Analyzing_Data_From_Airbnb_Edinburgh_Scotland.ipynb

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        "<a href=\"https://colab.research.google.com/github/syrilae/data_science/blob/master/Analyzing_Data_From_Airbnb_Edinburgh_Scotland.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "<center><img src=\"http://sigmoidal.ai/wp-content/uploads/2019/08/logo_color.png\" height=\"40px\"></center>\n",
        "\n",
        "# Airbnb Data Analysis - Edinburgh, Scotland\n",
        "\n",
        "[Airbnb](https://www.airbnb.com.br/) is already considered the **largest** hotel company in the world. Although it doesn't own any hotel, it provides a gateway service for travelers needing to host a place and real estate owners that want to rent their places to someone without going through the bureaucracy that is usually involved. Airbnb provides an innovating platform to give a way for this new type of hosting.\n",
        "\n",
        "At the end of 2018, the company, which started 10 years ago, had already hosted **more than 300 million** people around the world, challenging all of the traditional hotel companies.\n",
        "\n",
        "Airbnb also provides it's site data for some of the main cities around the world. Through the portal [Inside Airbnb](http://insideairbnb.com/get-the-data.html), you can download large datasets to develop **data science** projects and solutions.\n",
        "\n",
        "<center><img alt=\"Analisando Airbnb\" width=\"10%\" src=\"https://www.area360.com.au/wp-content/uploads/2017/09/airbnb-logo.jpg\"></center>\n",
        "\n",
        "**In this *notebook*, we are going to analyze data regarding the city of Edinburgh, and realize which insights can be extracted from raw data.**"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Gathering Data\n",
        "\n",
        "All data used here was obtained from the site where Airbnb itself provides it's datasets: [Inside Airbnb](http://insideairbnb.com/get-the-data.html).\n",
        "\n",
        "For this initial exploratory analysis, we're going to download only the following file:\n",
        "\n",
        "* `listings.csv` - *Summary information and metrics for listings in Edinburgh (good for visualisations).*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhp2wMPgJ6zX",
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      },
      "source": [
        "# importing packages needed\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
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    {
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      "source": [
        "# import the listings.csv file to a DataFrame\n",
        "df = pd.read_csv(\"http://data.insideairbnb.com/united-kingdom/scotland/edinburgh/2019-11-28/visualisations/listings.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "id": "qb5By2nIMS6m",
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      },
      "source": [
        "## Analyzing Data\n",
        "\n",
        "In this step we allow ourselves to brainstorm into our data given it's situation and to understand how the data is structured along the listings file..\n"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "RYgvuebKNg8-",
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      "source": [
        "**Variable Dictionary**\n",
        "\n",
        "* `id` - id number generated to identify the real estate property\n",
        "* `name` - name of the announced property\n",
        "* `host_id` - id number from the property's owner (host)\n",
        "* `host_name` - host name\n",
        "* `neighbourhood_group` - this column does not contain any valid value\n",
        "* `neighbourhood` - neighborhood name\n",
        "* `latitude` - latitude coordinate where the property is situated at\n",
        "* `longitude` - longitude coordinate where the property is situated at\n",
        "* `room_type` - informs which types of rooms are provided\n",
        "* `price` - price to rent the place\n",
        "* `minimum_nights` - minimum quantity of nights allowed to book\n",
        "* `number_of_reviews` - number of reviews which the place has\n",
        "* `last_review` - data from last review\n",
        "* `reviews_per_month` - amount of reviews per month\n",
        "* `calculated_host_listings_count` - quantity of listings from the same host\n",
        "* `availability_365` - number of available days inside a year\n",
        "\n",
        "Before starting any analysis, let us check what does our *dataset* look like, by reaching out for the 5 first entries."
      ]
    },
    {
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      "source": [
        "#  show 5 first entries\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15420</td>\n",
              "      <td>Georgian Boutique Apt City Centre</td>\n",
              "      <td>60423</td>\n",
              "      <td>Charlotte</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Old Town, Princes Street and Leith Street</td>\n",
              "      <td>55.95689</td>\n",
              "      <td>-3.18768</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>3</td>\n",
              "      <td>309</td>\n",
              "      <td>2019-11-19</td>\n",
              "      <td>2.86</td>\n",
              "      <td>1</td>\n",
              "      <td>287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24288</td>\n",
              "      <td>Cool central Loft, sleeps 4, 2 double bed+en-s...</td>\n",
              "      <td>46498</td>\n",
              "      <td>Gordon</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Meadows and Southside</td>\n",
              "      <td>55.94265</td>\n",
              "      <td>-3.18467</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>115</td>\n",
              "      <td>2</td>\n",
              "      <td>216</td>\n",
              "      <td>2019-09-08</td>\n",
              "      <td>1.93</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38628</td>\n",
              "      <td>Edinburgh Holiday Let</td>\n",
              "      <td>165635</td>\n",
              "      <td>Trish</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Joppa</td>\n",
              "      <td>55.94308</td>\n",
              "      <td>-3.09525</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>46</td>\n",
              "      <td>4</td>\n",
              "      <td>55</td>\n",
              "      <td>2019-10-09</td>\n",
              "      <td>0.83</td>\n",
              "      <td>2</td>\n",
              "      <td>316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44552</td>\n",
              "      <td>Double room - spacious Leith flat</td>\n",
              "      <td>195950</td>\n",
              "      <td>Shaun</td>\n",
              "      <td>NaN</td>\n",
              "      <td>South Leith</td>\n",
              "      <td>55.96600</td>\n",
              "      <td>-3.17241</td>\n",
              "      <td>Private room</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>195</td>\n",
              "      <td>2019-10-29</td>\n",
              "      <td>1.72</td>\n",
              "      <td>2</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>47616</td>\n",
              "      <td>City flat, close to nature and the Fringe</td>\n",
              "      <td>216203</td>\n",
              "      <td>Ben</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Canongate, Southside and Dumbiedykes</td>\n",
              "      <td>55.94732</td>\n",
              "      <td>-3.17851</td>\n",
              "      <td>Private room</td>\n",
              "      <td>100</td>\n",
              "      <td>2</td>\n",
              "      <td>35</td>\n",
              "      <td>2019-10-13</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "      id  ... availability_365\n",
              "0  15420  ...              287\n",
              "1  24288  ...               18\n",
              "2  38628  ...              316\n",
              "3  44552  ...               73\n",
              "4  47616  ...                0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
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      "cell_type": "markdown",
      "metadata": {
        "id": "6-B_qtEaYEd-",
        "colab_type": "text"
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      "source": [
        "### **Q1. How many features (variables) does our dataset have? What are the types of variables?**\n",
        "\n",
        "Now we proceed by identifying the amount of entries our dataset has and view what are the data types from each column.\n",
        "\n",
        "It's interesting to note that the dataset we downloaded is the \"synthesized\" version of Airbnb's dataset. In the same page we have downloaded the file `listings.csv` there is a more complete version with both more features and more training examples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFRK0tniLV-G",
        "colab_type": "code",
        "outputId": "843d3afe-ee8f-40e0-c371-6b444bab6b52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "# identify the volume of data in the DataFrame\n",
        "print(\"Training Examples:\\t {}\".format(df.shape[0]))\n",
        "print(\"Features:\\t {}\\n\".format(df.shape[1]))\n",
        "\n",
        "# display the data types in the DataFrame\n",
        "display(df.dtypes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variáveis:\t 13332\n",
            "Entradas:\t 16\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "id                                  int64\n",
              "name                               object\n",
              "host_id                             int64\n",
              "host_name                          object\n",
              "neighbourhood_group               float64\n",
              "neighbourhood                      object\n",
              "latitude                          float64\n",
              "longitude                         float64\n",
              "room_type                          object\n",
              "price                               int64\n",
              "minimum_nights                      int64\n",
              "number_of_reviews                   int64\n",
              "last_review                        object\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count      int64\n",
              "availability_365                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yz1urSUGW9iE",
        "colab_type": "text"
      },
      "source": [
        "### **Q2. What is the percentage of absent values in the *dataset*?**\n",
        "\n",
        "The quality of a *dataset* is directly related to the quantity of absent values. It is useful to understand, as you start your analysis, if these null values are significant when compared to the total of entries.\n",
        "\n",
        "* We acknowledge that the column possível `neighbourhood_group` has a 100% of it's values as absent.\n",
        "* The features `reviews_per_month` and `last_review` have null values in almost 12% of the values inside the DataFrame.\n",
        "* The feature `host_name` has approximately 0,1% of it's values as null."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plk2FjbCXOqP",
        "colab_type": "code",
        "outputId": "15dfd178-b106-4d09-e233-8c7a690b5355",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        }
      },
      "source": [
        "# ordenring in descending order the features with null values\n",
        "(df.isnull().sum() / df.shape[0]).sort_values(ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group               1.000000\n",
              "reviews_per_month                 0.115062\n",
              "last_review                       0.115062\n",
              "host_name                         0.000900\n",
              "availability_365                  0.000000\n",
              "calculated_host_listings_count    0.000000\n",
              "number_of_reviews                 0.000000\n",
              "minimum_nights                    0.000000\n",
              "price                             0.000000\n",
              "room_type                         0.000000\n",
              "longitude                         0.000000\n",
              "latitude                          0.000000\n",
              "neighbourhood                     0.000000\n",
              "host_id                           0.000000\n",
              "name                              0.000000\n",
              "id                                0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHXvvHIxaoNc",
        "colab_type": "text"
      },
      "source": [
        "### **Q3. What is the type of distribution from the features?** \n",
        "\n",
        "To identify the feature distribution, we now plot the histogram for each feature identified in the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2wWSfeRbmBo",
        "colab_type": "code",
        "outputId": "736e7ef9-83f4-436f-818c-b8e2d12bbb1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "# plotting the histogram from the discrete features\n",
        "df.hist(bins=15, figsize=(15,10));"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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0acovBR6zkBglaTaS7Ec32vAqukfSnFZVb0nyKrrH0XynLfryqjq/rfMy4ATg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\n",
            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7x4aw1j5wNmn",
        "colab_type": "text"
      },
      "source": [
        "### **Q4. Are there outliers?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ba1xYYTug6bX",
        "colab_type": "text"
      },
      "source": [
        "Through the histogram distribution, it is possible ot verify hints of the presence of *outliers*. Observe as an example the variables `price`, `minimum_nights` and `calculated_host_listings_count`.\n",
