRQWKi4sjlg8dOhS7d+8+1zp1Oha7O2ZKxLKqOljswsm+kk5potKprkREFF2txRl1/DjgT2nY/wItxr1YjlFPf4pZ6ypwrKEJddYz1/+ivKyYfSz7AhKSs7pTLhKJ0NjYGLHcZDIFZ/ek5KVT6iVTkxMGjRxLJwxAjlYBi92DTKUUJ812zF6/B6Ym4dTVbHdBLZdgWkk+ivKy4HB7oZRJglMrC+m8EhHRGaHT27u98UfZ2p1e/PM3w2B1eKBRSuFweeDyePHuvaVotLuRIZdg5kgjVpRVR02TeNrmhOVIPbLUcjjdXlgcbmhVsuBd90T1S1SWKFlnFZQPHz4cixcvxrp164Izeno8HixevBglJSVtWsHOIJ1SL2WqpFhzx1A8tin8q8QSox5r7hgKsVg40wfpVDIsm1gUNX3jsolFYZNREBGRMLSc3n755MFxy7s8Xly/7PPg76VGA+4ecQmmr94RDMID1/2W2VcAwGp3w+Tz4an3vwnr14YXGLBkwgDktnj+qGX94pUlao2zGr6ydOlSbN68GZdeeimmTp2KqVOn4tJLL8WWLVvwxz/+sa3r2OFplFKUxJgquMSoF1SecqVEEhGQA/5hNo9v2gelRDjpGxVSMVaWV0fUtbyqDqvKq8Ny3RIRUepFm96+4khDxKydASVGPT77Pvwav7WqFi98XIVpJfnBZeVVdVhZXh22DPAH6x6fL2pfseVgLeas3wOT7czwlmj1i1WWqLXOKirp27cv9uzZg1tvvRU1NTVobGzEr371Kxw4cAD9+wsnT3W6sDrcmBJj6vopxfmCmtHT4vTEH/8e5avBVDEnGKtvFtBYfSIiiv4s0IqyakyN0keWGg2YUpyPFWXVEdspr6pDUV5W3GWlBQZMLc6HSCRKesw5n1Wi8+msb8Hm5ubiySefbMu6dFqmJlfUqYIrjjTgnnUVWHvHkFRXMagxwZhxIY3TNqdRXUNxrCIRdVbRnrGyOT3BPvLh6/vA6fZCLhXD4fZi0uvboo4TBwCH2xuxLFMpw0uTBkEhFeOCLBVuefkzPH3rZXHrFOgrTDYnHG4PXpo0KOz5pND9C7VfofSQdFC+Z88e9O/fH2KxGHv27IlbdsCAAedcsc5Eq5RFnSo4QFhjytNn/HuiMeNCqmsAxyoSUWcW6xmrQB85fuAF6Jurw3c1Fhw32WMG5ACiDlHUKKW49S/+8efv3lsKALgwO/61NVMpi3ptjjZOXYj9CqWPpIevDBw4ELW1tcF/FxUVYeDAgRE/RUWx831SdIHpfKMR2nS+Sqk47vh3pYDGaWsTjNXXCmisPsCxikRE8frDn/XJgUYpxcGTjai3OdFDp0SpMXrZYqMeFUcaIpaJfCH/BjCtJB+VR00xx6wPLzBAo5RGvTa3HKcutP6a0k/SUUl1dTW6du0a/De1HZ1ajkXj+uOhKFPXLxrXX1BDFxweD+aP7YfHN+2LqOuCG/vBIaCUmA63F1OK8+EDwsYLBsbqR/tqM5WSGasopM8CEVFb06nlWDJhQMT09j/rk4P5Y/vigTe/CuYsV8slWDnlCkCEsGunP/uKEdNXbw8uKzbqMbU4H154g/+GCBh2sR53/nUHljVPINSyr3js5v6wOtwxr83lVXWYVpyP4QUGLJ0wgNdoOicin8/Xqhx2LpcLv/71rzF//nzk5+cnfgEFxZpm2GRz4qENlbhl0IXB3N8apRQ1Zjs27DqKJ8YXCqahf1djwV+2VOHO4ZfA7fHB3OQf9yyViPDalu/w6+FGXJKjSXU1AQAVh+tx5193xMyp/vqvBmNgz+xUVzOo4nA9xr/0Wcz1G+++SlD1TTfnMs03EZ2b1ra/ltPba5TSsIA8QC2X4NEb+6H/hTpU11qRk6nA96csqLU6UGL030i0OT3QKKSQiACbywNAhO9qGjE0vwtqLE78/JXPI+a0CDzXdU2fHHh8wPiXPos5R4c+Q4Z8g0Yw/TSlr1Z/fy+TybB+/XrMnz//fNSnU6qzOjF+0IVY0SIlU+Cv+TqrcO6QSkXA3Vcb8fDGvRF5yheNK4SodX/jnVc6lQxLJgyIel6XTBgguDzl6ZSvnojofGo5vf13NZaos3ranB78Yf0efHT/T7Fm2yFUHG7Ai7cPQl4XNZa+dyBqn3rPugpc3isbQy/WI7t5H7Ge6xo/8AIA/mE1seboWDROODfOKL2d1QDgcePGYePGjW1clc7L7Y2eIzUwXs2TYDaz9iQSiyICcsCfYnDexkqIxKIU1SySUibB2m2HUNQzG8snD8ZLkwZhxZQrUNQzG+u2HYJSJpyc6kB6PVtARNSeYs18rW6erdMn8mHWCCOKembhq6MNePHjqmA/FSgzrTgfIgB/mz4El+Vl4dF39kEpE6M0xnW3tPm6a9DI8cLtg2LO0TFvYyVOmu1terzUOZ3Vk24FBQV47LHHUF5ejssvvxwZGRlh6++55542qVxn4fX6YuZILa+qE1RQbk2Qp9wqoDzljQ4Xbh/SK+qMnlOL89HocAEQTkaTWGMpOVaRiDq7aN8kquWS4KzNK8qq8fKkQRhbmIv+F2rx7P8ORpSJ1g8ca7BhavFFgA9hd+KLjXrcfbUR5iYX8vQZ0Klkcfu+eqsT3bTKtj1o6nTOKihfvnw5srKysHPnTuzcuTNsnUgkYlDeSjZn/Els4qV8am+JcrAKKUerz4uY30AAwPwb+qaiWnHlZqnw/MSisLGUBg3zlBNR56ZRSlFqNIQFztNK8sOu8buPNqCrRgGr3ROzTEDg94eu74PPvj+N6wq7Y0rxRWHjyaev3o5BPbPw9K0D0ZhgsjlORkdt4ayC8tDsK4HnREUi4QxbSDc6VfyASyegsc+JxjULadxzy6wrocqr6iCc7x/CtRxLSUTU2Vkdbsy/sS8efefMEJKivKywu9/9c3WYvnoHlk8eHFzWskyo8qo6iESi4OuiCdwFT5RCV2gpdik9nXVS6eXLl6N///5QKpVQKpXo378/Xn/99basW6eRTmOJ0ylPudUR/85FovVERCQMpiYX3t17HDcU9sDyyYPxj7uGIkMeHggH0txWHGkI5h1PlPrWYncnLGO2u5GdIY/b92VnCKefpvR1VhHUggULcO+99+LGG2/EW2+9hbfeegs33ngjfve732HBggVtXccOLzCWuGVgLsSxxE6vF/PH9ou4OAXylDu9wsn9naGIf+ci0XoiIhIGrVKGv3z6PXK0Sqwor4bV6YG1xdDPwAyeK8qqMbU4H8VGfdRZPUOp5ZKEZbRKKbpplXhyfGHUvu/J8YUcT05t4qyikpdffhmvvfYaJk6cGFx20003YcCAAZg1axYee+yxNqtgZ5EuY4nlYjH++P4BTC3Ox+zrejfnVJegxuzAsx9+gz+M6Z3qKgZlNs/oWRZlCEuJUY9Mft1IRJQWDBo5BvfKxj3rKjCtJB8GjRz7fjSh1KjH1uZrfOAOeXlVXbBc10xFWJlQJUY9TprtYa+LViZwF7ynPgNP3zoQ9VYnzHY3tEopsjPkDMipzbR68iAAyMrKwvbt21FQUBC2/Ntvv8WVV16JhoaGtqpfh9IRJi/5tsYMmUgEsVgMm9MTnDxILZfA6/XC5fPhJznCObZDdVbM21AZdkEuNeqxaHwheukz4rySOpqO0P6I0lVbtL9jDU3B7FRquQSvTLocPfUq/Nhgh6nJBZVMAr1Gjlc+qcLFOZkoysuC2+vDJV0z8Og7+8Nn/SwwYOGN/VBrceDz7+tw2YVZeL3s+2Bg7k+DWASdSo5GuxtalRTZagbgdH6dVVA+a9YsyGQyPPPMM2HLH3jgATQ1NeHFF19sswp2JB0hKPj+lBlikQQPb6yMOoGC1+fBxV2Fc2zHG5rwyTenkKNVBJ+qr2l04OqfdEWPLOGkQ6TzryO0P6J01VbtLzDTp9Xh/0Z5wdt7W9x0MeCxcf3wxKb9+N+BU1DLJXjx9kE4bmpCd50KarkEHq8Pn39fhxVl1bA5PSgtMODRm/pBIgKa3F7YHG50yVBE7eeeHF+InryhQ+fJWQflf/3rX5GXl4ehQ4cCALZt24bDhw/jV7/6FWSyMxk4WgbunVmii1LgYmO2++8+GzKEN3zlSJ0VczZUxvyab/H4QuQJ5IJlsjkxc11F2N2RgOEFBjw/sUhw55fOHwblRKlzNu0vXp9osjkxc+2umMNSBvbMxgubqzBzpBEVh+tRXlUX9u+WSgsMWHRzf5y2OWHIkMft556+dSDvmNN5cVYPeu7duxeDBg1C165d8d133+G7776DwWDAoEGDsHfvXlRUVKCiogK7d+9u08ouWbIEIpEI9913X3CZ3W7HjBkzoNfrodFoMGHCBJw8eTLsdYcPH8YNN9wAtVqNnJwc/OEPf4DbHf6AyCeffIJBgwZBoVDAaDRi1apVEft/8cUXcdFFF0GpVGLIkCH48ssv2+zYjjU0Yea6Cox65lOMf+kzjHr6U8xaV4FjDU1tto+2YEkweZBFQDnVay3OqAE5AGw5WItai7Oda0RERMlI1CfWWpxRA3LA3xcV5WUB8KdEDE2hGKv/2nqwFlWnLBj/0mdosLsSThREdD6c1ZNuH3/8cVvXI6Ht27fjL3/5CwYMGBC2/He/+x3+85//4K233oJOp8PMmTNxyy23oLy8HADg8Xhwww03oHv37vjss89w/Pjx4N38J598EoA/7/oNN9yA3/zmN1izZg0++ugj3HHHHejRowfGjBkDAPjHP/6B+++/H6+88gqGDBmCZ599FmPGjME333yDnJycczo2k82JBW/vxWV5WZhylX/yAqVMgl2H6/HI23vxp19cJpg7uuam9Jk8qKEp/oUz0XoiImp/JpsTs9fvibipsuVgLR55ey8WjS/EaVv863eGQoqXJg0Ky7KVKPVhYL3FHv/mEicKovNFOEml47BYLJg0aRJee+01ZGdnB5ebTCYsX74czzzzDEaOHInLL78cK1euxGeffYYvvvgCAPDBBx9g//79+Pvf/46BAwfiuuuuw+OPP44XX3wRTqe/Ub/yyivIz8/H008/jT59+mDmzJn4+c9/jj//+c/BfT3zzDO48847MXXqVPTt2xevvPIK1Go1VqxYcc7HV2d14rYre6LicD2mr96Bu9fswrRV21FxuB6/vLIn6gT0V7k2wURGQpo8SC2P/zdnovVERNT+Yn3LqZZL8Msre+KBN3cnnGHT5vTg7jW7wuajSJT6MLBeo5TELZchlwjuW2zqGNIiKJ8xYwZuuOEGjB49Omz5zp074XK5wpb37t0bPXv2xOeffw4A+Pzzz1FYWIhu3boFy4wZMwZmsxn79u0Llmm57TFjxgS34XQ6sXPnzrAyYrEYo0ePDpY5F26vL+Y0wCvLq+HxCmfuyUCawWiElmZQBAQnkGip2KgH56AlIhIec4xvXKeV5GNleTW2VtXB5/PF7YsCj8uFTiQU+u+Wio16VBxpAADUmB1xt33CbMec9XtgSnC3nqi1hBNBxfDGG29g165d2L59e8S6EydOQC6XIysrK2x5t27dcOLEiWCZ0IA8sD6wLl4Zs9mMpqYm1NfXw+PxRC1z4MCBmHV3OBxwOBzB381mc9RyXq8v7nTwQgrKrQ43phTnR0xhX2zUY0pxvrBmyRQBU4vzAUTWdWpxPhiVd2zJtj8ianvn0v60Mb5xLcrLwgubqwAAEpEobl8kEfkv8CvKqrFsYlHEv6P1CfesqwAAzF6/B2vuGIrHN+0Lm+ciMEne7a99gVqL/yFUoQwtpY5B0EH5kSNHcO+99+LDDz+EUpl+TzovXrwYjz76aMJyLWcla+369tRgcwUnZZhWnB9MM1hxpAH3rKvAqqlXpLqKQQqJGP/aeQTTivMxp3mio0ylFCfNdqzfeURQEx2FSocsPOkg2fZHRG3vXNqfQSPH8AIDtjQPYVHLJZhWko+MkCGHNpcHD7z1Vcy+6E+/uMxfzukJ9lmzr+0Np9uLh6/vA5vTg1MWB7pqFPj2ZCPuWVcBW3OiApvTg/99fQJzr+sDq9ODDIUEKqkEHvgw8dUvgkkCGu0uXq+pTQk6KN+5cydqamowaNCg4DKPx4MtW7bghRdewPvvvw+n04mGhoawu+UnT55E9+7dAQDdu3ePyJISyM4SWqZlxpaTJ09Cq9VCpVJBIpFAIpFELRPYRjRz587F/fffH/zdbDYjLy8volw6TQevVclgc3qCdytaEtKYcpfPiz9c2xvzNu6NyDX7+Lj+cHnjP/STCscamiIecBpeYMCSCQOQy7zqrZJs+yOitncu7U+nlmPJhAGYs34Pdhyqx7KJRVhZXh3MqAL4x3/H64tCx48HyhXlZUEiEqGhyYXVn/2Arc2TEC2bWIRBPbNQVlUX/H1leTX++P63wW2UGg2YP7ZvMHAHAJVcEpF2l9drOheCHlM+atQoVFZWYvfu3cGfwYMHY9KkScF/y2QyfPTRR8HXfPPNNzh8+DCGDRsGABg2bBgqKytRU1MTLPPhhx9Cq9Wib9++wTKh2wiUCWxDLpfj8ssvDyvj9Xrx0UcfBctEo1AooNVqw36ilpOI445fU0iE8zZp5JK4ddXI4z8g057UMinmtwjIAX9Kq/kb90ItE84fO0D8jAMcv9h6ybY/Imp759r+crNUeH5iEd69pxSrm5+5Opvx4aHLTprt2HG4Hm98eRhPjuuPD383HGvvGIKLDRl46ueXYd2dQ7DmjiHB/YXaWlWLxzftw7QS/5DI4QUG7DrcwOs1tSlhRSUtZGZmon///mHLMjIyoNfrg8unT5+O+++/H126dIFWq8WsWbMwbNiw4KRG11xzDfr27Yv/+7//w1NPPYUTJ05g3rx5mDFjBhQKBQDgN7/5DV544QU8+OCDmDZtGjZv3ow333wT//nPf4L7vf/++zF58mQMHjwYV155JZ599llYrVZMnTr1nI/T4fHGHRvn8Ajnjq7N7cH8sf1ijrWzuYWTp9zh8obVMVRZVR0cLuGcVyC5vOr8WpSIOgudWh6WjzyZ8eGlRgPuHmHE9NVnnkMrNuoxc0QB7C4PNh+owW1X9sSY57ZicK/siLvaP9RaY+Y/31pVhynF+RheYMBjN/fH9cu2Ri3H6zWdLUEH5cn485//DLFYjAkTJsDhcGDMmDF46aWXguslEgk2bdqE3/72txg2bBgyMjIwefJkPPbYY8Ey+fn5+M9//oPf/e53eO6553DhhRfi9ddfD+YoB4Bf/vKXOHXqFBYsWIATJ05g4MCBeO+99yIe/jwbjXZ33HHaq6ddec77aCtmmxv3v7kbL04ahIckYjQ2uZCpksHt8WL6qu145pcDU13FILPdFRyLWJSXFZb/fUVZtaByqgPpV18iovMtNBNL6PjwacX5cHt9eOym/nC4vTA3uZChlKK20Y5dh0/jrd8Mg83hgbp5PLjT64XbI8FD1/XBO5XHAPiD59nr9+BPv7gM3bRKZMglyFLL8NKkQWHX3tAhKzqVDM9PLMIPddaw5S3Ful5zDDrFI/IF8gbReRdrmuEDJ8y49tnof3EDwHv3laJ3d2F89X7ghBlH65siUjgGnl6/MFslmLp+f8qC72utMet6sSEDF3fVpLCG4dKtvunmbKb5JqK2cbbt77saC0Y982nM9WvuGIJJr28L/h7aF/1Y34QcrQJL3j0QM9uKzenBG3cOQW6WCg9v3Bv2bWXLcgDw0f0/xSU5moT1CpQLxWeGKJG0v1PeEWiVMlzfvxsmXJ6HHK0iIktIrPRQqaCWSfDJgZN4Ylx/NLn8dye0KhlUMjGWb/0edw6/JNVVDFJIxTHzv4sALJ0wIPoLUyRDIY1b36dvHZiSehERpUpoJpa8bBVenDQI0uZvabUqGTLkEqy780q43IDL64VULIY+w3/nOVMpgxgizLm2DzZ/cxJ/+/wQbruyZ/CB0b9NH4KPv6mBSi7FQxsqI4Y7Bq7F00ry8cLmKgwvMMCgkUfUq6XQcgGJnhl6fmIR75gTg3IhcLk8ePDaPnh4Y2VElpBF4wrhcglnnLbT68GdpZdg3sbKsHF3pc11dXqFU9dGuztm/veyqrqEM8K1N0uC+lrsbnTjDV4i6kQCmVj+9P4BzBpVEJFNa1Tvrpg3ti+sDg9Omu3Qa+SoPNqAx//zdfDudrFRj3tGGnFd/x549J19YRlbio16jC3sEfP5o/KqOkxrHke+dMKAYOAcmiFmS4s736HlAvjMECWDQbkAiMQiPLShMmqWkHkbK/Hk+MIU1SySUizB3A2VEQ/CbK2qw7yNe7F4fP8Yr2x/iYJuoQXlsWaxC+CYciLqjDLkEvxu9E8wp0U/qZZLMHFIr4hAvdiox7KJRcFhJ+VVdbihsAfe3VwV9ZvIHxua4u9fIcXjN/dHjxZDTAIZYmotTjTaXchUymDQRB8jzus7JYNBuQBYmy8a0ZRV1cEa52GS9mZxeeI8mV4Li4Du6muU8T/eida3t0TDlISUA56IqL3UWpxwerwR/eS0kvyYQ/4C6wN3xbtplTH7rkSsDjfqbU70QkbEOp06uQc1eX2nZAgnAXYnZm5Kn7+gzU0J7j4nWN+exCLEzWMrFrVzhRIIjFGMJtoYRSKizsBsd0XtJ4vysmLe0CqvqgubbMjhjp0Ct+JIA0pjXHsDOc/PNWjm9Z2SwaBcALSq9PkLOkMRf3IgdYL17UkkAqYW50cE5oEn6kUCC8oDYxRbXrhjjVEkIuoMtEpZ1H4yXqDdcn3oDJ8trSirxsIb+0VMjBfoKw4cN59z0MzrOyVDWN/fd1IZzbNkRnvQpMSoR4agZsmUYGTvruibq4vIpb3/mAlqmXDqqlHI8N8932H2tb3DntZ3ebxYVV6NB8b0TnUVI7RmjCKllsfjQTIZZUUiESQS4bQLonRj0MhhbnJF9JPxAu2W60+a7Sg16rG1qi5iPohstQwOtweP3twPNqcHVocHMokIFrsbOZkK/O5nP8G3NRZolVKIRSJIxCLozyK/OK/vlAjzlLejWHlafzhlgU8EzN+4N2KWzMfHFULk8+EigeSn/rHOApdPFDNTjEzkwwV6YdT10CkLfKLYdRX5fOglkPNK519b5in3eDy4sOdFOHHsaMKy3XMvxNHDPzAwp07tXNvf4VoLvBBh3sYzqQtnjjRi9+H6qDe0io16FPXMxgubq5qzrxSgS4YcS987gNuu7BkxFr20wIDpJfm4e80uAMCqKVfA6/Ph+Y+rImYMnVpyEdZtO4xHb+7P/OLUphiUt6NYFyWTzYlH39mHKcX5wTu6gVkyV5VX45Eb+wnmL+mjdVY88s4+9Ilyp/zrYyY8emM/XKiPfBgmFQ7XWTE3SlYbwB+YPzm+ED0FUlc6/9oyKHe73ZDJZPj5Cx9DJI4dbPu8Hvxz5gi4XC5Ipfxikjqvc2l/JpsTHx2oQb5eBZVcBh/8z1r558iQYF6LSX9KCwx47OZ+qLM4IJf62+fH39Rg4IVZUMnF+K7GihytMmLW5Mt7ZuOynlkAgFydEv+pPB61/yg16nFZz2zsOdLA/OLUpthLCIBOLccDY3onne80lSwuDyYO6YWV5dURuV6nFucLKvtKOmW1ofQkEksglsS+jMYf8UpEyai1ODFv4168ePsgKGRSHDfZ4XB70Wh348AJM6YVX4QHr70UFrsHOrUMJ01NOHq6Ca9u+R5bq870qe/eUwKJWIxNlVUxUyhOKb4ouDxW/7G1qg5Tiv2ZXZhfnNoSg3KBSJuxZj7ETUE1/4a+qahVVOmU1YaIiKIz212wOT2YsXYXXvl/l2P66h0xy740aRDuXrMLv/tZAa4r7I4pxRfB4fZCIRVDJBLh8U374qZQTPTwaECgHPsRaksMygWk0eGG0+P1f6Xm8aLR4RZcUO5D7LsH5VV1ENJYqHTKakNERNEZMuR4Z1YJ7E43tGoZZo40oigvC26vD7k6FVweD8RiESRiMewuD/4zqwRe+HDj8+XBbajlEqz/7VUxc5WXV9VhesnFST28DQBKmQQzRxqhlElQcbgeWpUMhrN4+JMoFINygThUZ42Y1bPEqMcT4wvRS0Djnq2O+HnIE61vT1qlNG5WG63AJg8iIqJwgWeDKg434MXbByFDIUVF8xjwZROL8NInB/Hrnxrx9PsHwgLu5ZMHB/+tlkuwbGIRTludcfeVIZfgi+o66NVySCRiFBv1MceUGzRyVByuDxvGObzAgCUTBvDhTzprzFMuAEfrbXj0nX0o6pmN5ZMH46VJg7BiyhUY2DMbj72zD0frbamuYlBmgkA20fr2JAKw8KbI3LMlRj0W3tQfAktTTkREIU6a7cGH9aeV5ONUox27jzRgWnE+1twxBG9sO4TfXm3EH1sE5C0FZv5MNDTF4/XB5wN++pOuuNigxswRxoh5LkqNBiwaX4hn//dtRMC+5WAt5qzfA5MtfvBPFItwIqhOzOJw4/Z4D08K6O6zLM7dg2KjHjKJcP7OM9nd+L/l27B0wgDMvq43LHYPNEoJaswO3Pbq5/jb9CHITXUliYgoqnqrM9jXDO6ZjdwsFR5tHhO+fPJg9M7VodHujtofVRxpCPZVg5pTIxb1zEap0RD28GdAsVGPz76vw/X9u8Pi9GDqqh349U8vxuxr/fNZ2JweSMUilFXVosnlweYDp6LWecvBWj78SWeNQbkQ+IC12w6hqGc2phXnh6VpWrvtEB4U0CQ3dRYHphbnA0DE0+tTi/Nx2urAxQLJ/R3tQU9RyP1xPqBDRCRcZvuZG1LdtDJIJSLMH9sX8Pmfb9KpZNAopFg+eTBmr9+DWsuZO9SB4S1iAFKxKLhsw91X4bFN+6P2X/esq8Do3jlwuL2wOT3484cH8ecPD0bU66pL9BHLQrFvobPFoFwIRIh7p1xI4yw0Shkmr9yOaSX5wT8gFFIxKo404J51FVj/26tSXcWgrAwZ1twxFI+1eNq+xKjHmjuGAiIhPZZKREShAs/9/CRHA5Vcjsc27Qv2ldGu6ZNe/yIYmNucHtyzrgJv3DU0+A2uzenB8QZ72A2w0P7L5vQgUymDwhN/mEuiJAFMIkBni0G5AIgQP83ggrHCSTOokUswqGdW2B8PASVGPTRy4cxaqJZKok4eVFZVh8c37cPi8YUpqhkRESWiVckwqndXzB/bFw9vrERRz+yofWXgmr50woCwdImX98rGqUYHLtJnBB/633G4PuIBzYASox4apRROlydukgCNXILhBYaweUUChhcYYNBw6AqdHeEMAO7EfL4EaQYFdEO3yePB4+P6R3148vFxhWjyCGdCHovTE/WiCvgv4hZOHkREJEjHGprwp/cPYN7YvmhyeVFeVYeivKy4E8LlaBXB30uMejxwzaVYu+0Q3D4v5o/1P/S/oqwaU4vzIx7gLDbqMaU4H6YmJ5xeDxaNK4zazz0xvhBOrwdLJgzA8AJD2HohTvhH6YV3ygXA4nBDLZdgWkl+xNT1K8qqBfWgZ73Fhfkb9+LF/zcIbo8P5ib/VMdSiQi//usOLBrfP9VVDLLY45+3ROuJiKj9mWxOzF6/B5flZeHxTfvx4JjeWDXlCnTTKvHOrBLUW53weH3Y2dxH2ppvsNgcHrw0aRB0KhkylVI88OZXmH1db/i8wFPvf42BPbPx4LW9cdxkx+xre8Pp9uKUxQG55MwQlucnFiFDLsXfPv8Gi8cXwuL0BPs5jVyCJe/ux7TSS3BJL216TPhHaYVBuQBolFIsm1gUdUz5solF0AgozaBOLcOc63vj0X/vC7sLXWrUY871vRNO2NOeEp03IZ1XIiLyq7U4sfVgLaYX52NgXhb+9P4B3DakFxb9N/IBzWUTi8LGgzc5PbC7PHB7vXhifCFcXi+kEjG++P40Nh84haK8LPz6bzvj7t/n8+KBa/tgTpS5Qx4f1z/4OJJOzSCc2hajEgFQSMQxx5SLADx+s3DuPmtkEqwsq44YFrK1qg4QibB4nHDqKhYhbvpGsYAeoA1lsjlRa3HCbHdxlrgOwu1O/K2MSCSCRCKcZzKIUsXcnL1Ep5bhTx98E3MseeD3aSX52H24HifNTfD4Ip/RKjUagsH7NyfNePPXQ5GhkMJid0OrkkEmEeOD/cex61ADumYqkKGQ4PFN+6NmRFu0aT8ei9Mn8/pN54JBuQDY3d644+TsCSY8aE9WlyfmJA1bD9bC6hLOOG2RGFg0rj/mb9wb9kdEYPy7yyucugYca2jC7PV7sDXkASLOEpe+fF4PIJZApUr83nXPvRBHD//AwJw6PW1z9hKpRITyqjpML7k46oOZgD8wnznCiJ8PuhD/qTyGz76ri+hPt1bVwgsfZo68BKP7dMej70Rm5Fp4Uz/8rG93/PmDb/DgtX0wedhFeHXr91EzojXGGFLK6zedKwblAmBNMKZcSFPXNyYYh51ofXtSSSR45J19GNgzG1NbpL9atGkfFt7YL9VVDBMYR7m1xRP9gVninp9YxDsuacbn8wFeDyY8vxliSezLrc/rwT9njvCXJ+rETDYnvD4fVk25Anan/4aUNMHXmjKJGPVWB4rysvHH97+NWqa8qg4Lb+qHhf/eFzV7y8J/78M9owpwcU4m5m+sxPWFPWLemZ9/Q2RGNF6/qS0wKBeAdBpTnqGIX5dE69uTxemfdS3WzGsPjBHWnfLAOMpoOEtcehOJJXGD8sB3YckMcwE41IU6psCd5p2H6rFsYhGczfnCu2TEv+5lKKTweH1wuOP/Uevy+OJ+K/3QDX0xuHn2zynNk+S1VF5Vh2h74fWb2gJTIgpAhkwSc7zcqvJqZMiE0/mKgIhUUgHFRr2Q5jmKOqNnKKHNumZOUB+h1ZfaTugwF5lMlvDnwp4XwSOg9KNE5yr0TvO0knysLK/G59/Xodioh1wqjkhPGFBi1EMpFUMEwJZg+GRjgj7B3OSCTu0fOuOIM2zU6nDju1MWmGxnZhDl9ZvagnBua3ZiFqcn7l/vgsqnLYJ/llEg4il4oc0+migTjNBmXdNylrhOK9lhLgCHulDHFHqnuSjPP0FdxeEGLJtYBI/HiynF+fAhst+ZUpwPh8cLiHzYc7QBpQWGqHesS42GhNfQDMWZG2AKaex7lhkKKUY9/WnYeHFev6kt8E65ACT6611If2GrZRKs23YIRT2zsXzyYLw0aRCWTx6Mop7ZWLftENQCuqufIZfEvbuSIaDZRwHAoJFHTEYRwFniOofAMJd4PyKxsD63RG0h9E5z4C61zenBPesqYGpy4551FVH7nXvWVcBid0MqFuOCLBXmXtc74rpfWmDAIzf1Q6Yifp9QY3ag3uZCqVGPGrM9arnQb4QD48VNNiev39QmeKdcADLT6I6uw+vBvLF9MX/j3rDx74GMJg4BZTSxN88+Giv7il1gX//r1HIsmTAAc9bvCZu+mbPEEVFHF3qnWSEVhyU/yFBIYHN6YmZgUcslkEvFGHChDnes3oEVU67AqUYHGppcwYf7n/7gAP4w5idYNK4Q8zZWRvQJC27sh9tf+wLPTyzCovGF+KHWGpFSN9o3woHx4pfkaHj9pnPGoFwAMuQSlBoN2FoV/Ss3Id3RVYoleOHjg3jkpn4RM3q+8slBzBxRkOoqBqkk4XVtbHIhU6B1DcjNUnGWOCLqdAJ3mrccrMXeYyasmDwYz39chRc2V2HmSGPMOSdKjHq4vT4seHsf5t/QB3+dfiVOmOxwe324SK+G1wtc268bfP26we0B9v54GjNHGv9/e3ce3lSZ/g38mz3pkrYkdEEoFFJlK1B2bIsbIyIoIOOMyIxs7hRUHAUUUEHFXQRURhBhfi/gMg46woiDMMoigmCVHalUCkILLW3TtNlz3j9KQtNsLZTmpP1+LntdknOSPOckJ+c+z7mf+8GMYZ1hsjgRo5bhrNGKu5d9jy4pWsRHKSARBPxj529edcrdwf3aXScw89YuXm1w383m7zddLgblIuASBEy5oRNcEHyuyqfcaIBLRLmjZqcTk7I7+ZSVyjboMGdEN5hF1Ptscznx0PXpePoz31nZXhidAZuIevVr4yxxRNTS1L5TKJdK8Pb/8j2/2yu2F2DR2EwA8JkUaHxWB5RV2bH3RBkUMqnPndGcdD0evr4TJq/ag39M6o/Znx/EorGZWLwl3+d8O29kN9y5dCcW/7kX5ozohtmf7fe5I/z86Azcv2qPV9tr383m7zddDlHnlC9YsAD9+vVDbGwsEhMTMWrUKBw9etRrHYvFgilTpkCn0yEmJgZjxoxBcXGx1zqFhYUYPnw4oqKikJiYiCeeeMKn9Ng333yD3r17Q6VSwWAwYOXKlT7tefvtt9GhQweo1WoMGDAAu3fvbpTtNNtdmLRqj998uUkrf4DZLp7Jg1xO4OWNh73aumJCP/RKTcDLGw9DTHGuUirD7DoBOVAzePbpdfuhZG4uEZFoRCtlmD+yO27qnOQ1SZ07t9x93vn4gYH4YmoWJmR1wLS1eaiyOTApOw1zP78YkEcpZci90YAJ13ZAtc2J/5s8ANHKmn7IQPnp50023NU/FWUWB+7/xx48c3s3fPlIDj6+fyC+fCQHz9zeDfev2oNfzpo8bWO+ODUmUfeUf/vtt5gyZQr69esHh8OBp556CjfffDMOHTqE6OhoAMBjjz2GDRs24JNPPkFcXBxyc3Nxxx13YMeOHQAAp9OJ4cOHIzk5Gd999x3OnDmDe+65BwqFAi+++CIAoKCgAMOHD8eDDz6I1atXY/Pmzbj33nuRkpKCoUOHAgA++ugjTJ8+HUuXLsWAAQOwcOFCDB06FEePHkViYuJlbafJ4giaL2cS0YQ8kAB3D2jvt6a62KqvmGxOrx6T2kRX1YaIqAWrPRvmh/cP9Fle+xzpXj51bR6qbU6o5FJPxRagJiD3N/dHTroei8ZmYtraPL/n22Hdk5Fj0EOrUWDGsM4Y+15NSst9OR1RVWHB8u3HvQLyHOaLUyMTdVC+ceNGr3+vXLkSiYmJ2Lt3LwYPHoyKigq8//77WLNmDW688UYAwAcffIAuXbrg+++/x8CBA/Hf//4Xhw4dwtdff42kpCT06tUL8+fPx4wZM/Dss89CqVRi6dKlSEtLw+uvvw4A6NKlC7Zv344333zTE5S/8cYbuO+++zBx4kQAwNKlS7FhwwasWLECM2fOvKztjFIF77ENtbwpSYCANdUBYO4I35nOwiVU1RoxVbUhImqp6s6GGRNykjoZNh8+i2qbE1kGHfJOliOzXbxnubvOed3z1LZjJRAEAZOy03yC8iyDDlEKGYYv2g6gJlXlnw9ei59PliHvZBn+/u1xTMpO8+SYx2sU6JQYgyStuhH2AFENUaev1FVRUQEAaNWqFQBg7969sNvtGDJkiGedzp07IzU1FTt37gQA7Ny5ExkZGUhKSvKsM3ToUBiNRhw8eNCzTu3XcK/jfg2bzYa9e/d6rSOVSjFkyBDPOv5YrVYYjUavP380ClnQCXk0Iioz6BIQsKb6jvxSuMST/h6yTnmo5RTZ6nv8EVHja8jx565R7k45iVKGPie+t/W45w7tkdMVuCpe41kns1180Lk/BnX0fu0sgw5Tb0j3utG7Pb8Usz/bjxi1Av3at/L01E9etQcf7i5Emj6aATk1OlH3lNfmcrnw6KOPIisrC927dwcAFBUVQalUIj4+3mvdpKQkFBUVedapHZC7l7uXBVvHaDTCbDajrKwMTqfT7zpHjhwJ2OYFCxbgueeeC7ltCqkEuTcYAPhOjJB7QzoUUvHkhIRKpTFZxZNqo5RJA47YzzLooJRF1DUpNVB9jz8ianwNOf6MFrtXyskfuiQGPCdOvTEdDqcLHz0wEA6ngIpqO27skuRZviO/NOhsnG7vj+/rqapy1miB1eFESZXVa53t+aWYMawz7A4Bm6dfx4oqdMVFTFA+ZcoUHDhwANu3bw93U+pt1qxZmD59uuffRqMR7dq181nP5RKQpFVjREYbr/JLZ41WJGtVcImo+zlWE/wrE6sWz1fqfJU16Oyj56us6Ng6JlzNoyusvscfETW+hhx/WrXCK+WkLNsOiQQYkZHifU6stCJOI8df3t+NapsTD1zXETdck4hWMUqYrA68cKEGebDZOAEgWimHViPHqTIzAOD3CgvmbziMTx+61mddk8WJxFgVOiXGoKLahtIqG4orrThjtKDa6kBclBL6aAbp1DjEE0EFkZubi/Xr12Pr1q1o27at5/Hk5GTYbDaUl5d79ZYXFxcjOTnZs07dKinu6iy116lbsaW4uBharRYajQYymQwymczvOu7X8EelUkGlUoXcPidckElkEFA3+BYglUjgFMQzIFEVovdZJaLe5xiVAves+MErD9Bda3ba2jz8y88PMDUf9T3+iKjxNeT408cocW1HnSfP++CZCgzo0Aq92sXjfLXd0/P9e7kZmw4V4ZU/9oBcIoVCLsHLG494zkcrL1QCax2rQo5B51XBxS3HoEdpldUzSNQt26CDRuF7/tKq5dDHKHG63Iy5nx/AXf1TffLVB6fr8dKYHmhTK4WG6FKIOigXBAFTp07FunXr8M033yAtLc1reZ8+faBQKLB582aMGTMGAHD06FEUFhZi0KBBAIBBgwbhhRdewNmzZz1VUjZt2gStVouuXbt61vnPf/7j9dqbNm3yvIZSqUSfPn2wefNmjBo1CkBNOs3mzZuRm5t72dupkMowa91+v5VCsg06LBidcdnv0VjOV9uC9j6XVduQFujJTSxGLcegjq28HpNIalKBBnVshRgR9eoTEbVUcVFKKGv1bndN0eL3cjP+ufeU38Da7hAwbYgBb319zOs8VG2vyft21zV3wbeu+bxR3bCn4LzX67nnrpi65kefx2PVclgcLsz9/AA6p2j9DiDdeqwEMz/dh8VjM9ljTpdF1FHJlClTsGbNGnz