house data 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35083ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56baca4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9443178",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2539f1b6",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>statezip</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1340</td>\n",
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       "      <td>1955</td>\n",
       "      <td>2005</td>\n",
       "      <td>18810 Densmore Ave N</td>\n",
       "      <td>Shoreline</td>\n",
       "      <td>WA 98133</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>2384000.0</td>\n",
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       "      <td>3650</td>\n",
       "      <td>9050</td>\n",
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       "      <td>3370</td>\n",
       "      <td>280</td>\n",
       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "      <td>709 W Blaine St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98119</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
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       "      <td>2.00</td>\n",
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       "      <td>26206-26214 143rd Ave SE</td>\n",
       "      <td>Kent</td>\n",
       "      <td>WA 98042</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2000</td>\n",
       "      <td>8030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
       "      <td>857 170th Pl NE</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA 98008</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1940</td>\n",
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       "      <td>800</td>\n",
       "      <td>1976</td>\n",
       "      <td>1992</td>\n",
       "      <td>9105 170th Ave NE</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>WA 98052</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>490000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>880</td>\n",
       "      <td>6380</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>880</td>\n",
       "      <td>0</td>\n",
       "      <td>1938</td>\n",
       "      <td>1994</td>\n",
       "      <td>522 NE 88th St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98115</td>\n",
       "      <td>USA</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
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       "      <td>2616 174th Ave NE</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>WA 98052</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>482000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
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       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>23762 SE 253rd Pl</td>\n",
       "      <td>Maple Valley</td>\n",
       "      <td>WA 98038</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>452500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2430</td>\n",
       "      <td>88426</td>\n",
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       "      <td>0</td>\n",
       "      <td>46611-46625 SE 129th St</td>\n",
       "      <td>North Bend</td>\n",
       "      <td>WA 98045</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>640000.0</td>\n",
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       "      <td>6811 55th Ave NE</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98115</td>\n",
       "      <td>USA</td>\n",
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      "text/plain": [
       "                  date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "0  2014-05-02 00:00:00   313000.0       3.0       1.50         1340      7912   \n",
       "1  2014-05-02 00:00:00  2384000.0       5.0       2.50         3650      9050   \n",
       "2  2014-05-02 00:00:00   342000.0       3.0       2.00         1930     11947   \n",
       "3  2014-05-02 00:00:00   420000.0       3.0       2.25         2000      8030   \n",
       "4  2014-05-02 00:00:00   550000.0       4.0       2.50         1940     10500   \n",
       "5  2014-05-02 00:00:00   490000.0       2.0       1.00          880      6380   \n",
       "6  2014-05-02 00:00:00   335000.0       2.0       2.00         1350      2560   \n",
       "7  2014-05-02 00:00:00   482000.0       4.0       2.50         2710     35868   \n",
       "8  2014-05-02 00:00:00   452500.0       3.0       2.50         2430     88426   \n",
       "9  2014-05-02 00:00:00   640000.0       4.0       2.00         1520      6200   \n",
       "\n",
       "   floors  waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
       "0     1.5           0     0          3        1340              0      1955   \n",
       "1     2.0           0     4          5        3370            280      1921   \n",
       "2     1.0           0     0          4        1930              0      1966   \n",
       "3     1.0           0     0          4        1000           1000      1963   \n",
       "4     1.0           0     0          4        1140            800      1976   \n",
       "5     1.0           0     0          3         880              0      1938   \n",
       "6     1.0           0     0          3        1350              0      1976   \n",
       "7     2.0           0     0          3        2710              0      1989   \n",
       "8     1.0           0     0          4        1570            860      1985   \n",