        "\n",
        "These values do not follow a distribution, and distort all of the graph representation. To make sure that there are outliers, we have two quick ways that help us detecting the *outliers*:\n",
        "\n",
        "* Statistical synthesis through the `describe()` method\n",
        "* Plotting `boxplots` for the feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ff-8gUahLk-C",
        "colab_type": "code",
        "outputId": "1aa8dc95-160e-46a9-9c88-94a9a3d4b371",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "# view the statistical synthesis for the discrete variables\n",
        "df[['price', 'minimum_nights', 'number_of_reviews', 'reviews_per_month',\n",
        "    'calculated_host_listings_count', 'availability_365']].describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>35451.000000</td>\n",
              "      <td>35451.000000</td>\n",
              "      <td>35451.000000</td>\n",
              "      <td>18979.000000</td>\n",
              "      <td>35451.000000</td>\n",
              "      <td>35451.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>622.242222</td>\n",
              "      <td>4.735833</td>\n",
              "      <td>7.951962</td>\n",
              "      <td>0.607775</td>\n",
              "      <td>7.884827</td>\n",
              "      <td>190.142648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1588.131281</td>\n",
              "      <td>21.958524</td>\n",
              "      <td>22.308053</td>\n",
              "      <td>0.849336</td>\n",
              "      <td>32.626366</td>\n",
              "      <td>151.889963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>281.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>179.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>599.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>362.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40000.000000</td>\n",
              "      <td>1123.000000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>11.250000</td>\n",
              "      <td>265.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price  ...  availability_365\n",
              "count  35451.000000  ...      35451.000000\n",
              "mean     622.242222  ...        190.142648\n",
              "std     1588.131281  ...        151.889963\n",
              "min        0.000000  ...          0.000000\n",
              "25%      150.000000  ...          0.000000\n",
              "50%      281.000000  ...        179.000000\n",
              "75%      599.000000  ...        362.000000\n",
              "max    40000.000000  ...        365.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAx_GIAzhZTn",
        "colab_type": "text"
      },
      "source": [
        "Through the statistical synthesis above, we can confirm a few hypothesis, such as:\n",
        "\n",
        "* The `price` feature has 75% under the value 599, yet it's maximum value is 40000.\n",
        "* The minimum nights we are allowed to book (`minimum_nights`) has it's max value above the feasible limit of 365 days in a year.\n",
        "\n",
        "#### Boxplot for minimum_nights\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-X_Z58of_dC",
        "colab_type": "code",
        "outputId": "6757fa6b-256c-4dd9-abfe-ccd096cdccc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "# minimum_nights\n",
        "df.minimum_nights.plot(kind='box', vert=False, figsize=(15, 3))\n",
        "plt.show()\n",
        "\n",
        "# view amount of values above 30 days for minimum_nights\n",
        "print(\"minimum_nights: values above 30:\")\n",
        "print(\"{} entries\".format(len(df[df.minimum_nights > 30])))\n",
        "print(\"{:.4f}%\".format((len(df[df.minimum_nights > 30]) / df.shape[0])*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "minimum_nights: values above 30:\n",
            "51 entries\n",
            "0.3825%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1G8lOfGxfW0",
        "colab_type": "text"
      },
      "source": [
        "#### Boxplot for price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQhJ229OkX_o",
        "colab_type": "code",
        "outputId": "2bf29737-835f-467c-9a20-997f56ee9a3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "# price\n",
        "df.price.plot(kind='box', vert=False, figsize=(15, 3),)\n",
        "plt.show()\n",
        "\n",
        "# view amount of values above 1500 for price\n",
        "print(\"\\nprice: values above 1500\")\n",
        "print(\"{} entries\".format(len(df[df.price > 1500])))\n",
        "print(\"{:.4f}%\".format((len(df[df.price > 1500]) / df.shape[0])*100))\n",
        "\n",
        "# df.price.plot(kind='box', vert=False, xlim=(0,1300), figsize=(15,3));"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "price: values above 1500\n",
            "31 entries\n",
            "0.2325%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfxQ1PITFc5Q",
        "colab_type": "text"
      },
      "source": [
        "#### Histogram without *outliers*\n",
        "\n",
        "Since we have already identified *outliers* in the variables `price` and `minimum_nights`, we're now going to clean them out from the *DataFrame* and plot the histogram again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67JxJ6n_FrTn",
        "colab_type": "code",
        "outputId": "99d993ff-81e7-472c-ac02-640a180367f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "# remove *outliers* in a new DataFrame\n",
        "df_clean = df.copy()\n",
        "df_clean.drop(df_clean[df_clean.price > 1500].index, axis=0, inplace=True)\n",
        "df_clean.drop(df_clean[df_clean.minimum_nights > 30].index, axis=0, inplace=True)\n",
        "\n",
        "# remove `neighbourhood_group`, since it is empty\n",
        "df_clean.drop('neighbourhood_group', axis=1, inplace=True)\n",
        "\n",
        "# plot the histogram for discrete features\n",
        "df_clean.hist(bins=15, figsize=(15,10));"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AiZCGupJUG+oKPNSV2YTgyp6ZmZlZiUXEsQ1meairDhrJYOwLZg5wQl5+tIOx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\nlbRzXnd+RKwGyA3GKcAlLcuMmbXUsBt7Zag09VfQz5h1VV3vNZxKUxn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d4X/7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\n",
            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXn2oIo5qx-w",
        "colab_type": "text"
      },
      "source": [
        "### **Q4. What is the existing correlation between features**\n",
        "\n",
        "Correlation means that there is a relationship between two things. In our context, we're searching for a relationship or similarity between two variables. This relation can be measured, and it is the job of the correlation coefficient to stablish it's intensity. To identify the existing correlations between the interest features, we're going to:\n",
        "\n",
        "* Create a correlation matrix\n",
        "* Generate a *heatmap* from that matrix, using the `seaborn` library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2ayzBajqxyd",
        "colab_type": "code",
        "outputId": "8a8774e5-4094-42c6-d03c-ccced9762a74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# create a correlation matrix\n",
        "corr = df_clean[['price', 'minimum_nights', 'number_of_reviews', 'reviews_per_month',\n",
        "    'calculated_host_listings_count', 'availability_365']].corr()\n",
        "\n",
        "display(corr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.027891</td>\n",
              "      <td>-0.065640</td>\n",
              "      <td>0.007080</td>\n",
              "      <td>0.094251</td>\n",
              "      <td>0.132840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minimum_nights</th>\n",
              "      <td>-0.027891</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.094617</td>\n",
              "      <td>-0.166021</td>\n",
              "      <td>-0.020332</td>\n",
              "      <td>-0.059094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number_of_reviews</th>\n",
              "      <td>-0.065640</td>\n",
              "      <td>-0.094617</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675832</td>\n",
              "      <td>-0.044240</td>\n",
              "      <td>0.173794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reviews_per_month</th>\n",
              "      <td>0.007080</td>\n",
              "      <td>-0.166021</td>\n",
              "      <td>0.675832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015363</td>\n",
              "      <td>0.205586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <td>0.094251</td>\n",
              "      <td>-0.020332</td>\n",
              "      <td>-0.044240</td>\n",
              "      <td>0.015363</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.152908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>availability_365</th>\n",
              "      <td>0.132840</td>\n",
              "      <td>-0.059094</td>\n",
              "      <td>0.173794</td>\n",
              "      <td>0.205586</td>\n",
              "      <td>0.152908</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   price  ...  availability_365\n",
              "price                           1.000000  ...          0.132840\n",
              "minimum_nights                 -0.027891  ...         -0.059094\n",
              "number_of_reviews              -0.065640  ...          0.173794\n",
              "reviews_per_month               0.007080  ...          0.205586\n",
              "calculated_host_listings_count  0.094251  ...          0.152908\n",
              "availability_365                0.132840  ...          1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91UujDr6ueqE",
        "colab_type": "code",
        "outputId": "33a33b52-2e83-4c58-e5cd-732f86838acd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "sns.heatmap(corr, cmap='RdBu', fmt='.2f', square=True, linecolor='white', annot=True);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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094qd4zTJKaWUcl6VMrjv69VUsaNTSilVobmJOPRwhIj0E5EjIvKriEwops4w\n++LLB0Tk09La1J6cqpAmDK9YC1G+H1BwVaPy9UC9LuUdQiHvvDe1vEPIR0beWt4h5FM9xLu8Q7gq\nymq4UkTcgf8AfYEYYLuILLGvwXm5ThNgItDNGJNsv3F8iTTJKaWUcppb2X2FoBPwqzHmGICIfA5E\nAQfz1HkI+I8xJhnAGBNfqJWC8ZVVdEoppf73ODpcKSJjRGRHnseYAk3VAU7m2Y6x78urKdBURDaL\nyFYR6VdafNqTU0op5bQqDvbkjDHzgHkuns4DaAJEAnWBDSLSxhhzrqQDlFJKKaeU4R1PTgH18mzX\nte/LKwbYZoy5BBwXkaPYkt724hrV4UqllFJOK8PZlduBJiLSSESqAsOBJQXq/BdbLw4RCcQ2fHms\npEa1J6eUUsppZTW70hiTJSJjge8Bd+ADY8wBEZkO7DDGLLGX3SQiB4Fs4GljzNmS2tUkp5RSymll\neVsvY8wKYEWBfZPzPDfAOPvDIZrklFJKOU1XIVBKKVVpOTq7srxoklNKKeU0XYVAKaVUpVXB78+s\nSU4ppZTztCenlFKq0tKVwZVSSlVa2pNT6iq6/dUptOwfSeZFCwvvf4qYXQcK1Rkw4yk6jboN74Aa\nPF2jdb6y8KED6D/57xhjOLX3EB/f/aRL8XScPYnafXqSnW4h+vFJJO09WKhOzXYt6frGHNy9PDn9\n4wa2T5oNQEDr5tzw8lTcPatisrPZ9vR0zu7a53Qs86bcxy0925GQlEL40MlF1nll/Ej6dWtDuiWT\nB6a8z+7DJwAYNagrEx4cBMDc+UtZsDTa6TjyOrZnO6sXvIXVaqVdZH86Dx5eqM6hrevZ/PXHIEJw\n/cYMHjuJ8wlxfPvqVIzVSnZ2Nu1viiK8z6Ayian5c08TFNGd7HQL+yZMIfXg4UJ1/Fq1oPXcqbh7\neZGwfhOHZ76Ur7zB/XfTfMI41tzQm0vJxd5GsVT+HTvRcOzjiJsbcSuWc/qz/MulSZUqXDthEj5N\nm3IpJYVfpk8jIy4WDz8/mk6Zjk/zZiR8v5Ljr7/mdAxXqkoFXxq8gl8yVKp4LftHEtSkITOa9eKL\nRyYy7D8zi6x3YNmP/Ktz4bXFgq5tSN9nHuXfPe5gTtub+eYfM1yKp3afnvg2bsDiTv3YOm4KN7xU\ndGK54aUpbP3HZBZ36odv4wbUvrEHANdPeYq9L/2H5b2GsHvuG1w/9SmX4vl46WYGPvZKseX9urfh\n2vohtIyayKMzP+LNSfcAEOBXnWfHRNF91Ey63T2DZ8dE4e/r+lpoVms2P3z4BkPHz+bBF+dzcMta\nEmP+yFcnKTaGrUs+4+6pr/KkBiXuAAAgAElEQVTgi/O5cdSjAPgE1OTuqa9x35x3uWf6G2xd+gWp\nyYkuxxQY0Q3vhvXZ2DeKA8/PpOW0iUXWazltIgeem8nGvlF4N6xPYM+uOWVeoSEEdutC+qkzrgXj\n5kajvz/JoQnj2X3fvQT2vpFqDRrkqxLcfwBZqansGnUXZxZ9Rf0xDwNgzczk5P+9zx/v/PnrMIo4\n9igvmuSuAhFZJyId/sTzvWRfJfel0ms71f7g4lbpLU9tBvflpwXfAPD7tt1U8/fDLzSoUL3ft+0m\nJTah0P4uDw5n49sLSD+XAkBaQol3BypVvf69OfblYgASd+6hSg0/qoXkj6daSBBVfH1I3LkHgGNf\nLqbeLTfaCo2hiq8PAFX9fEiPLXWprBJt+vkoyecvFFs+KCKchctsPbSf9h3D39eb0MAa3NS1Nau3\nHiA55QLnUi+yeusBbu7WxqVYAM78dgT/kNr4B4fh7lGFFp0j+WVn/h7injXfcX3fwXhV9wWgeo0A\nANw9quBRpSoA2ZcuYYzV5XgAgm+M5PS3ywA4v2cfVXx9qRoUmK9O1aBA3H2qc36PrVd9+ttlBPfp\nlVPebNI/OfrSq2CMS7H4NG+B5dQpMs6cwWRlkbhmDQFdu+erU7NbNxJWfQ/A2fXrqXH99QBYLRZS\n9+/DmpnpUgzOcEMcepQXHa6sYETEwxiTdYWHjQFqGmOyr0b79nvGFbxRarmrUSeEcydzPz2fizlD\njTqhRSa0ogQ3bQTAkxu+ws3dne+mv8qh7zc4HY93WAgXTsXmbF88HUu1sGDS43LjqRYWzMXTcXnq\nxOEdFgLA9mfn0Oer92g/7WnEzY2V/Uc6HYsjagcHcDI2KWc7Ji6J2sEB1A7yJyYud/+p+GRqB/m7\nfL7UpET8auUmfd+agZz5Lf/QYHJsDACfTP07VquV7rffQ+N2HQFIORvPopeeIznuNL1GPIRvQP5k\n5AzPkGAssbnvhyUuHq+QIDITcnuJXiFBZOT5wGGJi8czxLYgddCNEWTExZN6+BeXY6kaGEhGfO55\nMhMT8G3RolCdzMt1rNlkX7iAh18NslLOu3x+Z1XwS3L/2z05EWkoIodE5D17T2iViFTL2xMTkUAR\n+d3+fLSI/FdEfhCR30VkrIiME5Fd9gX8auZpfpSI7BaR/SLSyX58dRH5QER+sh8TlafdJSKyBlhd\nTKxi77HtF5F9InKnff8SwAfYeXlfEcd+KCLviMg24MUS4tgqIq3yHLdORDrY43vTvi9IRL4Wke32\nRzf7/n0i4m+P86yI3GPf/7GI9BWRVvbz7RaRvfZl7AvGmbOo4n6T6vgb6SQ3D3eCmjTk9d4j+PCu\nJxj+7hyq1fC96uctTrP7hrPjubl80643O56bS5fXih5+rcys2dkkx51ixHP/YvDYSayc/28sF9IA\n8KsVzP1z5zHmlQ/Zv/EHLpxPLtdY3by8aPzI/fz62jvlGkd5cxPHHuUWX/mdusJogm059VbAOeD2\nUuq3BoYAHYFZwEVjTDiwBbgnTz1vY8x1wN+AD+z7ngXWGGM6Ab2Al0Skur3seuAOY0xEMecdAlwH\ntAP62I8NM8YMBtKNMdcZY74oIe66QFdjzLgS4vgCGAYgImFAmDFmR4F2XgP+bYzpiO21mm/fvxno\nBrTCtvRFD/v+LkA08Ajwmv016YBtXah8jDHzjDEdjDEdWkvRyabHo6MYv3M543cuJ+VMAv71wnLK\n/OuGcT5PT6o052Ji2b/0R6xZWST9HkP80eMENWnk8PEATe8fyYC13zBg7TekxyVQvU5oTpl37VDS\nz+Qfckw/E4937ZA8dUK4eMbWk2g8/FZOLPsBgD8Wr6TW9a4PEZbkdHwy9UJzP5fVDanJ6fhkTiec\no25I7v46wQGcTnB+MsVlvjUDSTmb26tNTUrEp0BvzLdmINde3xl3Dw/8g8OoGVaH5Nj8S4r5BgQS\nWK8hJw87Nymn3l3D6LL4M7os/ozMhAS8QnPfD6+QYCxx+UcCLHEJeIYG56uTERePd/26VKtbh65L\nPqfnmmV4hgbT5duFVA2s5VRcmYmJeAbnnqdqYBAZCYmF6lS9XMfNHffq1cu1Fwe2rxA48igvmuTg\nuDFmt/35TqBhKfXXGmNSjTEJwHlgqX3/vgLHfgZgjNkA+ImIP3ATMEFEdgPrAC+gvr3+D8aYJIrX\nHfjMGJNtjIkD1mNLtI76