++eeIjY315IDHxcVBo9EgLi4OkydPxvTp09GqVStotVpMnToVgwYNwsCBNSWTbr75ZnTt2hV//etf8corr6CoqAizZ8/GlClTPFfxDz74IJYsWYInn3wSkyZNwpYtW/Dxxx9jw4YNnrZMnz4d48ePR9++fdG/f38sXLgQVVVVnmosl6PK7sSPheXIvdGAzHbxsDpcUCtk+LGwDCu2F6DKLp6e8iilHH99f3fA3md/t//CxelwYc6Ibnja3wQQozLgrEfeIRERXVmny82wOy/+HqfEq9EmQYOe5xIw4cJ5pvY5cVt+CZ4a3sWnI8udtuKua37/4I6YOawzzhprcsXzTpZj+KLt6NM+ARumZqPUZEW0WoEYpQzvbf0V+36/OBg126DD3Nu64Y53v0OXFC3GX9sBUokkYOnircdKUGKyMSinyyLqoPzdd98FAFx//fVej3/wwQeYMGECAODNN9+EVCrFmDFjYLVaMXToULzzzjuedWUyGdavX4+HHnoIgwYNQnR0NMaPH4958+Z51klLS8OGDRvw2GOP4a233kLbtm2xfPlyTzlEAPjzn/+Mc+fOYe7cuSgqKkKvXr2wceNGn8Gfl6La6vBbUzXLoMOisZmoFtHgSakEGBig93lgx1YQ0ZhUOAXgmc/9Tx4057MDeG5ktzC1jIiIgIvlEHu2i0eWQYejRZWIVshhtruQV1jm95w4bW0eKsy+JW3zTpbjxs6t0bVNHDLbxUMhk6LS4kDeyXKs2F7gSVfZdqwEcz8/iBm3dMawt7bhXw8NQu5NV2PcwA4oN9sRrZLhrNGKu5d976kM4xIEPDC4U9BtYZldulyiDsqFekwvr1ar8fbbb+Ptt98OuE779u190lPquv7665GXlxd0ndzc3EZJV6krPkqJ1zf9ErD29/yR3Rv9PS+VRArMuKUL5q0/6NP7PGdEN0ik4hmUanW6/N76BIBt+SX1GqFPRERXjjvo3XuiDEvH9UGbBDXsLgHz1x8MeE6clJ3mt6jAh7sLsfregT7np9rBvCcwzy/BrAsZMxqlHElaNUwWB+5673u/7dyRX4qZwzoH3ZZYNcvs0uURdVDeUlgdrqC1v8UUPCokUjy/8RAyUxM86Svu24qvbDyM2cPFM3lQyPKNYpoplYioBTJe6F2utjnhgoDn/n0QM4d1CdihkldYjqdv7QKpRIL3x/eFRCLxpLXc1T8V80IE87WDdZPViZx0PaKVMq+2BOJwCgELHQxO10Mfw9QVujwMykUgkmp/W50ujB3Q3m+qzcSsNFid4rmAiKSZUomIWiJtrd7lxFg1DhdVelVFqc1dy3zBfw57Be3ZBh2+fCQH5gsDPf3ZkV+KSVneZQiilTLMGdEV89YfwryR3REd4pxgtTv9FjoYnK7Hy2N6MJ+cLhuDchGIpNrfEsDv6HP3v+eOEE9PefSFWeEClW90944QRSKHI/TFukQigUzG7zmJlz5GicHpeuw5UQaT1YGXx/QIeE6sXcu8tprZNw9g9vAuAQsmVNucXnedsw06RKtk+Pzn0/j68FnYHPvw8A2GoD3haa1jUGV14NnbusHpElBtcyJOw8mEqPGIJ9prwaIVMuSk67HtWInPspx0PaIV4jmpCgKCptrUYxhAk0mIUmLqjekA/M8Kl8AfUYpAgssJSGXQaELXRE5u0xanCn9jYE6iFRelxEtjeuDbX84hWimDRimFSi71e07MbBcfsCd827ESCEDQwaHu6ixZBh0mZKXBbHfh798eB1BTPWVCVlrAkr/zRnZHklbdmJtO5INBuQhIATx3ezfsOl6KRK3ac4VfXGHGgI46iGc6ntCpNGJKtQEAmb9Z4YwWyERUJYaoIQRBAFxOjFm8BVJZ4J9wweXEP3NvqNeAeaJwahOvQd/2CXAJAsqqHJAAmHZTTYdK7cBcJpEE7Qk/XW4OeBd3zoiuaBWtxPvj+3pK+L7xp15eqTIWuxN/++RnvyV/jWYbgOgrvi+oZWNQLgIOCDhXacWG/We86q7mGHTo2DoGyXHimZUwWhX8KxNqeVMqMdkw/oOaGT0Ta/VwuKdU/iI3m7ccKWJJpLKgQbl4RncQhWayOvDdryW4sXMS7v/HHvzf5P64rUcKHrkpHQ6XgBiVDNEqOZZtPx6wJ1wboPrJjvxSzBnRFX9ZvgslJpvncUWd3hmVXIpqm/+89NG9rmqkLSUKTDwRVAvmcgGLthzzucLfll8KF4DnxVQSEQiapy2mDmijxR7wBxZgTVkiIrHQqhV4+3+/4pZuKWivi8Jf39+Nt8f1hlwmhclih0Yhx9zPDgTuCR/eFXFRCkQpZX4HilZU23FX/1TP+SDLoEPeyXLP8px0Pc5WWv22jZVVqKmIKTOixTLbnUHztM0imtETEmBiVhqyDDqvh93VV8QUlQfqNXFjTVkiInHQxyjRt30CHvp/e/H8qAy010Xh9iU7cOtb26BWyPBbaVXAMok78kuRmRqPhf89ikVjMxHlZxB/jFqOzHbxAGoGeU7MSsOK7QUAgByDHvNHdsf1V7fG4HS91/NYWYWaEnvKRSBQ+af6Lm9K0QoZ1u464VWn3J1zt3bXCTx7m3hmyXSP6t/qZwAtez6IiMSj9oDPlzcexsSsNMwY1hkmixNWuyvkfB2nKyxIS4zFBzsKfOqRZxt0UMqliFbJsfHRHJwuN0OjkGHpX/tALpEgRi2HyWJDQqtozB/ZHVU2h6eySmKsigE5NRkG5SIQSXnaFWYr5ozohtmf7ff50XthdAaMZivEMhjG/SM/89N9XoE5ez6IiMTHPeBz1r/248sDxZ7HP31okKdySiCtY5Xom5qAJVvyveqRZxt0mHtbN5worYYEgNXhxKSVewAAq+8dAH2sCi99eRhzRnTF45/8jK8Pn/U8d3C6Hi+N6YG4qMbdTqJAxBPttWBquRTZBp3XIE+3bIMO6hA/Rk1JLpdj2tofsWhsJsx2FyrNdsRqFNAopJi65ke8cmfPcDfRS5t4DRaPzUSJyYZKix2xataUJSISK5PVgSilDJOy0zxVVlRyGaJVQsDSwVkGHfafqkCXFC2AmtTEtfcNRIxahliVHH9cuhOL7srEd8dL0fXCOm5fHjiDLUfOwe44gJ6pCV5B+dZjJZj56T4sHpvJcwY1CQblImB1ujAhKw0CfGujThDZLJlquRRxUUpc/9q3Psty0vWiuoBwi4tiEE5EFAniNAosGpvpM2v0kC6JmHd7N8z+/IDPeXJiVhqmrc3DRw8MBFAziH/yqj2e3PHOybFoHavCiu0FWDw20/NcrUbuqVO+Lb8U9w7u6NOercdKUGKy8RxCTYJBuQiYLA7M/HQfXh7TAzMv5NDFqOU4a7Rgxqf7sPQvfcLdRC9Tru8ElyD4/DBOud4QxlYREVGki1bJ/c7a+fXhs3j4+k7o16EVHhtyNRwuAVFKGSSQYMvRmlSXsmq7p6qKO23lrU2/YN7I7hi/Yjf6pMZ7Kq7kpOux+fBZrzFbGoX/kIiVuqipMCgXgViNHC+N6YEVdX6Isgw6vBRkyuFwsDhcmLRqj9/JFSat+gH/evjacDeRiGpxOEJP6CWRSDjrJ4lCldXhVUig9gRB0So5ul8Vhze//sXnXLlobCZUMgmeu7077C4XxmReBavLhTF92+JPf9+JLslaTMzugNw1ecgy6DB3RFeMfHuH13vHqv2fa1mpi5qKeKK9FkwplfrtGdiRXwoJgPkiqlNusjiC1v42WcQ1oydRSyW4nIBUBo1GE3Ld5DZtcarwNwbmFHaCAPxUWOZ1jskx6LDk7kwoZcHPlS+OzsAtb21Dtc2Jfz44CGqFFFFKOT6Y2A8SQYLfK8xYPDYTeSfLcbrc4tVLHmieDVbqoqbEoFwELA5XwDrl2/NLYQlRCqopRVKlGKKWTBAEwOXEmMVbgs78Kbic+GfuDTXrE4XR6XIznv33AZ+iBzX1ySV4aniXoOdK44VOI7cFXx7xu352PefZYKUuamqMoESgyhq8dznU8qYklQSf0VMqosmDiC6F0+kMGaDWJyVELCRSWdCgXDyX/NTSGS32gBMEbcsvgSnEufB8tQ1ATb64RinDMyO6Yf76g16vmW3Q4YmhnVFWZcM743p7zbPxzG3dsHn6dazURWHDoFwENH5mH2vI8qYkuTCjJ+BbKWZiVhokDMopgjmdTrRN7YCi06fqtT47l4kaj9EcPOj2N1NnbQ6ngCyDDvNGdsOdS3ei2ubEnBFdMXtEV5htTkSr5FDIpJi//iA2HznneV62QYdnb++OOI2CQTiFFYNyEVDLZUF7n9Vy8QTlcRolPtx91O+Mnh/uLsQLozPC3USiSyYIAopOn8Ifl/wPEmng485pt+FfjwwB0Hyicg4IpXCLVgX/bgU7V+YYdGjXSoMRPdrA5nRh+T19/fZ2F5Wbcd/gTnj0D1dfqHQmQ5XViViljAE5hR2DchGQSoDcG2rKCdbtfc69IV1UKSFJWjXmjuiKp9ft9xmI88LoDCRp1WFsHVHjCJny4Yyc9JVQOCCUxCJKEbyDSioBnhjaGcARn3Pl+Kw0lFRaMbBjK9icLvRKTfD7HsnxGmiUMpSYbHA6BUQp5EhNiGJATqLAoFwE5FIJZBIJhmekePU+FxstkElqlotJqi4ar/2pF8qqbDBaHNCq5UiIVjIgJ4pAHBBKYtEqWompN6YD8N9B9e99p7HvZDlm3NIZJSYbLHbnxTu1uwoxdkAqzptsaNcqKuj7cEI5EisG5SJgdjgR7DZ4zXJxSdKqGYQTNSMcEErhFhelRPtWURjRo41PB5XF7sTfvz2OapsTVkdNT7j7bm3OhZ7yqWvzsPyevlCJcGZpovpgUC4CThcwYWXNhDy1A93TFRY8v+EwPn2IE/IQUfNUn2o3APPZW4qUeA36tk+A3SXgt5IqABfPhe5yh9vzSzFjWGd0TdFCJZeidawKd733PTJT4/Hd8VIkadXsCaeIxKBcBKqswSfkEVNJRCKiUOobaDudTnTo2AlFp38PuW5989kZ5Ec+pUyKwvMmPLz6x4DrnDxvxsOrf0SWQYfM1ARkpsZjYlYapq3Nw5DOiU3YWqLGw6BcBDghDxE1Fw0tKwkAYxb/D9IgAXJ989kb8t4ctCpeNmfoZCmVXIocgx6zR3TBmXILAGDa2jxU25yIVSuudBOJrghGeyLACXmIKFKEKp3ocDjqVVYSuFhaUiKVNko+e31LWnLQqriZrA7knSwPUv5Qj5Q4NXqmxmP0O995zeI5OF0PfQxTVygyMSgXAalEEnRCHiln5CGiMGtI6UQAgCT4wFHgypWW5KDVyKZVK7BiewEWjc0E4HtefOb2rjh53oy8wjKfgPzlMT2YT04Ri0G5CGiUMqzddcLvhDxrd53AcyO7h7uJRNTC1bd0YnOcWImalj5GiT7tEzBtbR4mZad5nRfPGi1wugSkxKsx+9auECRApdmOhCgFB3hSxGNQLgImqwNjB7THBzsKvAZ7unvKTRzoSUQi0ZImVqLwiItSYt7t3TD78wN+z4mj3/kOnz50LayOmvzxq5JjGYxTs8CgXAQqqu1+ewTyTpZj2to8rJzYL9xNJCIKu/rks1PzUGKy+r177B7MWWmxo3+aLtzNJGpUDMpFQKtRBC2JyJHkRNSSNTSfvb7jN+sTxLtcLkil9ZuMhmUWG0+MWhHwnAjwvEjNE4PyBnr77bfx6quvoqioCD179sTixYvRv3//y3rNWLUc2QYdtvsZZZ5t0CFWzY+JiFquxs5nb0iQL1Uo4bLb6tVOlllsPDwvUkvEb3UDfPTRR5g+fTqWLl2KAQMGYOHChRg6dCiOHj2KxMRLn6ygbUIUXhidgafX7ff6Aco26PDC6Ay0TYhqjOYTEUW0xspnb2iQH2o94GKZRbvdHrLUInvUQ+N5kVoiBuUN8MYbb+C+++7DxIkTAQBLly7Fhg0bsGLFCsycOfOyXru9LhovjemBSosDlRY7YtUKxKrl/OEhIrpC6hvkh1oPAJwN6H1PSrkKJwp+DRmYt/TgnedFamkYlNeTzWbD3r17MWvWLM9jUqkUQ4YMwc6dO/0+x2q1wmq1ev5tNBqDvgd/aIgaT0OPP6LLUd/ed5fDhk8fHQq1Wh3yNSM5Haaxjj+eF6klYVBeTyUlJXA6nUhKSvJ6PCkpCUeOHPH7nAULFuC5555riuYRUR08/igc6tX7Xo/gPdLTYXj8ETVc/YaU0yWZNWsWKioqPH8nT54Md5OIWgwefyRm7uA90B8ATzqMQqEI+tc2tQOcTmfwN2xiPP6IGo495fWk1+shk8lQXFzs9XhxcTGSk5P9PkelUkGlUjVF84ioDh5/FMnqmw7j7lEP1Zve1Hj8ETUcg/J6UiqV6NOnDzZv3oxRo0YBqKlfu3nzZuTm5tbrNdw/msxtpZYqNjYWEokkLO9dn+PPXbfaYbNBIg3c8+i02z3ruZyuJlsvnO/NbQnPtjgdTghC4GNGcNV8T41GI+Ty4Kd0sR9/RM1ZfY4/iSC2y2sR++ijjzB+/Hj8/e9/R//+/bFw4UJ8/PHHOHLkiE+uuT+nTp1Cu3btmqClROJUUVEBrVYblvfm8UctHY8/ovCpz/HHoLyBlixZ4pk8qFevXli0aBEGDBhQr+e6XC6cPn06rL0VjcloNKJdu3Y4efJk2H7om6vmum/D+d2vffxVVlY2y/0rVs31+yxGwfa1WI4/iUTSrL4T3BZxEtu2sKecriij0Yi4uLiw9r40V9y3Vxb3b9Pi/m46kbKvI6Wd9cFtEadI3BZWXyEiIiIiCjMG5UREREREYcagnC6ZSqXCM888w7JXVwD37ZXF/du0uL+bTqTs60hpZ31wW8QpEreFOeVERERERGHGnnIiIiIiojBjUE5EREREFGYMyomIiIiIwoxBORERERFRmDEob0KCIMBoNIJja4maHo8/ovDh8UcUGoPyJlRZWYm4uDhUVlaGuylELQ6PP6Lw4fFHFBqDciIiIiKiMGNQTkREREQUZgzKiYiIiIjCjEE5EREREVGYMSgnIiIiIgozebgbQESRq6LahhKTDUaLHVqNAvpoJeKilOFuFhERUcRhUE5El+R0uRkzPt2HbcdKPI8NTtfjpTE90CZeE8aWERERRR6mrxBRg1VU23wCcgDYeqwEMz/dh4pqW5haRkREFJkYlBNRg5WYbD4BudvWYyUoMTEoJyIiaggG5UTUYEaLPejyyhDLiYiIyBuDciJqMK1aEXR5bIjlRERE5I1BORE1mD5GicHper/LBqfroY9hBRYiIqKGYFBORA0WF6XES2N6+ATmg9P1eHlMD5ZFJCIiaiCWRCSiS9ImXoPFYzNRYrKh0mJHrFoBfQzrlBMREV0KBuVEdMniohiEExERNQamrxARERERhRmDciIiIiKiMGNQTkREREQUZgzKiYiIiIjCjEE5EREREVGYMSgnIiIiIgozBuVERERERGHGoJyIiIiIKMwYlBMRERERhRmDciIiIiKiMGNQTkREREQUZgzKiYiIiIjCjEE5EREREVGYMSgnIiIiIgozBuVERERERGHGoJyIiIiIKMwYlBMRERERhRmDciIiIiKiMGsWQfnvv/+Ov/zlL9DpdNBoNMjIyMCePXs8ywVBwNy5c5GSkgKNRoMhQ4bg2LFjXq9x/vx5jBs3DlqtFvHx8Zg8eTJMJpPXOvv27UNOTg7UajXatWuHV155pUm2j4iIiIiat4gPysvKypCVlQWFQoEvv/wShw4dwuuvv46EhATPOq+88goWLVqEpUuXYteuXYiOjsbQoUNhsVg864wbNw4HDx7Epk2bsH79emzduhX333+/Z7nRaMTNN9+M9u3bY+/evXj11Vfx7LPP4r333mvS7SUiIiKi5kciCIIQ7kZcjpkzZ2LHjh3Ytm2b3+WCIKBNmzZ4/PHH8be//Q0AUFFRgaSkJKxcuRJ33XUXDh8+jK5du+KHH35A3759AQAbN27ErbfeilOnTqFNmzZ499138fTTT6OoqAhKpdLz3p999hmOHDlSr7YajUbExcWhoqICWq22EbaeiOqLxx9R+PD4Iwot4nvK//3vf6Nv37648847kZiYiMzMTCxbtsyzvKCgAEVFRRgyZIjnsbi4OAwYMAA7d+4EAOzcuRPx8fGegBwAhgwZAqlUil27dnnWGTx4sCcgB4ChQ4fi6NGjKCsru9KbSURERETNWMQH5cePH8e7776L9PR0fPXVV3jooYcwbdo0rFq1CgBQVFQEAEhKSvJ6XlJSkmdZUVEREhMTvZbL5XK0atXKax1/r1H7PeqyWq0wGo1ef0TUNHj8EYUPjz+ihov4oNzlcqF379548cUXkZmZifvvvx/33Xcfli5dGu6mYcGCBYiLi/P8tWvXLtxNImoxePwRhQ+PP6KGi/igPCUlBV27dvV6rEuXLigsLAQAJCcnAwCKi4u91ikuLvYsS05OxtmzZ72WOxwOnD9/3msdf69R+z3qmjVrFioqKjx/J0+evJRNJKJLwOOPKHx4/BE1XMQH5VlZWTh69KjXY7/88gvat28PAEhLS0NycjI2b97sWW40GrFr1y4MGjQIADBo0CCUl5dj7969nnW2bNkCl8uFAQMGeNbZunUr7Ha7Z51Nmzbhmmuu8ar0UptKpYJWq/X6I6KmweOPKHx4/BE1XMQH5Y899hi+//57vPjii8jPz8eaNWvw3nvvYcqUKQAAiUSCRx99FM8//zz+/e9/Y//+/bjnnnvQpk0bjBo1CkBNz/ott9yC++67D7t378aOHTuQm5uLu+66C23atAEA3H333VAqlZg8eTIOHjyIjz76CG+99RamT58erk0nIiIiomZCHu4GXK5+/fph3bp1mDVrFubNm4e0tDQsXLgQ48aN86zz5JNPoqqqCvfffz/Ky8uRnZ2NjRs3Qq1We9ZZvXo1cnNzcdNNN0EqlWLMmDFYtGiRZ3lcXBz++9//YsqUKejTpw/0ej3mzp3rVcuciIiIiOhSRHyd8kjCOq1E4cPjjyh8ePwRhRbx6StERERERJGOQTkRERERUZgxKCciIiIiCjMG5UREREREYcagnIiIiIgozBiUExERERGFGYNyIiIiIqIwY1BORERERBRmDMqJiIiIiMKMQTkRERERUZgxKCciIiIiCjMG5UREREREYcagnIiIiIgozBiUExERERGFGYNyIiIiIqIwY1BORERERBRmDMqJiIiIiMKMQTkRERERUZgxKCciIiIiCjMG5UREREREYcagnIiIiIgozBiUExERERGFmTzcDSCiyFVRbUOJyQajxQ6tRgF9tBJxUcpwN4uIiCjiMCgnoktyutyMGZ/uw7ZjJZ7HBqfr8dKYHmgTrwljy4iIiCIP01eIqMEqqm0+ATkAbD1Wgpmf7kNFtS1MLSMiIopMDMqJqMFKTDafgNxt67ESlJgYlBMRETUEg3IiajCjxR50eWWI5UREROSNQTkRNZhWrQi6PDbEciIiIvLGoJyIGkwfo8TgdL3fZYPT9dDHsAILERFRQzAoJ6IGi4tS4qUxPXwC88Hperw8pgfLIhIRETUQSyIS0SVpE6/B4rGZKDHZUGmxI1atgD6GdcqJiIguBXvKieiyCQAgCXcriIiIIhd7yonoknDyICIiosbDnnIikamotuHXsybkFZbh13MmUU7Ew8mDiIiIGhd7yolEJFJ6n+szeRBzy4mIiOqPPeVEIhFJvc+cPIiIiKhxMSgnEolImrqekwcRERE1LgblRCIRSb3PnDyIiIiocTEoJxKJSOp95uRBREREjYsDPYlEQh+jxB+6JOKaFC0y28XD6nBBrZDhx8IyHD1jFF3vMycPIiIiajwMyolEIi5KiTkjumLWuv1YsiXf83i2QYcXR2eIMtiNi2IQTkRE1BiYvkIkEhXVNjz92QHsyC/1enx7filmf3ZAVNVXiIiIqHExKCcSiUiqvkJERESNi0E5kUhEUvUVIiIialwMyolEIpKqrxAREVHjYlBOJBKs/U1ERNRyMSgnEgnW/iYiImq5WBKRSERY+5uIiKhlanY95S+99BIkEgkeffRRz2MWiwVTpkyBTqdDTEwMxowZg+LiYq/nFRYWYvjw4YiKikJiYiKeeOIJOBwOr3W++eYb9O7dGyqVCgaDAStXrmyCLaKWJi5KiU6JMeiVmoBOiTEMyImIiFqAZhWU//DDD/j73/+OHj16eD3+2GOP4YsvvsAnn3yCb7/9FqdPn8Ydd9zhWe50OjF8+HDYbDZ89913WLVqFVauXIm5c+d61ikoKMDw4cNxww034KeffsKjjz6Ke++9F1999VWTbR8RERERNU8SQRCEcDeiMZhMJvTu3RvvvPMOnn/+efTq1QsLFy5ERUUFWrdujTVr1uCPf/wjAODIkSPo0qULdu7ciYEDB+LLL7/EiBEjcPr0aSQlJQEAli5dihkzZuDcuXNQKpWYMWMGNmzYgAMHDnje86677kJ5eTk2btxYrzYajUbExcWhoqICWq228XcCEQXE448ofHj8EYUWtp5yp9OJ1157Df3790dycjJatWrl9ddQU6ZMwfDhwzFkyBCvx/fu3Qu73e71eOfOnZGamoqdO3cCAHbu3ImMjAxPQA4AQ4cOhdFoxMGDBz3r1H3toUOHel7DH6vVCqPR6PVHRE2Dxx9R+PD4I2q4sAXlzz33HN544w38+c9/RkVFBaZPn4477rgDUqkUzz77bINe68MPP8SPP/6IBQsW+CwrKiqCUqlEfHy81+NJSUkoKiryrFM7IHcvdy8Lto7RaITZbPbbrgULFiAuLs7z165duwZtFxFdOh5/ROHD44+o4cIWlK9evRrLli3D448/DrlcjrFjx2L58uWYO3cuvv/++3q/zsmTJ/HII49g9erVUKvVV7DFDTdr1ixUVFR4/k6ePBnuJhG1GDz+iMKHxx9Rw4WtJGJRUREyMjIAADExMaioqAAAjBgxAnPmzKn36+zduxdnz55F7969PY85nU5s3boVS5YswVdffQWbzYby8nKv3vLi4mIkJycDAJKTk7F7926v13VXZ6m9Tt2KLcXFxdBqtdBoNH7bplKpoFKp6r0tRNR4ePwRhQ+PP6KGC1tPedu2bXHmzBkAQKdOnfDf//4XQE0FlYYcyDfddBP279+Pn376yfPXt29fjBs3zvP/CoUCmzdv9jzn6NGjKCwsxKBBgwAAgwYNwv79+3H27FnPOps2bYJWq0XXrl0969R+Dfc67tcgIiIiIrpUYespHz16NDZv3owBAwZg6tSp+Mtf/oL3338fhYWFeOyxx+r9OrGxsejevbvXY9HR0dDpdJ7HJ0+ejOnTp6NVq1bQarWYOnUqBg0ahIEDBwIAbr75ZnTt2hV//etf8corr6CoqAizZ8/GlClTPBcIDz74IJYsWYInn3wSkyZNwpYtW/Dxxx9jw4YNjbRHiIiIiKilCltQ/tJLL3n+/89//rOnGkp6ejpuu+22Rn2vN998E1KpFGPGjIHVasXQoUPxzjvveJbLZDKsX78eDz30EAYNGoTo6GiMHz8e8+bN86yTlpaGDRs24LHHHsNbb72Ftm3bYvny5Rg6dGijtpWIiIiIWp5mU6c8ErBOK1H48PgjCh8ef0ShhXVGz//7v/9DVlYW2rRpgxMnTgAAFi5ciM8//zyczSIiIiIialJhC8rfffddTJ8+HbfeeivKy8vhdDoBAPHx8Vi4cGG4mkVERERE1OTCFpQvXrwYy5Ytw9NPPw2ZTOZ5vG/fvti/f3+4mkVERERE1OTCFpQXFBQgMzPT53GVSoWqqqowtIiIiIiIKDzCFpSnpaXhp59+8nl848aN6NKlS9M3iIiIiIgoTMJWEnH69OmYMmUKLBYLBEHA7t27sXbtWixYsADLly8PV7OIqAEqqm0oMdlgtNih1Sigj1YiLkoZ7mYRERFFnLAF5ffeey80Gg1mz56N6upq3H333WjTpg3eeust3HXXXeFqFhHV0+lyM2Z8ug/bjpV4HhucrsdLY3qgTbwmjC0jIiKKPGEJyh0OB9asWYOhQ4di3LhxqK6uhslkQmJiYjiaQ81cpPXm/l5WDaPFAaPZjjiNArFqOa5KiAp3s7xUVNt8AnIA2HqsBDM/3YfFYzNFvY+JiIjEJixBuVwux4MPPojDhw8DAKKiohAVJa6gg5qHSOvNPVFahafW7ceO/FLPY9kGHV4YnYH2uugwtsxbicnmE5C7bT1WghKTjUE5ERFRA4RtoGf//v2Rl5cXrrenFiBUb25FtS1MLfPv97Jqn4AcALbnl+Lpdfvxe1l1mFrmy2ixB11eGWI5EREReQtbTvnDDz+Mxx9/HKdOnUKfPn0QHe3dC9ijR48wtYyai0jrzTVaHD4Budv2/FIYLQ5c1cRtCkSrVgRdHhtiOREREXkLW1DuHsw5bdo0n2USicQzwyfRpYq03lyjOXLaq49RYnC6Hlv9XPQMTtdDHyOeix0iIqJIELagvKCgIFxvTS1EpPXmajUKRCllmJSdhsx28bA6XFArZPixsAwrtheIqr1xUUq8NKYHZn66zyswH5yux8tjeojqDgQREVEkCFtQ3r59ewDAoUOHUFhYCJvtYn6vRCLxLCe6VJHWmxunluP98X2x5H/5WLIl3/N4lkGH98f3RZw6bIerX23iNVg8NhMlJhsqLXbEqhXQx4i7sg0REZFYhe0sf/z4cYwePRr79++HRCKBIAgAagJyAExfocsWab250So53vlfvk9e+Y78UkglEiwZmxmmlgUWF8UgnIiIqDGELSh/5JFHkJaWhs2bNyMtLQ27du3C+fPn8fjjj+O1114LV7OomYmk3twSkw3bAgz03CbCgalERETUeMIWlO/cuRNbtmyBXq+HVCqFTCZDdnY2FixYgGnTprFcIjWaSOnNjbSBqURERNR4wlan3Ol0IjY2FgCg1+tx+vRpADW55kePHg1Xs4jCJtIGphIREVHjCVtPeffu3fHzzz8jLS0NAwYMwCuvvAKlUon33nsPHTt2DFezqBmqqLahxGSD0WKHVqOAPlqcPef6GCVGZCRjdO+2SNSqYLI4EauWo9howbofT4luYCoRhRYpvz9EFH5hC8pnz56NqqoqAMC8efMwYsQI5OTkQKfT4aOPPgpXs6iZOV1u9pnVc3C6Hi+N6YE28ZowtsxXXJQST9zS2WdWz2yDDi+MzuCJnCjCRNLvDxGFn0Rwlz0RgfPnzyMhIcFTgaW5MRqNiIuLQ0VFBbRabbib0+xVVNuQuzbP76yeg9P1WDw2U1SB7u9l1Xjy031+Z/XMNujw8pgeuCohKgwtax54/FFTirTfnyuNxx9RaGHLKfenVatWzTYgp6ZXYrL5PSECwNYL1UzExGhx+A3IAWB7fimMFkcTt4iILlWk/f4QUfiJKignakyRVs3EaI6s9hJRYJH2+0NE4cegnJqtSKtmotVEVnuJKLBI+/0hovBjUE7Nlj5GicHper/LBqfrRVfNRKuWI9ug87ss26CDVh22cdlE1ECR9vtDROHHoJyarbgoJV4a08PnxDg4XY+Xx/QQ3SCrGJUcz4/K8AnMsw06PD8qAzEqBuVEkSLSfn+IKPxEVX2luePo8/Bw1wmutNgRq1ZAHyPOOsG/njVhwge78fa43pDLpKg02xGrUcDhdGHK6h+xcmJ/dEqMCXczIxaPPwqHSPn9udJ4/BGFxq43avbioiLjJGi02HGyzIzbl+zwu5wDw4giT6T8/hBR+DF9hUgkODCMiIio5WJQTiQSHBhGRETUcjEoJxIJDgwjIiJquZhTTiQibeI1WDw2kwPDiIiIWhgG5UQiw4FhRERELQ+Dcmr2io0WlFXZYLQ4oNXIkRClRJJWHe5mBRRp7aXI5C7VZ7TYodUooI+O3IvB5rQtRNRyMSinZq2wtAqz1u3HjvxSz2PZBh1eHJ2BVF10GFvmX6S1lyLT6XIzZny6D9uOlXgeG5yux0tjeqBNvCaMLWu45rQtRNSycaAnNVvFRotPgAsA2/NL8dS6/Sg2WsLUMv8irb0UmSqqbT5BLABsPVaCmZ/uQ0W1LUwta7jmtC1ERAzKqdkqq7L5BLhu2/NLUVYlrhN2pLWXIlOJyeYTxLptPVaCElPkfM+a07YQETEop2bLaHFc1vKmFqo9lSJrL0UmY4iZYSNp5tjmtC1ERAzKqdnSqoMPmQi1vKnFaUK0N8RyovpoTjPHNqdtISJiUE7NVkK0EtkGnd9l2QYdEqLFVZ1BKZMiK0B7sww6KGU8XOnyNaeZY5vTthAR8SxPzVaSVo0XRmcgp06gm2PQ4YXRGaIrM1haZcPErDSfwDzLoMPErDScZ045NYLmNHNsc9oWIiKJIAhCuBvRUhiNRsTFxaGiogJarTbczWn2zhotOFFahfyzJiRq1bA6XFDJpThrtMCQGIP2umgkiigwP3i6Ancu3YlJ2WnIbBfvaW/eyXKs2F6ATx4chG5t4sLdzIjF48+bu7Z3c5g5tjltS3PF448oNCapUrNVZXVg4eZjfiuaZBl0eH5k9zC0KrAohQyZqfFYsiXfZ1mWQYcohSwMraLmqjnNHNuctoWIWi4G5dRsme3OgCUGd+SXwmx3NnGLglMrZJh6QzoAeLU7y6DD1BvSoWZQ3qJx1koiouaNQTk1W9W24EF3qOVNLSVeA7vThREZKZiUleaVbtMmXo0Uzk7YYnHWSiKi5o8DPanZilYFv+YMtbypVVTbMG/9Ifxe4T1z5+8VFsxff4izE7ZQnLWSiKhlEFdUQtSIJJKa1I9AOeUSSRgaFUSJyYavD5/F14fPBlzOdIWWpz6zVvJ7QUQU+SK+p3zBggXo168fYmNjkZiYiFGjRuHo0aNe61gsFkyZMgU6nQ4xMTEYM2YMiouLvdYpLCzE8OHDERUVhcTERDzxxBNwOLxnUPzmm2/Qu3dvqFQqGAwGrFy58kpvHl2mYCUGxcZosUMfo8T74/vii6lZWHvfQKyfmo33x/eFPkbJ2QlbqKaatbKi2oZfz5qQV1iGX8+Z2AMvMvx8iJq/iO8p//bbbzFlyhT069cPDocDTz31FG6++WYcOnQI0dHRAIDHHnsMGzZswCeffIK4uDjk5ubijjvuwI4dOwAATqcTw4cPR3JyMr777jucOXMG99xzDxQKBV588UUAQEFBAYYPH44HH3wQq1evxubNm3HvvfciJSUFQ4cODdv2U2BSAGt2nUBmaoJXjnbeyXKs2XUCM2/pEu4meonXKLD63oGYt/6gV+9+tkGH1fcOhFImsq59ahINmbXyUgeDMmdd3Pj5ELUMza5O+blz55CYmIhvv/0WgwcPRkVFBVq3bo01a9bgj3/8IwDgyJEj6NKlC3bu3ImBAwfiyy+/xIgRI3D69GkkJSUBAJYuXYoZM2bg3LlzUCqVmDFjBjZs2IADBw543uuuu+5CeXk5Nm7cWK+2sU5r0zp+zoTjJVX4YEeBTzWTiVlp6KiPRsfWMWFsobffy6rx5Kf7/KbbZBt0eHlMD1yVEBWGljUPkXr8VVTbMHVtHrb6SWEZnK7H4rGZiItSXnLgVlFtQ+7aPL8pMrVf/3Laz6oxl+5Kfz5NJVKPP6KmFPE95XVVVFQAAFq1agUA2Lt3L+x2O4YMGeJZp3PnzkhNTfUE5Tt37kRGRoYnIAeAoUOH4qGHHsLBgweRmZmJnTt3er2Ge51HH300YFusViusVqvn30ajsTE2keopWiXHf34+jRm3dIZcJkWluSYosDtdWLm9ADNuFVdPudHiCFjCcXt+KYwWB65q4jZFMjEffw0JVN2zVs78dJ9XYF571spQg0GDBW5XMmc9Unt4xXQhEaljCsR8/BGJVbMKyl0uFx599FFkZWWhe/eaiWGKioqgVCoRHx/vtW5SUhKKioo869QOyN3L3cuCrWM0GmE2m6HR+J5gFixYgOeee65Rto0azmZzYNqQdDz92QGfdJDnR2XAZnMEeXbTC5UbzJzyhhHr8XcpgWqbeA0Wj80MOGvl5QRuVypn/XIuFMJJbBcSTTWmoLGJ9fgjErOIH+hZ25QpU3DgwAF8+OGH4W4KAGDWrFmoqKjw/J08eTLcTWpRXJDg5Y1HMCkrzWvg5MSsNLyy8TBcEFeOdpwmeO5wqOXkTYzH3+WUN4yLUqJTYgx6pSagU2KMV0B7OYFbQ3LW/Sk2WnDkjBG7C87jSJERxcaakp71uVAQGzGWn7zczydcxHj8EYlds+kpz83Nxfr167F161a0bdvW83hycjJsNhvKy8u9esuLi4uRnJzsWWf37t1er+euzlJ7nboVW4qLi6HVav32kgOASqWCSqW67G0To2KjBWVVNhgtDmg1ciREKZGkVYe7WV4sTiem/+EazPviILbV6inPMegx97ausDjFNXmQQiYNWsJRIWtW19BXnBiPv4b0aDckheJyAjd9jBKD0/UBc9b1MYF7swtLqzBr3X6/d6IqzDasmNAPPxaWYcX2Ap/Juq5ED2+gfVbffSnGVJHL+XzC6Uoff06nEwUFBZ5/p6WlQSbjrMcU2SI+KBcEAVOnTsW6devwzTffIC3Nu9Rdnz59oFAosHnzZowZMwYAcPToURQWFmLQoEEAgEGDBuGFF17A2bNnkZiYCADYtGkTtFotunbt6lnnP//5j9drb9q0yfMaLUmgE/GLozOQqosOY8u8KWVSzP3sgFdADgDb8kswb/0hzBvZLUwt86/UZPWUavQ3MPV8lVVUA1Op4erbo93QFIpggVtOuh5OQcCv50x+g9H65Kz7U2y0+PwOADXjH2Z/th8Ts9IwedUeZBl0WDQ2E9PW5nkF5o3dw+tvn/2hSyLmjOiKpz87UK99KcZUkUv9fJq7goIC3Pf2l4jSJaO6tAjLpgyDwWAId7OILkvEB+VTpkzBmjVr8PnnnyM2NtaTAx4XFweNRoO4uDhMnjwZ06dPR6tWraDVajF16lQMGjQIAwcOBADcfPPN6Nq1K/7617/ilVdeQVFREWbPno0pU6Z4rvQffPBBLFmyBE8++SQmTZqELVu24OOPP8aGDRvCtu3hEOxE/NS6/Xj9T71E02Nudbh8AnK3bcdKYHW4mrhFwcWoFRj/wQ+YlJ3mU8Jx2to8fPrQteFuIl2mmHrMMnspudiBArdsgw7jr+2AUW/vQLXNGTAYrZ2zXmW1I06jhM3pQpHRgmq7028wX1ZlCzowecawzgAuXmBOyk7Dki35ABq/hzfQPrsmRev39yrQvrwSqSKNMWg01JiClipKl4zYxLahVySKEBEflL/77rsAgOuvv97r8Q8++AATJkwAALz55puQSqUYM2YMrFYrhg4dinfeecezrkwmw/r16/HQQw9h0KBBiI6Oxvjx4zFv3jzPOmlpadiwYQMee+wxvPXWW2jbti2WL1/e4mqUl1XZkFdYjtwbDchsFw+rwwW1Qua5RV1WZRNNUF5pdiBKKcOk7DS/ba00i2ugZ4xSht6p8Z7ApbZsgw4xSt6ajXTKEClKSpkUZyutl5RCUTtwqzDbYXM4UWVzQiKR4LU7e3q++898fgCv3dnTb2DfkNKKRkvw48dkudgrviO/FJMu3AXy18N7uYFroLSTzHb+jyfA/75sSKpIfdrcmING3Z8PETVfER+U16fMulqtxttvv42333474Drt27f3SU+p6/rrr0deXl6D29icmKwOLBqbiQ92FHid7Ny3qE1W8QS6MWpZ0LbGqMUV5FbZbHh+VAZmf7Yf2+ukBr0wOgNVNhsA8aQHUcOVm23BU5SqrThfdekpFO7AraZGvy1gjf7SKv+BfUN66bXq4KePusdXrFqBzdOv8+nhbYzANVDaSai7YXX3ZX1TRerT5kitPkNE4RPxQTk1LV20Eu98k+81S6a7B27NrhOYM7xruJvoEaWU+wQlQE0wJAHwwuiM8DQsAIVMgWlrf8SisZkw212oNNsRq1FAo5Bi6pof8eafM8PdRLpMMSoFxi7bFTBFafk9fb3WvzoxBm//pTecLgGo+Q9VVgcOnzFCAkAiAaIUMpSbrYhWyaGQymCyOSEBsCrAdx8Anr3N/3gKfz3O+hglXh7TA4laFY4WV0KrUSAhSomEaCWyDTqvC0i3bIMOZ41Wr8d00TXVY2prrMA1UNqJSh58cLS/dJQ28RosuCMDRovDcwxq1XKkNDDYFuOg0UhXe3DniRMn0LymPiRiUE4N5HAJuHtAe7+9zxOz0uBwiedX0mJ3Bs15tdjFVX1FH1NTweafP/7uSbcx2Zz4sbAMSVq1aKssUP3FqOVYfk9flJvtkEgkOHTG6KlMkpOux3fHa76vWQYdzhmteO+evpi3/qDnmPPX67121wnMGdEVUkgw80L+9IoJ/QKOp9iRXwqnn2imotoGq8OJd8b19lxof573O5aP74d56w/6DOxeMDoDL47OwFPrfO/szL2tG+5e9r3nsUA55I0VuAZKO8k7WR7wwiFQm06UVuEpPwPZXxidgfa66Hq3WYyDRiNd7cGdJb/uR8xV6eFuElGjYlBODSIAAXufgcA9cOEQKuc11PKmFhelxJwRXTFr3X6vCx53ZRv2qkWmimobSqtsEAA8+7l3NSB3KtVHuwsx89YuuG3xdgDA23f3RsfW0Xhq3X5kpiYEPeYyUxMw+7MDyL3R4HlMLg1eg7/K6sCvZ00wWuyI0yiglEkxa91+r2AzJ12P/5s8wCcgB2ouamet2483/tQLr/+pl6c8aqxajgqzHXcv+95ThzxYlZDGClzjopR4flR3nwuEw6cr8MLoDMz57EC9KpecLjf7BOTu7X163X688see9W6zVq3A1YkxePcvvWF3Cai2OqCLVqHa7oTRbIdaIUOx0SKaMTiRwj24s6q0KNxNIWp0DMqpQVwuIWDv84780prb7CIRGyLnNdTyplZRbfOZfRRwl5g7wBzUCOTOPe7ZLh55hWV+A2vphYGYVVYHqm1ORCllECCg2ub0DJAMNFix9nJ3tRMASIgOXiVELZfhpje+BQDk3mjw27Ztx0ow5/MD6NkuHluOnPN5je35pThfZUPnFK1XYFlRbcNH9w+qV5WQxqp2UlFtw7z1h9ArNQET66QFvbrxCF69sydMFkfINlWY7UHvrlWY7fVusz5GiWX39MXsz/bjcFElVt87EE99Jv5SskQUPuKKSkj0qkJMTR9qeVNSSENMxiMV12Q8kZiD2hjl3iJdsAlr3LnHE67tEDCw3nasBEZzTe/q++P7IjFWjZc3HsbUG2tuzYcarOheXrvaicMpBP3uO1yCp4JStFKO3qkJyExN8Jnkx932QIwWO3YXnPeaQKxulZCKapunV77ud6SxJsYpMdnw9eGz+PrwWb/LH/vDNT757H63xxy6F7y9LjpoLn3MhYt9k9WB+esPomdqAp4c1hmnyqoxObuj136uW0q2otqG8mo7qmwOVNmciNcokBiranHHFFFLxaCcGiS6HnWWxcJotmHuiG6Yv/6g35xXo1lc03wbLfbgJRxFloPamOXeIlWwfWC2OT2PBwqs3YMo7S4BFdV2JGnVUCtkOFJUM6ASCD1Y0b28drWTimp7wCovk7PTEKWUIa+wzG9VorqT/AS7KNAoZbht8Q4AF/PMtRoFSkw2mC7UO5/z2QFsy/f/HWmsiXEaKw3Gvc8DiVUrUGV1YEJWGgT47tsJWWmoulCBqtLqwF0Bxt/U3s/b80tRXm2DyyXgxPlqLN5yzOt1cy7si5ZyTBG1ZOKJoCgiqGTSoL1EKhFNBR8fpcT8Df5vab/05WFRVYoBatJpgpdwFM/hynJvoffBo0MuDkLzF1jrY5RYfe9Av4MoV987EHJZzf/nnSwP2uvtHsxYVasc6Z7CMhw6XeFVJcn93S+ttGHF9sA56rUn+QGAuACBat0KKz8WluNUuRnvrKsJwgOlxdT9jjTGxDiNlQYTp1EE/X2L0yhQbLRg2tq8gBV01tw7oOYJQvDxN7X3syAA3/xyDuv3nfabRtRSjimilk48Z3mKCFanK2gvkdUpnlkyrU4Xthw55zcfFgCevEU8bQVqLniClXBcIKISjpGYatPYQu2Dp27t4vm3v8D65TE9Ag6inL/+IB68rhOeH5WB+esP4v6cjhiRkYJErdpzB6W4wow28Rr8Y+dveH5UBqSAJ6Bcsb0Ai8ZmYqWfC7xh3ZLxxKf7/La79iQ/uPB6beLUPoGqvwork7LTsOR/+RcHoDZg4p7LnRinsdJg2sRr8MLoDDztp6LMC6Mz0CZeA7PNiWqbM+C2uS8A6v5G1lZ3PwsAEmNVAddvKcdUY6pdPhGomQBQJhPX3BREdTEopwaptDiC9hKtmtQ/3E30CJUfGmp5UzPZgpdwNNVKKQg3lnsLvQ9kUoknUHQHycDFQC1RGzgI255fiqdu7Yoz5dV4ZkRXuCDB8m3HvSq35Bh0mD8qA8+O6IZzVRZooxRYMDoDJpsTlRfytxeMzkBhWTUqzA7PcVpYVh203e50lewLF9r3rNiNZ2/vhhnDOsNkcUKrlsMpCF4VVgDfILyhE/dcjsZKgwGA9rpovPLHnqgw2z0993EahSd9pL4XAFUhJlKLVsmRe6MBh09XoNrmaNL91RLULp9YXVqEZVOGwWAwhLtZREExKKcG0arlQXuJQs3y15RC3bKu7y3tphLqpCumk3JjpQtEslD7QCaVeAWK7ovZKdcboFJIYQ5xkVVhtmPs8t348P4BWLwl3zetIb8Usz/bj4lZaZi8ao8nAO2SEo28wjLcsnCb39d9f3xfv4+7tddFYcPUbHx5sMiT9zx51R6f16gdkAO+QXioXPhA408udfCwvzSYGLUcVVYH8grLPK9VZXOiwmyH0VxTDjJWLYfd4UKZ+eL7RStlMNucUMikUMqliFZe7GENVH4x26DD86O6e9oaKt2syurAT4VleGF0BmwOF8qrgx/fLeGYulSCy4UTJ054/p2WVnMXwl0+kShSiCeCoogQpZDhngGpmJSTBrPdBeOFE5lGIcWKbQWIUojn9mCUUhY0PzRKKZ62AqGDvFDLm1JjpQtEslD7QHchmAyUL33kjDHo68dFKfDYH9Khj1FjcnZHPDC4E1pFK2F3umA0O5B3sgwSSU0w+smDg5AQpYAA4EiREXanCxumZsMpCCirqqnm4R40fOB0BXLS9X5Tbwan69E2XoP8c6aAF96B1A3CQ+XCK/2MP7ncwcO102BOl5vxt09+9ryWPkaJD+8fhGf+fcAnh3/+qO6YtjYPpVU2rJjQD29vyQ84OLWi2oaXNx7BxKw0z92DWLUcxUYLXtl4BC9cmFMgIUqJmzq3Rpc2cT4Dtw+drkDeyXJPudO5t3XFWaM14P5qKcfUpaouO4s5/zqJhJTznl5xokjEoFxEIqG8nMQl4N6cjtjxa4knv7XK5kRxhRn35nSERER1yq0OJ569vTue/fcBnx6tZ2/vDptTPOkgAEJOW54QLZ7vQmOmC0Qqk9WBp4Z3wVNCTU6wyeqAxe7EqTIzbri6tc8+EACg1pw+7s/baLZj0dhMWBwuSCU1pTyr7U6YrQ7c3rMN5n52AIeLKvHymB4QIMBqdyFRq8K1nXSQQMCYd78DAM8g4bzCck8FHwBI0qqRd7gM7209jj6pCZg2JB03XJMIl+A950Dtzy5ec7Ht/ioCtW8VhRs7t/Yar1FstCDHoPcEs/5SdoCLM5FWmG0Aoj3PrbY6MLfO5ErAxYGh7lrj9fl99DcI9+UxPXwCcqAmVWjuZwex7J6+EAAcOFWOvmkJmJDVwSuQfubzA3jtzp4orbJhdO+2WBFghtXSqprc7yStGnNv64an/EwGNmdEN4xbXpOPv+1YCX4/b0ZynBq5Nxh89pe7+gqAgKUlCdAkJLFXnCIeg3KRiJTyclKpBKfKzVi//4zPCSlNH4PUePHMTqeQSlFisiD3RoOnRytGLUOV1YGyKiv0MapwN9FLklYdcNryF0dniG7mv8aomhGpTpRW4bkvDuLuCyXv6gZRgzrqsP9UGWLVwUsCvjo6A1YBmLf+IMYP6gCVXIrFFwZLuquXHL0w8Yy/Ki3P3t4NuTd2QrXN5QnI/VXwyalVhm/R5mOYOawz/nbzNZg9XIZzJiscTgHFRotn/Ri1HNkGHX4M+Hp6PD70GtwzsAOq7U6o5FIcOF2BidkdAElNoFltc2La2jzMHt4FM4d1xqkyM5Syi+NPvsjNBgAUllZh1rr9mJSV5hOQu209VoJfz5pw9/JdfvdjXf4G4QbL4d+WX4Lfy82YvGoPcgx6PHxDJ0xetcdTGrJ2wO1wCfWa1bii2obZASYDm7f+IO7qn+rZp9V2J/72yc944LqOePrWLnAJNRd58VEKpGjVqLY5kbs2T/TnB7Hyl97CQZ8kRgzKRSCSystZXQJW7fwNk7LSMLPOrdtVOwvw5NDOoV+kidicLoz/YI9XL1+1rSYoWLG9AOsevjbcTfSRqov2mrZcq5YjIVopuoDc7XKrZkSi38uq8dS6/chMTfAbnG07VoJ56w/i0SFXY85n+/32/M74dB/m3d4NcqkET39W81qn61zsugdOvj++b8AqLc/++yCeub0bCkursWRLPnJvNPhvU34pXLhYhm/CtRZPnnjOhZ5bu9OF30qrIJVKPLW4b82wBHi9ErggIDM1wStY/0OXRLx2oUe73GyDxe7CzuOleH7DYeDC+2e2i8fisZlwCgJOl5ux4MvDyExNQOtYFd4Z19urNn/teunldQZmu/fjnBFdIZNKvHqO/Q3CNVmcQecBcOfEu7etdsnC2gF33TsMte3IL4VTqLlbGKw6T93qKyq5FNU2J97cdAxvbjqG98f3xeRVe/DZhd+oJyPk/CBW/tJbOOiTxIhBuQhEUnk5q9OJR4dc47fXbs6IbrCKKCXEaPGtfiCRSIIuF4MkrVq0QTjVfG9qT29fV5RShrsHtIfJ4vAKyN0BYd/UBMRFKWC5EATWDtBqH1PuIDFUlRa7Q4DtQinSYGUIa79P7UGZ2/JL8dz6g54AOyddj0duSse0tXn4x6T+eGrdAb/bmJmagOEZKRic3hqxajkEQYBcVnN8dUqMwfFzJhwvqUJeYRkA+O1xH9IlEU8M7Yx56w+GnMjI38DRbcdKcKrMjEkrf8DgdD1eHJ0Bm9Plta57v8dHK4LOAyCv9duwI78UTw/viqxOek+nw4xP98HpEkLPanyh6kqo6jzuz8Bda97fsli1IqLOD2LG9BaKBAzKRSCSysupZDLM/tz/Ldn56w9i/sjuYWqZrzhN8Ml44jT8+lPDuUtp+ithF6WUYfGF+uBjB7T3PK6PUWLVxP5QyqWwOV2osjqhVtSkmEcpZX5fyx1YmizBL3QrLXa0vpCKFaqsnnt53QC3dsC+7VgJHrquE6ptTpwzWX1eI0opC3hcTcxKw9pdR/DMbd0gkwBt4tSYM7wrlHIpnvHzu9E5Rev3LkDdCXZy/ASubu4LgT0nyrDt2DmkJ8VCJpUgx6DH3sIyT1tv7poUdB6AibV6rgHgt5IqPLz6RwAXJ3Sy2h31ntU41MBslVzq2WfT1ub5LHMP7jxeUhX0dcR0fogETGUhMWNUIgJatSLobVUxlcKyOFxBe+0sIYKCpqSWy4KehF8U0WQ8FDncU7FHKWTIvdGA3qkJUMokSInTwO5y1Uxxn90RWrUcUUoZ7h/cEUO7JUMmkWDeFwe9a42n67FobCYUUikcLu9jx129JEYdPGCIUcvhdAnIMuhCliF0B4L+AlylXIoopQzVNie+O16KnHS9J9ivbVJ2WtCc6szUBDz92QHc1iMFrWJU+GBHQcB88fr07GddmKjo9zIzvpia5ZUyN+PTfdBHq7BqYn/oYxTYd6oCFWY7bE4Xnh7eBeVmG97behw78ktxvsoW9LdrUnZHn31Ve/n89QexYHQG7E4heFUZac3zglXnyUnXIyVOjczUBK+7AUBNOlFKnBqP3JSOkiobYkJcBIjp/BAJmMpCYsagXAT0MUqsmNAPi7cc8+l5WjGhn6hKYVVa7EEvIMTUa1MVYjKeKhFNxkORQ6uW46bOrREfrcRPF773i8ZmYm6dyh6vjMnABxP6YcmWY3C4BL9Tzm87VgKlTIKZt3ZBlcWJ98f3hUQiwY+FZfhwdyFeGtMDVVZH0Ko8xUYL7E4BE7PSUGy0BA0Yi40Wvz2zACCTSLBobCZmfroPCpkE827vBrPd6fN69Qmkl2zJxyM3pePNr3/BjvxSjKt116C22j37/n5XUuLU6J/WCkaLHcu2H/dJmfvw/oEw2xw4WlwBlTzeZwB6Troe46/tgO+Pn/cKfP2x2C8u93fh8mNhORwuARaHE4/cdDVmD5dDLpOgrMoGmVSKX89WonWsGuXmmvrtoSoUAcC+k+XeAXm6HlOuN+CPS3d6Hn/jTz0ClrDMYanES8JUFhIrBuUi8bafyUF25JdCKpFgyYWyYmKg1QTPy3T3IoqBKUTOeKjlRP5clRCFZ2/rhlkXquTk3mjAml0nkJma4JnlVq2QQS4Flmw5hm35pZgQJP987ID2mPdv7x70LIPOE9A9NawLnh+Vgdmf+VblmTeyO34/b4Y+ToG73vseD1zXEc+M6Ib56w/6zP757O3d8eOJ8z49s+73++54KY6cMeKTBwZh7ucH8Pdvj2PZPX08aR3u36dQKTKKCz3uLgG4L7sj/nbzNVApZH4Hcbp7owOlxOQY9Bif1QE2P3fo3ANdn7u9O27snIQX/3PY5zP4sbAMa3adwKTstHrdRQCAGzu3xqNDrkaJyeZp875T5ejZNt6nZGPt9JM+7RMwOTvNa9IgCYBhGSkYf20Hz+zHZytrUoJS6lQvilbJsedEGSat+sHr82kVpcLD13fyGWCaZdDh4esNIT8PIoocDMpFoMRk8yqZVts2kQ3kiVYETwlZIKKUkChV8Nv+oZYTBWJzCp4AuW9qAnq1i/cJKNfcO8ATwAUKnIKlgkghwep7B+DLA0XolBiFBaMzYLQ4YLI6EKOSo8howZ/+vhMlJhtybzQgMzUeb246hr9/exyTstMwMbsj5DIJdFFKyKQS2JwO9ExNQJ/U+DoBux4Tszsgd03NjKMvbDiEnqkJmJCVhjiNEvf9YycmZad5gt12rYKX4HP3uAuCCw5BwGv/PeoTTLoHceadLEe2QYdegSrZ5JcAEDB7RFe/Qf32/FJYnS5IIHjKU/rLc5dLJPjpVLlXHfXactL1aJugwZeP5sBqd+HljUe8e9wNOgxIa4W9heU+n5P7c1yyJR8QBMwb2R2/njMhRiXHnM8P4OvDZ33eb3C63lM1xf3b/utZE2b9a7/PurpYJSZ+8ANeHtPDU/EqRi3HWaMFU9f+iP+bPAC/l5tZu5yoGWBQLgIVZluI5eJJCam2B08JqbYHv0XclKIUsqC38sU0+yhFltqDs+OiFJ7As3YKhkQiwYoJ/bD/93K0S4jC++P7+qR7BUsF2ZZfU1lk4dfH0D+tFexOC5RyGZyCgEqrA1FKGe7qn4oV2wuwYnsB3h/fF1JIsC2/xPOaWQYdnrmtG0a9vQMAsHRcH0y50YAnawV3VVYHbPaai4a+qQnok5qAMxVmAIDD5UKf9t5lD3NvNAQ9rnYeL8VPhWV48pbOWBTgDiBQE8geOV2BOSO6ocRkDbIfSnGqzOwZdFm3Mkul2Y44jSJonvvfbr4GEgnw8A2d4IL/HmcAkEsleP6rI34uDrxLStZ9D88g2fxSFJRWY9LKHzypM9/9WurV8x2llKFHu3icqbDg13MmRKnkkEokAVP/qq1OvDSmh9/Jil4a0wNGsx1/fq9mIiLWLieKbAzKRSBKGfxjENN08EZz8JSPUMubkkIq8TtDXpZBh9wb0qGQSgI9lSio2pU1FDIpduSXol2CBism9MO5Siuq7U60jlXB5nAiVqWHyWr31Mevtjk9gaW7lGEgCrkUQzon4offzuO2Hm0wp04Fk5x0Pf6dmw2L3YFopRzzRnZDlc2J89U2OJwCfiwsQ0FJFaptTjz2h3Qo5BIsrhMo1xwPBjxwXUckRCtQbXNiw4XcbHdaiSBcvDPguQCQSLzynGunclTbnJgpkQSt5/3UrV1wc9ck3LvqB7z+p15B90PtOw11e6dj1QoIQPD3GiZF9zZxmLxqj1evv0peM2/B5FU/4NOHroXLGbwG+aQ6FVr8tc+dm77tWAkUMgkWX0g/tDpciFbKERelwKtfHfGZ3Om5kd09A21r08UosWjLsYAXHM/d3s3zGGuXe1dXEcQzwTRRvTAoFwGpVBK050kmouAxklJCTHYHdDEqjMhI8ToJnzVaoI9VwmQXzwUERRa1QuoZfGmyOGpKHk7qjzmfH/DMwFl3SvfaPbzux5+4OfhkWzKJBE+P6IL/7D/jt6TgtmMleObfBzA8IwW92sXD4RJ8Ui/eH98XAHDDNYk+y4CLwd2MWzojWinHK19dTDdxz8o5KTsND11vgFMQYHe4sPu38/jbzddgQq1cafdMne6gsjLEmI3fy8zY93sFHrq+E+whLk4ClXDMNuigkEtQVhX8bmKVzQmtuuaCI1CPfKXFAZcreBQXKA2pdvtq58nfPaA9Vu4o8KQLuWdp9dcT/8znBzB7eBefuvDmIHcnd+SXwlzn7mRLr13urq7irDYi5qr0cDeHqEGCj3yhJiGXSjDxQumv2tw9T2IKymNUNdNv+5Nt0IUs39WUKs1O3L3seyRq1UjUqpAQpUSiVoVErRpj3/selSHqPxP5U1FtwzP/Poi/De2MbIMO0Wo5Xh7TwxM0vzymR8Da2x/sKMCk7IsDJzVKqc9x7+ZOBZn7+UHkpLcOOAX9jvxS9Gwbj/8dPYujZ4yYd3t35NR6TXfetnvdQK8hk0hgcfgGgO5AdtzyXbA7XJi8ag/e3HQMJSYrJq/ag4dX/4jJq/ZgyZZ8r17eUKUc2yZEeVJ4LHYncgz6gPshUI3yZ2/vjk2HimAOcYHtcgnQhLjjGKWUhRyo7m+waO321a6n7h4vUPtzy2wXH/Az2JZfioy2cT7fh5LK4OmNv5dZfB4TUxWscNAkJEET3zrczSBqMPFEUC2YLlqJ/x44jZdGZ8Bkc8JotkOrUSBGKcOGfb+jX/uEcDfRw+JwYkJWms/t4iyDDhOy0mBxiCfQ1WrkKDHZPNOJ+yxX8+tPDVdisuHrw2fxlwHt0Ss1AQqpBMlaNQ4XVeL98X3RJl5T7xSI30qrMTErzZML7lY3FeTJW64J2iaT1YGuKXFYvv04nvniECZlp2HChfdpm6DBzV2TYHO4agaE+illWm1z1qtEaO2e4ryTgQdOZht0OGu04sbOrdG1TZzPex46XQG1QoqsTjooFTKUmqyYM6IrXtl4GJ1rrR8fpUCMSo5JK3/weY9krRqQCOjUOhYJUUrkGHR+L1xy0vVoE6+G0yUEHuhp0AMCIJMh4F3LHIPeUznFrfbnVHvALOC/dGSoSimnysxeFWQ66qNDdsooZL7LWbucKDIxKhGBuCglhvW4CjPX7fepw/vC6AxR3YY0Vjs8t7Pr5mVOW5uHVZP6h7uJHvFRyqD1feNFtF8pcrgHeVbba3qQb+7SGg6XgNX3DsS89QeRe0PwW+a1AzO5VIKpa/Ow+t4BmJAVOBXEYguRey6T4v3tF/OOaweD2QYdHhzcCVclaJBXWBZwOnutWo5QKbi1e4oPna7A40Ov8Ttwcu5t3fDO/45hxi1dMG/9QZ/2zBnRDRXVNrwwOgPHz5nw+c+n8cpXR7F8fD+8Xiff2j2gsfb+yEnXIyFaCYvdiaRYFWJUciy4oweeWrffb03wlHgNfj5ZhonZHQAIPuUiJ2Z3wO8VZkgk8CkB6W7DxOwOSIlT48P7BkIiAeI0CihkUpRX2/DJA4PwzS9nkbvmYhuDzdIaiFIm9dr2zx6+Fmn66ICTEPm7izCYtcuJIhaDchE4VVaNp+oE5EBNNZOn1+3HS2N6oG1CVJha5y1WIw+alxkrot7nJK0aL4zqjqfXHfDqHcsx6PHCqO5I0qrD2DqKBBXVNpSYbDBaLty9UtXM0vnePX3QSR+DFRP6weoEEmPVnmN45rDgeeLuwCz7QkBVbXPifFXgOzoAoFHKAvYEZxl00GrkficXAmp+R+bd3h1z/eSku/89Z3hXOJwCXBB8erejlXI4XC5olDI4nAJWTOiH4goLOraOxr2rfsBd/VMxKSutZt+oZPjqYDHuXvY9Xh7TA8/7SePZnl+KeesPYv7I7qi2ObD4fzUDT3NvNOBVP5VP6g7szEnXY/7I7ohWynyqjNSu+x2rVkAfc7FEYIxKgbHLdnnuItS+AMpdk+cZkDk1QKdD7po8fPzAIJhsDp9KLzWT/nTyakucn1QY9yytgcYP1Q2wY9WKgJMQ5aTrMeUGg9ddBPdFiJg6coio/sQTQbVglRYH8grLA95aDjVgqinFKGRBe59jRFZmMFUXjdf+1BNlVTYYLQ5o1XIkRCsZkFNIp8vNmPHpPq/veo5Bj/kjuyG9dYzXRDJfPpLtSTuosjix5r4B+O7XUk9qiJs78MpJ12POiK546cvDAEIHaxCAh28wAH7SXJ4c2hmnzpuDbovZ4Qyakz5zWGeUVFrx0Z5CPD28K+Z+fgBLtuR7TepTt273327pjNf+2NNTBtVkdeDXcybkFZahxFSTBx3sPd09ye7fvqHdkoLOFDprWBdktotH3sly/HHpd3j85mvQOzUe1Tanp0a3mwBAIgEsDhdKzppgtNhr6oaP6Ir56w/5nTzJHRBnpvovU5ll0EEpl/qvp36sBBAEbJiajXMXLgosfmZDdc/+KgG8LqJqp8G41e7xblNnoiH3BQcAfJGb7fcihIgiD4NyETBZ7EFnyayyimfQjsMlBJ1dzhGiekE4JGnVDMKpQSqqbV4Bubv+eN/UBAgA5nx+wBNURSllUMhkPqkh2XXqaecY9Hjm9m6wO50YkZGCu5d9j7v6p2LcgPZwuASM6tkGz60/5H0RkK7HxKwOsDqdOFVmRp8OCZiYnYZYtRw2hws7j5eirMoGZ4jab1XW4Pni5yqt2HeqAp0SY/FsrR71QJMbbcsvhWvjEWSmXqxhHqWU4e27e2PqhTKkofKnKy12yKQSz29f1xRt0PXPV9fcTah9oVB7sp26Pcd+LybS9VgxoR8mrbw4a2b2hfEw7oB4xYR+PuUe3evYnYEroWzLL8WZCgtax6rwp7/v9LQTuNjbX21z4sPdhXh5TA9Y7C5UmO2IUsmQV+idruSvx7v2REO1MQgnaj4YlIuALkaFf+49iWdv7waHU/AM9JTLJFi+9Vc8cJ0h3E30MDtcmLo2z2t2uVi1HMW1ZpcjinQlJptXQL5obCbW7DrhqRTSt0MrPDrkajhdAuKjFLA7nHjouk6YNawL7E4XjGYHfjhxHut+PIUvp+XA7HCivNqO38uq0a5VFGxOF0pMNq8gPkopw/qp2bDYXTBZ7VArZFBIJXAKAuRSCTKuikOrC3nUWrUMP58qw8hebTy/GRumZaOowoIZn+7z9FQDNb3aoQY1J8WqIZFU+AxODDa5Ud1Bq9U2J6as+REfPzAQM2/pArmfAYi1xagUUCukeGPTL0FrgLslRNWkgwS8UDhWApcgeKrbBOzRBjw92lqNHFq1Ala7E2vuHeDpbX7tTvfdtZoe6GilDBaHExXVwe9alpvtuCpe48kBrzv+Jl6jQKfEGJ9OghStGv07tGKPN1ELx6BcFFx4+Pp07Pi1BElaNawOF6psThRXmPHw9elwCuKpaFJltQedXU5MvfpEl6q8+mJQOyk7DWt2ncDdA9pj7a4TeOKWLtj723ks/PqYZx13+sGED3Zj+h+uRkbbOPROjceIjBR892sJ5m847DVI8flRvhPFVNucKK2yYdHXv3ilfbgHfJ81WiCXSgE5EKtRYFj3FMz+7IDP4PDV9w7EuOXfo8RkQ5ZBhzm3dYNEgqBTzG/PL0HGVfGw1qmeFKq3u+7yPqkJsDsFlFXb0C5BE7TaicPlgtVxsRc5VAqPw1lzN6C+FwoBZwg9VgKTzYESkxUmqwN5xnJcf3VrpLWOAeA/bck9U6ZMEnygpkouRYXZ5pUD7m6Hu/fb3127QL3gRNSyMCgXAaVUhpPlZs8sem5ZBh066KPRTkRTJreKVuHNrwPPLjd/ZPdwNIuo0VRU26CuNTaib2oCMtvFY+WOAvRMTcDz6w/65ErvyC+FSi7F8vH98OpXR7wmgKk7Lfy2YyV4et1+v1O2O5wu9ExN8AxE1ChkcAkCzpvMSNRqaiaKEQCXCzhdacG92R2RmZrgyV3fnl+K+esPYs19A3HyfDWKjRZ8eeAMbrhaj2dHdsOz/z7oMwvnw9cboItRotRkQaso79+aUNVCai/PNugwPqsDxi3fhWqbE0M6J2LObV0x/4uDPtVOJuekIVop8+rRd+dbu/dn7TZOzEpDRXXNBX9DLxT8OXnejIdX/+h5/TR9tGfm5LoBOXBxpsxX7+wZcEyNOy99ZM82AXPAGXgTUTAMykXA5hKw5H/5ERHo2p2uoDWYQ83MRyR2JSYbVIqaSX3yCsuREq+uSWfJL8WELN9A2q1rmzi8Xo/qIUDNIL+JddI1cgx6RKvkXrnpUUoZ5o/shqsS4lBwrgptEjSYt/6gzyDB2kH/9vxS2B0urNhRgIlZafj6cDFuzUiBQirBsO7JPrNwTl71A3qnxuPF0RlwuASv3upgvdc5Bh1ax6rwj8n9IZNIsPN4qVde9NdHzkKAgAlZNdVOHC4BKXFq2Bwu2BwunK20IqZWWk3t2UPd6R7tWmnw1cFifLi7EJ0v5JyHulBo10qDKosTKyb086rDXlvt13Bv2wujusPpgt+AG6gJzKusDjw/sjue/my/34Gaa3adwJ292wJg7zcRNRyDchEw2xo2jXI4haoEI6ZKMUSXwmixw+ZwYWJWGoZnWPD8+kMYO6A9gOC9sA3Jv677WjkGHZ4b2Q3PbziEHfmliFLK8MB1HTGsWwrmrz+Ixz/ZVzNF+/bj9Qr6zXYnMlMT8OGuQtw9oD0sNiccMsFnCne37fmlqLI5UXi+2qtOd6BqITmGmkGVd733PRaPzcQ9Aco5bj5yDpOyO+K+f+zBorGZeHmj90XLi6O7e/U81y63mmXQITM1AXmFZXh+VHfMX38IQPALhWyDDl8dLPZ6jdoXLO7H6pYe3HFh+20hetmNZjtkUuDJWzrjIYsDFWa75+LGneJUYbYBiA76OkRE/jAoF4G6vTg+y0NUTmhK0argX5lQy4nETqtWoNruwMSVP+Afk/rjqXUHPLNjBuulbWhaRZo+Gh8/MBCxagUqLXbYXC5sOXLOM7D0rNGC52rV+Q4V9E/O7uj5d7RKjsx28bipcyLGLd+FFeP7QSIJ/jtiNNs9kxnV7q2WSySYmJWGSdkdoVXX1Gn/76FixKhlWDw2E1HK4Me8Si7Fl9NyMPsz37kYnt9wGO+P7wsAPqUnZ4/ogjPlNVPIl1Ra0bVNnKdSze092mB+nTSiulVU3PsFuHjB4i4hWVZlwzvjenvPamp1olV08J5t90yZty3ZjknZaZ4Stpnt4gEA09bm4Yvc7KCvQUQUCCMoEdBqgn8MoZY3JQkCT0OdZdAheL0FIvE6VVaNSosDRrMdyXFqXNtJB7lMgvfH94VCJsWa+wbAbHMGnMTH32QxtdUO6HMMepwuN2Oqp1xizSyY+hglXh7TA2qFDN2vivPq2Q4V9LurnbiPw8mr9uD98X2RmRoPuUwS8oI5VqPAtvySgHW6c9L1eO3OnqiyOrDw62NwuATkFZaFrJoSp1HA4RL87rNqmxOTV+3B+qnZKCip8kqrGf3Od54Oi1szUnwq1bgnAYpVyxGrVmDD/jNePeJuO/JL8dSwLhiRkQKb04WFX/+CLUfOeZa7e9PjouTQxygDzp5Zu2543/YJfvcRZ9MkossRPDmPmoRUUnPC8ycnXQ+pmCLdC9NQZxl0Xg+7cyoZlVMkOlFahRmf7sOwt7Zh4sof8PWhYjw9vAte/+ooJq/ag3tW7Mbdy3Zhza4TmHNbN+QYvI/XLIMOsWq5z3FRe7k7ZSLrwoDI1btOYNHYTEQpZdiWX4p3/5ePfz44CMlaNaQSCSx27yA8VC51QpTC6zjMMuhqettv7watWg4JanqT/ck26OBwurBie0HA43v8tR0we91+KGVS/KFLomfdYqMl4HbnpOuRGKuC0RK4KlO1zYkSkw0f7CjAw6t/xORVe7BkS75XHXFHnbEq7jSXyav2IFatQLXN4fWcuiyOmtKtL2884hWQAzVB+8odBYhWyj2zZw6u83tcu254fdYhIroU4umCbcFkUimm3mgABPhMBz/1RgNkUvFcO8UoZFi764Rn9sLaPVtrd53Ac7d1C3cTiRrkVFk1nlq335PLvWhsJs4ZLT7lBoGaHGkJJJh7W1c4XAJMVgdiVHKoL5TCe35kd8ytU+EkJ12PZ27ril/PVeH98X2Rd/LiRDFWhwuTstPw4e5CTLkpHU/Xek93WodbqJKBcqkUmakJWLvrBJ4e3hXP3NYNPxWW4eR5M+I0MkgAPD8qA7PrDFLMNugwf1QG7nl/V9DBlrXbvOCODMz6135MW5uHB67riGdu64b5Xxzy+v1yB6kAoAkx06/Z7sD8Ud0x57MDPm17fnQG3tp01O/zsg06tIpWQikL/hsZr1Gi0uoIOHZne34pKq0142HqUzmF1VWI6EpgUC4CNqcLEz744cLtWO/KCBM++AH/evjacDfRQwVg7ohuePqz/T6zF74wOgOq8DWN6JJUWhxeuccf7CjApKw0nwDOPfjyhmsSYbswl4BcKsFXB4vw3tbj6NM+Ac/e3g2392zjqXDSQR8NhUyC25fs8NuL6x4AmtkuHnM/974IqBuEhyoZWFxhwU+FZXhhdAaqrTYUG+0oLDPj2S8O4ZMHB+Gbo2cxvHsyFozOgMnm9ASTMUoZ3vz6F5wsMwPwP9iy9rG+9VgJLHaXV1Aql0rw2p96wmRxeAWp1TYnctfmoWe7+CBVXPRIilXD7nL5bVuVzYZHhlyDcyabT8D+4ugMJGnVUMttIdNOjhabgn4PjOaLg9TrUzmF1VWahtPpREFBAQDgxIkTCDF5LVFEY1AuAiaLw+tE6G+5WJyptkKnVlw8eZrtiNXUnDylLhfOWBxorWPlAYocRvPF1Ar3YMpxF6qtuLmnkFcrpD4VRHIMeiy5OxO5a/Lw7L8PYuawzrDYnXC6gEqzHYIQfDC3uze6bsBaNwh392LPGd4Fc4Z3xTmTFXangB8Ly/DhrkI8NbwLnrm9G06XVWPs8t2e18k26FBstODVr37BT4XlePb2blDKpJ4/rUaBvw3tjBKTzSuodQf7tQdOulVa7OiUGOMTlCZpL/5/RbUNT16o+b33RJnfC4rB6Xo8fIMBd7z7nc8+Gpyux+KxmWh34ffk9T/1ujDLpgNatRwJ0UrPRDzulBL3hD21X8OdUhJqVtNQyyk8CgoKcN/bXyJKl4ySX/cj5qr0RnttweXCiRMnPP9OS0uDTHbxrk7tCwJ/y4kaG3+FRECjDH6Qh1relKJVSoz8+068PKYHErUqOAXA4XLhl7M1s+B9dP+gcDeRqEG0tQZougdT1s3fnpSdhjMVvhN8Ae6UM8FT4WPCtRZMXrUHX0zNgkwqRbU1+EW1Si6FyeIbtNdOJZlxS2f8Xm5G2wQN9p+q8Api3cHziMXb0Ts1Hr1SEzyv4b6DVWm2YfP06wKmWMRFwavnW62QYX2AgZPAxSokwZSYbF6lDuumxXTURyMlTo1qmxN92ycEDKbdkrRqv7NhuoVKKUmIViLboPPqba+9nxJCVF6h8InSJSM2sS2qSosa9XWry85izr9OIiHlPKpLi7BsyjAYDAbP8toXBP6WEzU2BuUioFHIguaKhsrHbEr6GCW6pmgx2U9dYlYeoEgUq5Z7gjV3MF43dcRd8i5QTrJ7YiHgYmBvsjgRrQKOnzOFnAVyWLdkv6/rvoOW2S4eD/2/Hz1VRxaPzfSUUnTnqGemxmPubd3gEgQMTm9dpzc59N2r2ukYFdU27DtZ7jcgr+9xXndwZ927gZ89fO2F90Sj5WcHSylJ0qrx4ugMPLXON6fenQZD4uHupb7SKSuahCTEJrYNuNx9QUDUFBiUi4BCKkHuDTVX33VzRXNvSIdCROVX6nObmCiStE2IwgujM/D0uv2eYLxu6kh9pm6v28seo5bh4f/3I/5vcn+ktY4B4FuLe85tXSFAAAQE7MWtXbnFHdhmGXSYO6IrKi12dE3RYvHYTOSdLMerG4/gtTt7onPy5R2HjXGca0P0ptfubW+q/OxUXXTQNBgSD3cvdXX5uUZNWSESMwblImB2OpESp8aIjBSviiZnjRakxKlhdopn8iCAlQeo+Wmvi8ZLY3rAZHVgVK+r8NwXB73SLRK1Kpw1WoO+hkou9QqgzxqtaK+Lwl/f342lf+mNeSO7wepwodLsQJRKBo1CBpkEsAkCDp2qxNzbumHeFwd9Z8680YBJK3/wPOZOVzlTbvG6Y9XYF8aXe5zXt+Z3UwuVBkPiEaVLBsd1UkvCoFwENDIZ3th0FBOzO0Iuk3oGT7aOVeGtr49i+h+uCXcTfbDyADU3bROiPP+/pE4wGqOW48DvxiAVRHQ4a7R6DYz8596TnhKEwxfv8KSeDOqoA2xApdkBXawSY9/bhbv6p6JVlBJzb+tWU2rRYkeMWoHiCjP2njiPxWMzvaoyTVubh8+nZGHz9Ouu6IXx5RznvKtGkcQ96NN5oRNMJpOx2gs1OQblIqAA8MiQa/zWD35hdAZCD6kiosbkLxi9/urWSNPX5GZ7VV9J12PeyG6IVsphsjqw5t4BiFbJsedEGZ785094/c5eMFocMFociFHLcNZoxervf8OTt3TBX9/fhRKTDUu25GPJhddzlyEEgJ9PlvvNRR98YVIesQe2vKtGkcI96NNZ/S1kUVokpLRv9Gov7yDxXAAAFv5JREFURKEwKBeBKpcLKokE80Z2v3B7u6anXCWXQiYA1ULofFYiurJS4jWIUsrw/MjuqLY7UW11IlYjh1atQJt4DQAgsdb60So5vtx/Bncv34W3x/VGXJQClWY72sRr8PAN6Th1vhoddFGe+uCAdxnCrE46LLgwMDGSe5p5V40ihSYhCU6lCrLo+CtS7YUoFAblDfT222/j1VdfRVFREXr27InFixejf//+l/WaEkjw23kzdLFKCJBAACAAsDqcOF1pQ5sETaO0nYguT0MCzNq9xGXVNpyrtCLvZDlWbC9Atc2JKKUM9w/uiCeHdfbkq7tTU/q2T8C8kd2Rwp5mIqIWg0F5A3z00UeYPn06li5digEDBmDhwoUYOnQojh49isTExNAvEIAuWokF/zmMa1K0yGwXD6vDBZPVgbyT5Th6xojX7uzZiFtBRE3FHcRXVNswdW2eV493tc2JhV8fw8HfK/D86AyYLA7oopUY3esqr8CbPc1ERC0Dg/IGeOONN3Dfffdh4sSJAIClS5diw4YNWLFiBWbOnHnJrxsXpcRzI7tj5qf7vOr4RtptaiLyL9igx3kju1+oCBLGBhJRUKFm/yRqDAzK68lms2Hv3r2YNWuW5zGpVIohQ4Zg586dl/36HBBF1LzxGCeKXLVn/6w6dxpzbu+O9u3bA2CATo2HQXk9lZSUwOl0IikpyevxpKQkHDlyxO9zrFYrrNaLtY2NRmPQ9+BtaqLG09DjrynwGKeWQozH3+Vyz/5ZVVqEOf/KQ0LKeVSXFmHZlGEwGAzhbh41AwzKr6AFCxbgueeeC3cziFokHn9E4dPcjz93gB6K0+lEQUGB5/8BeHrVr0QPe+33u1LvQVcOg/J60uv1kMlkKC4u9nq8uLgYycnJfp8za9YsTJ8+3fNvo9GIdu3aXdF2ElENHn9E4XMpx1/tgDJSJu6pnWvuDoDrbsf89YcQrUtGya/7PTXQG9LD3pBAu6CgAPe9/SWidMnsxY9ADMrrSalUok+fPti8eTNGjRoFAHC5XNi8eTNyc3P9PkelUkGlUjVhK4nIjccfUfhcyvFXO6CMlIl73LnmSvkBT5553UA85qp0T9qLuwZ6KIECe3egnZaWFjBQj9Il1+s9GiLQhUGoC4bm2nNfe7sac5sYlDfA9OnTMX78ePTt2xf9+/fHwoULUVVV5anGEopw4bK/OeTWEV2K2NhYSCSSsLw3jz9q6cR+/FVWVsLpdMLlcMLldKKq5Ayc1RWQmc1QyhU1QW09/79JnxcVh6rKs5j+3n+g1bdB2aljiEnpCE2t7fB53vkiHDyoRGVlpd99UVhYiPmffA9NnN7r9ZxOJw4ePIiDBw96lpsrSjDnzoFITU1FYWEhKs/+DpfDGfI9GqJ2e+q+n7/HQz0v0rm3CwCWPf4ndOrUKeRz6nP8SQQhEm4QiceSJUs8kwf16tULixYtwoABA+r13FOnTvH2ObVoFRUV0GrDU/uPxx+1dDz+iMKnPscfg/Im5HK5cPr06bD2VjQmd47gyZMnw/ZD31w1130bzu9+7eOvsrKyWe5fsWqu32cxCravxXL8SSSSZvWd4LaIk9i2pT7HH9NXmpBUKkXbto2b5yUGWq1WFF/45oj7tvHUPv7cP4zcv02L+7vpiG1fBzr/ia2dl4PbIk6RtC3ScDeAiIiIiKilY1BORERERBRmDMrpkqlUKjzzzDMsO3cFcN9eWdy/TYv7u+lEyr6OlHbWB7dFnCJxWzjQk4iIiIgozNhTTkREREQUZgzKiYiIiIjCjEE5EREREVGYMSgnIiIiIgozBuXUYAsWLEC/fv0QGxuLxMREjBo1CkePHg13s5qdl156CRKJBI8++mi4m9LsvP322+jQoQPUajUGDBiA3bt3h7tJovLss89CIpF4/XXu3Nmz3GKxYMqUKdDpdIiJicGYMWNQXFzs9RqFhYUYPnw4oqKikJiYiCeeeAIOh8NrnW+++Qa9e/eGSqWCwWDAypUrfdrS3D6rrVu34rbbbkObNm0gkUjw2WefeS0XBAFz585FSkoKNBoNhgwZgmPHjnmtc/78eYwbNw5arRbx8fGYPHkyTCaT1zr79u1DTk4O1Go12rVrh1deecWnLZ988gk6d+4MtVqNjIwM/Oc//2lwWy6F2D7T+pzTmvI735j8nUciaVt+//13/OUvf4FOp4NGo0FGRgb27NnjWd6Ux0uTEIgaaOjQocIHH3wgHDhwQPjpp5+EW2+9VUhNTRVMJlO4m9Zs7N69W+jQoYPQo0cP4ZFHHgl3c5qVDz/8UFAqlcKKFSuEgwcPCvfdd58QHx8vFBcXh7tpovHMM88I3bp1E86cOeP5O3funGf5gw8+KLRr107YvHmzsGfPHmHgwIHCtdde61nucDiE7t27C0OGDBHy8vKE//znP4JerxdmzZrlWef48eNCVFSUMH36dOHQoUPC4sWLBZlMJmzcuNGzTnP8rP7zn/8ITz/9tPCvf/1LACCsW7fOa/lLL70kxMXFCZ999pnw888/C7fffruQlpYmmM1mzzq33HKL0LNnT+H7778Xtm3bJhgMBmHs2LGe5RUVFUJSUpIwbtw44cCBA8LatWsFjUYj/P3vf/ess2PHDkEmkwmvvPKKcOjQIWH27NmCQqEQ9u/f36C2NJQYP9P6nNOa6jvfmAKdRyJlW86fPy+0b99emDBhgrBr1y7h+PHjwldffSXk5+d71mmq46WpMCiny3b27FkBgPDtt9+GuynNQmVlpZCeni5s2rRJuO666xiUN7L+/fsLU6ZM8fzb6XQKbdq0ERYsWBDGVonLM888I/Ts2dPvsvLyckGhUAiffPKJ57HDhw8LAISdO3cKglATeEqlUqGoqMizzrvvvitotVrBarUKgiAITz75pNCtWzev1/7zn/8sDB061PPv5v5Z1Q3KXS6XkJycLLz66quex8rLywWVSiWsXbtWEARBOHTokABA+OGHHzzrfPnll4JEIhF+//13QRAE4Z133hESEhI8+1oQBGHGjBnCNddc4/n3n/70J2H48OFe7RkwYIDwwAMP1LstlyISPtO657Sm/M43lkDnkUjalhkzZgjZ2dkBlzfl8dJUmL5Cl62iogIA0KpVqzC3pHmYMmUKhg8fjiFDhoS7Kc2OzWbD3r17vfatVCrFkCFDsHPnzjC2THyOHTuGNm3aoGPHjhg3bhwKCwsBAHv37oXdbvfah507d0ZqaqpnH+7cuRMZGRlISkryrDN06FAYjUYcPHjQs07d7/jQoUM9r9ESP6uCggIUFRV5bXNcXBwGDBjgtW/j4+PRt29fzzpDhgyBVCrFrl27POsMHjwYSqXSs87QoUNx9OhRlJWVedYJtv/r05aGipTPtO45ram+840p0Hkkkrbl3//+N/r27Ys777wTiYmJyMzMxLJlyzzLm/J4aSoMyumyuFwuPProo8jKykL37t3D3ZyI9+GHH+LHH3/EggULwt2UZqmkpAROp9PrZAMASUlJKCoqClOrxGfAgAFYuXIlNm7ciHfffRcFBQXIyclBZWUlioqKoFQqER8f7/Wc2vuwqKjI7z52Lwu2jtFohNlsbpGflXu7gm1zUVEREhMTvZbL5XK0atWqUfZ/7eWh2tJQkfCZ+junNdV3vrEEO49E0rYcP34c7777LtLT0/HVV1/hoYcewrRp07Bq1SqvtjTF8dJU5E36btTsTJkyBQcOHMD27dvD3ZSId/LkSTzyyCPYtGkT1Gp1uJtDLdiwYcM8/9+jRw8MGDAA7du3x8cffwyNRhPGlhFdWZF+TmtO5xGXy4W+ffvixRdfBABkZmbiwIEDWLp0KcaPHx/m1l0Z7CmnS5abm4v169fjf//7H9q2bRvu5kS8vXv34uzZs+jduzfkcjnkcjm+/fZbLFq0CHK5HE6nM9xNjHh6vR4ymcyn0kBxcTGSk5PD1Crxi4+Px9VXX438/HwkJyfDZrOhvLzca53a+zA5OdnvPnYvC7aOVquFRqNpkZ+Ve7uCbXNycjLOnj3rtdzhcOD8+fONsv9rLw/VloYS+2ca6JzWVN/5xhDqPJKUlBQx25KSkoKuXbt6PdalSxdPKl1THi9NhUE5NZggCMjNzcW6deuwZcsWpKWlhbtJzcJNN92E/fv346effvL89e3bF+PGjcNPP/0EmUwW7iZGPKVSiT59+mDz5s2ex1wuFzZv3oxBgwaFsWXiZjKZ8OuvvyIlJQV9+vSBQqHw2odHjx5FYWGhZx8OGjQI+/fv9zoZbtq0CVqt1nOSHTRokNdruNdxv0ZL/KzS0tKQnJzstc1GoxG7du3y2rfl5eXYu3evZ50tW7bA5XJhwIABnnW2bt0Ku93uWWfTpk245pprkJCQ4Fkn2P6vT1saSqyfaahzWlN95xtDqPNI3759I2ZbsrKyfEpT/vLLL2jfvj2Apj1emkyTDy2liPfQQw8JcXFxwjfffONVMq26ujrcTWt2WH2l8X344YeCSqUSVq5cKRw6dEi4//77hfj4eK9KAy3d448/LnzzzTdCQUGBsGPHDmHIkCGCXq8Xzp49KwhCTUm11NRUYcuWLcKePXuEQYMGCYMGDfI8311S7eabbxZ++uknYePGjULr1q39llR74oknhMOHDwtvv/2235KIze2zqqysFPLy8oS8vDwBgPDGG28IeXl5wokTJwRBqCnxFh8fL3z++efCvn37hJEjR/ot8ZaZmSns2rVL2L59u5Cenu5V4q28vFxISkoS/vrXvwoHDhwQPvzwQyEqKsqnJKJcLhdee+014fDhw8IzzzzjtyRiqLY0lBg/0/qc05rqO38l1D2PRMq27N69W5DL5cILL7wgHDt2TFi9erUQFRUl/L//9/886zTV8dJUGJRTgwHw+/fBBx+Eu2nNDoPyK2Px4sVCamqqoFQqhf79+wvff/99uJskKn/+85+FlJQUQalUCldddZXw5z//2as2sNlsFh5++GEhISFBiIqKEkaPHi2cOXPG6zV+++03YdiwYYJGoxH0er3w+OOPC3a73Wud//3vf0KvXr0EpVIpdOzY0e9vSHP7rP73v//5/f0cP368IAg1Zd7mzJkjJCUlCSqVSrjpppuEo0ePer1GaWmpMHbsWCEmJkbQarXCxIkThcrKSq91fv75ZyE7O1tQqVTCVVddJbz00ks+bfn444+Fq6++WlAqlUK3bt2EDRs2eC2vT1suhdg+0/qc05ryO9/Y6p5HImlbvvjiC6F79+6CSqUSOnfuLLz33ntey5vyeGkKEkEQhKbtmyciIiIiotqYU05EREREFGYMyomIiIiIwoxBORERERFRmDEoJyIiIiIKMwblRERERERhxqCciIiIiCjMGJQTEREREYUZg3JqliZMmIBRo0aFuxlELdpvv/0GiUSCn376KdxNISISPU4eRM1SRUUFBEFAfHx8uJtC1GI5nU6cO3cOer0ecrk83M0hIhI1BuVERNTobDYblEpluJtB1OLZ7XYoFIpwN4PqgekrFNH++c9/IiMjAxqNBjqdDkOGDEFVVZVX+or7Fnrdv+uvv97zOtu3b0dOTg40Gg3atWuHadOmoaqqKjwbRSRC119/PXJzc5Gbm4u4uDjo9XrMmTMH7n6dDh06YP78+bjnnnug1Wpx//33+01fOXjwIEaMGAGtVovY2Fjk5OTg119/9Sxfvnw5unTpArVajc6dO+Odd95p6k0lErWNGzciOzsb8fHx0Ol0GDFihOcYch9zH330Ea677jqo1WqsXr0aQOhja8aMGbj66qsRFRWFjh07Ys6cObDb7U2+fS0Zg3KKWGfOnMHYsWMxadIkHD58GN988w3uuOMO1L35065dO5w5c8bzl5eXB51Oh8GDBwMAfv31V9xyyy0YM2YM9u3bh48++gjbt29Hbm5uODaLSLRWrVoFuVyO3bt346233sIbb7yB5cuXe5a/9tpr6NmzJ/Ly8jBnzhyf5//+++8YPHgwVCoVtmzZgr1792LSpElwOBwAgNWrV2Pu3Ll44YUXcPjwYbz44ouYM2cOVq1a1WTbSCR2VVVVmD59Ovbs2YPNmzdDKpVi9OjRcLlcnnVmzpyJRx55BIcPH8bQoUPrdWzFxsZi5cqVOHToEN566y0sW7YMb775Zjg2seUSiCLU3r17BQDCb7/95rNs/PjxwsiRI30eN5vNwoABA4QRI0YITqdTEARBmDx5snD//fd7rbdt2zZBKpUKZrP5irSdKNJcd911QpcuXQSXy+V5bMaMGUKXLl0EQRCE9u3bC6NGjfJ6TkFBgQBAyMvLEwRBEGbNmiWkpaUJNpvN73t06tRJWLNmjddj8+fPFwYNGtSIW0LUvJw7d04AIOzfv99zzC1cuNBrnUs5tl599VWhT58+V6TN5B9H3lDE6tmzJ2666SZkZGRg6NChuPnmm/HHP/4RCQkJAZ8zadIkVFZWYtOmTZBKa24U/fzzz9i3b5/nFh8ACIIAl8uFgoICdOnS5YpvC1EkGDhwICQSieffgwYNwuuvvw6n0wkA6Nu3b9Dn//TTT8jJyfGb31pVVYVff/0VkydPxn333ed53OFwIC4urpG2gCjyHTt2DHPnzsWuXbtQUlLi6SEvLCxE165dAXgfi/U9tj766CMsWrQIv/76K0wmExwOB7RabRNtFQEAg3KKWDKZDJs2bcJ3332H//73v1i8eDGefvpp7Nq1y+/6zz//PL766ivs3r0bsbGxnsdNJhMeeOABTJs2zec5qampV6z9RM1NdHR00OUajSbgMpPJBABYtmwZBgwY4LVMJpNdfuOImonbbrsN7du3x7Jly9CmTRu4XC50794dNpvNs07tY7E+x9bOnTsxbtw4PPfccxg6dCji4uLw4Ycf4vXXX2+CLSI3BuUU0SQSCbKyspCVlYW5c+eiffv2WLdunc96n376KebNm4cvv/wSnTp18lrWu3dvHDp0CAaDoamaTRSR6l7wfv/990hPT6930NyjRw+sWrXKbzWIpKQktGnTBsePH8e4ceMarc1EzUlpaSmOHj2KZcuWIScnB0BNoYJg6nNsfffdd2jfvj2efvppz2MnTpxovIZTvTAop4i1a9cubN68GTfffDMSExOxa9cunDt3Dl26dMG+ffs86x04cAD33HMPZsyYgW7duqGoqAgAoFQq0apVK8yYMQMDBw5Ebm4u7r33XkRHR+PQoUPYtGkTlixZEq7NIxKdwsJCTJ8+HQ888AB+/PFHLF68uEE9abm5uVi8eDHuuusuzJo1C3Fxcfj+++/Rv39/XHPNNXjuuecwbdo0xMXF4ZZbboHVasWePXtQVlaG6dOnX8EtI4oMCQkJ0Ol0eO+995CSkoLCwkLMnDkz5PNCHVvp6ekoLCzEhx9+iH79+mHDhg1+O7joymL1FYpYWq0WW7duxa233oqrr74as2fPxuuvv45hw4Z5rbdnzx5UV1fj+eefR0pKiufvjjvuAFDTe/ftt9/il19+QU5ODjIzMzF37ly0adMmHJtFJFr33HMPzGYz+vfvjylTpuCRRx7B/fffX+/n63Q6bNmyBSaTCddddx369OmDZcuWeXrN7733XixfvhwffPABMjIycN1112HlypVIS0u7UptEFFGkUik+/PBD7N27F927d8djjz2GV199NeTzQh1bt99+Ox577DHk5uaiV69e+O677/xWUKIri5MHERFRSNdffz169eqFhQsXhrspRETNEnvKiYiIiIjCjEE5EREREVGYMX2FiIiIiCjM2FNORERERBRmDMqJiIiIiMKMQTkRERERUZgxKCciIiIiCjMG5UREREREYcagnIiIiIgozBiUExERERGFGYNyIiIiIqIwY1BORERERBRm/x/zJGnGAca2TQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "c3e488c7", + "metadata": {}, + "outputs": [], + "source": [ + "# we can observe that the all the three size,area and price have \n", + "# nearly linear correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "0e14c4f8", + "metadata": {}, + "outputs": [], + "source": [ + "# value count for each feature\n", + "\n", + "def value_count(df):\n", + " for var in df.columns:\n", + " print(df[var].value_counts())\n", + " print(\"------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "bd8c0712", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "seller_name\n", + "Azuroin 93\n", + "G K GROUP 78\n", + "Prime property 49\n", + "seller 43\n", + "PropertyPistol Realty Pvt Ltd 38\n", + " ..\n", + "RAS PROPERTIES 1\n", + "Investor Floor 1\n", + "Liban Empire 1\n", + "Verma Real Estate 1\n", + "Shri Sidhanath Estate Consultant 1\n", + "Name: count, Length: 90, dtype: int64\n", + "------------------------------------\n", + "seller_type\n", + "AGENT 954\n", + "VERIFIED OWNER 43\n", + "BUILDER 3\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "size\n", + "2 476\n", + "1 303\n", + "3 182\n", + "4 34\n", + "5 5\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "type_\n", + "BHK 967\n", + "RK 33\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "type_of_house\n", + "Apartment 966\n", + "Studio Apartment 33\n", + "Independent House 1\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "name\n", + "Marathon Nexzone Aura 1 26\n", + "Paradise Sai World City 23\n", + "Haware Haware Citi 23\n", + "Godrej Tranquil 18\n", + "Godrej The Trees 15\n", + " ..\n", + "Dubey Gayatri Paradise 1\n", + "Wadhwa Dukes Horizon 1\n", + "Om Shivam Residency 1\n", + "Nilkanth Bhaveshwar Hill View 1\n", + "Puraniks Tokyo Bay Phase 2A 1\n", + "Name: count, Length: 362, dtype: int64\n", + "------------------------------------\n", + "location\n", + "Thane West 173\n", + "Panvel 122\n", + "Kalyan West 53\n", + "Kharghar 45\n", + "Wadala 33\n", + " ... \n", + "Dahisar 1\n", + "Madh 1\n", + "Koper Khairane 1\n", + "Malad East 1\n", + "Napeansea Road 1\n", + "Name: count, Length: 89, dtype: int64\n", + "------------------------------------\n", + "city\n", + "Mumbai 1000\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "price\n", + "14000.0 39\n", + "25000.0 26\n", + "30000.0 25\n", + "12000.0 23\n", + "10000.0 23\n", + " ..\n", + "55600.0 1\n", + "84000.0 1\n", + "51000.0 1\n", + "12400.0 1\n", + "12990.0 1\n", + "Name: count, Length: 153, dtype: int64\n", + "------------------------------------\n", + "area\n", + "650 57\n", + "1250 51\n", + "600 37\n", + "825 31\n", + "1100 30\n", + " ..\n", + "365 1\n", + "2850 1\n", + "1322 1\n", + "1552 1\n", + "4580 1\n", + "Name: count, Length: 238, dtype: int64\n", + "------------------------------------\n", + "area_type\n", + "Area in sq ft 1000\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "status\n", + "Semi-Furnished 448\n", + "Unfurnished 373\n", + "Furnished 179\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "deposit\n", + "No Deposit 1000\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "no_bathroom\n", + "2 bathrooms 577\n", + "1 bathrooms 215\n", + "3 bathrooms 163\n", + "4 bathrooms 31\n", + "5 bathrooms 12\n", + "6 bathrooms 1\n", + "Name: count, dtype: int64\n", + "------------------------------------\n", + "facing\n", + "East facing 352\n", + "NorthEast facing 192\n", + "West facing 173\n", + "North facing 95\n", + "NorthWest facing 17\n", + "SouthEast facing 9\n", + "South facing 8\n", + "SouthWest facing 7\n", + "Name: count, dtype: int64\n", + "------------------------------------\n" + ] + } + ], + "source": [ + "value_count(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "947c0ee2", + "metadata": {}, + "outputs": [], + "source": [ + "# unique categories in each feature\n", + "def unique(df):\n", + " for i in df.columns:\n", + " print(df[i].unique())\n", + " print(\"---------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "adbb4ae8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Kasturi Developers' 'seller' 'Neha' 'EstatesHUB'\n", + " 'Earth Property Consultant' 'First home' 'Rutu Real estate'\n", + " 'Aditya Properties' 'Blue Diamond Realtors' 'RAS PROPERTIES'\n", + " 'Hitech Realty Consultancy' 'Kuber property' 'Cordeiro Real Estate'\n", + " 'sahdev chaudhari' 'Rahul yadav' 'Kushvin Properties'\n", + " 'MANASVI PROPERTIES' 'Azuroin' 'VibrantKey' 'ADITYA PROPERTY'\n", + " 'Aashiyana property consultant' 'Mhaskar real estate consultancy'\n", + " 'Satyam Enterprises' 'Shree Homes Enterprises' 'Om Sai Siddhi Properties'\n", + " 'Bajrangi Realtors' 'Khalsa Propera' 'Om sai estate' 'Krishna Estate'\n", + " 'Shri Sidhanath Estate Consultant' 'PropertyPistol Realty Pvt Ltd'\n", + " 'Noronha Estate Agency' 'Royal Real Estate Agency' 'Swastik Reality'\n", + " 'Aadhar enterprises' 'Trishul property' 'Takshak Properties'\n", + " 'Shetty?s Realty' 'My Vastu Realtors' 'Horizon Real Estate'\n", + " 'Perfect Housing Dwell' 'Omkar Patil' 'Jyoti Enterprise' 'KD Real Estate'\n", + " 'Samadhan Real Estate Consultant' 'Laabh Properties' 'PREMIUM PROPERTIES'\n", + " 'Reliance Estates - Since 1985' 'Individual Agent'\n", + " 'Urban Investment Property Solutions' 'India Direct Homecom'\n", + " 'Prime property' 'City Home' 'SUNRISE REAL ESTATE' 'Sanjay'\n", + " 'Green Group Real Estate Consultants' 'Bricks Property Consultant'\n", + " 'Hari om realtors' 'Stilt Real Estate' 'Prem Housing'\n", + " 'Right House Properties' 'Verma Real Estate' 'Liban Empire'\n", + " 'Rajesh Rasale' 'Investor Floor' 'A A REAL ESTATE'\n", + " 'Disha Real Estate Consultant' 'Star Realtors' 'Rightside Properties'\n", + " 'Housing Guru' 'Om Properties' 'H K homes' 'Homehunt Realty'\n", + " 'Galaxy homes' 'Vijay Estate Agency' 'G K GROUP' 'Santosh Magar'\n", + " 'SMP Group Real Estate' 'Mumbai property solutions'\n", + " 'Dream solution Properties' 'KNR Woods' 'Sarvam Properties'\n", + " 'Tejasvi Realty Pvt Ltd' 'Mahadev Properties' 'Shree associat'\n", + " 'Eastern Coast Properties' 'DHARTI ESTATE CONSULTANT'\n", + " 'Om Real Estate Property Consultant'\n", + " 'Shree Riddhi Siddhi Estate Consultant' 'Jai Shree Ganesh Realtors']\n", + "---------------------------------\n", + "['BUILDER' 'VERIFIED OWNER' 'AGENT']\n", + "---------------------------------\n", + "[2 3 1 4 5]\n", + "---------------------------------\n", + "['BHK' 'RK']\n", + "---------------------------------\n", + "['Apartment' 'Studio Apartment' 'Independent House']\n", + "---------------------------------\n", + "['Shagun White Woods' 'Surana Tulsi Gaurav' 'Tricity Enclave'\n", + " 'Godrej Prime' 'Tanvi Eminence Phase 2' nan 'Atul Blue Arch'\n", + " 'Reputed Builder Genevieve' 'DSS Tivon Park' 'Neelkanth Lotus Court'\n", + " 'Atlanta Enclave' 'Sree Krishna Builders Shreeji Dham Neral'\n", + " 'Reputed Builder Suprema Casa Bella' 'Reputed Builder palm islend 3'\n", + " 'K R Godrej Vihaa' 'Reputed Builder Satya Darshan' 'Agami Emerald'\n", + " 'Charisma Navdurga' 'Sagar Platinum Sagar Jewels' 'Lodha Casa Rio'\n", + " 'Swaraj Homes Little Flower Apartment'\n", + " 'Shree Prathamesh Vasudev Sky High' 'Reputed Builder Rutu Park'\n", + " 'Reputed Builder Tagore Park' 'RNA Regency Park' 'Anant Infra Residency'\n", + " 'Bombay ICC' 'Sai Baba Complex' 'Gajra Bhoomi Heights' 'Godrej The Trees'\n", + " 'Piramal Vaikunth Thane' 'Swaraj Homes Retreat Apartment'\n", + " 'Godrej Tranquil' 'Lodha Casa Bella' 'Varsha Balaji Heritage'\n", + " 'Seasons Orchid' 'Veena Saaz' 'Gee Cee Aspira 206'\n", + " 'BSEL Infrastructure Realty Ltd Kasturi Villa' 'Lodha World One'\n", + " 'Indiabulls Greens' 'Reputed Builder Anand Nagar Barkha CHS'\n", + " 'Paradise Sai World City' 'Neel Sidhi Orbit' 'Maithili Pride'\n", + " 'Vishesh Balaji Symphony' 'Paradise Sai Pride' 'K Raheja Eastate'\n", + " 'Platinum The Springs' 'Reputed Builder Nibbana Apartments' 'Labh Aspire'\n", + " 'Haware Haware Citi' 'Reputed Builder Mount Alps A'\n", + " 'Marathon Marathon Nexzone' 'Shailesh Riddhi Siddhi Residency'\n", + " 'Kanakia Paris' 'Mayfair The View' 'Reputed Builder Model Town'\n", + " 'Mangla Mayurs Nature Glory' 'Space India Alliance'\n", + " 'Gundecha Gundecha Heights' 'Squarefeet Grand Square' 'Lodha Elisium'\n", + " 'Puneet Sanjivani Tower' 'Nahar Amrit Shakti' 'Gami Viona'\n", + " 'Marathon Nexzone Aura 1' 'ANA Avant Garde Phase 1' 'DB Ozone'\n", + " 'Lodha Aqua' 'Lodha Mira Road Project 1' 'Man Opus'\n", + " 'Reputed Builder Sagar Drishti' 'Nahar Cayenne'\n", + " 'Reputed Builder Ashmita Garden' 'Unique Poonam Estate Cluster 2'\n", + " 'RNA NG RNA N G Silver Spring' 'Ravi Gaurav City'\n", + " 'Abhay Sheetal Complex Wing D E' 'Neelsidhi Amarante' 'Ronak Residency'\n", + " 'Vision Phoenix Heights' 'Platinum Avior' 'Platinum Aura'\n", + " 'Akshar Valencia' 'Sai Avaneesh' 'Mahaavir Heights'\n", + " 'Giriraj Giriraj Tower' 'AP Bianca' 'New Millenium Paradise'\n", + " 'RK Vaishnavi Heights' 'Shreeji Phoenix Nest'\n", + " 'Reputed Builder The Springs' 'Paradise Sai Spring'\n", + " 'Reputed Builder Daulat Shirin' 'Reputed Builder Usha Sadan Apartment'\n", + " 'GHP Aston' 'Reputed Builder Sneh Sadan' 'Peninsula Celestia Spaces'\n", + " 'Omkar Ananta' 'ACME Complex' 'Reputed Builder Palm Island 7'\n", + " 'Royal Palms Piccadilly Condos' 'Reputed Builder Emerald Isle 2'\n", + " 'Royal Palms Golden Isle' 'Royal Palms Ruby Isle'\n", + " 'Reputed Builder Sunita' 'Tharwani Heritage' 'Kanakia Zenworld Phase I'\n", + " 'Godrej Platinum' 'Gajra Bhoomi Gardenia II' 'Siddhivinayak The Orien'\n", + " 'Lodha Quality Home' 'Lodha Amara Tower 20 21'\n", + " 'Lodha Amara Tower 24 And 25' 'Pratik Harmony' 'Arihant Sharan'\n", + " 'Sai Udanda' 'Runwal Forest Tower 1 To 4' 'Reputed Builder Sangam Bhavan'\n", + " 'Reputed Builder Ashoka Apartment' 'Ashar Edge'\n", + " 'Reputed Builder Spring Grove Uno Society' 'Lokhandwala Living Essence'\n", + " 'Runwal Forest Tower 5 To 8' 'Kalpataru Habitat' 'Mohan Nano Estates I'\n", + " 'Kanakia Zenworld Phase II' 'Dhartidhan Dharti 3' 'Sunteck One World'\n", + " 'Agarwal Paramount' 'Rustomjee Virar Avenue L1 L2 And L4 Wing I And J'\n", + " 'M Baria Yashwant Vihar' 'Virar Virar Bolinj Yashwant Krupa CHSL'\n", + " 'Ekta Parksville Phase II' 'Shree Ganesh Amrut Garden'\n", + " 'Reputed Builder Suraj Complex' 'Reputed Builder Mayur Park Building'\n", + " 'Reputed Builder Bhoomi Harmony' '5P Bhagwati Heritage'\n", + " 'Pooja White Flag' 'Marvels Nandan' 'Shanti Hari Heritage'\n", + " 'Nilkanth Bhaveshwar Hill View' 'Om Shivam Residency'\n", + " 'Wadhwa Dukes Horizon' 'Dubey Gayatri Paradise' 'Dharti Sai Archana'\n", + " 'Radiant Ravi Rachana' 'Reputed Builder Sai Prasad Residency'\n", + " 'Reputed Builder Chetan Building' 'Godrej City Woods Panvel Ph 1'\n", + " 'Reputed Builder Prefeb Shree Dutta Tower' 'Reputed Builder Ashok Towers'\n", + " 'Runwal Lily at Runwal Forest' 'Lodha World Crest' 'Omkar 1973'\n", + " 'L And T Crescent Bay' 'Joshi Sai Anuraj' 'Pyramid Aastha Alavio'\n", + " 'Mittal Phoenix Towers' 'Reputed Builder Balaji Bhavan'\n", + " 'Kalpataru Sunrise' 'HCBS Dheeraj Gaurav Heights 1'\n", + " 'Dheeraj Realty Dheeraj Insignia' 'Reputed Builder 30 Union Park'\n", + " 'Reputed Builder Fair Field' 'Reputed Builder Amiya'\n", + " 'Silver Springs Apartment' 'Juhi Greens' 'Reputed Builder Amar CHS'\n", + " 'Darshan Pride' 'Marathon Nexzone Acrux 1' 'Indiabulls Park'\n", + " 'Krishna Amrut View' 'Shree Ambe Vinayak Ashray' 'Rustomjee Elita'\n", + " 'Samarth Aangan' 'Lodha Majiwada Tower 1' 'Lodha Luxuria' 'Lodha Park'\n", + " 'Varun Garden' 'Reputed Builder Rajkamal Heights'\n", + " 'Indiabulls Blu Tower A' 'Reputed Builder Beaumonde Towers'\n", + " 'K Raheja Artesia Residential Wing Constructed On Part Of The Project Land'\n", + " 'Lodha Marquise' 'Agarwal Doshi Complex' 'Rajhans Kshitij Aspen Wing C'\n", + " 'Reputed Builder Galaxy Villa' 'Reputed Builder Ram Rahim Tower'\n", + " 'Rajhans Rajhans Kshitij Iris Wing E F G' 'Kaul Kingston Tower'\n", + " 'Manav Wisteria' 'Soham Crystal Spires' 'Cidco NRI Complex'\n", + " 'Cidco NRI Complex Phase 2' 'Akshar Shreeji Heights'\n", + " 'Tharwani Tharwani Heights' 'Sejal Suyash Pride'\n", + " 'Swaraj Homes Vaikunth CHS' 'DSS Mahavir Kalpavruksha'\n", + " 'Reputed Builder Haridwar House' 'Lodha Bel Air'\n", + " 'Reputed Builder Garden Estate' 'Paradise Sai Crystals' 'Rutu Estate'\n", + " 'Paradise Sai Sahil' 'DLH The Park Residences Phase 1'\n", + " 'L And T Crescent Bay T4' 'Vijay Orovia Phase 1' 'Dosti West County'\n", + " 'Sunteck City Avenue 1' 'Windsor Grande Residences'\n", + " 'Sheth Auris Serenity' 'Kabra Paradise' 'Supreme 19' 'Rustomjee Elanza'\n", + " 'Khandelwal Sai Iconic' 'Kabra Metro One Wing A and B Of Pratap CHSL'\n", + " 'Platinum Casa Millennia' 'Reputed Builder Bhoomi Green'\n", + " 'Ajmera Julian Alps' 'Lodha New Cuffe Parade Tower 11'\n", + " 'Lodha New Cuffe Parade Lodha Altia' 'Goodwill Goodwill Gardens'\n", + " 'L Nagpal Anupama Heights' 'Lodha Splendora' 'Wadhwa Shiv Valley'\n", + " 'Raunak City' 'Mehta Amrut Pearl' 'Reputed Builder Nebula Darshan'\n", + " 'Runwal Bliss Wing B' 'Kanakia Silicon Valley' 'Darshan Rico'\n", + " 'Reputed Builder Shilpa Tower' 'K Raheja Modern Vivarea South Tower'\n", + " 'Neelkanth Greens' 'Raghav One' 'Rajhans Mount Everest' 'Future Hill'\n", + " 'Bholenath Hresa Sainagar Apartment Pvt Ltd' 'Rustomjee Azziano Wing G'\n", + " 'Moraj Riverside Park' 'Lodha Quality Home Tower 2' 'Runwal Garden City'\n", + " 'Swaraj Homes Nirlac Solitaire Society' 'Paradise Sai Solitaire'\n", + " 'Bhuvnesh Westside' 'Shreenathji Mayuresh Delta' 'Platinum Emporius'\n", + " 'Reputed Builder City Heights' 'Shagun Paradise' 'Nisarg Hyde Park'\n", + " 'Lodha Casa Rio Gold' 'HDIL Metropolis Residences'\n", + " 'Swaraj Homes Neelkanth Darshan CHS'\n", + " 'Ayodhya Construction Co Saffron Residency Phase 1' 'Ashtavinayak Aangan'\n", + " 'Ajmera Girnar' 'National Plaza' 'Kalpataru Sparkle'\n", + " 'Runwal Runwal Forests Tower 9 To 11' 'Marathon Nexzone Zodiac 1'\n", + " 'Krishna Heights' 'Radhe Krishna Heights' 'Lodha Enchante'\n", + " 'Rustomjee Oriana' 'Hiranandani Regent Hill C D And E Wing'\n", + " 'Hiranandani Zen Atlantis' 'Today Grande Vista' 'Hubtown Seasons'\n", + " 'MICL Aaradhya One' 'Ram Pushpanjali Residency Phase III'\n", + " 'Puraniks Aarambh' 'Reputed Builder Raj Paradise' 'Godrej Hill'\n", + " 'Tharwani Rosalie' 'Puraniks Tokyo Bay Phase 2C' 'Lodha Casa Bella Gold'\n", + " 'Raunak City Sector IV D3' 'Raunak Heights'\n", + " 'Swaraj Homes Elora Complex CHS' 'Reputed Builder NG Complex'\n", + " 'Sheth Vasant Oasis' 'DSK Madhuban' 'Vihang Vermont' 'Khade KIPL Morya'\n", + " 'Reputed Builder Akash Darshan' 'Reputed Builder Pride Of Kalina'\n", + " 'Puraniks Hometown Phase 2' 'Puraniks Rumah Bali' 'Haware Citi'\n", + " 'Monarch Cosmos Enclave Chestnut' 'Maison Tarangan' 'Godrej Emerald'\n", + " 'Rizvi Nectar Apartment' 'Puraniks City Phase 3'\n", + " 'Swaraj Homes Hill Niketan Apartment' 'Gurukrupa Guru Atman'\n", + " 'Ajmera New Era Yogidham Phase IV Tower C'\n", + " 'Reputed Builder Vasant Valley' 'Lodha Casa Royale' 'Lodha Dioro'\n", + " 'Ashapura Neelkanth Shrushti' 'Puraniks Tokyo Bay Phase 1'\n", + " 'Reputed Builder Innovative Heritage' 'Vijay Yashraj Park'\n", + " 'Terraform Everest Marigold' 'Puraniks Puranik City Sector 6'\n", + " 'Aakar Manas Residency' 'Mohan Heights' 'Bhoomi Homes Maple Hills'\n", + " 'Reputed Builder Dharti Aangan' 'Prince Alisha Paradise'\n", + " 'Paradise Sai World City Panvel' 'Ashar Aria'\n", + " 'Lodha Quality Home Tower 1' 'Lodha Crown Kolshet'\n", + " 'Lodha Quality Home Tower 5' 'Nahar Olivia' 'MM Spectra'\n", + " 'A C Crystal Avenue' 'Reputed Builder Ashok Nagar Complex'\n", + " 'Kanakia Rainforest' 'Marathon Nexzone Ion 1' 'Runwal Elina'\n", + " 'Kanakia Kanakia Sevens' 'Balaji Delta Tower' 'Marathon Nexzone Triton 1'\n", + " 'FSK Sukhkarta I CHSL' 'Gami Amar Harmony' 'Sai Kaveesha' 'Shiv Corner'\n", + " 'Varad Varad Heights' 'Reputed Builder Bella Vista' 'National Harmony'\n", + " 'Marwah Group Apartment' 'Reputed Builder Shiv Kalptaru Apartment'\n", + " 'Vasant Park' 'Reputed Builder Madhav Shruti' 'Giriraj Horizon'\n", + " 'Kesar Exotica Phase I Basement Plus Ground Plus Upper 14 Floors'\n", + " 'Paradise Sai Mannat' 'Regency Regency Gardens' 'Mahaavir Heritage'\n", + " 'Juhi Niharika Residency' 'CGEWHO Kendriya Vihar'\n", + " 'Reputed Builder Kesar Harmony' 'Concrete Sai Saakshaat'\n", + " 'Reputed Builder Raghunath Vihar' 'Seawood Seawoods Concept Unnathi'\n", + " 'Bhagwati Greens 1' 'Regency Park' 'Devisha Hex Blox' 'Sai Yashaskaram'\n", + " 'Keshav Winds' 'B Chopda Oval Apartments' 'Vaibhav Paradise'\n", + " 'Reputed Builder Silver Avenue' 'Kamla Habitat' 'Hiranandani Evita'\n", + " 'Puraniks Tokyo Bay Phase 2A']\n", + "---------------------------------\n", + "['Ulwe' 'Chembur' 'Mira Road East' 'Ville Parle East' 'Kandivali West'\n", + " 'Nilje Gaon' 'Ghatkopar West' 'Fort' 'Shil Phata' 'Neral' 'Usarghar Gaon'\n", + " 'Goregaon East' 'Badlapur East' 'Worli' 'Vikroli East' 'Dombivali East'\n", + " 'Andheri East' 'Boisar' 'Malad East' 'Madh' 'Dombivali' 'Bandra West'\n", + " 'Thane West' 'Malad West' 'Santosh Nagar' 'Kalamboli' 'Kurla East'\n", + " 'Wadala' 'Airoli' 'Kharghar' 'Vikhroli' 'Malabar Hill' 'Santacruz West'\n", + " 'Kandivali East' 'Virar West' 'Kamothe' 'Adaigaon' 'Kalyan West' 'Panvel'\n", + " 'Vasai West' 'Lower Parel' 'Thakurli' 'Santacruz East' 'Bhandup West'\n", + " 'Sanpada' 'Borivali East' 'Andheri West' 'Karanjade'\n", + " 'kasaradavali thane west' 'Bandra Kurla Complex' 'Mulund West'\n", + " 'Kanjurmarg' 'Powai' 'Manjarli' 'Taloje' 'Dronagiri' 'Dahisar'\n", + " 'Jogeshwari West' 'Koper Khairane' 'Vashi' 'Kalyan East' 'Colaba'\n", + " 'Cuffe Parade' 'Sewri' 'Goregaon West' 'Napeansea Road' 'Parel'\n", + " 'Ambernath West' 'Virar' 'Naigaon East' 'Sector 21 Kamothe'\n", + " 'Ghatkopar East' 'Dadar West' 'Seawoods' 'Khar West' 'Mahalaxmi' 'Tardeo'\n", + " 'Hiranandani Estates' 'Prabhadevi' 'Vasai' 'Juhu' 'Khar' 'Agripada'\n", + " 'Kurla' 'Bandra East' 'Kalwa' 'Borivali West' 'Dombivli (West)' 'Taloja']\n", + "---------------------------------\n", + "['Mumbai']\n", + "---------------------------------\n", + "[17000. 22000. 12500. 55000. 18500. 28500. 9000. 35000. nan 5500.\n", + " 12000. 26000. 7000. 20000. 14500. 40000. 50000. 11000. 90000. 32000.\n", + " 3000. 9500. 25000. 23000. 10000. 60000. 64000. 78000. 42000. 25101.\n", + " 6500. 6000. 15000. 43000. 13000. 30000. 24500. 8000. 65000. 10995.\n", + " 72000. 28000. 14000. 16000. 98000. 45000. 18000. 85000. 89000. 27000.\n", + " 73000. 38000. 21000. 15500. 36000. 62000. 48000. 19000. 33000. 7500.\n", + " 17500. 16500. 11500. 70000. 66000. 58000. 24000. 35500. 20500. 75000.\n", + " 25500. 54900. 76100. 18020. 23011. 46000. 70011. 18011. 80000. 36100.\n", + " 36001. 37111. 34501. 27111. 27200. 26500. 26101. 37000. 67000. 34000.\n", + " 92000. 8300. 8200. 6900. 19500. 77000. 8500. 47000. 13500. 49002.\n", + " 52000. 68000. 95000. 39000. 38101. 57000. 29500. 67500. 56000. 12999.\n", + " 55600. 79000. 49000. 84000. 51000. 12400. 41000. 28003. 82000. 45001.\n", + " 10500. 29000. 46500. 47500. 34500. 5700. 60002. 5000. 86000. 54000.\n", + " 87000. 44000. 34440. 21500. 31000. 17100. 10990. 13999. 13900. 17900.\n", + " 88000. 91000. 10400. 48500. 17011. 28020. 25022. 59000. 13100. 12900.\n", + " 16800. 10999. 11999. 12990.]\n", + "---------------------------------\n", + "[1180 1720 1150 1050 1165 200 750 634 1089 1450 680 490 900 654\n", + " 260 300 1650 850 1500 1311 450 1950 700 600 535 1800 650 2346\n", + " 575 800 2500 1049 3450 590 1100 747 540 1275 715 1200 500 1035\n", + " 525 7000 1250 1000 1220 745 1785 1985 1600 1550 625 1315 729 1125\n", + " 980 1020 640 955 410 460 2000 825 782 1056 865 855 846 658\n", + " 670 550 1400 560 1395 995 350 920 710 1139 1120 1175 1010 690\n", + " 1240 605 2390 675 2303 545 430 330 698 417 553 740 1138 1320\n", + " 1080 695 598 576 418 602 1008 753 400 1085 1781 1185 615 950\n", + " 610 982 620 1295 1235 720 752 1090 1104 1552 1322 1065 1900 2200\n", + " 771 3400 3000 2850 365 1750 835 660 580 480 1350 975 790 2150\n", + " 1130 778 1190 1055 2400 3200 4500 2100 4900 990 1324 1380 2002 793\n", + " 853 635 4300 1375 642 655 840 870 931 693 909 2250 1897 425\n", + " 517 772 1135 1005 827 1215 1560 2357 1197 999 744 1106 1075 665\n", + " 1585 1570 963 969 1300 1027 716 970 1580 1160 574 803 1038 1155\n", + " 735 570 681 630 902 666 767 1170 780 324 960 967 587 685\n", + " 968 1701 1377 1105 645 520 1040 730 925 1337 1437 1060 864 1445\n", + " 1025 910 1024 465 456 887 1210 1460 1850 1715 1230 1700 486 4580]\n", + "---------------------------------\n", + "['Area in sq ft']\n", + "---------------------------------\n", + "['Unfurnished' 'Semi-Furnished' 'Furnished']\n", + "---------------------------------\n", + "['No Deposit']\n", + "---------------------------------\n", + "['2 bathrooms' '3 bathrooms' '1 bathrooms' '4 bathrooms' '5 bathrooms'\n", + " '6 bathrooms' nan]\n", + "---------------------------------\n", + "['NorthEast facing' nan 'East facing' 'NorthWest facing' 'North facing'\n", + " 'West facing' 'SouthWest facing' 'SouthEast facing' 'South facing']\n", + "---------------------------------\n" + ] + } + ], + "source": [ + "unique(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "8ebcfb5b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + " # correlation heatmap\n", + "num_vars=[\"size\",'area','price']\n", + "sns.heatmap(df[num_vars].corr(),cmap=\"coolwarm\", annot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "f25ba061", + "metadata": {}, + "outputs": [], + "source": [ + "# we can see that correlation of size is greater than a area with price" + ] + }, + { + "cell_type": "markdown", + "id": "41e10aad", + "metadata": {}, + "source": [ + "### 4. Prepare data for Machine Learning Model" + ] + }, + { + "cell_type": "markdown", + "id": "58c6f2de", + "metadata": {}, + "source": [ + "#### Data Cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "dabd64dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "seller_name 0\n", + "seller_type 0\n", + "size 0\n", + "type_ 0\n", + "type_of_house 0\n", + "name 223\n", + "location 0\n", + "city 0\n", + "price 113\n", + "area 0\n", + "area_type 0\n", + "status 0\n", + "deposit 0\n", + "no_bathroom 1\n", + "facing 147\n", + "dtype: int64" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum() # find the number of missing values available in each feature" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "c8736e13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "seller_name 0.0\n", + "seller_type 0.0\n", + "size 0.0\n", + "type_ 0.0\n", + "type_of_house 0.0\n", + "name 22.3\n", + "location 0.0\n", + "city 0.0\n", + "price 11.3\n", + "area 0.0\n", + "area_type 0.0\n", + "status 0.0\n", + "deposit 0.0\n", + "no_bathroom 0.1\n", + "facing 14.7\n", + "dtype: float64" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().mean()*100 # % of missing values\n", + "\n", + "# no_bathroom has 0.1% missing values\n", + "# Price has 11.3% missing values\n", + "# facing has 14.7% missing values\n", + "# name has 22.3% missing values\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "763ce36a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize the missing values using heatmap to get the idea where is the value missing\n", + "\n", + "plt.figure(figsize=(16,9))\n", + "sns.heatmap(df.isnull())\n", + "\n", + "# in name and facing randomly missing values are present (need to drop)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "adf6db08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 13)" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop ------> name feature and facing feature\n", + "df2=df.drop(['name','facing'], axis=1)\n", + "df2.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "ce00792b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_nameseller_typesizetype_type_of_houselocationcitypriceareaarea_typestatusdepositno_bathroom
0Kasturi DevelopersBUILDER2BHKApartmentUlweMumbai17000.01180Area in sq ftUnfurnishedNo Deposit2 bathrooms
1Kasturi DevelopersBUILDER3BHKApartmentUlweMumbai22000.01720Area in sq ftUnfurnishedNo Deposit3 bathrooms
2Kasturi DevelopersBUILDER2BHKApartmentUlweMumbai12500.01150Area in sq ftUnfurnishedNo Deposit2 bathrooms
3sellerVERIFIED OWNER2BHKApartmentChemburMumbai55000.01050Area in sq ftSemi-FurnishedNo Deposit2 bathrooms
4sellerVERIFIED OWNER2BHKApartmentMira Road EastMumbai18500.01165Area in sq ftSemi-FurnishedNo Deposit2 bathrooms
..........................................