Ks9wZnFxfAncYa8zDFhURDt9gDftxy6x/2w+wEagp/3xNtBGROoAycaY\nC9g+BEwSkWeABsaY9CuIPcfGtxfwYvsBvNh+AHsXr6LTqCEANLzhOiznUx0eqgTYt3gV10Z0BqB6\nrQCCmzYi8diJK4rn6AefsrzXEJb3GsLJFatpPCwKgMD27biUkppvqBIgPS6BS6lpBLZvB0DjYVGc\n/G6NrSw2npButrc0tEdnUo/ln5RR1pat381dA20TKDq1acz5tIvEJp5nVfR++nRphb+vN/6+3vTp\n0opV0ftdPl9Y42Ykx57iXPwZsrMucWjrOq5t3yVfnSYdunHy0F4ALqaeJ+nMKfyDw0g5m8ClzAwA\nLBdSiTmyn1ph9QqdwxEnF37JlqgRbIkaQdyP66h920AAarRrQ1ZaWr6hSoDMhESy0y5Qo53tQ0ft\n2wYSv3odaUd/ZV2XPmzoPZANvQeSERvPltvuIjPRuWu7aYcP41WnLp6hoYiHB4G9e5O8ZXO+OknR\nmwm66WYAakVEcH7XLqfOVZYq+sQTvSYHGXmeZwPVgCxyPwB4lVDfmmfbSv7Xs+BVaAMIcLsx5kje\nAhG5ASh+hkDZyNt+kXHYYzkrIm2BO7H1vgpyAzobYywFjtsAPIYtWT4L3IYtYW4EMMZ8ah8uHQCs\nEJGHjTFrXPmBDq5YS6v+vZh8dB2ZF9NZ+MD4nLLxO5fzYvsBAAyeO4EOIwZTxbsa0/+IZsv7X/Dd\n9Nc49P0GmvftwaR9q7BmZ7P4mTlcTHK+x3Lqh/XU6dOTW7d/T1a6hegnJuWUDVj7Dct72RLytvHT\n6Wb/CsGp1Rs5/aPtOuCWf0ym4+xJiLs71owMto4renamoxbMeZie7ZsR6O/DsZUvM/2dxVTxcAfg\nvUXr+G7TXvp1b8uhJXNJt2Ty4FTbgENyygVmv7eU6E+eB2DWvKUkp7j+39PN3Z2+o8fy5QsTMVYr\nbSJuJqhuQzYu+pDQRk1p0r4rjdp24Pi+ncx/+gHEzY3IkQ9RzdeP2H07WbvwXdtfS2PoNGAoQfWv\nrNddlMR1mwiK6E6PHxeTnW5h/8SpOWVdFn/GlqgRABycOofWc6fh7uVJ4oZoEtdvLqZFF1izOf7G\nq7R44WXE3Y3471aQ/vvv1Bt9P2lHD5McHU38ihU0mfQs4QsWkpWaytEZ03IOD//0czy8qyNVPAjo\n1p1D458i/Y+r+0EJynco0hFiXJwR9FcmIg2BZcaY1vbtp7Bd36oL7DTGvC0iTwJPGmMaishooIMx\nZqy9/u/27cS8ZSKyDjhsjHlERLoDbxtj2ojIbMAPeNwYY0Qk3Bizq2C7xcQ6BHgYuAWoCewAbjDG\nxIpImjHGp4RjP7T/nIvs20XGYS97DNsQY7h9CJcCP9unwC5jzEv2susu94TFthR9ijGmg73HNhYY\na4xZLCKNsfWajYi8DMQYY14tLuYn3BtVqP+YHQMKftYpXw/U61J6pT/ZO+9NLe8Q8qkzsvDXRsqT\nX93yu95bnC5r1rucoo4npjr0u9oo0Ldc0qEOVxbtZeBREdkFODuFy2I//h3gAfu+GUAVYK+IHLBv\nO+pbYC+wB1gDjDfGOH4BKr+S4liEbdn5L4s59gmgg33yyEHy9/a2AUftzzcCdYBN9u1hwH77MGdr\n4GMnY1dKVSBuIg49ysv/dE9OVVzakyuZ9uRKpz250pVFT+5EUppDv6v1a/qUS6bTa3JKKaWcpjdo\nVldERNoACwrszjDG3ODAsc8CQwvs/soYM6us4lNKqbwq+sQTTXIVjDFmH7bvwzlz7Cxs391TSqk/\nRQXvyGmSU0op5byKPntRk5xSSimnSQXvymmSU0op5TS9JqeUUqrSquBrpmqSU0op5TwdrlRKKVVp\n6XClUkqpSquC5zhNcqpiyrRWqLt6EWspddH1P1VFu4UWwCMPTS3vEPJZ6VWx/rwl/VK+i7xeLWV5\nX0oR6YdtzUp3YL4xZm4x9W7Hdp/djkWseZk/vjKLTiml1P8cN4xDj9KIiDvwH6A/0BIYISIti6jn\nC/wd2w3hHYhPKaWUcpaxOvYoXSfgV2PMMWNMJvA5EFVEvRnAC4CliLJCNMkppZRymhirYw+RMSKy\nI89jTIGm6gAn82zH2PflnkvkeqCeMWa5o/FVrEFrpZRSfy2O9dIwxswD5jl7GhFxA14BRl/JcZrk\nlFJKOa/s1iQ9BdTLs13Xvu8yX2wLLq+zfzcvFFgiIoNLmnyiSU4ppZTzHOzJOWA70EREGmFLbsOB\nkTmnMeY8EHh5W0TWAU+VNrtSk5xSSimniTWrTNoxxmSJyFjge2xfIfjAGHNARKYDO4wxS5xpV5Oc\nUkop55VdTw5jzApgRYF9k4upG+lIm5rklFJKOa8Mk9zVoElOKaWU86ya5JRSSlVSoj05pZRSlZa1\nYt3XtSBNckoppZynPTmllFKVlQ5XKnUVDXttCq1v6UXmxXQ+Gv0UJ3cdKFQnauZT3HDPELwDavCk\nb6uc/UNfeZ6mvboAUNXbC9/gQMYFtHUpnt4vPUfjmyLISk9nxcMTiN9zsFCd7lP+QasRt+Ll78dr\noeE5+zuMvY82o4disrK4mJjMykcnknLytNOxHNuzndUL3sJqtdIusj+dBw8vVOfQ1vVs/vpjECG4\nfmMGj53E+YQ4vn11KsZqJTs7m/Y3RRHeZ5DTcVw2b8p93NKzHQlJKYQPLXJWOK+MH0m/bm1It2Ty\nwJT32X34BACjBnVlwoO2GObOX8qCpdEux3NZk2f+Sa0eXbFaLBx8fjpph44UquPbojktZk7GzdOT\nsxuj+eWFfwFwzbjHCYzogbl0ifSTpzg0eTpZqWkuxdNy2jME9+5BdrqFPeOeJ2X/oUJ1/Nq0oN0r\nM3H38iR+zUYOTnnB9rP841HqjxxCxlnbsj5HXnidhLWbXIqnVJrklLo6WvePJLhJIyY3iaTRDeGM\nfHsWL3S+tVC9vUtXs/bNj5j+y7p8+78aNyPneeTYe6kX3gpXNLopgoBrGjK/XV/COraj76vTWNhr\naKF6v61Yw653PuHBPavy7Y/be5DdPYaQlW7hugdHEDFzPEvvfdKpWKzWbH748A3unPgCvjUD+ej5\nsVx7fRcC6zbIqZMUG8PWJZ9x99RX8aruy4Xztj+MPgE1uXvqa3hUqUqmJZ33n3mIa9t3wTcgsLjT\nOeTjpZt564vV/N+MB4ss79e9DdfWD6Fl1EQ6tWnMm5Puofs9Mwnwq86zY6Loctd0jDFs/XQKS9ft\n5lzqRZfiAajVvSveDeqxdeDt+LVtTbPnnmHnXfcXqtfsuWc4PG02KXv30+6tV6nZvQtJm7aQvOUn\njr32FiY7m2ueHEuDB0bz26tvOh1PUK/uVG/UgHU9BuIf3pbWs58jevBdheq1mf0c+8ZP49yuvXT8\n+C2CIruTsM6WzI7P/4Rj737kdAxXrIInOV2FwAUiskJE/Ms7jopGRPxF5G95tiNFZFlZn6dt1E1s\n/fgbAI5v20U1f1/8QoMK1Tu+bRcpsQklttVxxGB2fObUDRVyNBl4Iwc++xaAM9v34FXDl+ohheM5\ns30PF+IKx3Nywzay0m2rh5z+aTe+tUOcjuXMb0fwD6mNf3AY7h5VaNE5kl925u/97FnzHdf3HYxX\ndV8AqtcIAMDdowoeVaoCkH3pEqaM/oht+vkoyecvFFs+KCKchctsMf607xj+vt6EBtbgpq6tWb31\nAMkpFziXepHVWw9wc7c2ZRJTYK+exC61ffc4Ze9+PHx9qRpYK1+dqoG1cPepTsre/QDELl1BUK8I\nAJK2bMNk2yZenN+7H8+QYJfiCbmpF6e+XgrAuV17qeLni2dw/g8XnsGBePj4cG7XXgBOfb2UkJt7\nuXRel5TdUjtXhSY5O7G5otfDGHOLMebc1YqprIjIn91j9wf+VmotV09SJ4TkPMN552Ji8a8TesXt\n1Kxfh8BG9Ti8xrUhMJ+wEFJjYnO2U0/H4eNkompz71CO/bDB6VhSkxLxq5WbYH1rBpKWnJivTnJs\nDElnTvHJ1L/z8eTHObZne05Zytl4PpgwhreeGEnngXe63ItzRO3gAE7GJuVsx8QlUTs4gNpB/sTE\n5e4/FZ9M7aCy+WzpGRyMJTYuZzsjLh7P4OBCdTLi4nO2LUXUAah92yDObnLt/5BXaDDpp3P/D1nO\nxOEVGlyojuVMbszpBeo0uHc4PVYtou3L0/Co4etSPI6Q7CyHHuXlfzrJiUhDETkiIh8D+4FRIrJF\nRH4Wka9ExEdE+onIV3mOyemViMjvIhJof363iPwkIrtF5F0RcReRoSLyir387yJyzP68sYhstj+f\nKyIHRWSviLxcQqwfisg79nWYjorIQPt+dxF5SUS229t4OE+cG0VkCVD4wlDuz3/Y3vZREVkoIn1E\nZLOI/CIinez1aorIf+3tbxWRtvb9U0XkAxFZJyLHROQJe9NzgWvsr8VL9n0+IrLIfr6FYr+NeIF4\nctabOkiqY29iGegwfBA/L1qBqSBfam1552BCw1uz/dX5V/U81uxskuNOMeK5fzF47CRWzv83lgu2\n60l+tYK5f+48xrzyIfs3/pAzlKmK1uCh+zBZ2cQtX1mucfyx4AvWdh/AxpuHkhGfSMvnn7r6JzXG\nsUc50Wty0AS4F/gV+AboY4y5ICLPAOOA2cA8EalujLkA3IltxdocItLCvr+bMeaSiLwF3AWsAsbb\nq/UAzopIHfvzDSJSC7gNaG6MMQ4MfTbEtnruNcBaEbkWuAc4b4zpKCKewGYRuXyx53qgtTHmeAlt\nXgsMBe7HdhfwkUB3YDAwCbgVmAbsMsbcKiK9gY+B6+zHNwd6YVsG44iIvA1MsJ/3OvvrEwmEA62A\n08BmoBuQ74p43vWmHpGGRf5WRPxtFN0fGgHAH9v3EFCvdk6Zf91Qzp2KLeqwEnUYPojPH3v+io8D\nCB9zF21HDwPgzM59+NbN7Un61g4h7XRccYcWqUFkVzqPf5TP+91FduYlp2ICW88t5WzukGhqUiI+\nBXpjvjUDCbu2Oe4eHvgHh1EzrA7JsacIu6ZZbp2AQALrNeTk4X00v6Gn0/E44nR8MvVCa+Zs1w2p\nyen4ZE4nnKNn+9yY6gQHsGFn4ckhjqpz5x3Uvt127Tb1wEG8QkM4by/zDAkmIz4+X/2M+Ph8w5Be\nBeqEDh5AYM/u7HrIucGLBvfeSb0RtwNwfs8BqtUO5fJHCq+wECyx+eOxxMbjFZY7QlAtT53MxNwe\n74lPv6bjh85fH3SYXpOr8P4wxmwFOgMtsSWJ3dgSXwNjTBawEhhkH/YbACwu0MaNQHtgu/3YG4HG\nxphYbD0YX2zrJH0K9MSW5DYC57Et4f6+iAwBSruS/qUxxmqM+QU4hi3B3ATcYz/vNqAWtsQN8FMp\nCQ7guDFmn7FdeDkArDbGGGAftqQKtqS3AMAYswaoJSJ+9rLlxpgMY0wiEA8UNz73kzEmxn6e3Xna\nviLr31rArPBbmBV+C7v/u4rO9wwBoNEN4VjOp5Z67a2gkGbXUD2gBse2/OxMOOyat5CPukbxUdco\nfl32I61G3AZAWMd2ZKSkFXntrTjBbVtw0+vT+WbYI1xMSCr9gBKENW5GcuwpzsWfITvrEoe2ruPa\n9l3y1WnSoRsnD9mu61xMPU/SmVP4B4eRcjaBS5kZAFgupBJzZD+1wuoVOkdZW7Z+N3cN7ApApzaN\nOZ92kdjE86yK3k+fLq3w9/XG39ebPl1asSp6v9PnOfXFIrYPu5vtw+4mYc16QgfdAoBf29Zkp6aR\nmXg2X/3MxLNkp13Ar21rAEIH3ULiWttQcs1unWlw3yj2PvFPrJYMp+L546Mv2NRvGJv6DSPu+zXU\nud02i9Q/vC1ZqalkxOcfZs6ITyQrLQ3/cNtM4Dq3DyJu1VqAfNfvQvv1JvXIL07FdCUcXRm8vGhP\nDi5fCRfgB2PMiCLqfA6MBZKwLflQcCxNgI+MMROLODYauA84gi2x3Q90Af5pX1qiE7akeIf9HL1L\niLVg78bYz/24Meb7fAHZek/FX+XPlfc305pn24pj/z/yHp9dwjGO1nPY/hVraX1LL2b8ut72FYL7\nns4pe3bXCmaF2/54DXlhAh1HRlHVuxpzTm5h8/wvWDbtVQA6Dh/E9s+XuhoKAMe+X0fjmyN4aO+P\nXEpP57tHcv873Bu9mI+6RgEQMeNpWgwbRBXvajxyZAN7P/qK6NlvEDnrGar4eBO14HUAUk6e5ts7\nH3UqFjd3d/qOHsuXL0zEWK20ibiZoLoN2bjoQ0IbNaVJ+640atuB4/t2Mv/pBxA3NyJHPkQ1Xz9i\n9+1k7cJ3QQSModOAoQTVb+Ty67NgzsP0bN+MQH8fjq18menvLKaKhzsA7y1ax3eb9tKve1sOLZlL\nuiWTB6d+AEByygVmv7eU6E9sve1Z85aSnOLIf+3Snd24mVo9utJl+TdkWywcej53xm3HLz9h+7C7\nATgy60VazJyMu6cnZzdF51x7azrxadyqVuW6d209ppS9+zkyc67T8cSv2UhQ7x5EblpOdrqFvf/M\nHWHovvJLNvWzjRrsf3YW7V6ZiZuXJwlrN+V8TaD5pH/g16o5GEN6zGn2TZjudCwOq+A9OTHlOFZa\n3kSkIbDMGNNaRIKAnUBvY8yvIlIdqGOMOSoi7sBv2IbzvjLGfGk//negAxCMrXfXzRgTLyI1AV9j\nzB8iMhqYbn/8H7Zrf+nGmOtFxAfwth9TAzhmjMk/tSs31g/t5xkINALWYxtqvAe4BRhqHyptim3B\nwY7YFhQc6MjPn+ccy4wxiwq8Nq8DCcaYGfbk+W9jTLiITAXSjDEv24/fb48vFfjZGNPAvj8ybywi\n8ia2DwsfFhdbccOV5eWa6lXLO4R8aq37sbxDKOSRh6aWdwj5rMx2vrd3NaQnW8o7hEIGnNxb6Nr4\nlbL+utWh31W3azu7fC5naE/OzhiTYE9In9mvbQE8Bxw1xmTbJ5uMxjaMWfDYgyLyHLDKPkPzEvAY\n8Ae23ls9YIO9nZPAYfuhvsBiEfHC1iMbV0qYJ4CfAD/gEWOMRUTmYxv6+9k+mSMB23W0sjQV+EBE\n9mIbUi30GuRljDlrn7yyH/gOWF7G8SilKgiT5fy14z/D/3RP7q8kby+rvGP5M2hPrmTakyud9uRK\nVxY9ueyD6xz6XXVvGak9OaWUUn8xugqBuhIi8iy2Kf15fWWMGe1Cm7WA1UUU3WiMOVvEfqWUcsjl\nO75UVJrkKhhjzCxgVhm3eZbc77UppVTZqSA3USiOJjmllFJOM1mZ5R1CiTTJKaWUcp725JRSSlVW\nRieeKKWUqrQ0ySmllKq0dLhSKaVUZaVfIVBKKVV56exKpZRSlVVFWWy4OJrkVIV053XFLUtXPjJS\nKtanVRlZ1vfgdt1Kr4r156Sfe+vyDiGfAwNTyjuEq6MMJ56ISD/gNcAdmG+MmVugfBzwIJCF7Wb0\n9xtj/iipTV00VSmllPOs2Y49SmFf0uw/QH9sC1iPEJGWBartAjoYY9oCi4AXS2tXk5xSSimnGavV\noYcDOgG/GmOOGWMysS1WHZXvXMasNcZctG9uBeqW1qgmOaWUUs5zsCcnImNEZEeex5gCLdUBTubZ\njrHvK84D2NarLFHFGkRXSin1l2IuOXa92hgzD5hXFucUkbuBDkBEaXU1ySmllHJe2c2uPAXUy7Nd\n174vHxHpAzwLRBhjMkprVJOcUkop55Xd7MrtQBMRaYQtuQ0HRuatICLhwLtAP2NMvCONapJTSinl\ntLK6QbMxJktExgLfY/sKwQfGmAMiMh3YYYxZArwE+ABfiQjACWPM4JLa1SSnlFLKaWX5ZXBjzApg\nRYF9k/M873OlbWqSU0op5TRrZlZ5h1AiTXJKKaWcprf1UkopVWmZbE1ySimlKilNckoppSotq64n\np9TV02T8OGp264rVYuHQlBmkHT5SqI5Pi+a0mPY8bp6eJG2O5pcXXwHgmicfp1bP7phLl0iPOcXh\nKTPISktzKZ4Wk8cTGNkNa7qFfeOnkHLgcKE6fq1b0ObFabh5eZK4bjOHpue/x2zDB0bRfNI4Vnfo\nxaXkcy7FA9D8uacJiuhOdrqFfROmkHqwiJhataD13Km4e3mRsH4Th2e+lK+8wf1303zCONbc0Nvl\nmJo8809q9bC9Zwefn07aocLvmW+L5rSYORk3T0/Obozmlxf+BcA14x4nMKKH7T07eYpDk6eTlerc\nezZvyn3c0rMdCUkphA+dXGSdV8aPpF+3NqRbMnlgyvvsPnwCgFGDujLhwUEAzJ2/lAVLo52KoSDv\nltcRNOw+EDdSNq8medV/85WLhwch9z6OZ/3GWC+kcWb+K2QlJeDbsQcBfXNn0v9/e/cdJ1V1/3/8\n9V7KLh1hWRAVsCMqVlSKgqgJBgj23qLGxCS2xK5RwR5jEoO/JF9LorHEbhRULCBIVUGRJhh7oSxL\nbwvs7uf3x72zO9uXZZkzu3yej8c82Ln3ztz37Cxz5px7StOduvLt3dey6fuv6yRXVdL9mpzPXenq\nrXb9+tCsyy68P+wUFtxxD3vfeG2Fx+1947UsuP1u3h92Cs267EK7vr0BWD7tAz489Sw+PP0c1n/z\nLV0uPH+r8mQP6Efzbl2YOHAYc266gx4jbqzwuB4jbmTOjbczceAwmnfrQnb/vsX7snbsSHa/I9jw\nw6KtylKcqX/fKNNxw5j7+zvoMfyGijMNv4G5N9/BxOPiTEf1KcnUqSPZfXvXSab2/frQvOsuTBty\nMvNH3M3eN19X4XF735n86tkAACAASURBVHwd84ffxbQhJ9O86y606xe9ZyumfsAHJ53JB6eczfpv\nvqXrRRfUOsu/R01myK//VOn+Qf32Z48uHekx7AYuveNxHrzxPAB2aN2Cmy4ZRr9z76DvObdz0yXD\naNuqea1zFFMGHc64mB8evJNvRlxFq179aNqp9PzDrfscQ9H6dXxz62WsGDea7BPPAWDNhxP59q5r\n+Paua1j82Eg2L8tNSQEHUe/KmtxCqdNCTtIFkh6s5WMfk3RKDZ6/8xY+bzdJc6p5zlpl3tJsksZL\nOjT++XVJbas49kpJzZPuV3l8fVH2dW2N7P5HsXh0ND/r6tlzaNyqFU2z25c6pml2exq1aMHq2dGf\nwOLRb5A9IJrubsW097G4qWX17DlkdszZqjwdj+3PwpdHA7Bq5myatG5FZofsUsdkdsimccsWrJo5\nG4CFL4+m43EDivd3v+lqFtz7AJhtVZaEnGMGlGT6ZDZNWrWiaZlMTTtk06hlC1Z9UpIp59iji/fv\nfePv+Oy+v9RJpuyjj2LxqGgY1OpZVbxnLVuwelb8no16nQ5HR+/Z8qkl79mqWVv3nk366DNWrFpX\n6f6h/Q/iqdFRDe2D2V/StlVzOmW34Ud99mPstLmsWL2OlWvWM3baXH7cd/9a50jI6rYHm5cupiAv\nFwoLWDN9Mi0O6FXqmBYH9GL1tPEArP1oKs27lz9vq179WDt98lbnqak6XIVgm6hvNbkLgC0q5FLo\nArYgm5n9xMyqave5Emi+BcfXF6Ve19bIzOnAxsVLiu9vXJJLZk6H8sfk5lZ5DMCOw4ayfPLUrcvT\nMYcNCxcX389fvITMTqU/hDM75ZC/uCRP/qIlxR/UOccOIH9JLmvmf7ZVOcpmyk/6HeUvySWrY+nX\nn9WxAxuTMy3JLc7U4Zj+bFySy5r5/6ubPDml80TvR065YzYuKZMnp3xh1vnEoSybVDfNhBXpnLMD\n3y1eXnz/+yXL6ZyzA507tOX7JSXbf8hdQecOW//9s3HbdhSsyCu+X7BiGY3btqv8mKIiCjesJ6NF\nq1LHtDykD2umT9rqPDVVVFhUo1soNSrkJJ0naZakTyQ9IWmopPclfSzpHUnllnGW1FHSy/FjPpHU\np2ytStLVkm6r4LG3SPpQ0hxJDylyCtGs009JmimpmaRDJE2QNEPSm5J2jB9/SOK8wK9r8BI7Sxoj\n6X+Sii+QSDpT0uw4x73xtkZxrXNOvO+qirLV4Hf6taRsSS0kvRbnnSPpdEmXExWY70p6t8zx3SR9\nKulhSXMlvZU4n6Re8fs0U9J9id+1pH0lfRBvnyVpzypylXqv423dJI2Lt4+V1CXeXqr2LWlt/O+A\nuNb6gqT5kp6K38Nyr6vMuYuX4hidV6Np6epE14suwAoLWfL6mJSds6yMrCx2u/RCPv/z34NlKCsj\nK4vdfnkhnz/wj9BRyun6859hBYUseS3ce5aOMrvtiW3ayKaF31V/cB2xwqIa3UKptuOJpH2Bm4E+\nZpYnqR1gwBFmZpIuBq4FflfmoX8FJpjZiYpWfG0J7FDDXA+a2Yj4/E8AQ8zsBUXzml1tZtMlNQFG\nAsPMbKmk04E7gQuBfwG/MbP3JN1X2UmSHAgcBGwEFkgaCRQC9wKHACuAtySdQLTe0U5mtl+cr62Z\nrUzOVsPXmDAIWGhmg+Pna2NmqxQt8360meVV8Jg9gTPN7OeSngNOBp6MX/fPzWyqpORl438JPGBm\nT0lqSjQvXDmVvNcQ/Z4fN7PHJV1I9N6eUM3rOgjYF1gITAb6mtlfq3pdyUtxvHvQ4RW2je102ins\neFK0juKaufPI7FTy/SqzYw4bc5eWOn5j7tJStYCyx3QaOpj2R/Vj5i9q8l2ovC7nnMbOp58EwKrZ\nc2nWuRMrZ0T7sjp1LFVDAti4OJespNpd1o4d2bgkl+ZddqbZLjvR97Vno5ydcujz6tNMPfFcNuUt\n26JMu5x9GjufdiIAq2fPJSvpd5TVMYf8JaV/R/lLlpaqcWZ1zCnJtPNO9Hn1meJMvV9+immnnLdF\nmXY6/RQ6nxz9uayZO4+sTh1ZFe+L3o8yv6Pc3FLNkFlljun008FkH9WPj3/+qxpnqI2FuSvYpVNJ\nTWrnju1YmLuChUtXctQhexdv3ylnB96bUb7zzJYqWLmcxjuUNCU33qE9BSuXV3hMwcrlkJFBo2bN\nKVq3pnh/q0P7siaFTZWQ/kMIalKTGwg8n/hQMrPlREsgvClpNnAN0YdZRY/7e/yYQjNbVcExlTk6\nrinOjp+nouffG9gPeFvSTKIP550VXbdqa2bvxcc9UYPzjTWzVWaWD8wDugK9gPFmttTMCoCngKOA\nL4HdJI2UNAhYvQWvqyKzgeMk3SvpyBr+nr4ys5nxzzOAbvHrbmVmiTa3p5OOnwrcKOk6oKuZbajk\neSt6rwF6Jz3fE0C/GmT8wMy+N7MiYCbQrQaPqdYPz73A9DPOZfoZ55L37nt0GnI8AK3334+CtWvL\nffhuyltG4bp1tN5/PwA6DTmevAnRn0a7PkfQ5YJzmX3l1RTlV7tiR4W+ffI5pgw9gylDzyD3rXfp\nfOIQANocuD+b16xl49LSZfnGpXkUrF1HmwOjaymdTxzCkncmsPazz3n3sGOY0H8wE/oPZuPiXKb8\n9KwtLuAAvnvqOaYOO5Opw85kyTvjSzIdsH/0OyqTadPSPArXrqPNASWZcseOZ+1nnzO+97G8N3AI\n7w0cwsbFuUw98ewtzvTDsy/w4Wnn8OFp57B03AQ6Df0JAK177kfhmkres7XraN0zfs+G/oS8d+P3\nrO8RdP3Zucy6/He1fs9qavSEmZw9JOqAc9j+u7Fq7XoW563irSlzOLb3vrRt1Zy2rZpzbO99eWtK\npZf9ayz/m89pmrMjjdvnQKPGtDq0L+tmfVjqmHWzptP6iAEAtDy4N+sXJJ1XotUhvVPaVAnpf02u\ntkMIRgJ/MrNXJQ0Abqvh4wooXbBmlT1AUhbwN+BQM/subs4sdxwgYK6Z9S7z+No0jif/bymkit+L\nma2QdADwY6Ia0mlEtcdaMbPPJB0M/AS4Q9LYRC12C/JW2TxqZk9Leh8YDLwu6RdmNq62mZMUv5+S\nMoCmVWSs8+EqyyZNpl2/Phzx6osU5ucz/7bbi/cd+swTTD/jXAA+u/sPdB9+C40yM1k2eSrL4+s4\ne153NRlNm3LA30cCUeeTz+68t9Z5lo6fRPaAfhw17lUK8/OZfd1txfv6jHqGKUPPAGDerXez/x+G\n0ygzk6UTJpM3ftt9KOWNn0SH/v048p1XKNyQz5wbSjL1fuU/TB12ZpTptrvZ757hNMrKJO+9KeRN\n2Da1gWUTJ9P+yD70fu0lCvPz+fT3Je9Zr+ee5MPTot6CC+78A/vcEb9nk6YUX3vb64ZryGjalAP/\nL+ortnrWHBbccU/5E9XAE3f/gqMO2Zvsti35cswfGfGPV2jSOGrkePiF8bwxaRaD+vXk01fvYUP+\nJi6+7Z8ArFi9jrseHsWUJ38PwJ0PjWLF6so7sNRYURG5zzzCTpfdDBkZrJ4yjk2LvqfdkNPZ+O0X\nrJs1ndWTx9LxgsvpOnwkRevXsujRPxc/vNkePdi8YlnUcSWF0n3uSlk1PabiJqyXgd5mtixuwhoL\nXGxmMyT9C9jVzAZIuoCocPqNpGeAaWb2l6TmyvXAIqJa2FpgAjDGzG6T9BgwGngHWED0zb8RMA14\nIT5mFFHh+m7c7DYPODdunmsC7BUvzTAL+JWZTYqvpQ1ONC9W8PqKM8f3RwN/jDNMo6S58k2iwn0y\nsMnMVkvaD3jSzA5MzlbF73I8Jc2tXxNdx2sKLDezfElD4t/rCXEt9qdm9lX82MTxLYHRSc2lVwMt\n49/PHOAiM3tf0l3x4/eTtBtR7c8k/RH43sz+UpP32syWS3qVqIb3RPz7GhY3Q99MVHu8Lm7KfTk6\nhQbEr3NI/LwPEi2V8VjZ11WZyporQ9m4umarH6eKGil0hHKaZKXXsNtBjSr8Lx/M3CO2ttGn7u35\n9xe2+g/p6+vOr9H/1W73Ph7kj7bav8q40LgTmCCpEPiYqOb2vKQVwDhg1woeegXwkKSLiL7JXxoX\nRiOAD4gWxSs3KjW+vvUwMAdYTLSQXsJjwD8kbSBqQjsF+KukNvFr+QswF/gZ8E9JBrxV7W+h4te9\nSNL1wLtEtcbXzOyVuBb3r7jmApAYeFQqWxVNgmXtD9wnqQjYDFwab38IGCNpoZkdXemjS7sIeDh+\nrglQfOnjNOBcSZuJfqd3VfTgSt7rC4DLiF7zNcBSot8vwMPAK4o6+IwBavJ1tjavyzmXptL9mly1\nNTlXf0hqaWaJHo7XAzua2RWBY9WK1+Sq5jW56nlNrnp1UZP78qqzavR/dbc/P52eNTlXrwyWdAPR\n+/oNUS3MOee2mXSf1mu7KeQk/ZhoSECyr8zsxG1wrpcp34R7nZm9WdfnSmZmzwLPVnecpPZE11XL\nOsbMtrw7n3Nuu5XuHU+2m0IuLmC2aSGTdK46LzjrUlyQHRg6h3Ou/gs5m0lNbDeFnHPOubrnzZXO\nOecarHTvXemFnHPOuVqzwrTqCF2OF3LOOedqza/JOeeca7AKN3sh55xzroHya3LOOecaLL8m55xz\nrsEq8kLOuS1XkJ9esyg0TrN5GVt0bB46QjnL/7cidIRS5g5Jr7ki953WOnSEcupiRtZ0b66syaKp\nzjnnXIUKNxfV6FYTkgZJWiDp83iS+bL7MyU9G+9/X1K36p7TCznnnHO1VlRYVKNbdeJ1R/8fcDzQ\nAzhTUo8yh10ErDCzPYA/U34+4nK8kHPOOVdrVmg1utXAYcDnZvalmW0CngGGlTlmGPB4/PMLwDGS\nqlzCxws555xztVbTQk7SJZKmJ90uKfNUOwHfJd3/Pt5W4TFmVkC0MHT7qvKl19V055xz9UpNZzwx\ns4eAh7ZtmvK8kHPOOVdrVlRnQwh+AHZJur9zvK2iY76X1BhoA1S5BqYXcs4552qtcFNhXT3Vh8Ce\nknYlKszOAM4qc8yrwPnAVOAUYJyZVVnKeiHnnHOu1upqxhMzK5D0G6LFrRsB/zSzuZJGANPN7FXg\nUeAJSZ8Dy4kKwip5Ieecc67W6nLGEzN7HXi9zLZbkn7OB07dkuf0Qs4551yt+crgzjnnGiyfu9I5\n51yD5asQOOeca7DqsHflNuGFnHPOuVrzmpxz29DeN15D9lF9KczPZ+6Nt7Fm3vxyx7Tq0Z197x5O\no8xM8t6bzIK77gNg98svpcPA/lBUxKblK5h7w61sXJq3VXn2uuF3UZ4N+cy7aThrPl1QcZ47byUj\nK8rz2d33A7DbZb+kw9FHgRmbli1n7k3D2bQVedr2Ooxuv7kMZWSw5PXXWPifp0vtV5Mm7HH9jbTc\nay82r17N/0YMZ+OSxTRu3Zq9bh1By+57s/TNMXz11wdqnaEiPYZfR87AIynckM8nv/09q+d8Wu6Y\n1vvvwwF/uoNGWZnkjpvIvFujeXj3vOpSupx1EhuXRcv6LLj3ryx9d1KtszTvcSAdTvsZKIPVk8ey\n4q3/ltqvxo3peP5lZHbZjaJ1a1n0yJ8oWL6UVr2OZIfjflp8XNOduvLt3dey6fuva50F4KFbf8ZP\njjqApctXc9Cpt1R4zJ+uPYtBffdnQ/4mLrr1UWbO/xaAc4f24fqLhwJwzyOjeGLUlK3KUlNFVQ9T\nC87nrkwjkl6X1Db+eW01x3aTNKeSfY8kZu+W9LWk7PjnKUmPLTvIsqYZh0maJWlmPP9cv6R9XSS9\nJelTSfMSy2BIekzSV/FjZko6sDbnLiv7qL4077oLkwedwKe33sE+t9xQ4XH73HoDn95yO5MHnUDz\nrrvQ/sg+AHz96L+ZdsIZTDvpLJaOn8huv/r5VuVpf2QfmnftwpTjT+LT2+6i+y3lVgoBoPst1zPv\n1juZcvxJNO/ahfb9ojzf/PMJ3j/pLN4/+WzyJkxit0svrn2YjAx2veJKPr3+Wmb+7HyyBx5Ds65d\nSx2Sc/xgCtas4eNzz2bRC8/T5ZJfAFC0aRPf/etRvvnH32t//kp0OLofLXbtyvgjhzD7uhHsd9fN\nFR63/103M/va4Yw/cggtdu1KhwHFf2Z89ciTTBp0GpMGnbZVBRzKoMMZF/PDg3fyzYiraNWrH007\n7VzqkNZ9jqFo/Tq+ufUyVowbTfaJ5wCw5sOJfHvXNXx71zUsfmwkm5flbnUBB/DvUZMZ8us/Vbp/\nUL/92aNLR3oMu4FL73icB288D4AdWrfgpkuG0e/cO+h7zu3cdMkw2rZKzZqDhWY1uoXihVwaMbOf\nmNnKOniei81sXgXb+8Q/dqP8TAI1NRY4wMwOBC4EHkna92/gPjPbh2hG8dykfdeY2YHxbWYtz11K\nh4H9WfTKawCs+mQOjVu3pGmH7FLHNO2QTeOWLVn1SfR9YNErr5FzzAAACtetKz6uUbNmbO1/ww4D\n+7Po1SjP6llzaNyqFU2zS88d2zS7PY1btGD1rDjPq6/R4Zj+FeZhKz4YWnbfh/wffmDjokVYQQF5\n48axQ59+pY5p17cvS996E4BlEybQ5uCDASjKz2fNnNkUbaqLJTVL6/ijo/nhxVEArPx4Fk1atyIz\np/R7lpkTvWcrP54FwA8vjqLjj4+u8yxZ3fZg89LFFOTlQmEBa6ZPpsUBvUod0+KAXqyeNh6AtR9N\npXn3/cs9T6te/Vg7fXKdZJr00WesWLWu0v1D+x/EU6OjGtoHs7+kbavmdMpuw4/67MfYaXNZsXod\nK9esZ+y0ufy4b/ms20Kh1ewWihdydUTSfyXNkDQ3nm37l5LuS9p/gaQHKzo26ZjiWlfStpaSxkr6\nSNJsSclLTzSW9FRcc3pBUvP4MeMlHVpBxkTt8B7gyLhWdZWk95JrV5ImSTqgotdpZmuTptFpAVHZ\nENccG5vZ20nHra/p7682MjvmkL94SfH9/MW5ZOV0KHVMVk4H8pckHbNkCZkdc4rv737Frzhy3Gvs\nOHQQX/x162oumTkdSuXZuCS31LmKMy8pKfs3Ls4lMynz7pdfSr93RtNpyCC+ePD/ap2laXY2G3NL\nzrMpbymZZb8AZGezKXFMUSGF69bRuHWbWp+zJrI65bBh4eLi+/mLlpDVKafcMfmLSn6PG8oc0/X8\nMzjyrRfo+cfhNG7TqtZZGrdtR8GKkubgghXLaNy2XeXHFBVRuGE9GS1Kn7PlIX1YM30rapRboHPO\nDny3eHnx/e+XLKdzzg507tCW75eUbP8hdwWdO7RNSaZNRVajWyheyNWdC83sEOBQ4HLgZeDEpP2n\nE62PVO5YSVUtFZEPnGhmBwNHA/cnrZ+0N/C3uOa0GvhVDbNeD0yMa1V/Jpoq5wIASXsBWWb2SWUP\nlnSipPnAa0S1OYC9gJWSXpL0saT74kUQE+6Mmzn/LCmzkuctXorjtZVbd22spr544G9MHDiYRaPG\nsMvZp6fknFXm+evfmXTsEBaPHsMuZ50WOk7a+eaJZ3m332Am/vhUNubm0eP3VwfNk9ltT2zTRjYt\n/K76gxsob67cflwu6RNgGtEs2bsCX0o6Ii7EugOTKzl2zyqeV8BdkmYB7xCtp9Qx3vedmSWe80mg\nXwWPr4nngSGSmhAVWo9VdbCZvWxm3YETgNvjzY2BI4GrgV7AbsQFJ3AD0evvBbQDrqvkeR8ys0PN\n7NDBbbMrOoSdzzqVI156miNeepqNS/PI6tSxeF9Wpxzyc5eWOj4/dylZHZOO6diRjUtyKWvx6Dfo\n+KOBVb3sivOceSqHv/gUh7/4FJvylpXKk9kxp9y5Ni7JJSupdpfZKYeNZTIDLHrtDXKO2/I8CZvy\n8sjMKTlP0+wO5TrVbMrLo2nimIxGNGrRgoLVq2p9zsp0Pf90+o15jn5jnmNjbh7NOncq3pe1Y0fy\nF5f+HeUvziVrx5LfY7OkYzblLYeiIjDj26dfpO2BtW+SK1i5nMY7lPydNd6hPQUrl1d+TEYGjZo1\np2jdmuL9rQ7ty5o6aqqsiYW5K9ilU0ltc+eO7ViYu4KFS1eyc8eS7Tvl7MDCpVt95aNGvLlyOyBp\nAHAs0NvMDgA+BrKIam6nAScDL5uZVXFsZc4GOgCHxNfBliQdX/ZPp1Z/SnGz4ttEq+6eBjxVw8e9\nB+wWN7F+D8yMV/UtAP4LHBwft8giG4F/EV2vq5Xvn36eaSedFXUWGTueHYcNBqDNAftRsGZtud6I\nm5bmUbB2LW0O2A+AHYcNZum4CQA071qyqkeHgf1Z9+XXW57nP8/z/sln8/7JZ5M7djw7/jTK07rn\nfhSsXcumvNKrgGzKW0bBunW07hnn+WlJnmZdSvLkHN2fdV9teZ6EtfPnk7XTzmR26oQaNyZ74EBW\nTC39Ybx8ymQ6/OjHALTv359VH39c6/NV5ZvHny3uKLLkzXHsdHLUA7DtQT0pWLOGjbml37ONudF7\n1vagngDsdPJQlrz1LkCp63edBg1kzYL/1TpX/jef0zRnRxq3z4FGjWl1aF/Wzfqw1DHrZk2n9RED\nAGh5cG/WL0jq6yXR6pDeKWuqBBg9YSZnD4kurR+2/26sWruexXmreGvKHI7tvS9tWzWnbavmHNt7\nX96aUmG/tDqX7jU5H0JQN9oAK8xsvaTuwBHx9peBm4CDKKm9VHZsVc+da2abJR0NJHeR6yKpt5lN\nJepIUtP/bWuAshczHgFGETVjrqjsgZL2AL6IC+yDgUyi9ZxWAG0ldTCzpcBAYHr8mB3NbFHczHoC\nUCf/+/ImTCL7qL70ffMVCvPzmXfjbcX7jnjpaaadFPWtmT/iHva9+zYyMrPImziZvPeiD/s9fnsZ\nLXbtihUZ+QsX8eltd21VnmXvTSb7qL70eeNlivLzmXvziOJ9h7/4FO+ffHaU5/Z7oyEEmZksmzSF\nZROjjgR7/vY3NO/WFSsqIn/RYuYPv7v2YYoK+WrkX9jn3j+iRhnkvvE6G77+ml0uuJC1n81nxZQp\n5L7+OnveeBMHPfEUBWvW8Nntw4sfftDTz9C4eQvUpDE79O3Hp9dezYZvvql9nljuuIl0GHgkAya9\nRuGGfGb97vfF+/qNeY5Jg6Im2jk33ckBf7qDjKxMlr47qbgXZfcbr6L1vt3BjA3fL2T29SMqPE+N\nFBWR+8wj7HTZzZCRweop49i06HvaDTmdjd9+wbpZ01k9eSwdL7icrsNHUrR+LYse/XPxw5vt0YPN\nK5ZFHVfqyBN3/4KjDtmb7LYt+XLMHxnxj1do0jhq9X/4hfG8MWkWg/r15NNX72FD/iYuvu2fAKxY\nvY67Hh7FlCej3+edD41ixerKO7DUpTQfJoeqWYrH1UB8jem/RL0WFwBtgdvMbLyk0UAPM9utBsd+\nDRxqZnmS1ppZy7iWNApoSVRoHAEcH596TLztEGAecG5ceI4Hrjaz6ZU8ZxOi5SzaA4/F1+WIr7Nd\naWZjqnit1wHnAZuBDUS9JifF+44D7idqYp0BXGJmmySNI6qNCpgJ/NLMqhwi8fY+h6TVH6Yy0qvR\no0XH1HQP3xLL/1fpd6Mg9hqyV+gIpew7rXXoCOVs+vifqv6oqv29bfca/V+9dOX8rT5XbXhNrg7E\nzXDHV7JvyBYc2y3p55bxv3lA70pO3b2S5xlQzXNuJqppFZPUmaj5+q1KzpV4jnuBeyvZ9zbQs4Lt\ntb+45JxLayF7TtZEen09dUFIOg94H7jJzNJ73QznXFrxa3Iu7ZnZv4kGcheT9DPgijKHTjazX6cs\nmHMu7aX7NTkv5FyFzOxfRD0hnXOuUiFraTXhhZxzzrla85qcc865Bstrcs455xqszV7IOeeca6i8\nudI551yD5c2VzjnnGiyvyTnnnGuwvCbnnHOuwUr3ab28kHPOOVdr6d5c6asQuAZN0iVm9lDoHMnS\nLZPnqVq65YH0zJSufIJm19BdEjpABdItk+epWrrlgfTMlJa8kHPOOddgeSHnnHOuwfJCzjV06Xjd\nIt0yeZ6qpVseSM9Mack7njjnnGuwvCbnnHOuwfJCzjnnXIPlhZxzzrkGyws555xzDZYXcq5BktRV\n0rHxz80ktQqc5w+SWktqImmspKWSzgmYZ3dJmfHPAyRdLqltqDxxjj6SzpJ0XuIWMMvYmmxLUZae\nST83kXSzpFcl3SWpeYhM9YkXcq7BkfRz4AXg/+JNOwP/DZcIgB+Z2WpgCPA1sAdwTcA8LwKFkvYg\n6o6+C/B0qDCSngD+CPQDesW3QwPkyJLUDsiWtIOkdvGtG7BTqvPEHkv6+R6iv537gWbAP0IEqk98\ngmbXEP0aOAx4H8DM/icpJ2wkmsT/DgaeN7NVkkLmKTKzAkknAiPNbKSkjwPmORToYeHHNP0CuBLo\nDMwAEm/SauDBQJmS/1COAXqZ2WZJ7wGfBMpUb3gh5xqijWa2KVGISGoMhP7wHCVpPrABuFRSByA/\nYJ7Nks4EzgeGxtuaVHH8tjYH6AQsCpgBM3sAeEDSZWY2MmSWJG3iLyMZQKaZbQYwM5MU+u867flg\ncNfgSPoDsBI4D7gM+BUwz8xuCpgpE2gBrDKzQkktgJZmtiRQnh7AL4GpZvYfSbsCp5nZvSnOMYro\nC0gr4EDgA2BjYr+Z/TSVeZJJ6gN0I6kyYGb/DpDjX2U2XW9mSyR1Ap4ys2NSnak+8ULONTiSMoCL\ngB8RNfW8CTwSsilM0kdmdnB121KY5xhgipltCHH+pBz9q9pvZhNSlSVZfI1wd2AmUFgSxy4PkcfV\nnjdXuoaoGfBPM3sYQFKjeNv6VAeJv23vBDSTdBAl11daAyF7xp0H/F3ScmAi8B4wycxWpDJEohCT\ndK+ZXZe8T9K9QJBCjvS5RoikLkCumeUraoO/ADgYmAc8bGYFIfOlO6/JuQZH0jTgWDNbG99vCbxl\nZn0CZDmf6EPpUGB60q41wGNm9lKqMyWT1Bk4Bbga6GxmQb74VlLTnWVmPSt7zDbO8zxwuZkFvUYY\nZ5kDHGZm6+OC9ltnjQAAGNxJREFUf3ei3sIDAczswpD50p3X5FxDlJUo4ADMbG2o8URm9jjwuKST\nzezFEBkqEo/ROxLYH8gj6jk4MUCOS4mume4maVbSrlbAlFTnSZINzJOUDtcIM8ws0QpxLFHvyiLg\nSUneu7IaXsi5hmidpIPN7CMASYcQ9WoMabSksyjfkWFEoDx/Ab4gGmf1rpl9HSjH08AbwN3A9Unb\n15jZ8jCRALgt4LnL+k7SQDMbRzTGchfgG0ntw8aqH7y50jU4knoBzwALia6BdQJON7MZATONAVYR\njb1KdGTAzO4PmGlf4CiiAdh7AgvM7NyAeRoBHSn9JeDbUHnShaRdgH8DjYj+hvoRdYhpC1xtZkFm\nYqkvvJBzDZKkJsDe8d0FibFFAfPMMbP9QmZIJqk10BfoT9RsmQ1MM7PzA+X5DVHtaQlQFG+2gNfk\n1lAytrIp0RjCdWbWOkSeONM+wF5EXwK+Bz6Mmy1dFbyQcw1GoklH0kkV7Q/ZyUPSQ0Qzi8wOlSFZ\nfP1rUnx7z8y+D5znc+BwM1sWMkdF4h6Nw4AjzOz66o5PBUntAjfn1hteyLkGQ9JwM7u1gsGzENUK\nUt4LTdJsohpBY6ImwS+JOjKIgDWVBEnNkzo1hMzxLnBcOneHl/SxmR0U4Lx9gUeIargXAncAuxHV\nME8zs6mpzlSfeCHnGpR4IPgpZvZc6CwQrYZQ1X4z+yZVWZJJ6g08SjTrShdJBwC/MLNfBcrzKFHz\n8muU7s34p0B5klsDMoiGgPQ3s94BsnxANLlBS2AUcIKZTZJ0MFHrQN9UZ6pPvHela1DMrEjStUBa\nFHKJQiye2b6sNSmOk+wvwI+BVwHM7BNJRwXM8218axrfQhua9HMBUa/GYWGi0CTRzC1pqZlNAjCz\njyQ1C5Sp3vBCzjVE70i6GngWWJfYGPgaxkdEXb9XEDVVtgUWS1oC/DxEz08z+67MSgiFlR2bgizD\noXjgPsnjHAPl+VnI85eRvCTaDWX2pcMXgrTm68m5huh0ogHGE4hmGUncQnob+ImZZZtZe+B4YDRR\nzr8FyPNdPAGxxQtxXg18GiAHAJL2i5f6mQvMlTQjHuIQKs/Okl6WlBvfXpS0c6A4v09MZmBmxesi\nStqdaGiBq4Jfk3MNTtyE8yui8URGNJPHP0JORixptpntX2bbLDPrKWmmmR2Y4jzZwANEM2gIeAu4\nIlTvRklTgJvM7N34/gDgrhBTscXnf5tooPoT8aZzgLPN7LgQeWpC0kgzuyx0jnTjhZxrcCQ9R7TI\n5VPxprOANmZ2WsBMbwFjiQapQ1TbPA4YRDTeKchqBOlC0idmdkB121KYp9wXjxBfRrZEyFUt0plf\nk3MN0X5m1iPp/ruS5gVLEzkLuJVoYl2AyfG2RkDKCl9J15rZHySNpIKFZAMuJfOlpN9Tuub0ZaAs\nAMvi+T3/E98/E0i7MXyuel7IuYboI0lHmNk0AEmHE/ianJnlES3gWpHPUxglcd0t9DXKsi4EhgOJ\nAfsT422hXAiMBP5M9GVgCpBOnVFcDXlzpWtwJH1KNOYqMe9hF2ABUVfwlA7AlvQXM7syaQXsUkKt\nfJ08gbVrGEINVk93XpNzDdGg0AGSJJrf/hg0RXn3xwu6vgA8a2ZzQoaRdChwI+VXaQg1d+XjRB1x\nVsb3dwDuD7l2m6T9q5kW7oGUhalHvCbn3HYqLuROI+oE05qosLsjUJYFwDXAbEomaA45I0y5WlHo\nmpKkiUAm8BjwlJmtCpWlPvFCzrkUiOcfvA3oSlRTScxduVvIXBDVEIBriZYjCjK4WNIkM+sX4twV\niRcjHWBmK+L77YAJZYeBBMi1J9H1wlOBD4B/mdnbITOlOy/knEsBSfOBqyi/nlyocWn7ENXgTiFa\nGfxZ4EUzyw2U5xiiHoxjKT13ZZCVIySdR9R8+ny86VTgTjN7ovJHpUa87t4JwF+JhsoIuDHkKhvp\nzAs551JA0vtmdnjoHAmSphKN2XvezBamQZ4nge5EM54krycX8hpYD2BgfHecmc1L2rdDopaXwjw9\niXp4DiaaQefReP7KzsBUM6tyMvDtlRdyzqWApHuIxsS9ROmaSrAejvHMMF3MbEGoDElZFpjZ3tUf\nmR5CDLyWNIFoyZ0Xys7eI+ncdKhlpiPvXelcaiRqcYcmbTNKagopJWkoUY/PpsCukg4ERoQa0gBM\nkdQjubaU5lT9IXXu5bIFmaQrzOwBL+Aq5zU559KApPPN7PEUnm8GUQE7PtFjsKL5NVOY51Ngd+Ar\n0mhR2coEqsmVO2foHp/1gdfknEsPVwApK+SAzWa2qsxSOyG/8VY5tjHENbB0IelMoingdpX0atKu\nVkDI5aPqBS/knEsPqW7+mivpLKBR3C39cqKpq4KowXi4sUA6TT6cyvdrCrAIyAbuT9q+BpiVwhz1\nkjdXOpcGUt38Fa9PdhPwo3jTm8AdZpafqgxbItXNcvFabd+b2cZ42Z+ewL+TZkBpF3gRXldDXsg5\nlwZS+SEej7O618yuTsX56kKALwEziToJdQNeB14B9jWzn6QqQ1KWSWbWT9IaSjcpJ65btk51pvrE\nmyudSw+TU3UiMyuUlDazi6SpIjMrkHQiMNLMRsYrl6dcYiYYM2sV4vz1nRdyzqWApLbAeZSfgPjy\n+N/fpDjSx3EnhueBdUl50nXWjFRfs9wcd/g4Hxgab2uS4gxA8ZRilfJm06p5IedcarwOTKPMBMQB\nZREtApo8Ts8oWc8tZeLm07lm1r2Kw45JVZ7Yz4BfEk3l9ZWkXSlZUSLVZhC9NxUV9AYEn/80nfk1\nOedSIMS4qq0h6QYzuzuF53sFuMzMvq32YOe2gBdyzqWApKuAtcBoSk/rlZZNTQE6erwHHEQ0s35y\n82moRWVnU37c4CqiFdXvSOXE2pK6m9l8SRW+H774bdW8udK51NgE3EfUbT/x4ZnOTU2pvgb2+xSf\nrzpvEK0W8XR8/wygObCYaD23oRU/bJv4LXAJpcfIJQSbGq6+8Jqccykg6UvgMDPLC52lJgJNW9UV\n2NPM3onH8TUyszWpzJCUpaIptD4ys4NDTn/mtlxG6ADObSc+B9aHDrEFUlqTk/Rz4AXg/+JNOwH/\nTWWGMhpJOixxR1IvolUkAApCBJKUJem3kl6S9KKkKyVlhchSn3hzpXOpsQ6YKeldSl+TuzyVISTd\na2bXSTrVzJ6v4tCq9m0LvwYOA94HMLP/ScpJcYZkFwP/lNSSqMBfDVwsqQWQsg45ZfybaCqvkfH9\ns4h6fJ4aKE+94M2VzqWApPMr2p7KlQfiHLOJpqiakU69PROLyiZmfpHUGPgo9CoEktoAmNmqkDni\nLPPMrEd121xpXpNzLgVSXZhVYQywAmgpaTXx1FCJfwNOETVB0o1AM0nHAb8CRgXKgqRM4GTiwfuJ\n1RrMbESoTMBHko4ws2kAkg4n6u3pquA1OedSQNJXVLCUjZkF6V0p6RUzGxbi3BWRlAFcRDRhtIgm\njH7EAn1ASRpDNGRgBlEvSwDMrKIejts6S2I4QxNgb+Db+H5XYL7X5KrmhZxzKSCpfdLdLKLrKO3M\n7JZAkZDUEegV333fzJaGyhLnaQp0J/oAX2BmmwJmmWNm+4U6f7K412mlarBM0XbNCznnApE0w8wO\nCXTuU4E/AuOJak5HAteY2QuB8gwG/gF8EefZFfiFmb0RKM9DRBMzzw5x/qrEHXKKe1X6LDFV80LO\nuRQoM1tFBtEyLpea2QGB8nwCHGdmufH9DsA7AfPMB4aY2efx/d2B16qZz3Jb5pkH7AF8RdQbNnHN\nMlhHGEk/JRoQ3hnIJWqu/NTM9g2VqT7wjifOpUbytZwC4GvgtDBRAMhIFHCxZYQdN7smUcDFviTq\nLh/K8QHPXZnbgSOIvowcJOlo4JzAmdKeF3LOpYCZHR06QxljJL0J/Ce+fzrRSgmhTJf0OvAc0TW5\nU4EPJZ0EqVsCSFJrM1tN2AK2MpvNbJmkDEkZZvaupL+EDpXuvLnSuRSobj25QJlOAhKLp040s5cD\nZvlXFbvNzC5MUY7RZjYkqTds8swvFqo3LICkd4ATiAajZxM1WfYysz6hMtUHXsg5lwKSplDBenJp\nNH6uFElTzax36BwJqV76Jx3Fs63kExW8ZwNtgKdSuSJCfeSFnHMpUA/Xk/vYzA4KnSMhwNI/Y83s\nmOq2ufTn1+ScS40n4kmI68V6clQwcD2wlEwYHU943BzIlrRD0nlbE00anXKS1lDx+xF6lpp6wQs5\n51Kjvq0nl25SVej+AriSqJv+DEoKudXAgynKUIqZtQpx3obCmyudS4F6uJ5cujVXpjSPpMvMbGT1\nR257iR6fktpVtD+NWwPSgq8n51xqpM16cpIaxUv+VOXclISpuVQv/bNYUisASTfHa7iFuqaaWJ18\nBtGEzDOSbj5BczW8JudcCkh6GdgXCLqeXFKescBJ6bCEDICkPwB3ABuIVkroCVxlZk8GyjPLzHpK\n6hfnug+4xcwOD5HH1Z5fk3MuNf5L2JWuy1oLzJb0NtGCrkDQcXs/MrNrJZ1INBvMScB7QJBCjpKV\nBwYDD5nZa5LuCJSlWNwZZk9Kz135XrhE6c8LOedSIA3Hw70U39JF4rNoMPC8ma1KrOEWyA+S/g84\nDrg3Xl8u6OUdSRcDVwA7AzOJpviaCgwMmSvdeXOlc9uQpOfM7LSkNcFKCTzhbzOgi5ktCJUhKcs9\nRLN5bAAOA9oCo0M1D0pqDgwCZpvZ/yTtCOxvZm+FyBNnmk20NNI0MztQUnfgLjM7KVSm+sALOee2\nIUk7mtmiytYEC7UWmKShREvtNDWzXSUdCIwws5+GyBNnagesMrPCeHaPVma2OMUZ0rYno6QPzayX\npJnA4Wa2UdJcX4Wgat5c6dw2ZGaL4n/TbWHL24hqTOMBzGympJDzMk4CJgATJU02szUkXStMoaeB\nIUQ9F8vNXUnYcY3fx3Og/hd4W9IKIN3+rtKO1+ScS4F4MuR7gRyiD86gs1VImmZmRySPP0v0KAyU\nZ1eihVuPJLrWtJFo0uirQuRJd5L6E81dOSbkCur1gdfknEuNPwBDzezT0EFicyWdBTSStCdwOTAl\nVBgz+0pSPtHMMJuAo4F9Up2jurFwZvZRqrKUJemvwDNmNsXMJoTKUd94Tc65FIib4PqGzpEQd6y4\nCfgRUa3yTeB2M8sPlOcLII+ouXAiMNPMiqp+1DbJUdUgeTOzYD0ZJZ1PtO7f3sDLRAWeDwavhhdy\nzqWApAeATkTXU5IHgwftxi+pdRTDgi4SKukKorXtdgHmE12fe8/MvgiZqzKSjjOztwOdux1wMnAG\nUe/YPUPkqC+8kHMuBSpZFDRli4GWJakX8E8gMfnvKuBCM5sRIk+CpJbAz4CrgZ3NrFHIPJUJuXSS\npMOIanTDgE/NbGiIHPWFF3LObYckzQJ+bWYT4/v9gL8F7HhyP1FNriXRtcFJRB1PvgyRpzohJrCO\npz47EfgCeAb4r5mtTGWG+sg7nji3DUm61sz+IGkkFQ8GDzWNVmGigItzTJJUECgLRDN3/MHMlgTM\nsCVC1A6+APoQDWPIBHpK8mm9quGFnHPbVqI35XTSYCHSpN6DE+Jpq/5DlOt04jFzgbwEnCVpVzO7\nXVIXoJOZfRAwU7opAsbh03ptEW+udC4F4mtgNwLdKPlyaaluHkzX3oOS/k70IT7QzPaJJyJ+y8x6\nBcqTaWYbK9sm6aVUT6fl03rVjtfknEuNJ4FrgNlEH+ZBmNnRoc5djcPN7GBJHwOY2QpJTQPmmQqU\n7VhSvC1QwZJvZvmSEgXufEl7B8hRr3gh51xqLDWzV0OHSIinhzqP0jXLkNcIN0tqRNykK6kDAb4M\nSOoE7AQ0k3QQJdN6tQaapzpPGT6tVy14c6VzKSDpGOBMYCxpME5O0hRgGmVqlqGWBJJ0NtF1wYOB\nx4FTgJvNLKUrgscDri8ADgU+pKSQWwM8FnpcY4JP61VzXsg5lwKSngS6A3MpKVRCjpMLNs6rMvE1\npmOICpaxIadAk3Symb0Y6vyu7ngh51wKSFpgZmlz/UTSVUSrg4+mdM0ypUvJpOvSNvEMLP8iqsE9\nTFTDvD7kenKudvyanHOpMUVSDzObFzpIbBNwH9H8lYlvuiGWkim7tE2CAuVJuNDMHpD0Y6A9cC7w\nBOCFXD3jhZxzqXEEMFPSV0Q1p8RSO6FWBv8dsIeZ5QU6PwBmNiT+d9eQOSqQuBb3E+DfZjZXkqp6\ngEtPXsg5lxqDQgco43NgfegQCZJeJRqY/oqZpUOuGZLeAnYFbpDUioBDP1zt+TU557ZDkl4G9gXe\npfQ1uSBDCOLegqcDg4l6NT4DjA649E8GcCDwpZmtlNQe2MnMZoXI42rPCznntkNxV/lyQg0hSIjH\nyg0Efg4MCrVyepzlp8BR8d0JZjYqVBZXe17IOefSgqRmwFBKxsuNNrPLAmW5h2gKrafiTWcCH5rZ\njSHyuNrzQs657VDcAaaiVRGC9GaU9BxwGDAGeJao5hTsGli8FNGBiQxxDfPjgB2FXC15xxPntk+H\nJv2cBZwKVDhWLUUeBc40s8KAGcpqCyTG6bUJGcTVntfknHMASJphZocEOndz4LdAFzO7RNKewN5m\nNjpQnjOBe4g65ojo2tz1ZvZsiDyu9ryQc247lLSuHEAGUc3uUjM7IFCeZ4kGhJ9nZvvFhd4UMzsw\nRJ44045E1+UAPjCzxaGyuNrz5krntk/3U3JNrgD4mqjJMpTdzez0uAaFma1Pg8HXGUAe0efkXpL2\n8lW46x8v5JzbPh0PnEzppXbOAEYEyrMp7l2ZWGpnd5LG76WapHuJenmWmlAb8EKunvFCzrnt03+B\nlcBHQJAB1wlxje0fRD0rd5H0FNCXaMmbUE4guiYYrKB1dcOvyTm3HZI0x8z2C50jQdJsYADRHJ8C\npoWcV1PSG8CpZrY2VAZXN7wm59z2aYqk/c1sduggsY+A3czstZAhJI0kapZcTzShdtlFbkOtnO5q\nyWtyzm2HJM0D9gDSYlUESfPjPN8A60LlqWy6s4TQ0565LeeFnHPbIUldK9puZt+kOgukX57qSHrR\nzE4OncNVz5srndsOpVvhkW55aiDUYq5uC2WEDuCcc/WQN4HVE17IOeeca7C8kHPOuS0XejYWV0Ne\nyDnn3Ja7LnQAVzPeu9I552LxoPRKPxR9Pbn6x3tXOudciSHxv7+O/30i/vfsAFlcHfCanHPOlSHp\nYzM7qMy2j8zs4Moe49KTX5NzzrnyJKlv0p0++OdlveTNlc45V95FwD8ltYnvrwQuDJjH1ZI3Vzrn\nXCUShZyZrQqdxdWOV7+dc64MSR0lPQo8Y2arJPWQdFHoXG7LeSHnnHPlPQa8CXSO738GXBksjas1\nL+Scc668bDN7DigCMLMCoDBsJFcbXsg551x56yS1Jx4YLukIwK/L1UPeu9I558r7LfAqsLukyUAH\n4NSwkVxteO9K55wrQ1ImUfPk3kSTMS8AMsxsY9Bgbot5Ieecc2VUNLuJz3hSP3lzpXPOxSR1AnYC\nmkk6iJIldVoDzYMFc7XmhZxzzpX4MXABsDPwp6Tta4AbQwRyW8ebK51zrgxJJ5vZi6FzuK3nhZxz\nzlVA0mBgXyArsc3MRoRL5GrDx8k551wZkv4BnA5cRnRd7lSga9BQrla8Juecc2VImmVmPZP+bQm8\nYWZHhs7mtozX5JxzrrwN8b/rJXUGNgM7Bszjasl7VzrnXHmjJbUF7gM+Ipre65GwkVxteHOlc85V\nIZ79JMvXlKufvJBzzrmYpJOq2m9mL6Uqi6sb3lzpnHMlhlaxzwAv5OoZr8k555xrsLwm55xzZUi6\npaLtPhi8/vFCzjnnyluX9HMWMAT4NFAWtxW8udI556oR97B808wGhM7itowPBnfOueo1J1qZwNUz\n3lzpnHNlSJpN1JsSoBHQAfDrcfWQN1c651wZkpInYy4AlphZQag8rva8udI558rbEVhuZt+Y2Q9E\nK4UfHjqU23Jek3POuTIkfQwcbPEHpKQMYLqZHRw2mdtSXpNzzrnyZEk1ADMrwvsw1EteyDnnXHlf\nSrpcUpP4dgXwZehQbst5Ieecc+X9EugD/AB8DxwOXBI0kasVvybnnHOuwfI2Zueci0kaScn4uHLM\n7PIUxnF1wAs555wrMT10AFe3vLnSOedcg+U1OeecK0NSB+A6oAfRKgQAmNnAYKFcrXjvSuecK+8p\noqV1dgWGA18DH4YM5GrHmyudc64MSTPM7BBJs8ysZ7ztQzPrFTqb2zLeXOmcc+Vtjv9dJGkwsBBo\nFzCPqyUv5Jxzrrw7JLUBfgeMBFoDV4aN5GrDr8k551x5pxJdzpljZkcDxwEnBs7kasELOeecK6+n\nma1M3DGz5cBBAfO4WvJCzjnnysuQtEPijqR2+OWdesnfNOecK+9+YKqk5+P7pwJ3BszjasmHEDjn\nXAUk9QASg7/Hmdm8kHlc7Xgh55xzrsHya3LOOecaLC/knHPONVheyDnnnGuwvJBzzjnXYP1/0heV\nXDSf5GkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7b6RMWpuHKN-",