995SanjayAGENT2BHKApartmentKalyan WestMumbai15000.0650Area in sq ftFurnishedNo Deposit2 bathrooms
996Prime propertyAGENT1BHKApartmentThane WestMumbai11000.0625Area in sq ftUnfurnishedNo Deposit2 bathrooms
997SanjayAGENT1BHKApartmentKalyan WestMumbai9000.0650Area in sq ftUnfurnishedNo Deposit1 bathrooms
998Prime propertyAGENT1BHKApartmentThane WestMumbai12990.0600Area in sq ftSemi-FurnishedNo Deposit2 bathrooms
999Prime propertyAGENT2BHKApartmentThane WestMumbai20000.01050Area in sq ftSemi-FurnishedNo Deposit2 bathrooms
\n", + "

1000 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " seller_name seller_type size type_ type_of_house \\\n", + "0 Kasturi Developers BUILDER 2 BHK Apartment \n", + "1 Kasturi Developers BUILDER 3 BHK Apartment \n", + "2 Kasturi Developers BUILDER 2 BHK Apartment \n", + "3 seller VERIFIED OWNER 2 BHK Apartment \n", + "4 seller VERIFIED OWNER 2 BHK Apartment \n", + ".. ... ... ... ... ... \n", + "995 Sanjay AGENT 2 BHK Apartment \n", + "996 Prime property AGENT 1 BHK Apartment \n", + "997 Sanjay AGENT 1 BHK Apartment \n", + "998 Prime property AGENT 1 BHK Apartment \n", + "999 Prime property AGENT 2 BHK Apartment \n", + "\n", + " location city price area area_type status \\\n", + "0 Ulwe Mumbai 17000.0 1180 Area in sq ft Unfurnished \n", + "1 Ulwe Mumbai 22000.0 1720 Area in sq ft Unfurnished \n", + "2 Ulwe Mumbai 12500.0 1150 Area in sq ft Unfurnished \n", + "3 Chembur Mumbai 55000.0 1050 Area in sq ft Semi-Furnished \n", + "4 Mira Road East Mumbai 18500.0 1165 Area in sq ft Semi-Furnished \n", + ".. ... ... ... ... ... ... \n", + "995 Kalyan West Mumbai 15000.0 650 Area in sq ft Furnished \n", + "996 Thane West Mumbai 11000.0 625 Area in sq ft Unfurnished \n", + "997 Kalyan West Mumbai 9000.0 650 Area in sq ft Unfurnished \n", + "998 Thane West Mumbai 12990.0 600 Area in sq ft Semi-Furnished \n", + "999 Thane West Mumbai 20000.0 1050 Area in sq ft Semi-Furnished \n", + "\n", + " deposit no_bathroom \n", + "0 No Deposit 2 bathrooms \n", + "1 No Deposit 3 bathrooms \n", + "2 No Deposit 2 bathrooms \n", + "3 No Deposit 2 bathrooms \n", + "4 No Deposit 2 bathrooms \n", + ".. ... ... \n", + "995 No Deposit 2 bathrooms \n", + "996 No Deposit 2 bathrooms \n", + "997 No Deposit 1 bathrooms \n", + "998 No Deposit 2 bathrooms \n", + "999 No Deposit 2 bathrooms \n", + "\n", + "[1000 rows x 13 columns]" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "0e958d0c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "seller_name 0\n", + "seller_type 0\n", + "size 0\n", + "type_ 0\n", + "type_of_house 0\n", + "location 0\n", + "city 0\n", + "price 113\n", + "area 0\n", + "area_type 0\n", + "status 0\n", + "deposit 0\n", + "no_bathroom 0\n", + "dtype: int64" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fill mean value in ------> no_bathroom feature\n", + "# before that we need to convert 'no_bathroom' in integer form\n", + "df2['no_bathroom']=df2['no_bathroom'].str.replace('bathrooms','')\n", + "\n", + "df2['no_bathroom']=pd.to_numeric(df2['no_bathroom'],errors='coerce')\n", + "\n", + "df2['no_bathroom']=df2['no_bathroom'].fillna(df2['no_bathroom'].mean())\n", + "df2.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "e18651b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_nameseller_typesizetype_type_of_houselocationcitypriceareaarea_typestatusdepositno_bathroom
9sellerVERIFIED OWNER3BHKApartmentFortMumbaiNaN1450Area in sq ftFurnishedNo Deposit3.0
36Cordeiro Real EstateAGENT4BHKApartmentWadalaMumbaiNaN2346Area in sq ftUnfurnishedNo Deposit4.0
42VibrantKeyAGENT5BHKApartmentMalabar HillMumbaiNaN3450Area in sq ftFurnishedNo Deposit5.0
57Swastik RealityAGENT5BHKApartmentLower ParelMumbaiNaN7000Area in sq ftSemi-FurnishedNo Deposit5.0
73Urban Investment Property SolutionsAGENT3BHKApartmentBandra WestMumbaiNaN1600Area in sq ftFurnishedNo Deposit3.0
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922DHARTI ESTATE CONSULTANTAGENT3BHKApartmentSantacruz EastMumbaiNaN2500Area in sq ftFurnishedNo Deposit3.0
932Tejasvi Realty Pvt LtdAGENT4BHKApartmentJuhuMumbaiNaN2400Area in sq ftFurnishedNo Deposit4.0
933Tejasvi Realty Pvt LtdAGENT3BHKApartmentJuhuMumbaiNaN1400Area in sq ftSemi-FurnishedNo Deposit3.0
983DHARTI ESTATE CONSULTANTAGENT2BHKApartmentSantacruz EastMumbaiNaN1200Area in sq ftFurnishedNo Deposit3.0
994Rightside PropertiesAGENT5BHKApartmentPowaiMumbaiNaN4580Area in sq ftSemi-FurnishedNo Deposit4.0
\n", + "

113 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " seller_name seller_type size type_ \\\n", + "9 seller VERIFIED OWNER 3 BHK \n", + "36 Cordeiro Real Estate AGENT 4 BHK \n", + "42 VibrantKey AGENT 5 BHK \n", + "57 Swastik Reality AGENT 5 BHK \n", + "73 Urban Investment Property Solutions AGENT 3 BHK \n", + ".. ... ... ... ... \n", + "922 DHARTI ESTATE CONSULTANT AGENT 3 BHK \n", + "932 Tejasvi Realty Pvt Ltd AGENT 4 BHK \n", + "933 Tejasvi Realty Pvt Ltd AGENT 3 BHK \n", + "983 DHARTI ESTATE CONSULTANT AGENT 2 BHK \n", + "994 Rightside Properties AGENT 5 BHK \n", + "\n", + " type_of_house location city price area area_type \\\n", + "9 Apartment Fort Mumbai NaN 1450 Area in sq ft \n", + "36 Apartment Wadala Mumbai NaN 2346 Area in sq ft \n", + "42 Apartment Malabar Hill Mumbai NaN 3450 Area in sq ft \n", + "57 Apartment Lower Parel Mumbai NaN 7000 Area in sq ft \n", + "73 Apartment Bandra West Mumbai NaN 1600 Area in sq ft \n", + ".. ... ... ... ... ... ... \n", + "922 Apartment Santacruz East Mumbai NaN 2500 Area in sq ft \n", + "932 Apartment Juhu Mumbai NaN 2400 Area in sq ft \n", + "933 Apartment Juhu Mumbai NaN 1400 Area in sq ft \n", + "983 Apartment Santacruz East Mumbai NaN 1200 Area in sq ft \n", + "994 Apartment Powai Mumbai NaN 4580 Area in sq ft \n", + "\n", + " status deposit no_bathroom \n", + "9 Furnished No Deposit 3.0 \n", + "36 Unfurnished No Deposit 4.0 \n", + "42 Furnished No Deposit 5.0 \n", + "57 Semi-Furnished No Deposit 5.0 \n", + "73 Furnished No Deposit 3.0 \n", + ".. ... ... ... \n", + "922 Furnished No Deposit 3.0 \n", + "932 Furnished No Deposit 4.0 \n", + "933 Semi-Furnished No Deposit 3.0 \n", + "983 Furnished No Deposit 3.0 \n", + "994 Semi-Furnished No Deposit 4.0 \n", + "\n", + "[113 rows x 13 columns]" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# to deal with price feature null values let first get the rows with null_price_values\n", + "null_price_rows = df2[df2['price'].isnull()]\n", + "null_price_rows\n" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "1a4ad4cd", + "metadata": {}, + "outputs": [], + "source": [ + "# Group by 'size' and fill null values in 'price' with the mean of each group\n", + "df2['price'] = df2['price'].fillna(df2.groupby('size')['price'].transform('mean'))" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "836443fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "seller_name 0\n", + "seller_type 0\n", + "size 0\n", + "type_ 0\n", + "type_of_house 0\n", + "location 0\n", + "city 0\n", + "price 5\n", + "area 0\n", + "area_type 0\n", + "status 0\n", + "deposit 0\n", + "no_bathroom 0\n", + "dtype: int64" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.isnull().sum()\n", + "# we still have null values in price feature" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "798a6601", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_nameseller_typesizetype_type_of_houselocationcitypriceareaarea_typestatusdepositno_bathroom
42VibrantKeyAGENT5BHKApartmentMalabar HillMumbaiNaN3450Area in sq ftFurnishedNo Deposit5.0
57Swastik RealityAGENT5BHKApartmentLower ParelMumbaiNaN7000Area in sq ftSemi-FurnishedNo Deposit5.0
334Trishul propertyAGENT5BHKApartmentSantacruz EastMumbaiNaN1500Area in sq ftSemi-FurnishedNo Deposit4.0
403Swastik RealityAGENT5BHKApartmentWorliMumbaiNaN4900Area in sq ftSemi-FurnishedNo Deposit5.0
994Rightside PropertiesAGENT5BHKApartmentPowaiMumbaiNaN4580Area in sq ftSemi-FurnishedNo Deposit4.0
\n", + "
" + ], + "text/plain": [ + " seller_name seller_type size type_ type_of_house \\\n", + "42 VibrantKey AGENT 5 BHK Apartment \n", + "57 Swastik Reality AGENT 5 BHK Apartment \n", + "334 Trishul property AGENT 5 BHK Apartment \n", + "403 Swastik Reality AGENT 5 BHK Apartment \n", + "994 Rightside Properties AGENT 5 BHK Apartment \n", + "\n", + " location city price area area_type status \\\n", + "42 Malabar Hill Mumbai NaN 3450 Area in sq ft Furnished \n", + "57 Lower Parel Mumbai NaN 7000 Area in sq ft Semi-Furnished \n", + "334 Santacruz East Mumbai NaN 1500 Area in sq ft Semi-Furnished \n", + "403 Worli Mumbai NaN 4900 Area in sq ft Semi-Furnished \n", + "994 Powai Mumbai NaN 4580 Area in sq ft Semi-Furnished \n", + "\n", + " deposit no_bathroom \n", + "42 No Deposit 5.0 \n", + "57 No Deposit 5.0 \n", + "334 No Deposit 4.0 \n", + "403 No Deposit 5.0 \n", + "994 No Deposit 4.0 " + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "null_price_rows = df2[df2['price'].isnull()]\n", + "null_price_rows\n", + "# we can observe that there is no data inreference to the size of 5 BHK so we can take it in prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "7b0ee248", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(995, 13)" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop na value of rows with size ==5\n", + "df3=df2.dropna()\n", + "df3.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "04226b86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "seller_name 0\n", + "seller_type 0\n", + "size 0\n", + "type_ 0\n", + "type_of_house 0\n", + "location 0\n", + "city 0\n", + "price 0\n", + "area 0\n", + "area_type 0\n", + "status 0\n", + "deposit 0\n", + "no_bathroom 0\n", + "dtype: int64" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3.isnull().sum()\n", + "# we can observe that now our data has no missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "7104adfd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_nameseller_typesizetype_type_of_houselocationcitypriceareaarea_typestatusdepositno_bathroom
0Kasturi DevelopersBUILDER2BHKApartmentUlweMumbai17000.01180Area in sq ftUnfurnishedNo Deposit2.0
1Kasturi DevelopersBUILDER3BHKApartmentUlweMumbai22000.01720Area in sq ftUnfurnishedNo Deposit3.0
2Kasturi DevelopersBUILDER2BHKApartmentUlweMumbai12500.01150Area in sq ftUnfurnishedNo Deposit2.0
3sellerVERIFIED OWNER2BHKApartmentChemburMumbai55000.01050Area in sq ftSemi-FurnishedNo Deposit2.0
4sellerVERIFIED OWNER2BHKApartmentMira Road EastMumbai18500.01165Area in sq ftSemi-FurnishedNo Deposit2.0
\n", + "
" + ], + "text/plain": [ + " seller_name seller_type size type_ type_of_house \\\n", + "0 Kasturi Developers BUILDER 2 BHK Apartment \n", + "1 Kasturi Developers BUILDER 3 BHK Apartment \n", + "2 Kasturi Developers BUILDER 2 BHK Apartment \n", + "3 seller VERIFIED OWNER 2 BHK Apartment \n", + "4 seller VERIFIED OWNER 2 BHK Apartment \n", + "\n", + " location city price area area_type status \\\n", + "0 Ulwe Mumbai 17000.0 1180 Area in sq ft Unfurnished \n", + "1 Ulwe Mumbai 22000.0 1720 Area in sq ft Unfurnished \n", + "2 Ulwe Mumbai 12500.0 1150 Area in sq ft Unfurnished \n", + "3 Chembur Mumbai 55000.0 1050 Area in sq ft Semi-Furnished \n", + "4 Mira Road East Mumbai 18500.0 1165 Area in sq ft Semi-Furnished \n", + "\n", + " deposit no_bathroom \n", + "0 No Deposit 2.0 \n", + "1 No Deposit 3.0 \n", + "2 No Deposit 2.0 \n", + "3 No Deposit 2.0 \n", + "4 No Deposit 2.0 " + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3.head()" + ] + }, + { + "cell_type": "markdown", + "id": "201b79a8", + "metadata": {}, + "source": [ + "### Feature Selection" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "f9c38765", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['seller_name', 'seller_type', 'size', 'type_', 'type_of_house',\n", + " 'location', 'city', 'price', 'area', 'area_type', 'status', 'deposit',\n", + " 'no_bathroom'],\n", + " dtype='object')" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3.columns\n", + "# we can see that we have few features that are not necessary required in prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "e31cefc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroom
0BUILDER2ApartmentUlweMumbai17000.01180Unfurnished2.0
1BUILDER3ApartmentUlweMumbai22000.01720Unfurnished3.0
2BUILDER2ApartmentUlweMumbai12500.01150Unfurnished2.0
3VERIFIED OWNER2ApartmentChemburMumbai55000.01050Semi-Furnished2.0
4VERIFIED OWNER2ApartmentMira Road EastMumbai18500.01165Semi-Furnished2.0
\n", + "
" + ], + "text/plain": [ + " seller_type size type_of_house location city price area \\\n", + "0 BUILDER 2 Apartment Ulwe Mumbai 17000.0 1180 \n", + "1 BUILDER 3 Apartment Ulwe Mumbai 22000.0 1720 \n", + "2 BUILDER 2 Apartment Ulwe Mumbai 12500.0 1150 \n", + "3 VERIFIED OWNER 2 Apartment Chembur Mumbai 55000.0 1050 \n", + "4 VERIFIED OWNER 2 Apartment Mira Road East Mumbai 18500.0 1165 \n", + "\n", + " status no_bathroom \n", + "0 Unfurnished 2.0 \n", + "1 Unfurnished 3.0 \n", + "2 Unfurnished 2.0 \n", + "3 Semi-Furnished 2.0 \n", + "4 Semi-Furnished 2.0 " + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# features like 'seller_name', 'type_','area_type','deposit' are not required\n", + "# dropping features that are not required\n", + "\n", + "df4= df3.drop(['seller_name', 'type_','area_type','deposit'],axis=1)\n", + "df4.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "681f1c45", + "metadata": {}, + "source": [ + "### Finding Outlier and Removing" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "0a0e8e59", + "metadata": {}, + "outputs": [], + "source": [ + "# function to create histogram, Q-Q plot and boxplot\n", + "\n", + "# for Q-Q plots\n", + "\n", + "import scipy.stats as stats\n", + "\n", + "def diagnostic_plots(df,variable):\n", + " # function takes dataframe df and\n", + " # the variable of interest as arguments\n", + " \n", + " # define figure size\n", + " \n", + " plt.figure(figsize=(16,4))\n", + " \n", + " # histogram\n", + " \n", + " plt.subplot(1,3,1)\n", + " sns.displot(df[variable],bins=30)\n", + " plt.title('Histogram')\n", + " \n", + " # Q-Q plot\n", + " \n", + " plt.subplot(1,3,2)\n", + " stats.probplot(df[variable],dist='norm',plot=plt)\n", + " plt.ylabel('Variable quantiles')\n", + " \n", + " # boxplot\n", + " \n", + " plt.subplot(1,3,3)\n", + " sns.boxplot(y=df[variable])\n", + " plt.title('Boxplot')\n", + " \n", + " \n", + " plt.show()\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "id": "f45d2300", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "******* size *******\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ANIKET RAY\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n", + " self._figure.tight_layout(*args, **kwargs)\n", + "C:\\Users\\ANIKET RAY\\AppData\\Local\\Temp\\ipykernel_21500\\1989572192.py:23: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n", + " plt.subplot(1,3,2)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_var=['size','area','no_bathroom','price']\n", + "\n", + "for var in num_var:\n", + " print(\"******* {} *******\".format(var))\n", + " diagnostic_plots(df4, var)\n", + " \n", + " # here we observe outliers using histogram, qq plot and boxplot\n", + " \n", + "\n", + " # we can observe that there are many outliers present in area and no_bathroom features" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "e8281c7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroom
5VERIFIED OWNER1Studio ApartmentVille Parle EastMumbai17000.0200Semi-Furnished1.0
7VERIFIED OWNER2ApartmentNilje GaonMumbai9000.0634Unfurnished2.0
15VERIFIED OWNER1Studio ApartmentWorliMumbai20000.0260Furnished1.0
16VERIFIED OWNER1Studio ApartmentVikroli EastMumbai14500.0300Unfurnished1.0
53AGENT2ApartmentPanvelMumbai15000.0500Unfurnished1.0
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" + ], + "text/plain": [ + " seller_type size type_of_house location city price \\\n", + "5 VERIFIED OWNER 1 Studio Apartment Ville Parle East Mumbai 17000.0 \n", + "7 VERIFIED OWNER 2 Apartment Nilje Gaon Mumbai 9000.0 \n", + "15 VERIFIED OWNER 1 Studio Apartment Worli Mumbai 20000.0 \n", + "16 VERIFIED OWNER 1 Studio Apartment Vikroli East Mumbai 14500.0 \n", + "53 AGENT 2 Apartment Panvel Mumbai 15000.0 \n", + "\n", + " area status no_bathroom \n", + "5 200 Semi-Furnished 1.0 \n", + "7 634 Unfurnished 2.0 \n", + "15 260 Furnished 1.0 \n", + "16 300 Unfurnished 1.0 \n", + "53 500 Unfurnished 1.0 " + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# here we consider 1 BHK size required min 350 sqft area\n", + "df4[df4['area']/df4['size']<350].head()\n", + "\n", + "# here we found outliers" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "df7785e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(942, 9)" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# if 1 BHK area is < 350 then we are going to remove them\n", + "df5=df4[~(df4['area']/df4['size']<350)]\n", + "df5.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "2441d4d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 9)" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# if no_bathroom have value more than size+2, we are going to remove them\n", + "\n", + "df6=df5[df5['no_bathroom']" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,9))\n", + "for i,var in enumerate(num_var):\n", + " plt.subplot(3,2,i+1)\n", + " sns.boxplot(df6[var])\n", + "3 # we observe that we have removed outliers at large extent" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "692e34c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroom
0BUILDER2ApartmentUlweMumbai17000.0000001180Unfurnished2.0
1BUILDER3ApartmentUlweMumbai22000.0000001720Unfurnished3.0
2BUILDER2ApartmentUlweMumbai12500.0000001150Unfurnished2.0
3VERIFIED OWNER2ApartmentChemburMumbai55000.0000001050Semi-Furnished2.0
4VERIFIED OWNER2ApartmentMira Road EastMumbai18500.0000001165Semi-Furnished2.0
6VERIFIED OWNER2ApartmentKandivali WestMumbai28500.000000750Unfurnished2.0
8VERIFIED OWNER2ApartmentGhatkopar WestMumbai35000.0000001089Unfurnished2.0
9VERIFIED OWNER3ApartmentFortMumbai47357.0076921450Furnished3.0
10VERIFIED OWNER1ApartmentShil PhataMumbai18500.000000680Furnished1.0
11VERIFIED OWNER1ApartmentNeralMumbai5500.000000490Semi-Furnished2.0
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" + ], + "text/plain": [ + " seller_type size type_of_house location city price \\\n", + "0 BUILDER 2 Apartment Ulwe Mumbai 17000.000000 \n", + "1 BUILDER 3 Apartment Ulwe Mumbai 22000.000000 \n", + "2 BUILDER 2 Apartment Ulwe Mumbai 12500.000000 \n", + "3 VERIFIED OWNER 2 Apartment Chembur Mumbai 55000.000000 \n", + "4 VERIFIED OWNER 2 Apartment Mira Road East Mumbai 18500.000000 \n", + "6 VERIFIED OWNER 2 Apartment Kandivali West Mumbai 28500.000000 \n", + "8 VERIFIED OWNER 2 Apartment Ghatkopar West Mumbai 35000.000000 \n", + "9 VERIFIED OWNER 3 Apartment Fort Mumbai 47357.007692 \n", + "10 VERIFIED OWNER 1 Apartment Shil Phata Mumbai 18500.000000 \n", + "11 VERIFIED OWNER 1 Apartment Neral Mumbai 5500.000000 \n", + "\n", + " area status no_bathroom \n", + "0 1180 Unfurnished 2.0 \n", + "1 1720 Unfurnished 3.0 \n", + "2 1150 Unfurnished 2.0 \n", + "3 1050 Semi-Furnished 2.0 \n", + "4 1165 Semi-Furnished 2.0 \n", + "6 750 Unfurnished 2.0 \n", + "8 1089 Unfurnished 2.0 \n", + "9 1450 Furnished 3.0 \n", + "10 680 Furnished 1.0 \n", + "11 490 Semi-Furnished 2.0 " + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df6.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "8e5dc6fe", + "metadata": {}, + "source": [ + "### Categorical Variable Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "e2246410", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 94)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using the get_dummies function in pandas to perform one-hot encoding on \n", + "# the specified categorical columns in the DataFrame df6.\n", + "df7= pd.get_dummies(df6,drop_first=True, columns=['seller_type','type_of_house','location','status','city'])\n", + "df7.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "764bb4d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizepriceareano_bathroomseller_type_BUILDERseller_type_VERIFIED OWNERtype_of_house_Studio Apartmentlocation_Agripadalocation_Airolilocation_Ambernath West...location_Vasai Westlocation_Vashilocation_Vikhrolilocation_Virarlocation_Virar Westlocation_Wadalalocation_Worlilocation_kasaradavali thane weststatus_Semi-Furnishedstatus_Unfurnished
0217000.011802.0TrueFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
1322000.017203.0TrueFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
2212500.011502.0TrueFalseFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
3255000.010502.0FalseTrueFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
4218500.011652.0FalseTrueFalseFalseFalseFalse...FalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
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5 rows × 94 columns

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" + ], + "text/plain": [ + " size price area no_bathroom seller_type_BUILDER \\\n", + "0 2 17000.0 1180 2.0 True \n", + "1 3 22000.0 1720 3.0 True \n", + "2 2 12500.0 1150 2.0 True \n", + "3 2 55000.0 1050 2.0 False \n", + "4 2 18500.0 1165 2.0 False \n", + "\n", + " seller_type_VERIFIED OWNER type_of_house_Studio Apartment \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 True False \n", + "4 True False \n", + "\n", + " location_Agripada location_Airoli location_Ambernath West ... \\\n", + "0 False False False ... \n", + "1 False False False ... \n", + "2 False False False ... \n", + "3 False False False ... \n", + "4 False False False ... \n", + "\n", + " location_Vasai West location_Vashi location_Vikhroli location_Virar \\\n", + "0 False False False False \n", + "1 False False False False \n", + "2 False False False False \n", + "3 False False False False \n", + "4 False False False False \n", + "\n", + " location_Virar West location_Wadala location_Worli \\\n", + "0 False False False \n", + "1 False False False \n", + "2 False False False \n", + "3 False False False \n", + "4 False False False \n", + "\n", + " location_kasaradavali thane west status_Semi-Furnished status_Unfurnished \n", + "0 False False True \n", + "1 False False True \n", + "2 False False True \n", + "3 False True False \n", + "4 False True False \n", + "\n", + "[5 rows x 94 columns]" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df7.head()" + ] + }, + { + "cell_type": "markdown", + "id": "5cbea24b", + "metadata": {}, + "source": [ + "###### we can see that in ['location','seller_type','type_of_house','status'] contains multiple classes and if we convert them into OHE so it increases the sizee of DF so try to use those classes which are frequently present in the cat var" + ] + }, + { + "cell_type": "markdown", + "id": "5d861bf2", + "metadata": {}, + "source": [ + "### Working on <<<< seller_type_ >>>> feature" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "3af17c46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "seller_type\n", + "AGENT 899\n", + "VERIFIED OWNER 37\n", + "BUILDER 3\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df6['seller_type'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "95526857", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 11)" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df8=df6.copy()\n", + "\n", + "# apply Ohe-hot encoding on'area_type' feature\n", + "for cat_var in ['AGENT','VERIFIED OWNER']:\n", + " df8['seller_type'+cat_var]=np.where(df8['seller_type']==cat_var,1,0)\n", + "df8.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "cbc80524", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroomseller_typeAGENTseller_typeVERIFIED OWNER
0BUILDER2ApartmentUlweMumbai17000.01180Unfurnished2.000
1BUILDER3ApartmentUlweMumbai22000.01720Unfurnished3.000
2BUILDER2ApartmentUlweMumbai12500.01150Unfurnished2.000
3VERIFIED OWNER2ApartmentChemburMumbai55000.01050Semi-Furnished2.001
4VERIFIED OWNER2ApartmentMira Road EastMumbai18500.01165Semi-Furnished2.001
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" + ], + "text/plain": [ + " seller_type size type_of_house location city price area \\\n", + "0 BUILDER 2 Apartment Ulwe Mumbai 17000.0 1180 \n", + "1 BUILDER 3 Apartment Ulwe Mumbai 22000.0 1720 \n", + "2 BUILDER 2 Apartment Ulwe Mumbai 12500.0 1150 \n", + "3 VERIFIED OWNER 2 Apartment Chembur Mumbai 55000.0 1050 \n", + "4 VERIFIED OWNER 2 Apartment Mira Road East Mumbai 18500.0 1165 \n", + "\n", + " status no_bathroom seller_typeAGENT seller_typeVERIFIED OWNER \n", + "0 Unfurnished 2.0 0 0 \n", + "1 Unfurnished 3.0 0 0 \n", + "2 Unfurnished 2.0 0 0 \n", + "3 Semi-Furnished 2.0 0 1 \n", + "4 Semi-Furnished 2.0 0 1 " + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df8.head()" + ] + }, + { + "cell_type": "markdown", + "id": "19823c66", + "metadata": {}, + "source": [ + "### Working on <<<< type_of_house >>>> feature" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "059d07f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type_of_house\n", + "Apartment 918\n", + "Studio Apartment 21\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df8['type_of_house'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "6ac23992", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 13)" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply Ohe-hot encoding on'type_of_house' feature\n", + "for cat_var in ['Apartment','Studio Apartment']:\n", + " df8['type_of_house'+cat_var]=np.where(df8['type_of_house']==cat_var,1,0)\n", + "df8.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "063f58ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroomseller_typeAGENTseller_typeVERIFIED OWNERtype_of_houseApartmenttype_of_houseStudio Apartment
0BUILDER2ApartmentUlweMumbai17000.01180Unfurnished2.00010
1BUILDER3ApartmentUlweMumbai22000.01720Unfurnished3.00010
2BUILDER2ApartmentUlweMumbai12500.01150Unfurnished2.00010
3VERIFIED OWNER2ApartmentChemburMumbai55000.01050Semi-Furnished2.00110
4VERIFIED OWNER2ApartmentMira Road EastMumbai18500.01165Semi-Furnished2.00110
\n", + "