        "colab_type": "text"
      },
      "source": [
        "### **Q5. What is the most booked property type in our Airbnb *dataset*?**\n",
        "\n",
        "The column of the feature `room_type` indicates the type of renting that is announced in Airbnb. If you have already booked a property using the site, you know that there are options of entire condos/apartments/houses, the single renting of a room or even sharing the room with other people.\n",
        "\n",
        "We're going to count the amount of occurrences of each type of rent, using the method `value_counts()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueLPNWvPHgfB",
        "colab_type": "code",
        "outputId": "598b61ea-013c-4117-8c9c-01e61d94a698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "# show the quantity of each available property \n",
        "df_clean.room_type.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    8211\n",
              "Private room       4812\n",
              "Hotel room          198\n",
              "Shared room          29\n",
              "Name: room_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvwoO-XAHhIm",
        "colab_type": "code",
        "outputId": "72c5b438-c19e-4fc6-b8b9-aae67b5fd342",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "# show the percentage of each type of available property\n",
        "df_clean.room_type.value_counts() / df_clean.shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    0.619698\n",
              "Private room       0.363170\n",
              "Hotel room         0.014943\n",
              "Shared room        0.002189\n",
              "Name: room_type, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UqNajl41zvsV",
        "colab_type": "text"
      },
      "source": [
        "As we can see, for our Edinburgh dataset, the property type which is booked the most is Entire home/apt. This means that out of 10 times people book a place in Edinburgh, 6 of it happens to be the entire place."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFa5NbnLJMcd",
        "colab_type": "text"
      },
      "source": [
        "### **Q6. What is the most expensive neighbourhood in Edinburgh?**\n",
        "\n",
        "A way to verify a feature as a function of another feature is through the `groupby()` method. In this case, we want to compare the streets(*neighbourhoods* from the renting price. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD_xa143JZpi",
        "colab_type": "code",
        "outputId": "0ec82234-0b6e-4a29-f1ae-862c2e431f50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "df_clean.groupby(['neighbourhood']).price.mean().sort_values(ascending=False)[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood\n",
              "The Calders                                  257.388889\n",
              "Old Town, Princes Street and Leith Street    170.018981\n",
              "New Town West                                169.810811\n",
              "Moredun and Craigour                         154.555556\n",
              "Deans Village                                152.505000\n",
              "New Town East and Gayfield                   143.971246\n",
              "Tollcross                                    141.673877\n",
              "Murrayfield and Ravelston                    139.923077\n",
              "Fairmilehead                                 134.250000\n",
              "Balerno and Bonnington Village               133.478261\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTt-H7G0Q87F",
        "colab_type": "text"
      },
      "source": [
        "Above we can see that The Calders has a bigger average price than places such as Old Town, Princes Street and Leith Street.\n",
        "\n",
        "A person that doesn't know Edinburgh could show these results without questioning herself. As to provide an example of how a sample can be non-representative, let us see how many entries are there for The Calders."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07RIuc0ERlVc",
        "colab_type": "code",
        "outputId": "1cecdf0e-3b16-44b7-9697-e5e60ba3f145",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# view the amount of properties in The Calders\n",
        "print(df[df.neighbourhood == \"The Calders\"].shape)\n",
        "\n",
        "# view the first 8 entries for The Calders\n",
        "df[df.neighbourhood == \"The Calders\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22, 16)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1366</th>\n",
              "      <td>6574607</td>\n",
              "      <td>DOUBLE BIG BEDROOM IN A LOVELY FRIENDLY FLAT :)</td>\n",
              "      <td>34387725</td>\n",
              "      <td>Thiago</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91877</td>\n",
              "      <td>-3.29214</td>\n",
              "      <td>Private room</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2019-02-03</td>\n",
              "      <td>0.18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2430</th>\n",
              "      <td>10432576</td>\n",
              "      <td>1 bedroom in west edinburgh near airport 12-1</td>\n",
              "      <td>53736254</td>\n",
              "      <td>Dan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92196</td>\n",
              "      <td>-3.29308</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>599</td>\n",
              "      <td>3</td>\n",
              "      <td>2017-05-28</td>\n",
              "      <td>0.07</td>\n",
              "      <td>5</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2687</th>\n",
              "      <td>12155244</td>\n",
              "      <td>double room to rent for August</td>\n",
              "      <td>65270182</td>\n",
              "      <td>Joanna</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92070</td>\n",
              "      <td>-3.29814</td>\n",
              "      <td>Private room</td>\n",
              "      <td>32</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3433</th>\n",
              "      <td>13930036</td>\n",
              "      <td>a double  room near airport ,heriot watt 12-2</td>\n",
              "      <td>53736254</td>\n",
              "      <td>Dan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92114</td>\n",
              "      <td>-3.29423</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>555</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-05-28</td>\n",
              "      <td>0.03</td>\n",
              "      <td>5</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5002</th>\n",
              "      <td>18882037</td>\n",
              "      <td>1 double bed room near airport 35 bus</td>\n",
              "      <td>53736254</td>\n",
              "      <td>Dan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91955</td>\n",
              "      <td>-3.29647</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>599</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-06-01</td>\n",
              "      <td>0.07</td>\n",
              "      <td>5</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5021</th>\n",
              "      <td>18914400</td>\n",
              "      <td>a double bed room near airport 35bus tesco</td>\n",
              "      <td>131824828</td>\n",
              "      <td>Dan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92000</td>\n",
              "      <td>-3.29452</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-07-14</td>\n",
              "      <td>0.03</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5187</th>\n",
              "      <td>19179102</td>\n",
              "      <td>a double bed room near heriot watt, airport 32</td>\n",
              "      <td>131824828</td>\n",
              "      <td>Dan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92156</td>\n",
              "      <td>-3.29364</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5423</th>\n",
              "      <td>19536606</td>\n",
              "      <td>Edinburgh 2 bedroom house free parking</td>\n",
              "      <td>125296161</td>\n",
              "      <td>Andrew</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91603</td>\n",
              "      <td>-3.29007</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>74</td>\n",
              "      <td>2019-11-03</td>\n",
              "      <td>2.63</td>\n",
              "      <td>1</td>\n",
              "      <td>324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6061</th>\n",
              "      <td>20200443</td>\n",
              "      <td>Affordable Short Stay, Edinburgh.</td>\n",
              "      <td>143868296</td>\n",
              "      <td>Paul</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91899</td>\n",
              "      <td>-3.29439</td>\n",
              "      <td>Private room</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-08-26</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6713</th>\n",
              "      <td>21998435</td>\n",
              "      <td>Private Stylish Room in Edinburgh</td>\n",
              "      <td>105290948</td>\n",
              "      <td>Ana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92071</td>\n",
              "      <td>-3.29339</td>\n",
              "      <td>Private room</td>\n",
              "      <td>100</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2018-01-22</td>\n",
              "      <td>0.09</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6717</th>\n",
              "      <td>22011721</td>\n",
              "      <td>Private Cozy Room in Edinburgh</td>\n",
              "      <td>105290948</td>\n",
              "      <td>Ana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92126</td>\n",
              "      <td>-3.29184</td>\n",
              "      <td>Private room</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2019-08-31</td>\n",
              "      <td>0.22</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6718</th>\n",
              "      <td>22016823</td>\n",
              "      <td>Private Cozy Single Bedroom in Edinburgh</td>\n",
              "      <td>105290948</td>\n",
              "      <td>Ana</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92013</td>\n",
              "      <td>-3.29364</td>\n",
              "      <td>Private room</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2018-04-30</td>\n",
              "      <td>0.17</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7977</th>\n",
              "      <td>25703384</td>\n",
              "      <td>Lovely double room, Edinburgh West</td>\n",
              "      <td>193393151</td>\n",
              "      <td>Julia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91878</td>\n",
              "      <td>-3.29422</td>\n",
              "      <td>Private room</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>54</td>\n",
              "      <td>2019-06-26</td>\n",
              "      <td>3.24</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9066</th>\n",
              "      <td>27545789</td>\n",
              "      <td>Lovely Double Room in Main Door Flat</td>\n",
              "      <td>51342327</td>\n",
              "      <td>Lorraine</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92227</td>\n",
              "      <td>-3.28834</td>\n",
              "      <td>Private room</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>2019-09-27</td>\n",
              "      <td>1.01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9651</th>\n",
              "      <td>30175029</td>\n",
              "      <td>A cozy private room in an old 2 bed apartment</td>\n",
              "      <td>140087641</td>\n",
              "      <td>Megha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91861</td>\n",
              "      <td>-3.29441</td>\n",
              "      <td>Private room</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>2019-10-10</td>\n",
              "      <td>1.73</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10101</th>\n",
              "      <td>31928066</td>\n",
              "      <td>A quiet and cozy room (K2L)</td>\n",
              "      <td>70207141</td>\n",
              "      <td>Kalli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91870</td>\n",
              "      <td>-3.29524</td>\n",
              "      <td>Private room</td>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>2019-09-28</td>\n",
              "      <td>2.84</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10138</th>\n",
              "      <td>32098617</td>\n",
              "      <td>The little red house!! Close to airport &amp; city!</td>\n",
              "      <td>164989804</td>\n",
              "      <td>Sara</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92184</td>\n",
              "      <td>-3.28939</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>81</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>2019-11-24</td>\n",
              "      <td>3.77</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10491</th>\n",
              "      <td>33291968</td>\n",
              "      <td>Beautiful &amp; spacious quiet double room (K2L)</td>\n",
              "      <td>70207141</td>\n",
              "      <td>Kalli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91773</td>\n",
              "      <td>-3.29664</td>\n",
              "      <td>Private room</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>2019-09-14</td>\n",
              "      <td>2.52</td>\n",
              "      <td>2</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11536</th>\n",
              "      <td>35867470</td>\n",
              "      <td>Double room. Close to city centre / airport</td>\n",
              "      <td>268780608</td>\n",
              "      <td>William</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91815</td>\n",
              "      <td>-3.28389</td>\n",
              "      <td>Private room</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2019-11-24</td>\n",
              "      <td>3.53</td>\n",
              "      <td>2</td>\n",
              "      <td>359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11986</th>\n",
              "      <td>36635821</td>\n",
              "      <td>Double bedroom near airport, Edinburgh college</td>\n",
              "      <td>272200535</td>\n",
              "      <td>Isaac</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92059</td>\n",
              "      <td>-3.29277</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-09-13</td>\n",
              "      <td>0.88</td>\n",
              "      <td>3</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12233</th>\n",
              "      <td>36994344</td>\n",
              "      <td>A double bedroom near heriot watt, airport</td>\n",
              "      <td>272200535</td>\n",
              "      <td>Isaac</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.92002</td>\n",
              "      <td>-3.29392</td>\n",
              "      <td>Private room</td>\n",
              "      <td>999</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-08-08</td>\n",
              "      <td>0.27</td>\n",
              "      <td>3</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12719</th>\n",
              "      <td>38150468</td>\n",
              "      <td>Fabulous large home in Sighthill near Heriott ...</td>\n",
              "      <td>29624831</td>\n",
              "      <td>Lorna</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Calders</td>\n",
              "      <td>55.91881</td>\n",
              "      <td>-3.28572</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>145</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18</td>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "1366    6574607  ...                0\n",
              "2430   10432576  ...              365\n",
              "2687   12155244  ...              252\n",
              "3433   13930036  ...              365\n",
              "5002   18882037  ...              365\n",
              "5021   18914400  ...                0\n",
              "5187   19179102  ...                0\n",
              "5423   19536606  ...              324\n",
              "6061   20200443  ...                0\n",
              "6713   21998435  ...                0\n",
              "6717   22011721  ...                0\n",
              "6718   22016823  ...                0\n",
              "7977   25703384  ...              141\n",
              "9066   27545789  ...                0\n",
              "9651   30175029  ...                0\n",
              "10101  31928066  ...                0\n",
              "10138  32098617  ...              140\n",
              "10491  33291968  ...               56\n",
              "11536  35867470  ...              359\n",
              "11986  36635821  ...              365\n",
              "12233  36994344  ...              365\n",
              "12719  38150468  ...              237\n",
              "\n",
              "[22 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nMyAfrBS3s1",
        "colab_type": "text"
      },
      "source": [
        "Since we are given the georreferencing coordinates for the properties, it is possible to plot each point. For this, we consider `x=longitude` and `y=latitude`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxVJqJ_3W59I",
        "colab_type": "code",
        "outputId": "659994ae-f3d2-4fde-d2ec-07d4a53e332f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "source": [
        "# plot the properties as per latitude-longitude\n",
        "df_clean.plot(kind=\"scatter\", x='longitude', y='latitude', alpha=0.4, c=df_clean['price'], s=8,\n",
        "              cmap=plt.get_cmap('jet'), figsize=(12,8));"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4PF7yeYnH4wU01q8foKcnzcsv97Bx4zDV1UH++Mc8+byg+FJuIaXN4KDJtm0Q\niQTIZoMMDRVoaXn739VMBm68sZM//GE3juNQXe3h0UeHufXWJVRWHpsrlCnKhCkJwamqbGAinS2l\n7BVCVANrhBB7gNuA71IMbL8L/Bvw2dc3klLuFkL8AHgKyABbKNbNAtwEfFlK+RshxMeAu4CLeBMh\nxOeBzwM0HasTJb5HQgiuvnoG559fRTCov6usBEBLS5BPf3oGPT155s4NUVZWDFYjEfXifjxrbzdx\nHJ3WVkE4DJGITjisAtfJUFICuRz4fEFmz4bS8XtELc3Q2tTFYP8IHj3PHzsOYJoWHo+H//qvPJFI\nCXV1kp6eNP/4j8XP445jUlGRR9cFAwM2pllA1118vhD9/Q4vPBenUFdCVVOEPVsSiBI/51e+cTDn\njBlB7rhjLvfdF2dkxMF1R/mHf9hKa6uPfD7Nnj1JkkmXgQGT227rxDQlUuroumDv3hwPP7yYDRsG\nufPOfuLxcgYG+ineENPxeg10PUxtVZyT2kJs2RbAcfy4bimu6xAIuBiGg+s6CCEYG7M5+WQPGzfG\n6ejI0dcH+bxNcfiCQfEtIYBpZkgkCuzfn2FkxCEaPfLrUybj0NFhUlFhsHt3nuee20UikaJQ8DA8\nXMaePS4bN7bz3HNzaWhQJTLKcSybhi2qbGDCSCl7x/8fEkL8FlgupXztOyyEuAN49Cht76IYmCKE\n+GegZ/ypG4Bbxr/+FXDnUdrfDtwOsHTpUvkXX8wxYmgoz+7dKerrA0ed6/XtzJkTYs4cVcd6oujs\ntLjrriS6DuFwOWecITnnnMi7qm88HkkpGR4u4PfrBIM6+bxDKDRx34vLLisumVoowKpVb3yuIuJw\n0kydF17IYhgahqGTTgukBNNMkExq+P3FALahIUQ6bRGJ+PB6dUZGsng8KXK5SnQdXNcm5DfIdw1y\n1qJ63P4ymho0CjtcenocGhr+NLK/utrHLbfU8eCDHfzP/wzjOC6rV2eYPTtEXV2QdDrF9u0ZDEPD\nNCWBgEtDQ4BEwiYet9i0yWJkpJrR0SRS1mIYg7guWJYgEvGweGGasWQVg8OlWJYFuICBlC6aJrAs\nHz6fQVNTGX19Bfbvz2KaXrJZB/CM769RfDvyAFFSaYks0aiq9eD1vnWWAtN0ueOOGENDNkJAU5NN\nPm9TKITGj1P819fn8POfW3zjG+oDunIcC6rM64QRQpQAmpQyNf71JcA/CiHqpJT947tdRbFE4Ejt\nq8eD3iaK9a4rxp/qA84DngcuAPZP1jUca9Jpm9tvP0Sh4GLbkr/+6xZmzlSBqHJ08XjxhkVjowfX\nhYsuKqOp6cQMXAEef7yPlx93zhUAACAASURBVF+OYVkSwygghGTRoiqWLWukutogFPrLCsfCYfjI\nR4783CWXNDI0lKOy0kNVVSmjo1l0HTweD7ZtI4RGc3OY3bvjVFUF+Ju/mcU3v7mZ9oMpXOmjqsqh\ntHSUtjaoqamjuTnA6JYc/ett3BEdI6KjlzqMjUkaGt54bstyefrpAbZuHcXnM5BSYpo2yWQB2/bi\nOALXLeC6DpmMl0TC4KqrojQ3+4lGy8jlDqJpBYQA1zUoKYGrr27G7y8jmyljaKSDgpUDSihWemWx\nbR8+nx9N8yCExv79BkKMYFkC0JHSoBi0pgELqAMkCA3petjXPYQbMYjFstTXh2hvT/LII93U1IRZ\nvryG3t48FRUao6OCRMIaLz+wxvvgw3GKM2z8+MfbGBuz+fa3T5vQDyqKcszIpmGryrxOlBrgt+Mj\nXw3gXinlk0KIX4xPdyWBDuBvAIQQ9cCdUsrLxtv/Zrzm1QK+IKUcG99+I/AfQggDyDNeGnAiSCYt\ncjmH5uYSOjoyxGImM4++xoCiMHOmh8pKjc5Oi9ZWD3V1J265gGW5vPLKCNXVfl54YYBEIsvy5RG+\n970DtLY6lJU5fOADQS67rI7y8j8/U7djR4Jf/7qXYFDnc59robLS94bn02mLZNJi7twyVq6cQTxe\noL+/wAsvjJFMQkuLh7Exk6ef7mHfvgQ33jifBx88n5v+dz979uxmoD9BebmH+nqoq/Py0ksp9u33\nogmH2lqdAwcc5szRaGt7a6ayoyON368RCnkYGirQ2hqmra2Oz3++iocfHmDjxji7d+fx+TTa2kzm\nzfPx7W+3oOsaZ5wRoKoqgGWVAhk0Da67rombbmrhRz/aQ1evTX+6joqaOB9Y8DtaGwd5Ye0cXt00\ni3y+BNABh3w+jaZlkNId33b4Vr4XKACjgA9kGSDRTIfu/SP8+LY9fPqvZnPlla/Q329jO4KlyzLk\nMsP09hYIBsOcc04NFRUVDAxkKAbDAggDNsPDe7nvPsHs2eX89V/P/bN/ropyzDtcNn4CmbR3Minl\nQWDREbb/1VH276M4MOvw43OOst9LwGkT1M1ppbrax6xZIQ4cyFBe7mH2bJV1Vd5eaanOzTdHSCZd\nIhENXT9xa/8MQ1BfH+Chh7oZHMwQi8XZuTPOyAh0dQmCQQ8lJaWk0yaf//xJf9axHUfy0592snt3\nkmTSIhYr8IMfLHzDtFVPPtmNz6dTVuZldDTHN79ZfBmLxXLk8xb79iVZs6aH1tZSOjtTxGJ5qqpK\nmDm7Do/XxXbaqZ8hWbWqmQsuaOLqjybwBvz0dOmUlDhcfLGfW24RBIN/OqeUkj17Uuzdm8AwNJYs\nqWTXriTnnltLJOLnpJMqCIWyLFwocZwkhmFRWipoafFTWloMphOJNJ/5TIRHH40xPGyydGmUfN7h\nl788hMfj0DY3xL6OPmY372HZ4oP09pXyoUs2sP9QJfFRm+JwBR+uC67rxeu1MC0NhB9cCwiM99ak\nGMgKIE+8U+IpyfLEo10UsjA8bJHPu5hmKeteTVBeFqalJYjPlyUW91LQwgg9g3Q0ioFwAjCx7QLD\nw4IdO+Lv9VdHUY5twRAsVmUDyjHKMDSuv76ZeNwkHDbw+9WKNco783oF0aj6XRFC8MEP1rNhwzCL\nF4f5/e/TdHcLXNcikxnDsiqoq/MzMlJ4D8eGWKxALucQDOrs3DnK+vUxliypfG1O1mjUT2dnCsMQ\nNDaGX2sbjQaAAFJqPPNMLx0dScrLfVRU+PB44JorHR59rIJVq+r44OU6gYBgeBhs10c4bFJeabBg\noc7f/q1GyetW4UomTe65p4vHn4pTWx8i7Ne46KIa5s+vJBj0cs45VTzwgE5JSRO6bnHrrS2sX99B\nIKBz5ZUz0TTBHXfsp6cnRzYrmTu3CdcdIpUKEgwKRkbyrF07wFjKJZ02SAc0dN1A08B1daTUKWZY\nbYq38gUej4eyMosCUdKJBK7ppxjcllDMvur8qf5Vw8pobN+eZWSkHcsKks+7gIdCPkshINm5M015\nucXcMzSqm4KMDTJ+nBKKMyqmgByFgsXvftfHFVcMc9FFarVA5TiTTcM2VTagHMN0XVBV5XvnHRVF\neQPXlWzY0E9X1wjr1rl0djqYZgEQ+P0eQiEbx7H50Iea/+xja5pg5coId9wxhuOA6zr86lcdHDiQ\n4rrr2gC4/PJmSks9pFIWLS1htm+PEY0GqKsrRpyNjSG+8IWTicXytLSE8fsN4nGT3z3cQTbr0N3p\nRcpmwCAchhWnB9i3z8AfcLjwgjxe7+EBT7Br1yh///eb2LUnT9oqJ9RZwKdZLF7k4e//vg2vV6Oj\nQ2KaLi0tGh0dGuXlAb7ylYrXrikeL7BtW5JYzGZwME91tZ9ly6Js2BBjZMTL0qUhtm4NsHNnknTa\nZutgI/VVs2isH+ThJ09ndCyE1+vH6w8iHS/gUii4CKGhuw66oePaHnBtirf4oVglJgEfxVkILABS\nKUFdnYdEIg8UcBwPyYRFWVmeWCzHi2s2Y+Y1pOOB8CzwRSCbg6wBxIA8uVyBr33tVTZsuFwt0KEc\nX4IhWKQyr4qiKMed3t40P/vZQQ4d0jl0SKJpPkpKJIWCpKkpwg03tPF//s/MN6xe9W7t2TPC7t39\nzJ4NnZ0FzjyznhkzguzaNUo6bfLMMz2YpsMZZ9TywAPtPPDAAfr7s8ydG+Hiixu59NJGSku91NeX\nUF//p/RpT0+ebNahuTlIZ2eWwUGT1lYDvx9uvknjuedtnnlmP398JUdnRwk33XQyhqHxm98cwnXB\n8Hqx0ylGB3LUt1azc2eSzZvHOP30CurrobERuroklZXQ2grxuERKSWWlRnm5l1xO4jgOkYiHbNYl\nmQxw1llNfPnL5axfn2D3bptUSiClxOPR+P1TZ+C6Ol6/Rkm55KJLW0nnInjMEXp7khw8mCUSEYyO\nxtEcB9wkcDgTqlGceWCUYgbWAgwcB2zboKPDpRjUJgA/tp0jlytQKLi4uSyuWwI+H/jDYJkQ9IOZ\nBht03Usg4MU0LRW4KsefXBp2qMyroijKcce24eDBNL29pUAa182RyQj8fklzs86HPlT9ngLXjo4M\nv/51J5mMy4oVNWjaENmsRWdnmmXLojzxRCebNw+jaYLdu0cpFCTZrI1hCLZti9Pbm+PQoQw33zz/\nLXM219b6MAxBR0eWUMhASnjmmRHq6nzMnx8iGIjR15umq9Pg0KEk111XoLIyQCjkoaEhSFfPKGXl\nQYIlYU492Y8QheLqXcDwcJZodIimphArV1aya5fDQw/ZAFxxhcEZZxh85CNNvPjiCKYJo6MGgUAO\nn08nENBxHA9erw+Pp1iz6zgm5eUBPB4DtCitp9Sy6IxWdF0yM+LlRz908Ho1enpSWNYoXq8Pvx8S\niSTFoDT1uisvLqji8RjYtotpFhdEgBDFsoAkUrpkMhrF8oAIkAPpAg5oJiBApoAwjjNGOm3z3e++\ni6UIFWU6UgO2FEVRjj9NTSEKBR3TLGbuioOD8miaSS6X4957e/mnfyr9szJzsViBu+46SCrlsmlT\nBinhrLNqufjiVizLpbk5xJ137mTfviSJRLFWfdmyWjwejdFRE13XmD8/QjptE4vlaWp64yDM6mof\nX/xiKz09OfbujfH1r2+gri6C3+/ns5+tRwgfsZhDOOySSHjI5YrtPvjBZn7wgx0smFfKxz4xF6Hp\nbN4xSqTs8Lyxee66ay8A6fQQdXWCp54KYNs2kYjBCy/AGWcYXH11Pa2tQTo7LTZtytHW5qezs8DI\niEU0Kpkxo4y6ugo6OrpYsaKKkREDx7FYtqyBCy+vw5aC1HCMu+/oo6fHJpm0cZwQHs8opmljGDqh\nUIF02qRYLqABEq9XYNkGjmMipYtp5jH0ED6vjWXnMC0DTZO4boDi21im2NbMQCYG3gBkBsDJUszm\nBpHSy7XXFqdncV2JphV/zj09edasiVNRYXDJJZUEAqo+XJlmAiFYqMoGFEU5zrz+zfpopISCBT5P\ncQDS8WZ4OEc6nSMcdkilbIr1oQWyWdi7N868eTVv2F9KSaHg4Pcf/WUymbRwHMn8+RGEEHzgAzVc\ncEE9hvGnNMicORHuvXc/4bCHysogp51WxbJlVRQKLk891YfPZ1Be7qWqyn/Ec1RV+dizZ5iXX+5h\ncDBDoZBj3rxWhodNWlrCnHpqNcEgCKG/Fnht3JjAND1Eox42rhvkvGvnMDTgZc3qDrZsTzG7wSCb\ntWlpCWOaLt3dGTZsGGH7doHPp3HLLbUAtLdneOKJGPm85NVX8zzySB8LFgSIRuupqQlzxRUhenpy\nfPCDs0ink/T3F2hra6WmtoauAx5WnA6buwr09bkkEgaOUxyM5Th+vF4Tj0ejstJLPp/DtgXFwV06\nUpQhRBbXERQHX2mctmgbjl3K4LBO3tSJJcvGZys4vPiiBxCQ7YdsgeLbm5/D03LF4yZ3391Pebmf\nrVszLFgQ5JprKvjZz/oQAvbtc/D7NVativ55v1iKMtXyadilygYURTlOpFI299zTTV9fgQsuiLJy\n5ZHfmC0b7nsB9vbASTPgunPBO83nc4/FTJ56KkZ5ucaqVdX4/RplZR40TZDN5nBde3zOUY1MxuHS\nS6tfy7oWCg733LOHgweTLFhQwcc+dtIbAtLDGhoCtLUV512eNauUZcuq3jIdWUtLKUuW1NDTk2Lr\n1hi7d4+SycDll7fyD/+wmLExi/r6IIHA0V+OUymTsjKDqqoKdu2ymTXLw/z5IcrLDT760VoOHsyx\nbFkplZXF+WnXro2zf38Gr1dD02xezEZw4kk8mkvaW8LatQMIAQcPxli6tJTq6gDx+F5qakKMjVkk\nkzYvvhjl978fwuv1sG2bzYEDKWbNqmBwMMfevUmWL6/kS1+aTSZjEw57cF2J60r279f52S8gWgn3\n3Q9XfqgCr3cYjydPPu9DSj+u24Km9TFrlg/HMfF4BLbtAAYeD1RWSAp5l9HRMMVSAY2R+BxOXfgy\nUIOUOWLpEMVg93AA6wMqxrcdDnqrABNNDCI0yX//91ZOP30ms2eX84c/JMhkshw8mOKUU8opFCS5\nnPuX/MopytQIhOBklXlVFGWac13JoUMp1q0bo7s7T2NjgKefHmbJkrIjLg3bOQS7u6GluhjAHhqE\nOQ1HOPA0MTyc44YbNtHVlcAwBHfeGWLlynq+851TefDBgyQSBdavz5JMSnRdEokEOf30ytfat7cn\nOHAg8dqsAE1NZZSV+Zg5M/yGulSvV+ezn20jkTD5wx/6+Nd/3UJ9fZAbbphLKOThuefiPPNMnM5O\nsCyHVMpkYCBHdXWQ++7bzVVXNXHaaRVv6LuUkgcf7OWZZwaR0qS83GD+/AiOE6C/v5QlS8J4PGXY\nto6mCc49N8K550becIzSUoMZDSWMjZmYBZO2iMPWVIB8Xw6fm8DJuFxySQ2rV/fQ3Z1maChLoeCg\n6ynKynTuu2+AX/2qj2QShCinUNDJZr2EQi6xmEAbL5w1DI2ysmLAfDizb5rFzH04DGMJaGwM8uUv\nN/GVr3STzYJtx/F4fDiOQT4PY2M25eU6hmHiujb19QGqq2327DHQNB+uawAmXb01pDIfwNBN0tkC\nUlZQXOfGoVgCIoFaIEkxC9sECISIIIRJSWCITEZn0+YRduwKcOBAjJdetjD0DIODSS69tIXzznvj\n91FRpoWcyrwqf4F4XJJMQl0d5POCYBA80zx7pUxPTz7Zw4svDtHXZ5LP64TDBh6Phtd75HqA4Pjs\na/vai3dcA9N8Kfif/3w/u3fH8fkkY2MSXU+RSFgEAj5+85sLGRkxOeecp9F1DcOwsG2H7353I1/7\n2hJaW8vGA1RJLJZndNTiV7/qpqTEoL4+yE03nYSua7iuZGzMJBQysCyHdeuKg5+6ulJs3z7CvHmV\nPP30CDNm+Nm+XScaLSORyNHbmwM0PB5JPu+8pe9//GOcH/3oIL29SYaH84TDFosWVXDOOXM5++xq\nZs7U6ehwSSQk1dVHvv7aljrWvNKPdP1I2+LAU/swDC833tDGnGqDZ54eZv/+BPm8S0NDiEce6aSh\nIcT+/SkCAR9er6SpqYR9+2wcx2XFinI2by4hlbK56qoKliwpO+r3ft48mD8PDrTD8mUufX157rnn\nIJY1DKICyGJZNkIYDA4WSKctvF4XKXVmz/aTSBrs2mPi94PXm6ZQCCKlH9PyMzisAUHQwyB7KA7W\nGqZYGmBQzLqG0LU8jqsBOQxdx7G9lJSkaG8PUFYO1dUZzHyKQx0uM+ogEnG4/voaSkvVC7YyDQVC\nsEBlXpX3oLtbcuedDqYpiMehokIjGhV89rNQKEBvL8yYAbW1U91T5XiWzzs88sgQv/hFN7W1GplM\nAdPUqKvzce65lW8ZzX5YfSWc5IdHnoPKUti9FZoufp87PwHGxgoMDGQYGsqwYEEp27aNUijYtLZW\nYNsumiaIxWxWrx6itTWMZUn6+jI0NXnZsGGIG298kS9/eTEXXVTHxz8+m717R6msDDEwYFFfH2Dj\nxhF+8INdNDUVJ8w/dChNJOLl2mub0DTYvz+BZbmUlnrxeDQMQyOZtGluDiNlikgkzMyZznh9bCtL\nl1a85Rr278+STrvk8xZCaHg8GmNjOSBHU5NGZ6dLU5OgqenIw4tN0yHlVnDlR8OkEgWefdKkpT6P\naUrOmONl+fIoJUGdJ5/s46STPNi2TTzucOqpUc49t4ZNm5KMjQk6OhKUlgrmzasmEvHxuc95+eQn\nKykre/thzX4/3HA9ZDIut96a4tYfx/jjHzVsR2JbPRxetlVKGBvTKC0NUlcXoKTEwuvTOHhI4Dhe\nkmM6kMHjTSOEFyklruPFcQXSkMUFuQhTLBvwAOVAHr/PQTfyGFoX+UIjfl8GrzFANluJbftJJmxS\nyT5MMwtIurok8+eX4POdYMO1leNHPg17VOZVeQ/27pWAIBoVvPCCyzXXwMgIvPIKrN8AplWsIbz5\ni1BZ+Y6HU5T35JVXRtm8OUFpqZcnnxymtVVQX1/CsmWltLWVvG3bxBCcfxpoOmzZCqumWfA6Olrg\nv/5rB7mcTSpVYM4cg4aGKOefX08iAR6PhusG+Pd/H6S7O0dLS4SGhiDPPZenqgr6+kykLPD884P4\n/QYXXliLlJKf/nQ3O3dmaW0tY8uW4ij5zs40qZTN+efX0N6e5tlnhxgdlfT2pqmsDFJe7qekROeG\nG+p48cUxli0rZccOg6qqEk4+uYZZs0J85jMz33INAwMFdu7MAhr5vE4wWEDXi5P2B4MGH/uYQNe9\nlJUVFyxxXcnWrQlisQILF5byxBN9rFs3jKNHGDPb6G43GOrKMNKbwucTjI0V0DTBeedFOeusCu67\nr53Vq4eZMaOckRGTdNrmggsqOfXUWvbuTeA4Lq4raG0NEg4L+vrSlJQUZ0TYtm0E03RYtChKIFCc\n0urVV4dJp22WL48Sj2vE4xa9vQPkcglctzhv658GWGkI4eL3Bxkd1bn00kZyuTSbNg2TyxbLJyCI\n4ySBHMUpsqoADzhBkD6KMwmUgvCCMIAc1dEUI6OlLDp5N4lEB5mczUi8hURKB9JYVp5i5GtQXNnM\nQyoV4DvfOcQppwS48srat60/VpRj0nE4yPbtqL/QCdLcDM8+K+nvh7IywehocV5JVxYHw7Q0Q2cX\nDA+r4FWZPK5bXAZ19uwwe/emOO+8CIWCS39/ga6uYaqrfSxZEmbnToft213mzdNYsqT4MrBoIax5\npnic886Zwot4j/r7M2Szh7OckmuvncWcORFAZ/Ua2N/usHfXMKcu8WLbLqOjBcrKdL797YU8+mg7\niQScdlo1Xq/Oq6/2I6XF00930NISIhDQOXAgAzh0dKTw+z3U1JTQ05Mdnzs2R3t7igULyqmr83Hw\nYJoZM4K0tRX/ATz5ZBem6VBe7sW2j3wNQ0MmyaRLa6sXIYpLxFZU+GhujpJKuTz00AB/8zdNr+2/\nefMYDz7Yg9er8dhjfWzePIAQGu3tcQIlpbS2hDkwmubUU/YyZ2aMQwcAisXMui7o7HRY/P+z995R\nctRnvv5TuTqH6Z48owmSRjkhIaJAgMm2hUEYWONsFozDud5zvevjs/s7d319beO9G7z2eu11wngd\nwFwbjEUUSDIICSWUpdFocp7pns6p0u+PGiGwwIBt0m4/5/RRq7qqv1Vd3VOfer+f931XxJiZKePx\nCFx9dSNLl4ZRFBFRtPjhD4fx+yUefniCcNhBFGHJkiDFosTjjw9TXy+ydu0MH/7wQrZuHeexx0aJ\nRZKkpyZYe95qfve7Ar29CWzbOzuuBJSAEp2dKUTRT2trDdFohDlzNO66q49SUcBxLESpgm1puGJX\nwrUIFN3tTRPX52oCDmh+cIpQEZlOhggHc1iGQi4fpH84hmXps+sWZ9/Pnv1/CUXx4/Np9PYm2Lq1\nzLFj43zuc0sJhaqdDKu8Q/D4YVHVNlDlj2DePJFPftIVrV4vdHcLNDZCZyd0n4DBIYhG3Y42Var8\nqdi2w/h4CV2XiEZPG1TPPTfMyEiJ8fEyH/tYK2NjObq6gmzfnsO2oVi0SSYdnnpKIxAQOHjQoKZG\npLVVZP1F0N7mlsxqe/0dUt9yGhp86LrEwEAGn0+hsdHPiRNpDhxS2HfATygk0NPvJxzKzvo9xzl0\naIbeXi/XXtuGbY/y7LOT6PoUq1ZFGRvLsW3bEOGwQnOzj8lJA1G0GRsrsmxZLX/7t0vo789j2xAI\nKOTzBt3dGerq4rS0uILVNG1GRwsMDGSYmspx4kSBWEzl4x93o67d3Wm2bJmkqcnDu97VAIg891yG\nwcEEsmzjODAxUUGSVNaurZ8t8XWa6ekyui7R0KCzaVOSnp48guChUrGxrEG2PJXjwrV7uOS8veTy\nfuY2/Racy7AdhW3bptm7N01zs0Z39yh+v4Ao5mlrW0Y06iGTMRFFgXhcZfv2BMGgSnd3iqeeGmNm\nxkTTBKamIghKiVXnwsBQmdpYgQvPeoB8rkx24jlELsSyZdzpfYdT7WvBS19fiVAozdSUQl1djkSi\nTDJpIwgWggC2JSEIBRzHwA0r2bMPFTdyKgAeIAeiCXYFJJNCEXS9jC+U48J5faQfO5fphIIrgDUg\nxekwlYUspygWfeRyJtGoRi5n0N+fZfnyqnit8g6hlIPjVdtAlT+S5maB5tkM7c4XzQjeeQdMT0Ms\n5vrBqlT5Uzh4MMk3vnGU0dESLS1B5s4N0tjo4aKL4qTTFuvWRRFFi2hUIxBQmZkx+Id/GKClRWN4\nuMz4uIHjaESjkE4LFAoO4GaIt7e9pYdGoWCyefMIxaLJJZc0EYv94R9MsWgyOVkgHvcQiWh8+tNL\nGR8v0NDgZdOmQfbvn6Z/KICizqG1OcDy5R4uvtjBtsc4dKiApmns3Vtg9+5DKIqJadrkchbj4wqR\niI9IxEN7u5+ZmQrz5gWZnKyQSpXo7Z3m29/ezw03zGP9+lqefHKKhoYQkYiPeDxMMOjBsmz+5m92\nsW9fkpkZA0XRqKnRqauTiURUnn56mr/7u4NEoxrxWpWyqWKbNoZRBCpMT5sUi26SWHd3gdbW0mzU\nPENXlx9ZFlm5MszevSkGBwsoikpHh5cDB0pUKhqWPY5hFpGlDIZp4+ChVCqTSJTYsnWKe+4ZIRKR\neeKJSXS9RFdXjCefHMPr9fKpTy1m0SI/O3a41SrWrYuyY8cYuZxJKCQzPJxFVVV6+qaJzl/Ij+43\nKGZCdIQPUsgVaWpdSCo5gW1X+Oxtz/HcnjXsfr6TYsmLG/m0sG2NmZk8ilLBNLM891yRSkXHshQU\nxcTnK6MoJlNTWdwOWhquv9WZPftl3CjuOBRGZ5e5glYLx6joGtu7V6D7y6iZHBUDXPHsw426gqLI\n1NaqeDwlVFUjHrfRdYl43PPn/FpXqfLGovthYTXyWuXPjK7zgqitUuVPIZ2u8NOf9vD006OUyyaH\nDqVZsCDGmjU1PPTQNC0tfvbvH6WzU6OxUef22xcSjWqsWRNk164MoZDMxRf72Lt3invvtbnwQh+d\nnW8fH8ujjw6za9cUqirR25vjgx+cS12dB0k6M5kmnzf4938/yMxMmWBQ5fbblxIOawSDKrt3l3jw\nwQmWLw+wxG9zojdDcibATTdInL0mQDzezPHjYxw8mCMcVshmwTAEVFXE7xdIpw0CAZuWFj+xmJeO\njgiKIiIIafbsKVNXp/Kzn52gtzfP8uV1fPSjHfzyl25y3MwMPPBAjpUrDbZvn6Slxc+xYyUiEQOf\nT8Y0NTIZk7vvHqa31+DYsQoVS+KpZwRUBfr6TCqVIJaVolAokk6LrFjRxOhogWeeSdDSonPjjU1s\n3NhIba3OX/3VfAoFk+9/f5CDBxXS6TSKP8Lze48jeiSe2b+Q5sYU558j8uQzZ7Ntbze9vWUSCYeD\nB5OIooUoWjzyyCgtLTrHj+c4eDDFypVRPvWpOZRKNl6vxK9/rfHII8NMTubx+VTi8Xpq6ixqohqP\n/HKAbN5izdKFnL1qkPr4GKLUwvrz9lEuGaxdtRPbKfPMznm4lx0RNwIqYhgVEokcHo9KMFgmnZaJ\nx1W6uvycOFFkasrCFZ2noq02pyOnRVwxe2qZA2QpOg2EPN2kdJmKV8C0hNn3KAEyqupBlkvIMszM\nOFx3XRPXXttCf3+WhgYfjY1/2B9epcrbirco8ioIwg+Aa4FJx3GWzC77OvBu3B/sSeAjjuOkZl/7\nAvAx3DvYzziO8+js8iuBf8GdHvme4zhffbWxq+K1SpV3EJZlUy5bs88lymVQVTdqmkxWWL5cJJ+v\noGkB8nmT/v4cNTU6GzbUcumlUXRdZP/+aVR1mKuv9pFMjpHLBYhE3h5TpJlMBZ9PwTRtHn10iEym\nwpIlEW6+ufOMtq2jo3mSyTJz5gQYGMgwPJxj0aIo+/aVuf/+HIIQ4uGHp1m71sNn7mzgrLNOb7t6\ndS3f+c7F/N3fHUAUlHQ0HQAAIABJREFURU6cSJNI5EilMgSDGn/1V8vo7Ixy11176e1Nc+WVrVx/\n/VweeWSQvXsn0DQZxxGIxTQMw8KyHGRZJJEocfCgwfbtNoGAWxM2kSih6wINDT5E0a3ZWizaHD+e\nI583SKeLWLaCZRmUSwLFvI0rymwEwYNtiwQCEoZh0tCgUSo5HDmSfeFYVFVEVVVWrw7y1FOj2LaB\nFDGRQhpGIkneFHh0x5VkzRj5vEFHh83YWJb6+gAHDqSZM8eDxyPgOB7WrWslnz+VLOXWbvV63a5d\n7353C7Is8vOf97FwIVhWljs/s4R//oFFvmgjaSEe2e3nRPKjnL8sw7suLGMY30JWLHStSEvDUdwp\nf3DFqw9R1FEUC1mWaWrykc+XWbkyxte+tpCODg+XXbaX3t5TftUCrkBVcC9dGm4UNYd7LRRnHwqp\nhMzz3QuJ1eQJeSZna8Kesh+4ybXRaBhZdjj77DjnnNNIPO6httb7Z/suV6nypiHgyr43nx8B3wR+\n/KJljwNfcBzHFATha8AXgL8WBGERcBOwGGgEnhAEYf7sNt8C3gUMA7sEQXjQcZwjf2jgqnitUuUd\nRDSqc+WVLTzySArbdmhsVJk7N4DfL7NuXS1TUwaKIlOpVPB4ZOrqTk9/BgIv/blLb8MW7u96VxPf\n+c4Jjh9PsXBhhDlz/Bw+PEM2axAMvrT4bCymo2ki/f0ZVFWkttY91pkZC1UVuOCCZo4eDfGxj0Xo\n6gqcMVZDg48vfWkFW7ZMsWFDC4EA/PrX3cTjXnbunKBQsGhvD1Bf76W/P4uiiNxyy3ySySIPPNDL\nvHlBQiEvkYhGba2Oafbym9+kGR62aGjQ+dznxlmyRKRclmlqCpBMWnR16UxMVPjZz/oZGEiTTBZm\nk5k8pBJlXCGmctrfabNwoY+NG2P85jdTnDiRp7HRw5o1YcAVmY7jisxnnplk2bIgQ0MF+g+NEWzx\nIDfUkhnLoQglxsfTLFgQoa3Nx4kTbjOCVatC+HwymUyF979/LomExbx5HhYvPrOO66OPTvOb38zQ\n1ydw+eXNDAwU2bWjSFeDTG4Ejo44mIbJ8ECFn/R5eH7HKGMjS3nXuv2MjgfYtqNt9ph0VFVBEGTC\nYZELLwzS3W0yPZ2hpiZAJiPw7W+n6OxMs2/fDKJoYNs5kB1oaAUlBOMZKCi4ojXyos9NAsqQ62Hg\nWJgBR0CwJC67LMxzz2WwLAFRFPD5KghCDbbtoKoSd989zvXX13L22a9cv7ZKlbctuh+63nzbgOM4\n2wRBaPu9ZY+96L87gBtmn78X+LnjOGWgTxCEHuDs2dd6HMfpBRAE4eez61bFa5UqbxTpNGjam+tl\nbmuLcM01HXi9kEhY/OVfzmHBAh/lss3QUAloJJerUFurv+z057JlNQwOZunry7JhQ8fbJuoKsG+f\nD8NYTk1NEjhJf3+Oujr9ZevTRiI6d9yxjJGRHI2NPmIxV7yuXKmzd2+Z4WGLK66I0tXlf8Xx6us9\nrF8f4wc/OMzBg1OIokNnZ5h0ukIs5qFQmGZgIEss5nmhfNKnPrWcT31qOYWCyfR0iXhcp6enwBNP\n5JmelikUKvT2ZnAcyGQKSJKEplmzZadEbNtkYMCirk4mndZJp30IgoXj6GjaFPXxPhIzMUTJS22t\nxP/5P4tZsybG8uURJibKtLR4aG31MDxc4O67T3LyZJauriCmafPUUxNMTFRwLA+F3hl8PgOfbVAu\nivT1FUilLFrmZLnscg9jI34GB1M4js11183hnHNqeeCBUXp78zz66Djvec9pr1OlYvPYYxOMj89w\n8mSWH/6wQCCg0t4eQZOzrL8oyOG7k6CoZAoKXnmGfD5PJhfkhz9fg22bgI0oOgiCQyCg0tTkRZYV\nikWBNWvC9PT42L8/T6Vi0ts7SjgsI8tgGA62XYKaOeD1Q8WGpno4kcWNpvoBnYA/T65ogSPi2BG3\n1RcyguhD1xW+9KXlGI7Fv/2wyNiIH9JZLMukp6dCR4eHvr5iVbxWeWdSysGJt2XC1keBX8w+b8IV\ns6cYnl0GMPR7y9e+2htXxWuVKn8kjzwCv/sdeDzw0Y9CY+ObM24oJJPP2+zfX0DTBOTZX7Gmicyd\n++rTnqoq8b73nVlj9K0mn4dnn4XWVoGJiRrq62XOOqtCV1cIWX75AvLxuIeaGh3TPN2TvqZG4nOf\ni1AqOfj9f7jwvGFYfOUre3jooRNUKiaappDJhFi0qI4FC+ppavIzM1Nm/vwwDz7Yy7FjM5x7bgPr\n1zfj9cq0trrCePv2At3dJdJpE9sWcSOCRSzLJpdz8PnKFIsmiiLi9Zr4/QbHjslksx5Aw6GIqpb5\n+C1baaidxKNbPLvvUm771E1omsDf//0u/H6FD35wAfX17jl++OERpqfL9PXlGR0tsmxZhLo6jd5e\nmWjUh6+soaoZpnIpZmZEgkGFtRf0UrRH2PbEeZw47qdciLNokcm99zo8/HA3Xm+Zyy5rZOfOaS6/\nvAFdd8PzkgTHj0+wa1d+tiuYgWkaDAwkqKmJUi5n0Ywk2CZGqYgojdCfMmcjwzZuRFRBUQRiMZ3O\nzgCVisPSpX7OP19n27YRpqYM0mkfoiiRSpXw+USWLAly7NgUxSKURctN1RJUXOtABtfDWkAQREyz\nTCwqUjKbyGZ1sKcAg3DEz1VXNfHhD7fxz/eIGHYvpcIwVMCrB8lmHWZmbFatOjM6X6XKOwLdD/Pf\nkMhrTBCE3S9a8F3Hcb77WrYUBOGLuH6fNyTrqypeq1T5IyiVXOHa3OzW7t25E6677s0Zu6ZGQVWh\nrU1DVUWOHs0xd+47O8FkeLhAMmni8/no7q6QzZZZv97P6tV/OBI2NVXkRz86Rjpd4aqrWjn//AYA\nZFnA73/1qt3lss3wcBbLsggENNJpP+FwAz5fkHvuyfD5z9cgigKHDiXYuXOC5mY/mzb1o2kyCxdG\nXohanziRx7JUdF3ANCtIUg7bzlAue5EkEVV1i/hfckmA3t4hHCeIIIjoeolSSQZRorZlmvrmJF7Z\n5JxV/Sxb+hStdY385/3txOMeUqkyTz01ws03uzaxQEAhlapQqdjU1ekYhsDKVcs5dnwa27KYM0fG\n6/WSz0fI55Pkcg6h6AjP713K5EQN7XMH2PLYKixrkpoaG0lqZGbmGIcOpVi8OML0NDz0UAVNgyuv\nFJiYKGOaAoYB5bLI0qV+enpmuOyyFjZtShMMQKWSRhGLLFvqJ5XKE4l42L+/iGWZmKbD0qW1rFoV\nZ+5cGUWRWb++hh//uI9USiCZlNE0C8MoUirBvHlBrrnGQyJRQ12dype/1sNAzgS5FsZKuA0LegAJ\nARtdL9DWlGLvkRCSHEB0YqiaiM8v8OuHRJYsTZLL1yCaEwRDPoqZMoGAwfveV8+dd9ZRV6dhGJBI\nQSgAnmplmCrvFMo5OPmGRF6nHcdZ/Xo3EgThw7iJXJc6pwz0MAK8uFho8+wy/sDyV6QqXqtU+SNQ\nFLdu7+io2/73lXrMv1FEIjLNzRozMwai+M5urXL8eIa77x7EcUCWRSYmMmiaw/PPeznnnCWo6iub\nc7dvH2N4OEcgoPCjHx1D1yWWL4+9YqT29/H7FS67rJmDBycol02amoIEAhrd3RWKRZtMxiYclhBF\nAcdxS3nt25ekUukjGh3njju6qK310NQk0NWlc/iwh2KxgN+fwTR1li+PMTpaxOuVaG0NEIvZpNM6\nsiwiigLligQUIWqSFDRyzc2sjWylUAywc28TTXOOEgh0MTNTolg0CQaVF/b92mubAAdVFfH5ZASh\nDssMc/37QmQyNtdcY7JlS449e8roukK5XOHE0RY6508xM93I0EALjmORTtu0tXlxHIElSyK85z3N\nXHhhHd/7nkmx6FAuw4MPQm1tiFRqhnxeRJJE5s+XWb26jgsuCHL//aNEozpTUwYXXxwmHi9w5EiB\nhgaNyUlX7GqaxMqVUdatC3PTTQ3s2JHgq189zNNPTxMOqxSLCqWSBAhEIhaXXAK6LlEoiGzZkiUe\nCTJ+sEy5nAfygJdTyVuiUmH18kksK0pTrUOxVKRsBfAGNM67QGdiQmbv3hzXXxFny8M6olAm0mRy\n7dVhLrzQz5YtWZYtd9i6X2doHEJ+uG0jhIOnvyuDg3l27UrR0eFnxYrgGQmEVaq8pbxNuhvPVg74\nPHCR4ziFF730IPBTQRD+ETdhax7wHG662TxBENpxRetNwC2vNk5VvFap8kcgSa5VYM8eCIVg1ao3\nb2xZFrnllgY2bZpmwQIfF10UffMGfwPo6cmj6xL19TpbtgxTKGTwej3095vMzJSpq3tlK8TkZJGd\nOyfJ5dwIZCikMjCQfV22iPe8bymOEsQoZlm9qp6vfKVELmfS3q6weXOe668P0tUV5pJLmnjuuUla\nWoIsXhylvz/H8HCB2loP111Xz7PPTlFf7zaCKJVacJwe+vsLxGIeLrsswl//dRc1NTrDw1l++tNj\nbN01glO2wSyB5qGgqHyn51YShRjndPZSU6Pw4GMr+egnFrJlyzB+v8L69ad9qH6/wk03tXPTTe2Y\nps3X7sqSmskRiwWIxUSuvFLmoouWMDBwhGefdahUcvR2z2dkJEcprzMy7EVVR/H7o4yOxli8WORD\nH1rExRe7is22y0iSgCQ5qKrADTe0cd99EqWSxfvfX8uFF9Ywf36Yvr4cnZ1QV6exfXuZuroA8+fX\ncMcdi/nWt04wPa2SzUJjo0gymWHXrjJ79iTYvHmcEycy2LZFIlGmUBBQlDC2LWDbNsGgxOhomfp6\nBXCYmqrQ2Ah9fQ6uz9UCoqD40HxFDhy/iI/c8D0CvinGJ2s4cGwehtZEb6+G7jFZszrIqkXwq58s\nZNOmfp54YoLDh6fYvHmSxYvjPPSIjBSJMb9DI5UJ0DcMKxe5n/XQUI6Pf3wP+bxNJKLxv/7XYs46\nq+qPrfI2QfPD3Dc/YUsQhJ8BFwMxQRCGgf8Pt7qABjw+e4O3w3Gc2x3HOSwIwr24iVgmcKfjONbs\n+3wKeBTXX/QDx3EOv9qeVcVrlSp/JJEIXHbZWzP2vHl+PvvZV05EeifR0eHjmWcS9PfnEQSbqakC\niUTphcYDp7Bthy1bhnnmmTHmzg3z3ve24/W6paeOHUshigJNTX6OHUu95rEnE/DvvxAwzTY0FRYu\nhjVrJvH5RAoFh2LR9dJKksiVV7axbl0z3/zmUQYGcsiySGOjmyTW3KyzZImPvr4CpmlhGGkcRyCV\nymCaWTZvzrFuXYB3vaudsTGZFSvm4+m0CHlnSPeXQRBheS3qmI8nd3+YfTum8fktFizqJB73sHHj\nvD94HL/97QBjoxM8/7xIV1czn/6rRp4+DPURmbPP9nPyZJpiUaV2rg9x3nL23DuMJ5THKMtUTA9L\n5+a58MJ6jh6VuPhi9z1vvFHh/vsNAgGBa69ViEabuPrqOD6fSCh0OgLc0eFn/vwQDz00TXu7xvLl\nQUZGKnR2htm/HzIZi0plhr4+SCQCiGKGfL6CaVZm/bAAAoIg4DglbFvGNGW+8Y0RRFEnlZJpb9e4\n7roYR45U+MlPMuTLAgheUDtA0smXsoS8fUwndXbunUOsJkfQV2bOnALrLvTygb+IsXyZl0OHUvz0\npyPce+8A09NZZFnCshy6u5OkMw6y7tAxv44Va7uIRepeOMa9e6cpFm3a2jwMDhbp7c1XxWuVtw+V\nHPS++QlbjuPc/DKLv/8H1v8y8OWXWb4J2PR6xq6K1ypV/ovRMwUzBeiqheDbvFHQs89OsWnTKLZt\nc+21LRw+LNLaqmHbNl6v8hLLwI4dk3zrW4eYni6yadMAfX0ZNmxo58gRt2zY5GSBwcEc11772nvb\nTs+AZUFLvc2R40X6hxRuvDHE/fdn8ftFLr/cT7ls0deXZWysSGurjzvuWMDQUJ54XCced42Rjz8+\nyujoOBMTHsrlIo6TxrZF8nm3RJkkmTz55DATE2F6ekySSZNYSx2SLJCdMLCnSkj3D+BpDHP2ufUM\njoTJ5i0+cMtr+xO9b980hhFA02BoJMP3fxslWKtjWg7ZEZHLL69nx840z52sxdgdoDTtBaMEDhQd\nkXTaYs+eNLfeerphRUuLyB13SIyN5dF1AUlSaWw8szKFqkp85CMdrFkT5+c/n2RoqMKiRV6CQZmO\njiB79pzEnRm0yGTys89F3OCMW7tVEHTicT9QoViEtWt1tm3LYlkRLCvFwMAE27ZlUBQ/5bJvdvsZ\nMIug+cBSSCUlfvXwe7DtMcYmvBRKIppq0NRQRMCgry/Hd77Ty9NPp5mcLGKaAsWigWnaOI6OINjg\nSCTHJ+hqjBOPxMhkbIJBhY4OP7GYW7EhFFI455zwq56TkZECqVSF9nb/y1bLqFLlz8ZbFHl9K6n+\noqpU+S/EoVG4ZxeIAsT98JmLQH4b1nMFN5K6adMo9fU6qVSFgYEs7373HO655ziVisXGjXNfWHdk\npMiPfzzE4cMZBAFaW3XGxoo0Nwf47GeXsm3bKFu3juE4zgv1Xl8LLQ0Q8NlsevAExVyG+y2JOz+5\ngC9+MQbA+HiB//2/j7BlyzhNTV46OkLcfnsXixa9VLwMDxeYns4zd26WZFKgVBIRRRnHMcnni8iy\nQFOTj6Ehi9ZWhVBIJH/EJNcUQpkoU0ql0TUNWTYZGpphbqdJba2PuZ2vrYzZ0qVRNm3KUak41NR5\n6DlS4rolOgOTAqFaL2PHp5ieEin0+7GcEhizflHBwBHzdHbK1NVluOqq09HGctnku989yNBQFo9H\n5jOfWUlNjedFr9skkyY1NTKqKrJ0aZD6ep183pr1Y1dobXVQlAqGIXK6O5aCO2soADrgxXEUZmb8\n3Hqrn4mJGfbvn6BS8eB6Ww0gj23blCsCyM3gKGANgTMMFQVRjjJ/7nH6B5vw6iLjU24N4PHxBPfc\nk+PrXx8kHldpaXFviMplCdt2AIdIxEcqVcFxBAxDZHqiRKWY5667DlMu21x6aT2XXFLPV7+6jP7+\nPMuWRUkkSvzrvx4mHtf52MfmE42+9DydPJnl+98/ieM4NDR4+OQn579mH3aVKq+bcg763palst4w\nquK1SpX/QgynwKNAQxAGZyBXhvBb0DTouQPw2HZoroMbrwTvy+hJURSoqVGZnCxTKlnEYhqNjT4+\n//mVAC9JiBkYKBKNeli5spZnnhlB1wOsXh3D71cIBlUOHEjS2RnENB22bRtl6dJXbnlrmjZ79iQp\nlSzOOivKX1xlMXIow4ILAoyMZHnssUnWr2+ktVXlueemyOcN/H63kP/AQJqvfW0fa9bEufHGTrxe\nd/p83bpafv7zASRJ4PLLIzzyiANYeDyg6zJ33tnBddfNZdcug8cfzyNJ8IW/rsXvd/jmNw3uuy9L\nKlWgUDAoFsEwbJYvD9LQ8NpS3jdsaOcnPxlgcNAmnxaJtDsMTIDfCx/4UA27tzskEimOHs9gGQU3\n3CxAa+skId8EkXATn/zkMnT99Gc+OVnkwIEER4+WGBwsc/KkwNe/vpKGBlegfve74yQSJvG4wic+\nUYfXKxGPq8Tj7vY//vEkzz5b5qyz2tm16ySWJeNeck5FXk1OdbtSVRlJskinZS64IEQmk8K2PYyO\nqrjR2Vl7gdQATgEIghgAewLsLCGvSqHgQxTLFEsCkmBjmAWKRQlNk8lmLUAmlSpTqZTQNJVQyEMm\nI+E4EURxCsOwEUWDtjY/W7ZMsX59I83NHjZvHmPdulpmZizuuWeYQGCM7u4E4J4nr1fizjsXveR8\n9PXlUBT3hmVwME8mY5whcKtU+bOh+aGjGnmtUqXKO5SljbBzwBWui+oh9BbYBjI5eOBJqKuB7n7Y\ndRAuOvvl1/3gBzt49tkpfD6F885zo50vl8Xd1uZFliU6OupZurSBjRsbaG8PIAjwq1/18vzz0+Ry\nBkuX1nD55S1nbP9innxynM2bJ5AkOHYsw0c+0kFbq8bQUIaDB4skEhWOHZvi/e+PUFvrQRRFJEkg\nmzUoFg2WL2+muzvF7t1TrFvnFvft6grz1a+u4pe/HMHrlWloCLF1axJRFLjhhi5uvdXdp/XrVZYt\n05BlgVDIDYl/5StLWLYsyPe/38fwcBFNk1BV8YX6va8FSRJZscJDV5crzufNt7huAwQ84NEkWq6r\no6lJZ9euEUbHKgAsnHuMD9z4FJpSZMmSdha1HoKcCZ4bQGpGVRW2b7cZG1ORJJn+/iK/+c04t93W\nxtBQmelpkzlzNAYGSgwPV5g//6VfttFRGVUFr1ciGo0Qj2v09IhUKhncaKqFm5/hYBhgWQITE0ny\neY183sbjqeDzyeTzXpDmub5gIYxb29UGxwRMvN4Ktj1OQ+0oLY1Z9uxfimV7sS0bVVWZmXEtC9Eo\npFICkYiCKEI2J2BZAerq4uRyJbzeFJWKTMUQQPXTfdJg9+48lYrEAw8kuOuu5xkcLJHPG0iSybp1\nNSQSJaany2ecj/nzg2zbNsnAQJ45c3wv8QhXqfJnp5KD/mrktUqVKu9QmsLwufWQLUNdAN6Kaj6S\n6D7KFbBst6zYy2Ga0N2jofuaWbkS1Jd2f8WybEolC59PobFR5zOfaSeZNGhp8eDxuMJverrIrl2T\nvOtdLfzud2OkUgbptEkuZ+D3v/zAo6NFIhGFYFBhZKSAooh8/OMLOHo0RTabZtmyMImExfHjZTZu\njCPLAtPTJerrPdx7bw+CIGDbzhnTwGvW1LB0aZje3iLFosOll8YQRYFLLvFjmjai6LYmDQYlFOX0\niVFVkYULQ7S1hSgU3Nq1qipyzTWvr+vF9deHuO++NIGAxNVXBan9PVtmJKKyapXD5KNlTFOhY84I\nxYLCdCHK6mVPkk6fRygUgeJ94P8fJBI2nZ0xisUZSiUFXVdf8CDX1CjYtk1fXwlVFYlGz7yUXHVV\nDaOjNsVimQ98IM62bYNUKgUghXvpMXAjqhpQRlUtJiay/Ohuk3TGxrFKVCpBEKIgyODMil7RBNEC\no4DH4yMYVBFFmb7hFlIzkM1JOJQBnYkJiwULZNrbgwiCTFOTTak8w7GTBeyyCIj09k4CJoGAD1m2\nEHSVrrPPYrg/TclKc+2VQbZtyzE2ViKXM6hULATBZni4RGOjzsaNbWcce2urj898pot02qClxa31\nW6XKG8p/s69YVbxWqfJfjIDuPt5MKhWb7u48qioyb56Xm68R2PIcdLXD6sUvv83vnoGHHwNVgSNH\n4VO3nxbbmUyFH/zgOFNTZc46q4YNG9qIxTRisZdOvXq9Mh6PTCJRZGKiQm2tyre/fZKnn05w110r\nX1bAXnBBnHvu6SebLXLFFQ0UixaBgMI559TS0yNx+HAJQYAlS3REUWD16vgL22qaxJYtI3R11b9k\n+SmeemqGrVtnAFi3LsLVV8fYsmWSf/zH45imw6pVLdi2ytKlHjZujJDLwS9/aVIqBbnssmZaWz20\ntuq8970N1NW9vpO4ZImHBQt0BAEk6cy7lp07c6xcGWD//hQjIzZHujtZu/Io3miavkGd0ekprr/e\nj6x6ERyoqZFZvDiCrsuMjJS55poo731v3ex7jZPPJzBNkXPPjXLkSJI1a2peaKELcPHFCnPmNJDP\n22zfnuG3vwU3airjXmkFXOGaRBBUQiGNYllgrNSKpUhQGAYjCZSBZhA84AggZtygrcePVzdmb3Iq\nlEoKpRK4VgQRKM1+L206O6MIQoDly2X+3wMD2OUirvcWbDuKrluAQ01NhVBtA20tEl5PDSeOqBiG\nQSCgsmJFiMcem0TTZDyeIKFQjEWLYgQCL98gJBbTicWqnQ6qvAlofmiv2gaqVKlS5XVx771jHDyY\nBeDd767lgguiLPy9UqvpdIVUqkJDgwdVlRgbh1AQohEYHnVtmKemyg8dSjI5WaK11c+ePdNccEH9\nyyZieb0KV1wxhy9/eT8nTyYZHCzQ2hpkbKzErl1J1q+vO2ObuXODfP7zi6hULH70oz7+6Z966Ory\n8sUvLuGmm6IMDFTw+UTq6xWKRZP77jtJf3+GSy9t5vzzG1i4MILjOIyPF1BVkZoaD47j8MQT43zj\nG700NXlYvDjKkSM5Lr88yre/fZJSyaRQkLjvvgk++9l2DhwosmKFxt///SDHjjk0NwdZu7aBL32p\n9k+KlsvySzceGYcnnnGL7dfVaxw4UGTOnDDptEEm38G/33MzwcAUsZjGlZc8zYnRMA/3bsQW4NYL\nVO68s5HBwTJtbRqNjRqmaXPyZJZnnplg0SI/jz8+xi9/mUZRJJYvj/DFLy55oWmGIAhEowZHjxr8\n4hfj5PIV3Jau7jQ+yAiChCSJBAJFwmGBAnUIpgfyKZC8YAhA0g3hUwLHAFkEKYwkK8iKSiZlUamI\nWJYXSAOnhKmJYQhYlsiRIw6aNkE2G6OQPSWeLSCP41jMnVdHNmdwyaUx+sfS/OInB1i6vIG1Sxz8\nfoVrr43yiU+EuP323YyPl6lUopxzThyvV2H79jwbN6rsfA5Gx2DtGrfzXpUqbxqVHAxWbQNVqlSp\n8ppxHIdjx/K0tXnIZk26u/NccMFLGyeMjxf4zne6KZdN5swJ8LGPzeOC80R6TrrC9bL1vMTjGQ5r\nmKbD5GQRQRDYvXsSv1/h7LNr0fXTK2azBv/6ryfYsmWYfL6CZRVJJApceWX7C9aCl8Pnk+npyXHf\nfePE4xpbt2Y477wJ3vOeZjpflOG/b980R47M0NzsY9OmQRYujBCN6jz66ADbto0iCAI33zyfUMjD\npk1jNDaqHDyYRhQVbr65iWSyxIkTMwwMFNA0P5qmMTpqIgiwbdsIu3YNYdsa2WyKOXMiwJ/W5tc0\nbUolG79fxrbhx/8PbAeOnYQL1wS49VaJOW1einaKQtZiarKRdM6DP5Rjy84N7EwsZNW5IZQA/OyJ\nAlctMVm92o+qipRKFj/4wUkGBnIcOJCiZ0hkz/4yjqOg6R4OHJqkvX0MhAZMo8LQUB//+Z8S6bSE\nbduYZhnw4CYWv7pTAAAgAElEQVRgyUiSiq7bgMOqVTVceWUrP/lFinQpjaM4ZEZE3GoDMjhlXGFq\ng6ODrmHlC8wU8lQqKlALVIAgmnYUXUnj95soCoyMezBNAVnWqFRMLOtUbVl79pHn4MFpYnVz2LnL\nopgdIhhW2PLASbobQ9TW+mhrm8+FF9bwi1+cz/HjWR56qIBliWSzFg0NMgcOwa9+AwE/HDoC//N/\ngPctSJSs8t8U1Q9t1chrlSpVqrxmBEFg9eoQTz01g64LXHHFmdPp3d0ZDh2aIZ83OXw4xTXXNNPa\n4uPzn4NKBYLBl66/cGGYG29sZ3Awy/79CZ58cgRFEZmaKnL99adDut/8Zg+PPjpOPm8DAooCmiYS\nDArMnx/m17/OYllw6aVewuGXilnLchOdbNuiUjERhDNNY6ZpUSqZFIsWguB2N3Mch2eeGaelxU82\na7BjxzhdXVF27ZpE0wSiUZW/+IsG1q2L8OCDfYTDIiMjOsmkzty5XrZvr/C+9/nI5Qw0TaO/P45h\nlBgY6CWV6iISUc/Yj9dCMlnhhz8cIpGocM45Ea66qo5SWaAmAhUDSmWBRYt97Ba8eAp+pk4YLHdK\nhKwyPsPm6NEyvb87SP+hOCsvbyLTM8LkHujq8nP11Q1s25bg8OEMy5eHONJtcrjPixPsZOzkFAFP\nFpEyX/rSJA2NFvmcQXd3lnA4SqFQxDS9WJaEz6chCGXq6lRyOYFAIMLFF9cSjwd4+OFxakNj1ARm\nKFs2J0thxifCQBDIuQcpWWDkoDKMIIQwhQxum1gAmRWLB/mLGzaxZtkuFNmmJlpk+5613P75OxCE\nAF5vFtMUyWadF31yMjg205MV0imDcEDBMGwymQqhkMkzz0yhKDK9vQU+/ekO1qyJMm9ekD17CgSD\nEqtWeXl6OygyxGMwOAylUlW8VnkT+W8Yef1vZvGtUuWdRy5ncffdCb7+9XEOHSq8+gZvMqWSw9CQ\nH9uOEQzWMm9e4Ix1RBGGh/PYtkMmYzA9XQLANB0SCZtCwXnJ+oIgsHJlDamUybZtkxw5kkUUZbZv\nN9mzx8Q0HU6cyPDggyPE4zqWJWJZ7hRxfb3OJZfUc//9aXbvLrN/f5n77sudsU9LlgS55pp6hoaK\n+Hw2PT1TFIvmC68fOpTg0UcHGR/PkUwWuPnmeQSDKoLgCuP+/iyJRInOzhBDQ1kWLw7g8ykIgsGi\nRV4EQcDvV5masshkRAyjSE9PiqNH8+zZUySRCGDb9UhSjmBQp7fX4Cc/GcaynDP29bWwf396traq\nh507Z8jlDN53JSRTEA3BRWthqgg9GYG2Fp221R4KzhTpvENPT4JKxWDenADF5BTeYprlHTLt7V4O\nHUrz2c8e4l/+pYetW10BK8oq4cY5dC2aiy88B9sykCWJZDLLsWNjIIBhWORyBratoqoGiiJRLHrJ\n5+vo71eYmnIYGKjw059WGB4ucPKkwdRElnJhhkrRwe87ZTHQcS9Vedc2ILq2A8exse2Qu5xuYJq1\nq7Zz/KSHfYfbaG1OEK/NcdE5u7nqsgFWrKgwNJJkpqiA4gPxlLo0Zv9VMMpZTEsjZbWQ15fQPSST\nyxU4fnySffuSL5ybcFjm0kuDnHWWl0SixNxOg9paGBqG8891u+9VqfKmIr4Bj7cx1chrlSpvc7Zv\nz9PdXSIWU7jvvhRz5+ro+tvnL8vQkMXYmM3SpV76+y0GBiwWLnzpn5bOziBnnVWDLIsIgkAgoJBO\nO3zrWyXGxvKEwwJ/8zdRfL7Tns1EoszJk1nOP7+eLVtG2bpVYcGCOu6912By0qGz00FRRBwHZFnH\nFSECti2xffs4vb0Fli+vIxrVSaWsM/ZbVUU2bIiRTCbo7AwyMJBlcDBLV5erPJ56aphoVKO2tp5i\n0WTx4tNWiBtvnMfSpTWoqkQwqLJ16yjDwykSCYtoVOOHPzzJpz+9kGXLYvj9p6bKwxhGhFIJnn/e\nYGrKw7JldRQKFoKgIUllHnooRzI5wTXvDbNnQCedh/deAF2tr34eIhGVSsVhfLyM1yvh8UgsXwjL\nFpxOhCsY4JVBD0BxCsxMmeWtEjuOa3R3F+npmaShwcvV56o8+2yO/n4TVZUYGEjS2uqnUDCor/dx\ny63N/MuPPfxuM3i8TVgaOMVDiGKRZMImlZrG76sgy0nmzathZGSckRHXYgISlqUAMrZtYdsG999f\nplCQGWAhmnY2jqNhmjmgH1ecTgMm2ApuFNbitAXBcl9jgp4+i3lzLSYTIX7z+Eref8M+fD4vS5bE\nONGvcuh4kVwZN6fL40e2RMySAAjgjCMKBt5YjBm5CbmUxVBbwMhw8mSeBQsKiL/3s/v1rwfYsyeB\nqkp8+MPzqKvzoVdztKq82ah+aK3aBqpUeVUqFYvp6RLRqPYSD2KVPw3TdM5IuhFFsG2wLAdBePXy\nV5blrvP7F9o3ikjErUk6NGQhihCJnLmDDQ1ePvax+ezbl2DhwjAdHQGOHLE5eHCEmZkxMhmHhQs7\nuOWW05kuwaBCKKSSSlVYu7aOYrGVRYs8FIswMOBwxRVBPvKRFv7v/z2GIEhYloQgmPT0FKirS9HW\nJjMyUkDXVW65xX/GPjmOQ6FgUi5bDA/nkSSRSOS037W52c+OHeOIosCSJS/18GqaxIoVcWzb4a67\n9iFJIrIsEgyKXH11K2NjBZLJMsGgypo1MUZHc4yP12FZUCr5OXw4z/S0QjwucckltQwNuVG9FSvC\n+Hwi/3pPmTmLdMJ++Plm+NsPvfr5XLYsiGHYTExUOOusELru2iRe/H3xKnDbEjg2A8oSmYm5DXz/\n344zMOBBkjQsK0s6XeCf//k4X/rSYurrfaRSZfbunSGZrBAKKWzY0MTChX42DjmUpw1GUg6lpMLI\nmIdiPoFlFbBtkfp6jUAgzcaNYe6/32B4WOJ0lQEH12/qw7IMCoVT3be8lMtRXMFaA0zh3pSIuDVh\nDdzLlkQ0MoMiFZhJe6kYgKDwu91rsfT9zJeHCJeKODj8bs8GxqZXMDAwjCI7UHFQgjreuAcmprAV\nk0LJg2NbKKpGXUOYXMLBF4SpKQEBFcty2Lw5yWWXbeOcc8JcckkdZ58dZc+eBC0tPqamSuzbN82G\nDX+aZ7lKlT8KIwcj/71sA1XVUeV1UyqZ/Md/HGd8vEg0qnLbbQsIBP44n14VF8dxePTRDE8/naOp\nSeXWW6P4/a74OO88P4mEycSEyc03R9C0V1Yx+3rhV8+CT4MPXQr1b8L0ZSwm8olPeOnttWhrk6iv\nf/lEqZUra1i58nTnq7o6gampaUTRQyjkMDg4BZwWr7ouc9tt8zlxIkMspjM87OHhh01EEa6+WkIU\nBc47r56f/jTBsWMJbLuMKGp4PAUymQrFYo4vfGE+S5ZEX8iAfzGPPz7EU0+NYhg2dXU673lPO7W1\np42K11zTRl2dF8uyWb36zKoF4La4LRRMams9zJ8fZmioyNhYgbo6D7GYjmnCzTc3Uamk2bHDx9SU\nMWshiGAYAuPjAh//uM7VV9dz5EgRw7BJpy1CQQHLdr2qsvza6vWKosCaNe4JHx8vc/x4jjlzPC+I\n2FPEve4DoFIf5z++2YvP51AqFTAMh3DYFZSJRInzzqvFtr3cdlsrTzwxyXnnxaipUbnvvjG2bClx\n9LCfobEUolAmkyiA5QAmopjm5MkQPp/C978/zsCA21jAo9usW3uc2vg0mzZfTGLGhySVsG0LxznV\nfQvcagGzEVGygJ/T7WVtRFFiwxXbONHXgGUJPLd/GabTScU0eX5/LQ3Nv2Husj56etsoF0bpmmcR\niy3A2NxL+lASI5cnMzOCohjIchzH9hGpsViwso3WBoeSVqLkBDHSR8lbBjYK2azD1q0T9PXlSSRK\nxGIatbU6AwN5LMumqalqcq3yFqH6oaUaea1S5Q8yOlpgfLzAnDkB+vuzDAzkzohMVXl9JBIW27bl\naG1VGRiocPBgkXPPdaOFXq/I+9//6p+vbcOvd0A8COkCPHkAbrnojd5zl5YWiZaWV87ufzliMZEP\nfzjO5s1jBAKwalXtGeuEwxpr1rgJYO3tsHixhCRBKOSKnF/+coxMxsbr1TEMG9sWWL06wty5Oh/6\nUBfLlsVecfzdu6doavIRj3twHIFKBe6/f4SODh8rV4ZRVYnzzmv4g8cgyyLvfW87Dz7YT0ODl9tv\nX4QgiIiiyD/90wC7duVobtZpadE4csSgtzeBrssUixHSaQldL7J5s82KFTqplMCCBSr19TJrzvGx\n9SBk8nD1Oa+v2UR/f4HvfW8I24amJo2//MvWMxoqnKJctmhsdEgmJZJJB0FwmJ5OsmRJjPr601l0\nk5MG4bDK3r1Jtm6dIZ+3eeSRUXSPjipHyRRkd/aeGsCDbRdwnDKSZHP8eAXLAhC49YZHiNfkAJ3r\nr93Ld3+ywZ2xdyYBFTey6uBemvK4FoFTntRT0VrAsZiajhKLGGhant4hnfFphbOWPU97W4lPXvcI\nHa1DFIoBBpUUtjnC2HiMw0dMjLIf0HBwS16ZZhxBlCiVPETDCnNbHD76gQLp8X8jGszwD99YyRPb\nGnAc9wahWLTIZi0qFZuPfGTe/8/em0fZVd9Xvp8z3XmqW3OpRk0llWZAWEKIUQaDI2MzJAY7ENsB\nm4WH2E5Iv5fXSbp7OU7S7X6dTr84MW1n2WAbY4INbQYbWcySEAIJDaWppJqne2/VnYcz/t4f55aE\nkAQCA8bk7rW0qnTumevcc/bZv/3dXw4cyBCNeli6NHruf6QaangnYRZgoqa81lDDG6KuzovHIzM0\nlEeWqQVxvwPweiU0TWJ21sa2BcHg2dVVIcQZW6jKMkQDMJuHiuX+/n7HzTf3sHZtHEmChQtjbzp/\nPH7qcfv9Cl6vjaYJfL4YfX0evvvdPqJRD7HYG/eS7+uLs2PHFAAXXtjM978/gtersHPnLJGIyoIF\np1oNZmd1Hn54BNN0+NjHOmhpcU/w+ec3smZNwynq7re+dZwXX8wxO2syPJwnkQCvt0J9vUKpZODx\npInHm+noULAsiaamIMWiw4UXBlmzxv0+3dwMpZJNOm1hmhqadm4+kIGBEooi0dnpY2SkQi5nEY+f\neWQkHPZw3XXtjI8PIEllZDlEqeRh//5ufv7zEPm8w3nnmezalcayTPbsSTIyYpLPy9i2DJkykjSL\nJHlxGw74qz/D6HqCRKJYnSYBBRRFxjC9HBroZWq6DkQa25krUJtXXbYEDOOSVreVLbIBjl1dj4wj\nHAoljSsuPko6GyOXV2moT9LaAs3xKVYuOYKi2AQDFVYs2U2q4kFWFCzDj+ufVXHJsg+w6O5Sqa8P\n8+ENMps3x+lueRq7EgB5AeHQfgbHWxgf9+E4Eh6PxLJlEVasiOHzKaxff/pLVw01vOd4/5RBvCeo\nkdca3jLq6rx8/vNLGRoq0N4ePPEQr+HtIxxWuO22enbsKNDREWT58tMD+Q1D8MADJocOOaxbp3Dt\nteppw+F/eDk8vQ+CPrhi5Xu1928fiiKfKJB6O/jUp9pJJEoEg2na2z1s2NBAV5ebdpDJGOTzFq2t\nvjMqj5s3d7FkSQxFkfB4NHbtGqSx0UOhYFEsnizwGhjI8sgjI+zbl6a52U847OGBB4b48pf7Tswz\n93eoVBy8XolKxaGpSWNkRCeXc4jHvczOlojFNAxD0NNTxOcboa8vysKFbQwPW7S3qyxefJJkzs6a\n/PM/uzFgHR0ePvvZZjyeN39CLVwY4OmnZxkaKtPR4SMSeePb/DXXdPKjH5UoV2Zx7AAQZ3RURpZt\ntm6FgYEpXnopw+HDs1gWKIqGbRu4RFNBCBtJ0nFVUQs3c1Xm5NC/zJyC+tBjF7PxQ/sZG2/i6FCD\nO48zg0sk/bgduALVdZdxc28FODon/bI24PDUtg4OHOkgmw+g6xO0NjnUh4fJZy12vrKclcuOUCp5\nmU2HuXiDTTqXRvXGsEsaLkFOoShZwuF6/P4QfX0lNm9up7vbD3oURTLoP3SUqWmDnm7o6ZGwLB89\nPQEcp0giUaKjI3TGF8kaanhPoYVgXs02UEMNb4qWlkCNtL7DmD/fy/z5Z1cLjxxx2LfPprtb5oUX\nbFatUujoOPXB2RiFmy5+t/f0vcHevVlGRyusXBmho+N0Mg/Q3OzlG99YRn9/lkrFYcUKV70dHS3x\n3e8OYZoOvb1hPv3pztOIvqLILFniEmfbFixaFOTJJ6cRwua++4p0d4e5+eYF/PjHx/D7VVKpCqWS\nxZo1DafFWVmW4IEH0hw4UKanx8t11zXx0EPTKEqIbLZCNCpz8GAIVRU0NHi55JI4w8NFbrutg40b\nmyiVBH6/dMo+Dg7qFAo23d0+hocrJBIm7e1vrCYDdHcH+PKXu8hkLDo7z0zcX4tMxkLTFGS1EceW\ngBCmqfBP/1Rm0SKDxsYkMzNgWV6E0LEsB9eDCi6ZjOI4ORTFg22buES1gjvMH6j+9AIG08kQD/7i\nElxSaoNkoHlMTN0twnLnV4EoLplN46qvDic9sDZzRDaRUqvzawyN+ll33kEmpuv453uvZumiPupi\nWQRB1kZaKBZzfPFOjX/8XwkMPQNI+P2wcmWaK6/04Dg2+/Zl6O4OgudCwGT/wed5/Ner0A2JfD7L\nqlX1dCwWbP31GKOjDuvXN3H11a00N/vP6KuuoYb3BGYBJmu2gRpqqOF9CI8HQCKfdz2Q3jfnMb+z\nGBgo8sMfjhMIKLz0UpqvfW0BkYh2yjyW5fD885Ok0zoXXdRCc/PJl6kDB3IAdHUFOXw4Ty7nejbP\nBkWRkCQ3q/PFF5PIcpTxcR0hZGRZwjBsYjEPuZyJbQtuuqn7lOXHxlyfcne3h2PHKqxbF+Qv/3IR\n4CrAExNlWlp8xGIetm6dYv/+LFdc0cwll7iFYK+NCJtDY6OGJMHQUIVgUCEWO/fbdVOTl6amkxfI\n5KTOww8nUVWJT3yikfr6k+ciGFQAD5oKlmEBJYSQEULm2LEEu3frmKaGEEHcR4YPl4zKzCUEQBHb\nnlOrdUABnwa2H+z8CauqJBURwgAUPD4FRa1QX6cxPiYhxDQuqTVwFdy5Nq4aJ7thzQVQzsVjeXGJ\nroRtC3740CY0TWCaNqpSwuNRaG/38tmvdDA6Ns0//mM/mprF55VpaPDT2xvFtmWeeCJJc7OHG27o\nqO6oCt5LsT3NHDr6KrGYRFdXBG/XNOn2Cfr+QDDwZIVH/58R7r8/xh/8QTe33roIRXn7Y7eG4fDg\ng9McPFhk3boo117bUFN1azg3eELQVlNea6ihhvchFi2S2bxZ5cgRh6uvVmhq+t03OTmO4PjxAooi\n0d0dPPGwzuVMZFmipcXL8HCZQsE+jbxu2zbF44+PEAxqHD6c4U//dPUJlbGzM8Azz6QYHCzS3Owl\nFHrzW93kZIlwWKZSsdixI01zcwDHyXDrrV3cf/9hjh8vYJpw8GCR6WmD9vaTsUjBoIwsQyrl+jJD\noZN/m1jMcwpx3rSplU2b3rgQzD0GL5//fCvT0wbz5/tOpE+8HTzwwDSFgoVpCh55JMlnPjPvxGea\nJhGPB7hko8GOFzWKBQlVlVDUMoW8juMA2MiyguP4qsVjrvIsxJwPtQ13KL4EVMAjg1JVSgMB5LyE\nLGtYVrXdKz5s4SXga2J6ykGIIi4prXDSjzrXxtXDScI6R17L1ekBXMVW4HpkTUwTwMGyvQQ9EXrm\ny5TLkEyaHDqUx3EkTNMhndYZHCyzZk2UtWvrmJ016e09tdXbTTctZnKyzOBgntbWIGtvL3PsSBvP\nPZ9gppymUGiiVIJXXpmhoaGJRELl/PM9rF791t8sjxwp8eqrBbq7fbzwQoaVK8N0dNTqCWo4B5gF\nmKoprzXUUMP7EJIkcfHFKhd/QGwBAI89Ns7zzyeRJFi7tp7588N0dQVZtChES4uXkZEyy5eHaW4+\nnQyk0zp+v0pDg4+JiSKW5Zwgr319EW6/vYds1mTRotApQ+eGYTM7W6GuzovXe/IWuG5dA3/5ly+h\nKDKmKZg/P0JnZwxN0+jqCpPLgW07GIbJs8+mOP/8k17dxkaNW2+Ns3dvhUWLvHR3vzOyeGenl87O\n33xdQggURaJUsrGsUy0PsiwRjdpkM1lamgS0+DHtBhIJDYdiNTnAbYurqg5+v0KhAEKkcclmlr7F\nE1yy7mWOHm/lqW1rcUSFiKfC8rWjHJyeT/Gga02wrCBILgm39QoZsw6EjquuzhFWBTceSwVacdXV\nIiczXyVcS0G5uv1gdZqCS3ANwI8kWUQiOosXt/IXfzHEL385Ri5nIklatSVwhVyuRDI5y6ZNHSxZ\nEqNUMvF43PNdKNg8+OAshtHE9dd3sm5dlAm/Q4WDeF5WGN3bSblsMjBg0Nsr89RTJi0tMg88UKS1\nVaW5+a29bHg80ontvvb/NdTwptBqymsNNdRQw3uGPXvStLcHSCQqfOc7xzjvvHpCIZWvfGUxd93V\nTalkEw6rZxw+veiiFg4fzjAxUeTDH+44rVnG/PmnB8aXSib33LOPZLJEQ4OfO+5YSSCgVeePsG5d\nPV1dYbZuTePz+YlENFatCmPbzezblyWZLNPbW0939+l+795eP729Z/bm/rZx441N/NVfDTE+buD1\nqmQyJrHYSSW7q6vMeeeBx6Py/R95CAXLRKJQUkNkUrO45FLGsnQqBgRDFcoVHVsNEo6W+OQtO0lP\ngqaFaG7QyRaKhIJT6PkUF887yJOHN1HWDVDqQDjgCJA8IJdAyGDLuKRTwiWrDq79oAGXyM4VcmVx\nldc5y4CB64v1cNLK4KYItLcHaW318ZOfZLGsHLpuIIQHIeTqvFlsW8EwZI4fT9LSIvNP/9TPXXct\nIxp1O4wdOVKmqUlj69YSa9fGKPcvxp5VGdo6habXEYu5RasXXdTF4cMq4bBEOi2h62+9xe+iRQE+\n+tEGjhwpsWlT/IwvbDXUcEaYBZiuKa811FBDDe8aJiYKlMs2HR0h1qyp4/nnk0xOlqmv91BXp7Fz\n5yxPPjnFxz427zSrwGvR2Ojn619fhWUJvN5TVa5kssxPfjJAuWxxww3ziUQ8bNs2TTZbYWKiwIIF\nMYaGsoyM5AkGvfzwh0NYlkNzc4ipqSKXXRbjhht6aWjw4vUqXHXVPHp7Yxw5kqeuzsuqVW8e6/V+\ngs+n0NzsZe3aCKOjOvv2Fdm48eQxNDREGR21CAZt6iIS2YKHQi6PcHK4xVnVyCpUTLtAY7yZwnAW\npByKpCIrISpyPboZxeOR0StByvk4dephUtMhtLBAVzqhUB3yly3IG2BVcNXUEC7ptICR6u9TuMS1\nHpfUWrj2AAtXYXVtAqriw7Jfa6Fx8PsNmpqCJJNl8nkHw/DiOHMKr4lLjlVAR9cFw8MyhUKeJUtK\nTE4WiUY9yDIIAZblNmb55jcPMTJiE4sFiIgmOjs0hobcpIpjx2bo6WlndNTmoou8tLe/dYuHJElc\nckkdl1zyHnQWqeGDhblQj39HqJHXGmr4gOHAgRyPP56gqcnL9de3npPf87Vw45DeWjD+ueK+Hx3h\n//vuMTyOwUev6eQrX1nBkiVRcjmThx8e49FHJ/B6NR54YJpSycMnP9n4hlXciiKjnIEnPP74MLOz\nOoGAyv33D+DxKOTzJrOzZTKZIprmtnOtr/fx4INjgMDrlZEkH3/2Z8sIBrVTrAaSJNHdHaK7262y\nF0IwPl5EliVaW9//qRuBgILfL5NImFiWoK5OZXLSYnLSIhyGn/40g6Y5CBFHpoxZSWAbJqo6Rxi9\nuEpoESyYGLaBDmQ5S1Gu57Ftl3HZih14QzJjk03YjortyCRnYqSyrUgNPYiJegiXIF8ESYDPgkoF\nxNwf0MIllj5guro9QEmBHcYl0K4CPNcy1ufNEgp5yeY8SNgYph/IUC4bvPpqAcuaU3IDyLKGoAzC\nV11eAwT19XWUyzZTUxY+n4mqznW2CzM9bbB37yyzs0mGhgAEdXVxliwJE4vBnj0VLr00zOBgkfPP\nd/j4x73kcgam6T3thaqGGt41aCFoqdkGavgtQgjB8eN5TNNh4cLIm8bc1PDBh2E4bN8+S7Fos359\nHXV1Z6+a13Wbn/xkgro6jcOHC7zwwgxXX33m1qZnwrYd8Pgvoa4ObvsU1Ne/+TLnikTa5B+eEsw0\nLyCAwYu7ppid1VmwwM1lbW72kUoZJJMBhocr/OxnJdrbLS655Ozq69ng+isdDMMtKBocdJg3L0RT\nk8Ty5XFWrKijqytCY2OAQEBlcrKMJEFbW4Bo9M2Ha7dunWDLlgkkCTZv7mT9+nM/x+81dN3hqaeK\nGEaASsVk48YohYLEV786SDJZoVIxKZVSBIMOudwQ2WwXPh8EAoJSCVxSqeNGYBWADly/qY1q+/Db\nY0wnm7j3sVuxyyDLNrZdJpdXebV/KfHGMLl8Q1W8zYFVQXFMHASqnMRyGhEijEtKBa41IOJuV9GI\nxfP83kd3kxwO8KtnNyJsy10PZerrTGzbQlVAkhwMc66oCyxL56Rf1sZxTGTZxBGgaCo+r4/29gCK\n0o2aKyCwaGr2kEqV+Na3XuXQoWK1kNBHf3+Kjo4GJiZk9u2bYfPmJm68sR1ZtnnllTT79mUYGspR\nqbgvRmvXNvKFLyxnYKBMMKjQ2xtEUd7a26AQsGsXDA/DeefB/Pm/4YVQwwcXZgESNdtADb9FPPfc\nFI89NgrABRc0cOONtTvWv3c8/XSKrVtTeDwyR44U+ZM/eeNrQpYlbJtqO8tzf/mpVODRJ6CtBRJJ\n2P4i/N617meplMnMjMmhQxOMjeW46KJ5rFnT/JplLY4fzxMOa3R0hM64/qmighoOIicSzGp+nHCQ\ne+8dxO9XuemmLtraAlxzTRt/8zeTNDd76O2NMTLinHFdZ4KuuzmdiiJx1VXt2LagXLYwjA7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5MIIWht9XLnnW21uK3fEVx0UROLF0eQJIn6+jePa3r8lw7xuIxjw5NbTyWvAF/6UjsrVkSRZbj8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bF2Bioogsu5k1axaPPrqNtjY38XiJRx5JviV5zed1fvjDXtJpDVXVEQQLv9+HKBaY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Pa3x5kxw8T3v59LQYHi6qJw4URJcZjPD3Bf21xq8TpfluUeQRBcwF8EQTgK/Br4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\ngg==\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkvAg623RjoR",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "We did only a superficial analysis from Airbnb's database, yet we have already perceived that there are *outliers* in some of the features. \n",
        "\n",
        "We have noticed as well that in some neighbourhoods where there are few available properties which can distort the statistical information from some features.\n",
        "\n",
        "Finally, we remind you that this *dataset* is a synthesized vresion, functional only for a starting approach. We recommend that you use, in a future exploratory analysis, the whole dataset, with 106 available features.\n",
        "\n",
        "This analysis was done as an exercise on the **Data Science na Pratica** course, teached by **Carlos Melo** [sigmoidal.ai](https://sigmoidal.ai) [GitHub](https://github.com/carlosfab) [Twitter](https://twitter.com/carlos_fab) [Youtube](https://www.youtube.com/channel/UCxaCu5ht7d3jvisTpiW6vxw)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXbugccQa9jp",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}