" + ], + "text/plain": [ + " seller_type size type_of_house location city price area \\\n", + "0 BUILDER 2 Apartment Ulwe Mumbai 17000.0 1180 \n", + "1 BUILDER 3 Apartment Ulwe Mumbai 22000.0 1720 \n", + "2 BUILDER 2 Apartment Ulwe Mumbai 12500.0 1150 \n", + "3 VERIFIED OWNER 2 Apartment Chembur Mumbai 55000.0 1050 \n", + "4 VERIFIED OWNER 2 Apartment Mira Road East Mumbai 18500.0 1165 \n", + "\n", + " status no_bathroom seller_typeAGENT seller_typeVERIFIED OWNER \\\n", + "0 Unfurnished 2.0 0 0 \n", + "1 Unfurnished 3.0 0 0 \n", + "2 Unfurnished 2.0 0 0 \n", + "3 Semi-Furnished 2.0 0 1 \n", + "4 Semi-Furnished 2.0 0 1 \n", + "\n", + " type_of_houseApartment type_of_houseStudio Apartment \n", + "0 1 0 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 1 0 \n", + "4 1 0 " + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df8.head()" + ] + }, + { + "cell_type": "markdown", + "id": "64f5ff66", + "metadata": {}, + "source": [ + "### Working on <<<< location >>>> feature" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "779e70bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "location\n", + "Thane West 152\n", + "Panvel 120\n", + "Kalyan West 51\n", + "Kharghar 45\n", + "Wadala 33\n", + " ... \n", + "Madh 1\n", + "Koper Khairane 1\n", + "Naigaon East 1\n", + "Ambernath West 1\n", + "Sector 21 Kamothe 1\n", + "Name: count, Length: 86, dtype: int64" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "location_value_count = df8['location'].value_counts()\n", + "location_value_count\n", + "# we can see that we have many locations but some location have count value very less" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "91990e5a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Thane West', 'Panvel', 'Kalyan West', 'Kharghar', 'Wadala',\n", + " 'Kalamboli', 'Andheri East', 'Kanjurmarg', 'Vikhroli', 'Ulwe',\n", + " 'Santacruz East', 'Chembur', 'Kandivali East', 'Kamothe', 'Seawoods',\n", + " 'Andheri West', 'Powai', 'Bandra West', 'Mira Road East', 'Dombivali',\n", + " 'Lower Parel', 'Worli', 'Dombivali East', 'Colaba'],\n", + " dtype='object', name='location')" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we will get the location whose count values are freater than 10\n", + "location_gert_10 = location_value_count[location_value_count>=10].index\n", + "location_gert_10" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "88cdd0ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 37)" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# location count is greater than 9 then we create a column of that feature\n", + "# then if this location present in location feature then set value 1 else 0 (ohe hot encoding)\n", + "df9= df8.copy()\n", + "for cat_var in location_gert_10:\n", + " df9['location'+cat_var]=np.where(df9['location']==cat_var,1,0)\n", + "df9.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "93caa1ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroomseller_typeAGENT...locationSeawoodslocationAndheri WestlocationPowailocationBandra WestlocationMira Road EastlocationDombivalilocationLower ParellocationWorlilocationDombivali EastlocationColaba
0BUILDER2ApartmentUlweMumbai17000.01180Unfurnished2.00...0000000000
1BUILDER3ApartmentUlweMumbai22000.01720Unfurnished3.00...0000000000
2BUILDER2ApartmentUlweMumbai12500.01150Unfurnished2.00...0000000000
3VERIFIED OWNER2ApartmentChemburMumbai55000.01050Semi-Furnished2.00...0000000000
4VERIFIED OWNER2ApartmentMira Road EastMumbai18500.01165Semi-Furnished2.00...0000100000
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5 rows × 37 columns

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" + ], + "text/plain": [ + " seller_type size type_of_house location city price area \\\n", + "0 BUILDER 2 Apartment Ulwe Mumbai 17000.0 1180 \n", + "1 BUILDER 3 Apartment Ulwe Mumbai 22000.0 1720 \n", + "2 BUILDER 2 Apartment Ulwe Mumbai 12500.0 1150 \n", + "3 VERIFIED OWNER 2 Apartment Chembur Mumbai 55000.0 1050 \n", + "4 VERIFIED OWNER 2 Apartment Mira Road East Mumbai 18500.0 1165 \n", + "\n", + " status no_bathroom seller_typeAGENT ... locationSeawoods \\\n", + "0 Unfurnished 2.0 0 ... 0 \n", + "1 Unfurnished 3.0 0 ... 0 \n", + "2 Unfurnished 2.0 0 ... 0 \n", + "3 Semi-Furnished 2.0 0 ... 0 \n", + "4 Semi-Furnished 2.0 0 ... 0 \n", + "\n", + " locationAndheri West locationPowai locationBandra West \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "\n", + " locationMira Road East locationDombivali locationLower Parel \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 1 0 0 \n", + "\n", + " locationWorli locationDombivali East locationColaba \n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "\n", + "[5 rows x 37 columns]" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df9.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e7a4e7f9", + "metadata": {}, + "source": [ + "### working on <<<< status >>>> feature\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "57980e05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "status\n", + "Semi-Furnished 426\n", + "Unfurnished 352\n", + "Furnished 161\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df9['status'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "fd59541a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 40)" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for cat_var in ['Semi-Furnished','Unfurnished','Furnished']:\n", + " df9['status'+cat_var]=np.where(df9['status']==cat_var,1,0)\n", + "df9.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "c69f8087", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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seller_typesizetype_of_houselocationcitypriceareastatusno_bathroomseller_typeAGENT...locationBandra WestlocationMira Road EastlocationDombivalilocationLower ParellocationWorlilocationDombivali EastlocationColabastatusSemi-FurnishedstatusUnfurnishedstatusFurnished
0BUILDER2ApartmentUlweMumbai17000.01180Unfurnished2.00...0000000010
1BUILDER3ApartmentUlweMumbai22000.01720Unfurnished3.00...0000000010
2BUILDER2ApartmentUlweMumbai12500.01150Unfurnished2.00...0000000010
3VERIFIED OWNER2ApartmentChemburMumbai55000.01050Semi-Furnished2.00...0000000100
4VERIFIED OWNER2ApartmentMira Road EastMumbai18500.01165Semi-Furnished2.00...0100000100
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5 rows × 40 columns

\n", + "
" + ], + "text/plain": [ + " seller_type size type_of_house location city price area \\\n", + "0 BUILDER 2 Apartment Ulwe Mumbai 17000.0 1180 \n", + "1 BUILDER 3 Apartment Ulwe Mumbai 22000.0 1720 \n", + "2 BUILDER 2 Apartment Ulwe Mumbai 12500.0 1150 \n", + "3 VERIFIED OWNER 2 Apartment Chembur Mumbai 55000.0 1050 \n", + "4 VERIFIED OWNER 2 Apartment Mira Road East Mumbai 18500.0 1165 \n", + "\n", + " status no_bathroom seller_typeAGENT ... locationBandra West \\\n", + "0 Unfurnished 2.0 0 ... 0 \n", + "1 Unfurnished 3.0 0 ... 0 \n", + "2 Unfurnished 2.0 0 ... 0 \n", + "3 Semi-Furnished 2.0 0 ... 0 \n", + "4 Semi-Furnished 2.0 0 ... 0 \n", + "\n", + " locationMira Road East locationDombivali locationLower Parel \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 1 0 0 \n", + "\n", + " locationWorli locationDombivali East locationColaba \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "\n", + " statusSemi-Furnished statusUnfurnished statusFurnished \n", + "0 0 1 0 \n", + "1 0 1 0 \n", + "2 0 1 0 \n", + "3 1 0 0 \n", + "4 1 0 0 \n", + "\n", + "[5 rows x 40 columns]" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df9.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ee8bbe0b", + "metadata": {}, + "source": [ + "### Drop Categorical Variable" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "cb3d3a20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(939, 35)" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we also dropped city column as only Mumbai is there so wont affect the prediction\n", + "df10=df9.drop(['location','seller_type','type_of_house','status','city'],axis=1)\n", + "df10.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "7c142322", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizepriceareano_bathroomseller_typeAGENTseller_typeVERIFIED OWNERtype_of_houseApartmenttype_of_houseStudio ApartmentlocationThane WestlocationPanvel...locationBandra WestlocationMira Road EastlocationDombivalilocationLower ParellocationWorlilocationDombivali EastlocationColabastatusSemi-FurnishedstatusUnfurnishedstatusFurnished
0217000.011802.0001000...0000000010
1322000.017203.0001000...0000000010
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5 rows × 35 columns

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" + ], + "text/plain": [ + " size price area no_bathroom seller_typeAGENT \\\n", + "0 2 17000.0 1180 2.0 0 \n", + "1 3 22000.0 1720 3.0 0 \n", + "2 2 12500.0 1150 2.0 0 \n", + "3 2 55000.0 1050 2.0 0 \n", + "4 2 18500.0 1165 2.0 0 \n", + "\n", + " seller_typeVERIFIED OWNER type_of_houseApartment \\\n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 1 1 \n", + "4 1 1 \n", + "\n", + " type_of_houseStudio Apartment locationThane West locationPanvel ... \\\n", + "0 0 0 0 ... \n", + "1 0 0 0 ... \n", + "2 0 0 0 ... \n", + "3 0 0 0 ... \n", + "4 0 0 0 ... \n", + "\n", + " locationBandra West locationMira Road East locationDombivali \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 1 0 \n", + "\n", + " locationLower Parel locationWorli locationDombivali East locationColaba \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "\n", + " statusSemi-Furnished statusUnfurnished statusFurnished \n", + "0 0 1 0 \n", + "1 0 1 0 \n", + "2 0 1 0 \n", + "3 1 0 0 \n", + "4 1 0 0 \n", + "\n", + "[5 rows x 35 columns]" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df10.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "id": "a695a7f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 939 entries, 0 to 999\n", + "Data columns (total 35 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 size 939 non-null int64 \n", + " 1 price 939 non-null float64\n", + " 2 area 939 non-null int64 \n", + " 3 no_bathroom 939 non-null float64\n", + " 4 seller_typeAGENT 939 non-null int32 \n", + " 5 seller_typeVERIFIED OWNER 939 non-null int32 \n", + " 6 type_of_houseApartment 939 non-null int32 \n", + " 7 type_of_houseStudio Apartment 939 non-null int32 \n", + " 8 locationThane West 939 non-null int32 \n", + " 9 locationPanvel 939 non-null int32 \n", + " 10 locationKalyan West 939 non-null int32 \n", + " 11 locationKharghar 939 non-null int32 \n", + " 12 locationWadala 939 non-null int32 \n", + " 13 locationKalamboli 939 non-null int32 \n", + " 14 locationAndheri East 939 non-null int32 \n", + " 15 locationKanjurmarg 939 non-null int32 \n", + " 16 locationVikhroli 939 non-null int32 \n", + " 17 locationUlwe 939 non-null int32 \n", + " 18 locationSantacruz East 939 non-null int32 \n", + " 19 locationChembur 939 non-null int32 \n", + " 20 locationKandivali East 939 non-null int32 \n", + " 21 locationKamothe 939 non-null int32 \n", + " 22 locationSeawoods 939 non-null int32 \n", + " 23 locationAndheri West 939 non-null int32 \n", + " 24 locationPowai 939 non-null int32 \n", + " 25 locationBandra West 939 non-null int32 \n", + " 26 locationMira Road East 939 non-null int32 \n", + " 27 locationDombivali 939 non-null int32 \n", + " 28 locationLower Parel 939 non-null int32 \n", + " 29 locationWorli 939 non-null int32 \n", + " 30 locationDombivali East 939 non-null int32 \n", + " 31 locationColaba 939 non-null int32 \n", + " 32 statusSemi-Furnished 939 non-null int32 \n", + " 33 statusUnfurnished 939 non-null int32 \n", + " 34 statusFurnished 939 non-null int32 \n", + "dtypes: float64(2), int32(31), int64(2)\n", + "memory usage: 182.7 KB\n" + ] + } + ], + "source": [ + "df10.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "a96246ce", + "metadata": {}, + "outputs": [], + "source": [ + "df10.to_csv('ohe_data_reduce_cat_class.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "3f80fb67", + "metadata": {}, + "source": [ + "### ML Model Developed on different algorithms\n" + ] + }, + { + "cell_type": "markdown", + "id": "580690e7", + "metadata": {}, + "source": [ + "#### processed dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "3294d4c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizepriceareano_bathroomseller_typeAGENTseller_typeVERIFIED OWNERtype_of_houseApartmenttype_of_houseStudio ApartmentlocationThane WestlocationPanvel...locationBandra WestlocationMira Road EastlocationDombivalilocationLower ParellocationWorlilocationDombivali EastlocationColabastatusSemi-FurnishedstatusUnfurnishedstatusFurnished
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5 rows × 35 columns

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" + ], + "text/plain": [ + " size price area no_bathroom seller_typeAGENT \\\n", + "0 2 17000.0 1180 2.0 0 \n", + "1 3 22000.0 1720 3.0 0 \n", + "2 2 12500.0 1150 2.0 0 \n", + "3 2 55000.0 1050 2.0 0 \n", + "4 2 18500.0 1165 2.0 0 \n", + "\n", + " seller_typeVERIFIED OWNER type_of_houseApartment \\\n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 1 1 \n", + "4 1 1 \n", + "\n", + " type_of_houseStudio Apartment locationThane West locationPanvel ... \\\n", + "0 0 0 0 ... \n", + "1 0 0 0 ... \n", + "2 0 0 0 ... \n", + "3 0 0 0 ... \n", + "4 0 0 0 ... \n", + "\n", + " locationBandra West locationMira Road East locationDombivali \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 1 0 \n", + "\n", + " locationLower Parel locationWorli locationDombivali East locationColaba \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "\n", + " statusSemi-Furnished statusUnfurnished statusFurnished \n", + "0 0 1 0 \n", + "1 0 1 0 \n", + "2 0 1 0 \n", + "3 1 0 0 \n", + "4 1 0 0 \n", + "\n", + "[5 rows x 35 columns]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df10.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fb2e4758", + "metadata": {}, + "source": [ + "### Split Dataset in Train and test\n" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "26e957c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of X= (939, 34)\n", + "Shape of y= (939,)\n" + ] + } + ], + "source": [ + "X= df10.drop(\"price\", axis=1)\n", + "y=df10['price']\n", + "print('Shape of X= ', X.shape)\n", + "print('Shape of y= ', y.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "c2343944", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of X_train= (751, 34)\n", + "Shape of X_test= (188, 34)\n", + "Shape of y_train= (751,)\n", + "Shape of y_test= (188, 34)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2, random_state=51)\n", + "print('Shape of X_train= ', X_train.shape)\n", + "print('Shape of X_test= ', X_test.shape)\n", + "print('Shape of y_train= ', y_train.shape)\n", + "print('Shape of y_test= ', X_test.shape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6dc12078", + "metadata": {}, + "source": [ + "### Feature Scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "dcff4232", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "sc= StandardScaler()\n", + "sc.fit(X_train)\n", + "X_train= sc.transform(X_train)\n", + "X_test= sc.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "id": "4b9c43d8", + "metadata": {}, + "source": [ + "### Machine Learning Model training" + ] + }, + { + "cell_type": "markdown", + "id": "4da835b8", + "metadata": {}, + "source": [ + "### Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "42700dc0", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.metrics import mean_squared_error\n", + "lr= LinearRegression()\n", + "lr_lasso= Lasso()\n", + "lr_ridge= Ridge()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "8f38f32f", + "metadata": {}, + "outputs": [], + "source": [ + "# creating function to get the mean_squared_error\n", + "def rmse(y_test,y_pred):\n", + " return np.sqrt(mean_squared_error(y_test,y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "e9202249", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6233479403008555, 12533.616109826015)" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Linear\n", + "lr.fit(X_train, y_train)\n", + "lr_score = lr.score(X_test, y_test) # with all num var 0.6233479403008555\n", + "lr_rmse= rmse(y_test, lr.predict(X_test))\n", + "lr_score,lr_rmse" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "id": "6892035c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6233322525561815, 12533.877122764852)" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Lasso\n", + "lr_lasso.fit(X_train, y_train)\n", + "lr_lasso_score = lr_lasso.score(X_test, y_test) # with all num var 0.6233322525561815\n", + "lr_lasso_rmse= rmse(y_test, lr_lasso.predict(X_test))\n", + "lr_lasso_score,lr_lasso_rmse" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "c3ed1557", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6235074346180465, 12530.962132286975)" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Ridge\n", + "lr_ridge.fit(X_train, y_train)\n", + "lr_ridge_score = lr_ridge.score(X_test, y_test) # with all num var 0.6235074346180465\n", + "lr_ridge_rmse= rmse(y_test, lr_ridge.predict(X_test))\n", + "lr_ridge_score,lr_ridge_rmse" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "id": "d2c3fdbc", + "metadata": {}, + "outputs": [], + "source": [ + "# overall we can see that linear regression model is not able to give that much\n", + "# good accuracy" + ] + }, + { + "cell_type": "markdown", + "id": "eb550464", + "metadata": {}, + "source": [ + "### Support Vector Machine\n" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "id": "8e01f27d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.09455931379784888, 21366.129960937014)" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.svm import SVR\n", + "svr=SVR()\n", + "svr.fit(X_train, y_train)\n", + "svr_score = svr.score(X_test, y_test) # with all num var -0.09455931379784888\n", + "svr_rmse= rmse(y_test, svr.predict(X_test))\n", + "svr_score,svr_rmse\n", + "\n", + "# we can say that by SVM we get a really bad accuracy\n" + ] + }, + { + "cell_type": "markdown", + "id": "2bd709f7", + "metadata": {}, + "source": [ + "### Random Forest Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "id": "4090438f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.760682283801976, 9990.647594786818)" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "rfr= RandomForestRegressor()\n", + "rfr.fit(X_train, y_train)\n", + "rfr_score = rfr.score(X_test, y_test) # with all num var 0.760682283801976\n", + "rfr_rmse= rmse(y_test, rfr.predict(X_test))\n", + "rfr_score,rfr_rmse\n", + "\n", + "# We can say that Random Forest Regressor is giving a pretty good accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "7b92b29b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(y_test,rfr.predict(X_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "id": "65961950", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ANIKET RAY\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n", + " self._figure.tight_layout(*args, **kwargs)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot((y_test,rfr.predict(X_test)),bins=50)" + ] + }, + { + "cell_type": "markdown", + "id": "dcad555f", + "metadata": {}, + "source": [ + "### XGBoost\n" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "id": "96180525", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.7076982065308773, 11041.344368785665)" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xgboost\n", + "xgb_reg= xgboost.XGBRegressor()\n", + "xgb_reg.fit(X_train, y_train)\n", + "xgb_reg_score = xgb_reg.score(X_test, y_test) # with all num var 0.7076982065308773\n", + "xgb_reg_rmse= rmse(y_test, xgb_reg.predict(X_test))\n", + "xgb_reg_score,xgb_reg_rmse\n", + "\n", + "# We can say that XGBoost is decent" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "id": "7d6cf792", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model Score RMSE\n", + "0 Linear Regression 0.623348 12533.616110\n", + "1 Lasso 0.623332 12533.877123\n", + "2 Support Vector Machine -0.094559 21366.129961\n", + "3 Random Forest 0.760682 9990.647595\n", + "4 XGBoost 0.707698 11041.344369\n" + ] + } + ], + "source": [ + "## lets make the output from all the models at one place\n", + "print(pd.DataFrame([{'Model': 'Linear Regression','Score':lr_score, \"RMSE\":lr_rmse},\n", + " {'Model': 'Lasso','Score':lr_lasso_score, \"RMSE\":lr_lasso_rmse},\n", + " {'Model': 'Support Vector Machine','Score':svr_score, \"RMSE\":svr_rmse},\n", + " {'Model': 'Random Forest','Score':rfr_score, \"RMSE\":rfr_rmse},\n", + " {'Model': 'XGBoost','Score':xgb_reg_score, \"RMSE\":xgb_reg_rmse}],\n", + " columns=['Model','Score','RMSE']))\n", + "\n", + "## clearly we can see that Random Forest is giving the best accuracy and minimum rmse" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "13ab6c8d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WkjRkyBBr+Pl+/fopX758mj59uhISEjR37tx0pxreqAoVKig6OtppOHJJGjlypNXnwQcf1KeffqrAwECVL19esbGxWrp06Q0N8Z2ZxzOzz2cAuG24ZjA/ALhzXTkc+bX8ezhyY/4ZuvrZZ581hQoVMh4eHqZUqVLmrbfesobwTnPhwgXTr18/kz9/fpM7d27TvHlzc+DAgXTDkRtjzJEjR0zv3r1NWFiY8fDwMKGhoaZBgwZm8uTJVp/MDke+Zs0a07t3b1OhQgUTGBhoPDw8TNGiRU3Xrl3Nnj170vX/+uuvTe3atY2Pj48JCAgwNWrUMJ9//rlTn1mzZpkqVaoYLy8vky9fPtOxY0fzxx9/OPXp0qWLyZ0791Xrmjx5somMjDQ+Pj7G39/fVKxY0Tz//PPm4MGDxhhjNm7caNq3b2+KFi1qvLy8THBwsHnwwQedhvK2M2fOHNO4cWOTL18+kytXLlOwYEHTtm1bs2LFCqd+e/bsMa1btzZ58uQx3t7epkaNGmbhwoVOfdKGI589e7ZT+9WeO8OHDzeSzNGjR602SaZ3795mxowZplSpUsbLy8tUqVLFLF++3GnekydPmm7dupmgoCDj5+dnoqOjzfbt202xYsVMly5dbNd95bS04cgz+3hm9vmcti3/9u8aAcCVHMZw1SUAALcbh8Oh3r17pzsVDgCQM7jGCQAAAABsEJwAAAAAwAbBCQAAAABsMKoeAAC3IS5RBoCbiyNOAAAAAGDD5cFp4sSJCg8Pl7e3t2rWrGn9iODVTJgwQWXKlJGPj4/CwsL07LPPpvtxRQAAAADITi49VW/WrFkaMGCAJk2apJo1a2rChAmKjo7Wjh07FBwcnK7/zJkzNWTIEE2dOlW1a9fWzp071bVrVzkcDo0bNy5T60xNTdXBgwfl7+8vh8OR3ZsEAAAA4DZhjNHZs2dVqFAh2x8pd+nvONWsWVPVq1e3foMiNTVVYWFh6tu3r4YMGZKuf58+fRQfH69ly5ZZbQMHDtQvv/yi1atXZ7iOixcv6uLFi9b9P//8U+XLl8/mLQEAAABwuzpw4ICKFClyzT4uO+KUnJysDRs2aOjQoVabm5ubGjZsqNjY2AznqV27tmbMmKF169apRo0a2rt3r7777jt16tTpqusZPXq0Ro4cma79wIEDCggIuPENAQAAAHBbOnPmjMLCwuTv72/b12XB6dixY0pJSVFISIhTe0hIiLZv357hPB06dNCxY8d03333yRijy5cv66mnntILL7xw1fUMHTpUAwYMsO6nPTgBAQEEJwAAAACZuoTH5YNDXI8VK1bo9ddf1wcffKCNGzdq3rx5+vbbbzVq1KirzuPl5WWFJMISAAAAgKxw2RGnoKAgubu768iRI07tR44cUWhoaIbzvPzyy+rUqZMef/xxSVLFihWVlJSkJ554Qi+++KLtBV0AAAAAkBUuSxqenp6KjIx0GughNTVVy5YtU1RUVIbznD9/Pl04cnd3l8QPAQIAAADIOS4djnzAgAHq0qWLqlWrpho1amjChAlKSkpSt27dJEmdO3dW4cKFNXr0aElS8+bNNW7cOFWpUkU1a9bU7t279fLLL6t58+ZWgAIAAABuN2nX76ekpLi6lDuOh4dHtmQFlwantm3b6ujRoxo2bJgOHz6sypUra9GiRdaAEfv373c6wvTSSy/J4XDopZde0p9//qkCBQqoefPmeu2111y1CQAAAMANSU5O1qFDh3T+/HlXl3JHcjgcKlKkiPz8/G5sOa78HSdXOHPmjAIDA3X69GkGigAAAIBLpaamateuXXJ3d1eBAgXk6emZqRHekDnGGB09elTnz59XqVKl0h15up5s4NIjTgAAAMDdLDk5WampqQoLC5Ovr6+ry7kjFShQQImJibp06dINnbLHMHQAAACAizE6dM7JriN4/IUAAAAAwAbBCQAAAABscI0TAAAAcAsas+nYTV3fkCpBN3V9txuOOAEAAADIkqNHj+rpp59W0aJF5eXlpdDQUEVHR2vNmjWuLi3bccQJAAAAQJa0atVKycnJmj59uooXL64jR45o2bJlOn78eI6sLzk5WZ6enjmybDsccQIAAABw3U6dOqVVq1bpjTfeUP369VWsWDHVqFFDQ4cO1UMPPWT1efLJJxUSEiJvb29VqFBBCxcutJYxd+5c3XvvvfLy8lJ4eLjGjh3rtI7w8HCNGjVKnTt3VkBAgJ544glJ0urVq1W3bl35+PgoLCxM/fr1U1JSUo5uL8EJAAAAwHXz8/OTn5+fFixYoIsXL6abnpqaqqZNm2rNmjWaMWOGtm3bpjFjxli/pbRhwwY9+uijateunbZu3aoRI0bo5ZdfVkxMjNNy3n77bVWqVEmbNm3Syy+/rD179qhJkyZq1aqVtmzZolmzZmn16tXq06dPjm6vwxhjcnQNt5jr+XVgAAAAICf9/fffSkhI0D333CNvb2+nabfD4BBz585Vz549deHCBVWtWlX16tVTu3btFBERoR9++EFNmzZVfHy8SpcunW7ejh076ujRo/rhhx+stueff17ffvutfv/9d0n/HHGqUqWK5s+fb/V5/PHH5e7uro8++shqW716terVq6ekpKR0j+O1HuPryQYccQIAAACQJa1atdLBgwf19ddfq0mTJlqxYoWqVq2qmJgYxcXFqUiRIhmGJkmKj49XnTp1nNrq1KmjXbt2KSUlxWqrVq2aU5/NmzcrJibGOuLl5+en6OhopaamKiEhIfs38v9jcAgAAAAAWebt7a1GjRqpUaNGevnll/X4449r+PDheu6557Jl+blz53a6f+7cOT355JPq169fur5FixbNlnVmhOAEAADuGDf71KY7Bb/fg+xUvnx5LViwQBEREfrjjz+0c+fODI86lStXLt2w5WvWrFHp0qWt66AyUrVqVW3btk0lS5bM9tqvhVP1AAAAAFy348eP6z//+Y9mzJihLVu2KCEhQbNnz9abb76pFi1aqF69evq///s/tWrVSkuWLFFCQoK+//57LVq0SJI0cOBALVu2TKNGjdLOnTs1ffp0vf/++7ZHqgYPHqy1a9eqT58+iouL065du/TVV1/l+OAQHHECAAAAbkG3+pFAPz8/1axZU+PHj9eePXt06dIlhYWFqWfPnnrhhRck/TN4xHPPPaf27dsrKSlJJUuW1JgxYyT9c+Toyy+/1LBhwzRq1CgVLFhQr7zyirp27XrN9UZERGjlypV68cUXVbduXRljVKJECbVt2zZHt5dR9QAAwB2DU/Wy5lb/gH4nu9aIb8gejKoHAAAAADcJwQkAAAAAbBCcAAAAAMAGwQkAAAAAbBCcAAAAABe7y8Zru6my67ElOAEAAAAu4uHhIUk6f/68iyu5cyUnJ0vSNX9UNzP4HScAAADARdzd3ZUnTx799ddfkiRfX185HA4XV3XnSE1N1dGjR+Xr66tcuW4s+hCcAAAAABcKDQ2VJCs8IXu5ubmpaNGiNxxICU4AAACACzkcDhUsWFDBwcG6dOmSq8u543h6esrN7cavUCI4AQAAALcAd3f3G74OBzmHwSEAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABs5HJ1AQAAAMDtasymY64u4bY0pEqQq0u4bhxxAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsEFwAgAAAAAbBCcAAAAAsJHL1QVAGrPpmKtLuC0NqRLk6hJuSzzfsobnGwAAdzeOOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADZcHp4kTJyo8PFze3t6qWbOm1q1bd83+p06dUu/evVWwYEF5eXmpdOnS+u67725StQAAAADuRi79AdxZs2ZpwIABmjRpkmrWrKkJEyYoOjpaO3bsUHBwcLr+ycnJatSokYKDgzVnzhwVLlxY+/btU548eW5+8QAAAADuGi4NTuPGjVPPnj3VrVs3SdKkSZP07bffaurUqRoyZEi6/lOnTtWJEye0du1aeXh4SJLCw8NvZskAAAAA7kIuO1UvOTlZGzZsUMOGDf9XjJubGjZsqNjY2Azn+frrrxUVFaXevXsrJCREFSpU0Ouvv66UlJSrrufixYs6c+aM0w0AAAAArofLgtOxY8eUkpKikJAQp/aQkBAdPnw4w3n27t2rOXPmKCUlRd99951efvlljR07Vq+++upV1zN69GgFBgZat7CwsGzdDgAAAAB3PpcPDnE9UlNTFRwcrMmTJysyMlJt27bViy++qEmTJl11nqFDh+r06dPW7cCBAzexYgAAAAB3Apdd4xQUFCR3d3cdOXLEqf3IkSMKDQ3NcJ6CBQvKw8ND7u7uVlu5cuV0+PBhJScny9PTM908Xl5e8vLyyt7iAQAAANxVXHbEydPTU5GRkVq2bJnVlpqaqmXLlikqKirDeerUqaPdu3crNTXVatu5c6cKFiyYYWgCAAAAgOzg0lP1BgwYoClTpmj69OmKj4/X008/raSkJGuUvc6dO2vo0KFW/6efflonTpzQM888o507d+rbb7/V66+/rt69e7tqEwAAAADcBVw6HHnbtm119OhRDRs2TIcPH1blypW1aNEia8CI/fv3y83tf9kuLCxMixcv1rPPPquIiAgVLlxYzzzzjAYPHuyqTQAAAABwF3BpcJKkPn36qE+fPhlOW7FiRbq2qKgo/fzzzzlcFQAAAAD8z201qh4AAAAAuALBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwAbBCQAAAABsEJwAAAAAwMYtEZwmTpyo8PBweXt7q2bNmlq3bl2m5vviiy/kcDjUsmXLnC0QAAAAwF3N5cFp1qxZGjBggIYPH66NGzeqUqVKio6O1l9//XXN+RITE/Xcc8+pbt26N6lSAAAAAHcrlwencePGqWfPnurWrZvKly+vSZMmydfXV1OnTr3qPCkpKerYsaNGjhyp4sWL38RqAQAAANyNXBqckpOTtWHDBjVs2NBqc3NzU8OGDRUbG3vV+V555RUFBwerR48etuu4ePGizpw543QDAAAAgOvh0uB07NgxpaSkKCQkxKk9JCREhw8fznCe1atX6+OPP9aUKVMytY7Ro0crMDDQuoWFhd1w3QAAAADuLi4/Ve96nD17Vp06ddKUKVMUFBSUqXmGDh2q06dPW7cDBw7kcJUAAAAA7jS5XLnyoKAgubu768iRI07tR44cUWhoaLr+e/bsUWJiopo3b261paamSpJy5cqlHTt2qESJEk7zeHl5ycvLKweqBwAAAHC3cOkRJ09PT0VGRmrZsmVWW2pqqpYtW6aoqKh0/cuWLautW7cqLi7Ouj300EOqX7++4uLiOA0PAAAAQI5w6REnSRowYIC6dOmiatWqqUaNGpowYYKSkpLUrVs3SVLnzp1VuHBhjR49Wt7e3qpQoYLT/Hny5JGkdO0AAAAAkF1cHpzatm2ro0ePatiwYTp8+LAqV66sRYsWWQNG7N+/X25ut9WlWAAAAADuMC4PTpLUp08f9enTJ8NpK1asuOa8MTEx2V8QAAAAAFyBQzkAAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYCNbgtOZM2e0YMECxcfHZ8fiAAAAAOCWkqXg9Oijj+r999+XJF24cEHVqlXTo48+qoiICM2dOzdbCwQAAAAAV8tScPrpp59Ut25dSdL8+fNljNGpU6f07rvv6tVXX83WAgEAAADA1bIUnE6fPq18+fJJkhYtWqRWrVrJ19dXzZo1065du7K1QAAAAABwtSwFp7CwMMXGxiopKUmLFi1S48aNJUknT56Ut7d3thYIAAAAAK6WKysz9e/fXx07dpSfn5+KFi2q+++/X9I/p/BVrFgxO+sDAAAAAJfLUnDq1auXatSooQMHDqhRo0Zyc/vnwFXx4sW5xgkAAADAHSdLwUmSqlWrpoiICCUkJKhEiRLKlSuXmjVrlp21AQAAAMAtIUvXOJ0/f149evSQr6+v7r33Xu3fv1+S1LdvX40ZMyZbCwQAAAAAV8tScBo6dKg2b96sFStWOA0G0bBhQ82aNSvbigMAAACAW0GWTtVbsGCBZs2apVq1asnhcFjt9957r/bs2ZNtxQEAAADArSBLR5yOHj2q4ODgdO1JSUlOQQoAAAAA7gRZCk7VqlXTt99+a91PC0v//e9/FRUVlT2VAQAAAMAtIkun6r3++utq2rSptm3bpsuXL+udd97Rtm3btHbtWq1cuTK7awQAAAAAl8rSEaf77rtPmzdv1uXLl1WxYkX98MMPCg4OVmxsrCIjI7O7RgAAAABwqes+4nTp0iU9+eSTevnllzVlypScqAkAAAAAbinXfcTJw8NDc+fOzYlaAAAAAOCWlKVT9Vq2bKkFCxZkcykAAAAAcGvK0uAQpUqV0iuvvKI1a9YoMjJSuXPndprer1+/bCkOAAAAAG4FWQpOH3/8sfLkyaMNGzZow4YNTtMcDgfBCQAAAMAdJUvBKSEhIbvrAAAAAIBbVpaucbqSMUbGmOyoBQAAAABuSVkOTp988okqVqwoHx8f+fj4KCIiQp9++ml21gYAAAAAt4Qsnao3btw4vfzyy+rTp4/q1KkjSVq9erWeeuopHTt2TM8++2y2FgkAAAAArpSl4PTee+/pww8/VOfOna22hx56SPfee69GjBhBcAIAAABwR8nSqXqHDh1S7dq107XXrl1bhw4duuGiAAAAAOBWkqXgVLJkSX355Zfp2mfNmqVSpUrdcFEAAAAAcCvJ0ql6I0eOVNu2bfXTTz9Z1zitWbNGy5YtyzBQAQAAAMDtLEtHnFq1aqVffvlFQUFBWrBggRYsWKCgoCCtW7dODz/8cHbXCAAAAAAulaUjTpIUGRmpGTNmZGctAAAAAHBLytIRp++++06LFy9O17548WJ9//33N1wUAAAAANxKshSchgwZopSUlHTtxhgNGTLkhosCAAAAgFtJloLTrl27VL58+XTtZcuW1e7du2+4KAAAAAC4lWQpOAUGBmrv3r3p2nfv3q3cuXPfcFEAAAAAcCvJUnBq0aKF+vfvrz179lhtu3fv1sCBA/XQQw9lW3EAAAAAcCvIUnB68803lTt3bpUtW1b33HOP7rnnHpUtW1b58+fX22+/nd01AgAAAIBLZWk48sDAQK1du1ZLlizR5s2b5ePjo0qVKqlu3brZXR8AAAAAuNx1HXGKjY3VwoULJUkOh0ONGzdWcHCw3n77bbVq1UpPPPGELl68mCOFAgAAAICrXFdweuWVV/T7779b97du3aqePXuqUaNGGjJkiL755huNHj0624sEAAAAAFe6ruAUFxenBg0aWPe/+OIL1ahRQ1OmTNGAAQP07rvv6ssvv8z2IgEAAADAla4rOJ08eVIhISHW/ZUrV6pp06bW/erVq+vAgQPZVx0AAAAA3AKuKziFhIQoISFBkpScnKyNGzeqVq1a1vSzZ8/Kw8MjeysEAAAAABe7ruD0wAMPaMiQIVq1apWGDh0qX19fp5H0tmzZohIlSmR7kQAAAADgStc1HPmoUaP0yCOPqF69evLz89P06dPl6elpTZ86daoaN26c7UUCAAAAgCtdV3AKCgrSTz/9pNOnT8vPz0/u7u5O02fPni0/P79sLRAAAAAAXC3LP4CbkXz58t1QMQAAAABwK7qua5wAAAAA4G5EcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAGwQnAAAAALBBcAIAAAAAG7dEcJo4caLCw8Pl7e2tmjVrat26dVftO2XKFNWtW1d58+ZV3rx51bBhw2v2BwAAAIAb5fLgNGvWLA0YMEDDhw/Xxo0bValSJUVHR+uvv/7KsP+KFSvUvn17LV++XLGxsQoLC1Pjxo31559/3uTKAQAAANwtXB6cxo0bp549e6pbt24qX768Jk2aJF9fX02dOjXD/p999pl69eqlypUrq2zZsvrvf/+r1NRULVu27CZXDgAAAOBu4dLglJycrA0bNqhhw4ZWm5ubmxo2bKjY2NhMLeP8+fO6dOmS8uXLl+H0ixcv6syZM043AAAAALgeLg1Ox44dU0pKikJCQpzaQ0JCdPjw4UwtY/DgwSpUqJBT+LrS6NGjFRgYaN3CwsJuuG4AAAAAdxeXn6p3I8aMGaMvvvhC8+fPl7e3d4Z9hg4dqtOnT1u3AwcO3OQqAQAAANzucrly5UFBQXJ3d9eRI0ec2o8cOaLQ0NBrzvv2229rzJgxWrp0qSIiIq7az8vLS15eXtlSLwAAAIC7k0uPOHl6eioyMtJpYIe0gR6ioqKuOt+bb76pUaNGadGiRapWrdrNKBUAAADAXcylR5wkacCAAerSpYuqVaumGjVqaMKECUpKSlK3bt0kSZ07d1bhwoU1evRoSdIbb7yhYcOGaebMmQoPD7euhfLz85Ofn5/LtgMAAADAncvlwalt27Y6evSohg0bpsOHD6ty5cpatGiRNWDE/v375eb2vwNjH374oZKTk9W6dWun5QwfPlwjRoy4maUDAAAAuEu4PDhJUp8+fdSnT58Mp61YscLpfmJiYs4XBAAAAABXuK1H1QMAAACAm4HgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYOOWCE4TJ05UeHi4vL29VbNmTa1bt+6a/WfPnq2yZcvK29tbFStW1HfffXeTKgUAAABwN3J5cJo1a5YGDBig4cOHa+PGjapUqZKio6P1119/Zdh/7dq1at++vXr06KFNmzapZcuWatmypX777bebXDkAAACAu4XLg9O4cePUs2dPdevWTeXLl9ekSZPk6+urqVOnZtj/nXfeUZMmTTRo0CCVK1dOo0aNUtWqVfX+++/f5MoBAAAA3C1cGpySk5O1YcMGNWzY0Gpzc3NTw4YNFRsbm+E8sbGxTv0lKTo6+qr9L168qDNnzjjdAAAAAOB65HLlyo8dO6aUlBSFhIQ4tYeEhGj79u0ZznP48OEM+x8+fDjD/qNHj9bIkSOzp+AcMqRKkKtLwF2E5xtutjGbjrm6hNsSr9Ws4XHDzcZz7u7h8lP1ctrQoUN1+vRp63bgwAFXlwQAAADgNuPSI05BQUFyd3fXkSNHnNqPHDmi0NDQDOcJDQ29rv5eXl7y8vLKnoIBAAAA3JVcesTJ09NTkZGRWrZsmdWWmpqqZcuWKSoqKsN5oqKinPpL0pIlS67aHwAAAABulEuPOEnSgAED1KVLF1WrVk01atTQhAkTlJSUpG7dukmSOnfurMKFC2v06NGSpGeeeUb16tXT2LFj1axZM33xxRf69ddfNXnyZFduBgAAAIA7mMuDU9u2bXX06FENGzZMhw8fVuXKlbVo0SJrAIj9+/fLze1/B8Zq166tmTNn6qWXXtILL7ygUqVKacGCBapQoYKrNgEAAADAHc5hjDGuLuJmOnPmjAIDA3X69GkFBAS4uhwAuOMxql7WMFIXAOS868kGd/yoegAAAABwowhOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGCD4AQAAAAANghOAAAAAGAjl6sLAADc2YZUCXJ1CQAA3DCOOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACADYITAAAAANggOAEAAACAjVyuLuBmM8ZIks6cOePiSgAAAAC4UlomSMsI13LXBaezZ89KksLCwlxcCQAAAIBbwdmzZxUYGHjNPg6TmXh1B0lNTdXBgwfl7+8vh8Ph6nJuaWfOnFFYWJgOHDiggIAAV5eDOxzPN9xsPOdwM/F8w83Gcy5zjDE6e/asChUqJDe3a1/FdNcdcXJzc1ORIkVcXcZtJSAggBccbhqeb7jZeM7hZuL5hpuN55w9uyNNaRgcAgAAAABsEJwAAAAAwAbBCVfl5eWl4cOHy8vLy9Wl4C7A8w03G8853Ew833Cz8ZzLfnfd4BAAAAAAcL044gQAAAAANghOAAAAAGCD4AQAAAAANghO2cjhcGjBggWuLuOuM2LECFWuXNnVZQDALSMz+8X7779f/fv3vyn13M54bweQhuB0Hbp27aqWLVtedfqhQ4fUtGnTm1fQdXI4HNYtICBA1atX11dffeXqsm7Yc889p2XLlrm6jLua3WsD2e/o0aN6+umnVbRoUXl5eSk0NFTR0dFas2aNq0vLtBUrVsjhcOjUqVNX7TN37ly5u7vrzz//zHB6qVKlNGDAgBuuJTw8XBMmTLjh5VxN165d5XA49NRTT6Wb1rt3bzkcDnXt2jXH1p+RefPmadSoUTd1nVmR9tg5HA55eHjonnvu0fPPP6+///7b1aXlqCu3+8rb7t27XVoT+/qsS0lJUe3atfXII484tZ8+fVphYWF68cUXrba5c+fqP//5j/LmzSsfHx+VKVNG3bt316ZNm6w+MTExTs8NPz8/RUZGat68eTdtm6S760sYglM2Cg0NdfmQj8YYXb58+arTp02bpkOHDunXX39VnTp11Lp1a23dujVHa0pOTs7R5fv5+Sl//vw5ug7gVtOqVStt2rRJ06dP186dO/X111/r/vvv1/Hjx11dWqZcunQpU/0eeugh5c+fX9OnT0837aefftLu3bvVo0eP7C4vy661vwsLC9MXX3yhCxcuWG1///23Zs6cqaJFi96M8pzky5dP/v7+N329WdGkSRMdOnRIe/fu1fjx4/XRRx9p+PDhri4rx6Vt95W3e+65J0vLyun3Ythzd3dXTEyMFi1apM8++8xq79u3r/Lly2c9pwcPHqy2bduqcuXK+vrrr7Vjxw7NnDlTxYsX19ChQ52WGRAQYD03Nm3apOjoaD366KPasWPHTd22u4ZBpnXp0sW0aNHiqtMlmfnz5xtjjElISDCSzNy5c839999vfHx8TEREhFm7dq3TPKtWrTL33Xef8fb2NkWKFDF9+/Y1586ds6Z/8sknJjIy0vj5+ZmQkBDTvn17c+TIEWv68uXLjSTz3XffmapVqxoPDw+zfPly2/qMMebMmTNGknnnnXestv3795s2bdqYwMBAkzdvXvPQQw+ZhIQEa/qlS5dM3759TWBgoMmXL595/vnnTefOnZ0el3r16pnevXubZ555xuTPn9/cf//9xhhjtm7dapo0aWJy585tgoODzWOPPWaOHj1qzTd79mxToUIF4+3tbfLly2caNGhgPRbLly831atXN76+viYwMNDUrl3bJCYmGmOMGT58uKlUqZK1nJSUFDNy5EhTuHBh4+npaSpVqmS+//57a3pm/zbIvGu9NsaOHWsqVKhgfH19TZEiRczTTz9tzp49a01PTEw0Dz74oMmTJ4/x9fU15cuXN99++60xxpgTJ06YDh06mKCgIOPt7W1Klixppk6das27ZcsWU79+fes507NnT6dl36lOnjxpJJkVK1ZctU/a83zTpk3p5kvbR6TtPxYuXGgqVqxovLy8TM2aNc3WrVuteaZNm2YCAwPN/PnzTcmSJY2Xl5dp3Lix2b9/v9P6PvjgA1O8eHHj4eFhSpcubT755BOn6ZLMBx98YJo3b258fX1Nly5djCSnW5cuXTLclgEDBphSpUqla+/SpYupWbOmtW09evQwQUFBxt/f39SvX9/ExcU59f/6669NtWrVjJeXl8mfP79p2bKlMeaffda/a0kzZ84cU758eePp6WmKFStm3n77badlFitWzLzyyiumU6dOxt/f/6rbkPYaqVChgpkxY4bV/tlnn5mIiAjTokULp3m///57U6dOHWtf26xZM7N7926nZR44cMC0a9fO5M2b1/j6+prIyEjz888/G2P+t1/85JNPTLFixUxAQIBp27atOXPmjDV/vXr1zDPPPOO0La+99prp1q2b8fPzM2FhYeajjz5yWqfde0ROyGj/8sgjj5gqVapY948dO2batWtnChUqZHx8fEyFChXMzJkzneapV6+e6du3rxk0aJDJmzevCQkJMcOHD3fqs3PnTlO3bl3j5eVlypUrZ3744Yd07512+520el977TUTHBxsAgMDzciRI82lS5fMc889Z/LmzWsKFy7stC/L7HZfacWKFaZ69erG09PThIaGmsGDB5tLly45bW92vhcPHz483evkap83cG3vvPOOyZs3rzl48KBZsGCB8fDwsPZXsbGx6T6bXSk1NdX6f9r++UopKSnGw8PDfPnll1bbiRMnTKdOnUyePHmMj4+PadKkidm5c6fTfHb7uokTJ1rvAcHBwaZVq1bGGJPhvjyn9wmuRHC6DlkJTmXLljULFy40O3bsMK1btzbFihWzdmy7d+82uXPnNuPHjzc7d+40a9asMVWqVDFdu3a1lvnxxx+b7777zuzZs8fExsaaqKgo07RpU2t62gefiIgI88MPP5jdu3eb48eP29Z36dIlM378eCPJfPjhh8YYY5KTk025cuVM9+7dzZYtW8y2bdtMhw4dTJkyZczFixeNMca8+uqrJl++fGbevHkmPj7ePPXUUyYgICBdcPLz8zODBg0y27dvN9u3bzcnT540BQoUMEOHDjXx8fFm48aNplGjRqZ+/frGGGMOHjxocuXKZcaNG2cSEhLMli1bzMSJE83Zs2fNpUuXTGBgoHnuuefM7t27zbZt20xMTIzZt2+fMSZ9cBo3bpwJCAgwn3/+udm+fbt5/vnnjYeHh7WTyMzfBtfnWq+N8ePHmx9//NEkJCSYZcuWmTJlypinn37amt6sWTPTqFEjs2XLFrNnzx7zzTffmJUrVxpjjOndu7epXLmyWb9+vUlISDBLliwxX3/9tTHGmHPnzpmCBQuaRx55xGzdutUsW7bM3HPPPVf94HonuXTpkvHz8zP9+/c3f//9d4Z9ric4pX1A3LJli3nwwQdNeHi4SU5ONsb888bs4eFhqlWrZtauXWt+/fVXU6NGDVO7dm1rufPmzTMeHh5m4sSJZseOHWbs2LHG3d3d/Pjjj1YfSSY4ONhMnTrV7NmzxyQmJpq5c+caSWbHjh3m0KFD5tSpUxluy++//24kWc8LY4w5e/asyZ07t5k8ebIxxpiGDRua5s2bm/Xr15udO3eagQMHmvz581v7w4ULFxp3d3czbNgws23bNhMXF2def/11Y4wxx48fN0WKFDGvvPKKOXTokDl06JAxxphff/3VuLm5mVdeecXs2LHDTJs2zfj4+Jhp06ZZdaSFkrffftvs3r07XbhJk/YaGTdunGnQoIHV3qBBAzN+/Ph0wWnOnDlm7ty5ZteuXWbTpk2mefPmpmLFiiYlJcXa/uLFi5u6deuaVatWmV27dplZs2ZZXwANHz7c+Pn5Wa+Pn376yYSGhpoXXnjBWkdGwSlfvnxm4sSJZteuXWb06NHGzc3NbN++3RiTufeInPDv/cvWrVtNaGioFZqNMeaPP/4wb731ltm0aZPZs2ePeffdd427u7v55ZdfnLY3ICDAjBgxwuzcudNMnz7dOBwO88MPPxhj/vnAWaFCBdOgQQMTFxdnVq5caapUqeL03pmZ/U6XLl2Mv7+/6d27t9m+fbv5+OOPjSQTHR1tXnvtNbNz504zatQo4+HhYQ4cOJDp7b7SH3/8YXx9fU2vXr1MfHy8mT9/vgkKCnIKgtn9Xnz27Fnz6KOPmiZNmlivk5z8u9/JUlNTzf33328aNGhggoODzahRo6xp/fr1M35+fpn6PPLv4HT58mUzdepU4+Hh4bQveuihh0y5cuXMTz/9ZOLi4kx0dLQpWbKktZ+329etX7/euLu7m5kzZ5rExESzceNGK9idOnXKREVFmZ49e1rPi8uXL2fDo3RrIjhdh6wEp//+97/W9LQ3//j4eGOMMT169DBPPPGE0zJWrVpl3NzczIULFzJcx/r1640k69uttA8+CxYssK1fkvH29ja5c+c2bm5uRpIJDw+3Plh8+umnpkyZMk7fZly8eNH4+PiYxYsXG2OMCQkJMW+99ZY1/fLly6Zo0aLpgtOV3wQaY8yoUaNM48aNndoOHDhgfWjasGGDkWQdRbrS8ePHr/nt+r+DU6FChcxrr73m1Kd69eqmV69expjM/W1wfexeG1eaPXu2yZ8/v3W/YsWKZsSIERn2bd68uenWrVuG0yZPnmzy5s3rdIT222+/NW5ububw4cOZL/42NWfOHJM3b17j7e1tateubYYOHWo2b95sTb+e4PTFF19YfY4fP258fHzMrFmzjDH/vDFLso5kGGNMfHy8kWR9KK1du7bp2bOnU31t2rQxDzzwgHVfkunfv79Tn7T1nzx50nZ7a9Wq5fTh9OOPPza+vr7mzJkzZtWqVSYgICBdiCxRooR1xCQqKsp07NjxqssvVqyYGT9+vFNbhw4dTKNGjZzaBg0aZMqXL+80X9qRq2tJe4389ddfxsvLyyQmJprExETj7e1tjh49mi44/dvRo0eNJOto4EcffWT8/f2v+kXZ8OHDrcfnytqvDBsZBafHHnvMup+ammqCg4OtL9cy8x6RE7p06WLc3d1N7ty5jZeXl5Fk3NzczJw5c645X7NmzczAgQOt+/Xq1TP33XefU5/q1aubwYMHG2OMWbx4scmVK5f5888/renff/+903t7ZvY7Xbp0McWKFbNCrjHGlClTxtStW9e6f/nyZZM7d27z+eefZ2q7026tW7c2xhjzwgsvpPtbTJw40fj5+Vnrze734rSaMruvx7Wl7UcrVqzoFJKaNGliIiIinPqOHTvW6XmQ9iVT2v45rd3Nzc14eXk5fbmzc+dOI8msWbPGajt27Jjx8fGxjkrZ7evmzp1rAgICnPYnV/r3vuROxjVOOSwiIsL6f8GCBSVJf/31lyRp8+bNiomJkZ+fn3WLjo5WamqqEhISJEkbNmxQ8+bNVbRoUfn7+6tevXqSpP379zutp1q1apmqZ/z48YqLi9P333+v8uXL67///a/y5ctn1bN79275+/tb9eTLl09///239uzZo9OnT+vIkSOqUaOGtTx3d3dFRkamW8+/2zZv3qzly5c7bWvZsmUlSXv27FGlSpXUoEEDVaxYUW3atNGUKVN08uRJSf+ch9+1a1dFR0erefPmeuedd3To0KEMt+/MmTM6ePCg6tSp49Rep04dxcfHO7Vd62+D7LN06VI1aNBAhQsXlr+/vzp16qTjx4/r/PnzkqR+/frp1VdfVZ06dTR8+HBt2bLFmvfpp5/WF198ocqVK+v555/X2rVrrWnx8fGqVKmScufObbXVqVNHqampd8W53a1atdLBgwf19ddfq0mTJlqxYoWqVq2qmJiY615WVFSU9f98+fKpTJkyTq+XXLlyqXr16tb9smXLKk+ePFaf+Pj4TL3mMrufykj37t01Z84cnT17VpI0depUtWnTRv7+/tq8ebPOnTun/PnzO+1jEhIStGfPHklSXFycGjRocF3rvNp27dq1SykpKVnargIFCqhZs2aKiYnRtGnT1KxZMwUFBaXrt2vXLrVv317FixdXQECAwsPDJf1v3x8XF6cqVapY+++MhIeHO13DVLBgQdt93JX7RYfDodDQUKf3rGu9R+Sk+vXrKy4uTr/88ou6dOmibt26qVWrVtb0lJQUjRo1ShUrVlS+fPnk5+enxYsXp3uvvHL7JOfHJD4+XmFhYSpUqJA1/crXRlqfzOx37r33Xrm5/e8jVkhIiCpWrGjdd3d3V/78+W3/HmnbnXZ79913rTqioqLkcDic6jh37pz++OMPqy0734uRvaZOnSpfX18lJCQ4/c0y0r17d8XFxemjjz5SUlKSjDHWNH9/f+v5sWnTJr3++ut66qmn9M0330j657mSK1cu1axZ05onf/78Tvt5u31do0aNVKxYMRUvXlydOnXSZ599Zr2H320ITjnMw8PD+n/aDi41NVWSdO7cOT355JNOO8XNmzdr165dKlGihJKSkhQdHa2AgAB99tlnWr9+vebPny8p/UWeV+7EryU0NFQlS5ZU48aNNW3aNLVt29bacZ87d06RkZFO9cTFxWnnzp3q0KHDdW33v+s5d+6cmjdvnm7Zu3bt0v/93//J3d1dS5YssQLde++9pzJlylgBctq0aYqNjVXt2rU1a9YslS5dWj///PN11fRv1/rbIHskJibqwQcfVEREhObOnasNGzZo4sSJkv73HH788ce1d+9ederUSVu3blW1atX03nvvSZKaNm2qffv26dlnn9XBgwfVoEEDPffccy7bnluNt7e3GjVqpJdffllr165V165drYuL0z60XfkGm9kBGXJKZvdTGWnXrp0k6csvv9SuXbu0Zs0aa1CIc+fOqWDBgun2Lzt27NCgQYMkST4+Pje+AVdxvdvVvXt3xcTEaPr06erevXuGfZo3b64TJ05oypQp+uWXX/TLL79I+t/rJjPbc+U+TvpnP2e3j7vWPNn5HnG9cufOrZIlS6pSpUqaOnWqfvnlF3388cfW9LfeekvvvPOOBg8erOXLlysuLk7R0dHp3iuz8phkRUbrycq607Y77Zb2JV9mZfd7MbLH2rVrNX78eC1cuFA1atRQjx49rH11qVKltHfvXqf9dZ48eVSyZEkVLlw43bLc3Nys50dERIQGDBig+++/X2+88Ua21evv76+NGzfq888/V8GCBTVs2DBVqlTpmiOi3qkITi5UtWpVbdu2zWmnmHbz9PTU9u3bdfz4cY0ZM0Z169ZV2bJls/WISI0aNRQZGanXXnvNqmfXrl0KDg5OV09gYKACAwMVEhKi9evXW8tISUnRxo0bM7Wtv//+u8LDw9MtO23H7nA4VKdOHY0cOVKbNm2Sp6enFRQlqUqVKho6dKjWrl2rChUqaObMmenWExAQoEKFCqUbknnNmjUqX758lh4nZN2GDRuUmpqqsWPHqlatWipdurQOHjyYrl9YWJieeuopzZs3TwMHDtSUKVOsaQUKFFCXLl00Y8YMTZgwQZMnT5YklStXTps3b1ZSUpLVd82aNXJzc1OZMmVyfuNuQeXLl7cejwIFCkiS09HZuLi4DOe78kuIkydPaufOnSpXrpzVdvnyZf3666/W/R07dujUqVNWn3LlymXpNefp6SlJTkdvrsbf319t2rTR1KlTNW3aNJUuXVp169aV9M/+5fDhw8qVK1e6/Uva0ZyIiIhr/myBp6dnujqutl2lS5eWu7u7bc1X06RJEyUnJ+vSpUuKjo5ON/348ePasWOHXnrpJTVo0EDlypVL961/RESE4uLidOLEiSzXcb3s3iNuFjc3N73wwgt66aWXrBEK16xZoxYtWuixxx5TpUqVVLx4ce3cufO6lluuXDkdOHDA6TXz7y/obpX9Trly5RQbG+v0xciaNWvk7++vIkWKXHW+G30vzuh1gutz/vx5de3aVU8//bTq16+vjz/+WOvWrdOkSZMkSe3bt9e5c+f0wQcfZHkd7u7u1mujXLlyunz5svXli/S/fUzaPjoz+7pcuXKpYcOGevPNN7VlyxYlJibqxx9/lHR3PS8ITtfp9OnT6b6pOXDgQJaWNXjwYK1du1Z9+vSxvvH56quv1KdPH0lS0aJF5enpqffee0979+7V119/ne2/udG/f3999NFH+vPPP9WxY0cFBQWpRYsWWrVqlRISErRixQr169fPOozct29fjR49Wl999ZV27NihZ555RidPnnQ6XSAjvXv31okTJ9S+fXutX79ee/bs0eLFi9WtWzelpKTol19+0euvv65ff/1V+/fv17x583T06FGVK1dOCQkJGjp0qGJjY7Vv3z798MMP2rVrl9MHuysNGjRIb7zxhmbNmqUdO3ZoyJAhiouL0zPPPJOtjx2cZfTaCAoK0qVLl6zn8Keffmq9OaTp37+/Fi9erISEBG3cuFHLly+3/rbDhg3TV199pd27d+v333/XwoULrWkdO3aUt7e3unTpot9++03Lly9X37591alTJ4WEhNz07b+Zjh8/rv/85z+aMWOGtmzZooSEBM2ePVtvvvmmWrRoIemfIxK1atXSmDFjFB8fr5UrV+qll17KcHmvvPKKli1bpt9++01du3ZVUFCQ02+1eHh4qG/fvvrll1+0YcMGde3aVbVq1bJO2x00aJBiYmL04YcfateuXRo3bpzmzZtne3SwWLFicjgcWrhwoY4ePapz585ds3+PHj20du1aTZo0yelITcOGDRUVFaWWLVvqhx9+UGJiotauXasXX3zRCnzDhw/X559/ruHDhys+Pl5bt251+kY2PDxcP/30k/78808dO3ZMkjRw4EAtW7ZMo0aN0s6dOzV9+nS9//77N3zU093dXfHx8dq2bVuGASxv3rzKnz+/Jk+erN27d+vHH39M91tV7du3V2hoqFq2bKk1a9Zo7969mjt3rmJjY2+otmvJzHvEzdKmTRu5u7tbR7BLlSqlJUuWaO3atYqPj9eTTz6pI0eOXNcyGzZsqNKlS6tLly7avHmzVq1a5fS7OtKts9/p1auXDhw4oL59+2r79u366quvNHz4cA0YMMDpFMF/u5H3Yumf18mWLVu0Y8cOHTt2zOVHsW9HQ4cOlTFGY8aMkfTPY/r222/r+eefV2JioqKiojRw4EANHDhQAwYM0OrVq7Vv3z79/PPP+vjjj+VwOJz+xsYYHT58WIcPH1ZCQoImT56sxYsXW+8FpUqVUosWLdSzZ0+tXr1amzdv1mOPPabChQtbfez2dQsXLtS7776ruLg47du3T5988olSU1OtLwvCw8P1yy+/KDExUceOHbuzz95x5QVWt5uMhlyUZHr06GGMyXhwiGtdmG2MMevWrTONGjUyfn5+Jnfu3CYiIsJpYIOZM2ea8PBw4+XlZaKioszXX3/ttNzrubj6yvrSpKammrJly1qjnB06dMh07tzZBAUFGS8vL1O8eHHTs2dPc/r0aWPMP6N59enTxwQEBJi8efOawYMHmzZt2ph27dpZy7zaRYI7d+40Dz/8sDUcZtmyZU3//v1Namqq2bZtm4mOjjYFChQwXl5epnTp0ua9994zxhhz+PBh07JlS1OwYEFrmMxhw4ZZF8BmNBz5iBEjTOHChY2Hh8dVhyO3+9sg86712hg3bpwpWLCg8fHxMdHR0eaTTz5xes726dPHlChRwnh5eZkCBQqYTp06mWPHjhlj/rmQuVy5csbHx8fky5fPtGjRwuzdu9da7906HPnff/9thgwZYqpWrWoCAwONr6+vKVOmjHnppZfM+fPnrX7btm0zUVFRxsfHx1SuXNkaWvnfg0N888035t577zWenp6mRo0aToNMpI3aNHfuXFO8eHHj5eVlGjZsaI1qmSYzw5H/e/9jjDGvvPKKCQ0NNQ6HI1MjIpYpU8a4u7ubgwcPOrWfOXPG9O3b1xQqVMh4eHiYsLAw07FjR6dh0+fOnWsqV65sPD09TVBQkHnkkUesabGxsSYiIsIafCBN2hC9Hh4epmjRok6D4xiT8aASGbG7qP7fg0MsWbLElCtXznh5eZmIiAizYsWKdI9hYmKiadWqlQkICDC+vr6mWrVq1oAd/94vGvPPCJfFihWz7mc0OMS/t6VSpUpOI7XZvUfkhKs9dqNHjzYFChQw586dM8ePHzctWrQwfn5+Jjg42Lz00ksZ/lTGv9+b/v2479ixw9x3333G09PTlC5d2ixatCjLw5FfKaN12z13smM48ux8LzbGmL/++sv6zMJ75vVbsWKFcXd3N6tWrUo3rXHjxuY///mPNeDHrFmzzP33328CAwONh4eHKVKkiOnQoYPTQD1pg0Ok3dL+Zq+99prTyHZpw5EHBgZa78VXG448o33dqlWrTL169UzevHmtn3BJG0DImH9eN7Vq1TI+Pj53/HDkDmOuOM4LXKfU1FSVK1dOjz766G3xC/QA/mfFihWqX7++Tp48qTx58mTYJyYmRv37978rz2UHAOBKuVxdAG4vaafK1atXTxcvXtT777+vhISEHL8wGAAAAHAlrnHCdXFzc1NMTIyqV6+uOnXqaOvWrVq6dOlVrzcCAAAA7gScqgcAAAAANjjiBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AAAAAYIPgBAAAAAA2CE4AgLvaihUr5HA4rutHfsPDwzVhwoQcqwkAcOshOAEAbmldu3aVw+HQU089lW5a79695XA41LVr15tfGADgrkJwAgDc8sLCwvTFF1/owoULVtvff/+tmTNnqmjRoi6sDABwtyA4AQBueVWrVlVYWJjmzZtntc2bN09FixZVlSpVrLaLFy+qX79+Cg4Olre3t+677z6tX7/eaVnfffedSpcuLR8fH9WvX1+JiYnp1rd69WrVrVtXPj4+CgsLU79+/ZSUlJRhbcYYjRgxQkWLFpWXl5cKFSqkfv36Zc+GAwBuGQQnAMBtoXv37po2bZp1f+rUqerWrZtTn+eff15z587V9OnTtXHjRpUsWVLR0dE6ceKEJOnAgQN65JFH1Lx5c8XFxenxxx/XkCFDnJaxZ88eNWnSRK1atdKWLVs0a9YsrV69Wn369Mmwrrlz52r8+PH66KOPtGvXLi1YsEAVK1bM5q0HALgawQkAcFt47LHHtHr1au3bt0/79u3TmjVr9Nhjj1nTk5KS9OGHH+qtt95S06ZNVb58eU2ZMkU+Pj76+OOPJUkffvihSpQoobFjx6pMmTLq2LFjuuujRo8erY4dO6p///4qVaqUateurXfffVeffPKJ/v7773R17d+/X6GhoWrYsKGKFi2qGjVqqGfPnjn6WAAAbj6CEwDgtlCgQAE1a9ZMMTExmjZtmpo1a6agoCBr+p49e3Tp0iXVqVPHavPw8FCNGjUUHx8vSYqPj1fNmjWdlhsVFeV0f/PmzYqJiZGfn591i46OVmpqqhISEtLV1aZNG124cEHFixdXz549NX/+fF2+fDk7Nx0AcAvI5eoCAADIrO7du1unzE2cODFH1nHu3Dk9+eSTGV6nlNFAFGFhYdqxY4eWLl2qJUuWqFevXnrrrbe0cuVKeXh45EiNAICbjyNOAIDbRpMmTZScnKxLly4pOjraaVqJEiXk6empNWvWWG2XLl3S+vXrVb58eUlSuXLltG7dOqf5fv75Z6f7VatW1bZt21SyZMl0N09Pzwzr8vHxUfPmzfXuu+9qxYoVio2N1datW7NjkwEAtwiOOAEAbhvu7u7WaXfu7u5O03Lnzq2nn35agwYNUr58+VS0aFG9+eabOn/+vHr06CFJeuqppzR27FgNGjRIjz/+uDZs2KCYmBin5QwePFi1atVSnz599Pjjjyt37tzatm2blixZovfffz9dTTExMUpJSVHNmjXl6+urGTNmyMfHR8WKFcuZBwEA4BIccQIA3FYCAgIUEBCQ4bQxY8aoVatW6tSpk6pWrardu3dr8eLFyps3r6R/TrWbO3euFixYoEqVKmnSpEl6/fXXnZYRERGhlStXaufOnapbt66qVKmiYcOGqVChQhmuM0+ePJoyZYrq1KmjiIgILV26VN98843y58+fvRsOAHAphzHGuLoIAAAAALiVccQJAAAAAGwQnAAAAADABsEJAAAAAGwQnAAAAADABsEJAAAAAGwQnAAAAADABsEJAAAAAGwQnAAAAADABsEJAAAAAGwQnAAAAADABsEJAAAAAGz8P9COM48ynuEuAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame([\n", + " {'Model': 'Linear Regression', 'Score': lr_score},\n", + " {'Model': 'Lasso', 'Score': lr_lasso_score},\n", + " {'Model': 'Support Vector Machine', 'Score': svr_score},\n", + " {'Model': 'Random Forest', 'Score': rfr_score},\n", + " {'Model': 'XGBoost', 'Score': xgb_reg_score}\n", + "], columns=['Model', 'Score'])\n", + "\n", + "# Plotting the bar graph\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Bar positions\n", + "index = range(len(df))\n", + "bar_width = 0.5\n", + "\n", + "# Bar graph for 'Score' only\n", + "bar1 = ax.bar(index, df['Score'], bar_width, label='Score', color='skyblue')\n", + "\n", + "# Adding labels\n", + "ax.set_xlabel('Models')\n", + "ax.set_ylabel('Scores')\n", + "ax.set_title('Model Scores Comparison')\n", + "ax.set_xticks(index)\n", + "ax.set_xticklabels(df['Model'])\n", + "ax.legend()\n", + "\n", + "# Display the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b512948c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/House-rent-analysis-and-prediction-Bombay/Model/ReadMe.MD b/House-rent-analysis-and-prediction-Bombay/Model/ReadMe.MD new file mode 100644 index 000000000..297c8f40c --- /dev/null +++ b/House-rent-analysis-and-prediction-Bombay/Model/ReadMe.MD @@ -0,0 +1,55 @@ +## **House Rent Analysis and Prediction of Mumbai** ## + +## **Goal:** ## + +Analyse and predict the rent of house in Mumbai. + +## **Dataset:** ## + +https://www.kaggle.com/datasets/lokeshgupta2020/house-rent-in-mumbai + + +## **What I Had Done:** ## +* Reading the data in python +* Basic Exploration of data +* Cleaning the data and transforming it to be useful for basic analysis +* Visual Data exploration +* Made Different ML Models + +## **Analysis of Data Visulization:** ## +* Pairplot of numerical values +![Screenshot 2024-01-15 154053](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/e13b7734-6251-436d-9a4d-c4077564019b) + +* Heatmap +![Screenshot 2024-01-14 203212](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/6b063d9e-81ad-49ce-8447-8bd889812cf8) + +* Boxplot for size +![boxplot for size](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/9bf4511b-15c5-42d8-9d69-0fe2fb85cc35) + +* Boxplot for area +![boxplot for area](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/1bc6b665-911e-4a6a-9495-eb0cb48a1718) + +* Boxplot for price +![boxplot for price](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/1d81dbbe-f70e-431d-98ab-5a46155e7df5) + +* Boxplot after removing the outliers +![image](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/e6742f02-2f14-40cd-abfe-bccec4a62cd3) + + + + +## **Libraries Needed:** ## +* pandas - To manipulate dataframes +* seaborn - To visualise data +* matplotlib - To visualise data +* Scikit-learn - To Import Classical ML Models + +## **Model Conclusions:** ## +![scores of model](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/e14dbb9e-0ba8-48ad-a5c0-8684d5c1ff77) + +* So for our model Random Forest Regressor is the best for our model as it is having the highest accuracy among other Models we have made. +## **Model Visualization:** ## +![Screenshot 2024-01-14 203348](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/1d76e829-ce82-47d2-a09e-58476702b45a) + +## **Contributed By:** ## +Aniket Ray \ No newline at end of file diff --git a/House-rent-analysis-and-prediction-Bombay/requirements.txt b/House-rent-analysis-and-prediction-Bombay/requirements.txt new file mode 100644 index 000000000..755b27340 --- /dev/null +++ b/House-rent-analysis-and-prediction-Bombay/requirements.txt @@ -0,0 +1,6 @@ + +matplotlib==3.4.2 +seaborn==0.9.0 +numpy==1.21.1 +pandas==1.3.0 +scikit_learn==1.0.2 From 10cdf0efe0c525b1bb8410dc922c2ec5d957123d Mon Sep 17 00:00:00 2001 From: Aniket Date: Tue, 16 Jan 2024 10:53:11 +0530 Subject: [PATCH 2/3] renamed folder name --- .../Dataset/house_rent_mumbai.csv | 0 .../Images/Screenshot 2024-01-14 203212.png | Bin .../Images/Screenshot 2024-01-14 203348.png | Bin .../Images/Screenshot 2024-01-15 154053.png | Bin .../Images/Screenshot 2024-01-15 154155.png | Bin .../Images/boxplot for area.png | Bin .../Images/boxplot for price.png | Bin .../Images/boxplot for size.png | Bin .../Images/scores of model.png | Bin ...se_Rent _Analysis_and_Prediction_of_Mumbai.ipynb | 0 .../Model/ReadMe.MD | 0 .../requirements.txt | 0 12 files changed, 0 insertions(+), 0 deletions(-) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Dataset/house_rent_mumbai.csv (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/Screenshot 2024-01-14 203212.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/Screenshot 2024-01-14 203348.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/Screenshot 2024-01-15 154053.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/Screenshot 2024-01-15 154155.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/boxplot for area.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/boxplot for price.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/boxplot for size.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Images/scores of model.png (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/Model/ReadMe.MD (100%) rename {House-rent-analysis-and-prediction-Bombay => House rent analysis and prediction of Mumbai}/requirements.txt (100%) diff --git a/House-rent-analysis-and-prediction-Bombay/Dataset/house_rent_mumbai.csv b/House rent analysis and prediction of Mumbai/Dataset/house_rent_mumbai.csv similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Dataset/house_rent_mumbai.csv rename to House rent analysis and prediction of Mumbai/Dataset/house_rent_mumbai.csv diff --git a/House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-14 203212.png b/House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-14 203212.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-14 203212.png rename to House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-14 203212.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-14 203348.png b/House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-14 203348.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-14 203348.png rename to House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-14 203348.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-15 154053.png b/House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-15 154053.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-15 154053.png rename to House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-15 154053.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-15 154155.png b/House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-15 154155.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/Screenshot 2024-01-15 154155.png rename to House rent analysis and prediction of Mumbai/Images/Screenshot 2024-01-15 154155.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/boxplot for area.png b/House rent analysis and prediction of Mumbai/Images/boxplot for area.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/boxplot for area.png rename to House rent analysis and prediction of Mumbai/Images/boxplot for area.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/boxplot for price.png b/House rent analysis and prediction of Mumbai/Images/boxplot for price.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/boxplot for price.png rename to House rent analysis and prediction of Mumbai/Images/boxplot for price.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/boxplot for size.png b/House rent analysis and prediction of Mumbai/Images/boxplot for size.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/boxplot for size.png rename to House rent analysis and prediction of Mumbai/Images/boxplot for size.png diff --git a/House-rent-analysis-and-prediction-Bombay/Images/scores of model.png b/House rent analysis and prediction of Mumbai/Images/scores of model.png similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Images/scores of model.png rename to House rent analysis and prediction of Mumbai/Images/scores of model.png diff --git a/House-rent-analysis-and-prediction-Bombay/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb b/House rent analysis and prediction of Mumbai/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb rename to House rent analysis and prediction of Mumbai/Model/House_Rent _Analysis_and_Prediction_of_Mumbai.ipynb diff --git a/House-rent-analysis-and-prediction-Bombay/Model/ReadMe.MD b/House rent analysis and prediction of Mumbai/Model/ReadMe.MD similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/Model/ReadMe.MD rename to House rent analysis and prediction of Mumbai/Model/ReadMe.MD diff --git a/House-rent-analysis-and-prediction-Bombay/requirements.txt b/House rent analysis and prediction of Mumbai/requirements.txt similarity index 100% rename from House-rent-analysis-and-prediction-Bombay/requirements.txt rename to House rent analysis and prediction of Mumbai/requirements.txt From 131fd2d7e5653704868561b565fcb659003cf00c Mon Sep 17 00:00:00 2001 From: Aniket Ray Date: Tue, 16 Jan 2024 11:48:02 +0530 Subject: [PATCH 3/3] Update ReadMe.MD --- .../Model/ReadMe.MD | 43 +++++++++---------- 1 file changed, 21 insertions(+), 22 deletions(-) diff --git a/House rent analysis and prediction of Mumbai/Model/ReadMe.MD b/House rent analysis and prediction of Mumbai/Model/ReadMe.MD index 297c8f40c..d7850c95a 100644 --- a/House rent analysis and prediction of Mumbai/Model/ReadMe.MD +++ b/House rent analysis and prediction of Mumbai/Model/ReadMe.MD @@ -16,40 +16,39 @@ https://www.kaggle.com/datasets/lokeshgupta2020/house-rent-in-mumbai * Visual Data exploration * Made Different ML Models -## **Analysis of Data Visulization:** ## -* Pairplot of numerical values -![Screenshot 2024-01-15 154053](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/e13b7734-6251-436d-9a4d-c4077564019b) +## Analysis of Data Visualization: -* Heatmap -![Screenshot 2024-01-14 203212](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/6b063d9e-81ad-49ce-8447-8bd889812cf8) +### Pairplot of numerical values + -* Boxplot for size -![boxplot for size](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/9bf4511b-15c5-42d8-9d69-0fe2fb85cc35) +### Heatmap + -* Boxplot for area -![boxplot for area](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/1bc6b665-911e-4a6a-9495-eb0cb48a1718) +### Boxplot for size + -* Boxplot for price -![boxplot for price](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/1d81dbbe-f70e-431d-98ab-5a46155e7df5) +### Boxplot for area + -* Boxplot after removing the outliers -![image](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/e6742f02-2f14-40cd-abfe-bccec4a62cd3) +### Boxplot for price + +### Boxplot after removing the outliers + - - -## **Libraries Needed:** ## +## Libraries Needed: * pandas - To manipulate dataframes * seaborn - To visualise data * matplotlib - To visualise data * Scikit-learn - To Import Classical ML Models -## **Model Conclusions:** ## -![scores of model](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/e14dbb9e-0ba8-48ad-a5c0-8684d5c1ff77) +## Model Conclusions: + * So for our model Random Forest Regressor is the best for our model as it is having the highest accuracy among other Models we have made. -## **Model Visualization:** ## -![Screenshot 2024-01-14 203348](https://github.com/raysofani/House-rent-analysis-and-prediction-Bombay/assets/129651614/1d76e829-ce82-47d2-a09e-58476702b45a) -## **Contributed By:** ## -Aniket Ray \ No newline at end of file +## Model's Score Visualization: + + +## Contributed By: +Aniket Ray