       "9     1.5           0     0          3        1520              0      1945   \n",
       "\n",
       "   yr_renovated                    street          city  statezip country  \n",
       "0          2005      18810 Densmore Ave N     Shoreline  WA 98133     USA  \n",
       "1             0           709 W Blaine St       Seattle  WA 98119     USA  \n",
       "2             0  26206-26214 143rd Ave SE          Kent  WA 98042     USA  \n",
       "3             0           857 170th Pl NE      Bellevue  WA 98008     USA  \n",
       "4          1992         9105 170th Ave NE       Redmond  WA 98052     USA  \n",
       "5          1994            522 NE 88th St       Seattle  WA 98115     USA  \n",
       "6             0         2616 174th Ave NE       Redmond  WA 98052     USA  \n",
       "7             0         23762 SE 253rd Pl  Maple Valley  WA 98038     USA  \n",
       "8             0   46611-46625 SE 129th St    North Bend  WA 98045     USA  \n",
       "9          2010          6811 55th Ave NE       Seattle  WA 98115     USA  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc0e86b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4600 entries, 0 to 4599\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           4600 non-null   object \n",
      " 1   price          4600 non-null   float64\n",
      " 2   bedrooms       4600 non-null   float64\n",
      " 3   bathrooms      4600 non-null   float64\n",
      " 4   sqft_living    4600 non-null   int64  \n",
      " 5   sqft_lot       4600 non-null   int64  \n",
      " 6   floors         4600 non-null   float64\n",
      " 7   waterfront     4600 non-null   int64  \n",
      " 8   view           4600 non-null   int64  \n",
      " 9   condition      4600 non-null   int64  \n",
      " 10  sqft_above     4600 non-null   int64  \n",
      " 11  sqft_basement  4600 non-null   int64  \n",
      " 12  yr_built       4600 non-null   int64  \n",
      " 13  yr_renovated   4600 non-null   int64  \n",
      " 14  street         4600 non-null   object \n",
      " 15  city           4600 non-null   object \n",
      " 16  statezip       4600 non-null   object \n",
      " 17  country        4600 non-null   object \n",
      "dtypes: float64(4), int64(9), object(5)\n",
      "memory usage: 647.0+ KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25369374",
   "metadata": {},
   "outputs": [
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       "      <td>Kent</td>\n",
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       "      <td>9105 170th Ave NE</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>WA 98052</td>\n",
       "      <td>USA</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.081667e+05</th>\n",
       "      <td>2014-07-09 00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1510</td>\n",
       "      <td>6360</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1510</td>\n",
       "      <td>0</td>\n",
       "      <td>1954</td>\n",
       "      <td>1979</td>\n",
       "      <td>501 N 143rd St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98133</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.343333e+05</th>\n",
       "      <td>2014-07-09 00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1460</td>\n",
       "      <td>7573</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1460</td>\n",
       "      <td>0</td>\n",
       "      <td>1983</td>\n",
       "      <td>2009</td>\n",
       "      <td>14855 SE 10th Pl</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA 98007</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.169042e+05</th>\n",
       "      <td>2014-07-09 00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3010</td>\n",
       "      <td>7014</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3010</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>759 Ilwaco Pl NE</td>\n",
       "      <td>Renton</td>\n",
       "      <td>WA 98059</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.034000e+05</th>\n",
       "      <td>2014-07-10 00:00:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2090</td>\n",
       "      <td>6630</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1070</td>\n",
       "      <td>1020</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "      <td>5148 S Creston St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98178</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.206000e+05</th>\n",
       "      <td>2014-07-10 00:00:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1490</td>\n",
       "      <td>8102</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1490</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>18717 SE 258th St</td>\n",
       "      <td>Covington</td>\n",
       "      <td>WA 98042</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4600 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             date  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "price                                                                           \n",
       "3.130000e+05  2014-05-02 00:00:00       3.0       1.50         1340      7912   \n",
       "2.384000e+06  2014-05-02 00:00:00       5.0       2.50         3650      9050   \n",
       "3.420000e+05  2014-05-02 00:00:00       3.0       2.00         1930     11947   \n",
       "4.200000e+05  2014-05-02 00:00:00       3.0       2.25         2000      8030   \n",
       "5.500000e+05  2014-05-02 00:00:00       4.0       2.50         1940     10500   \n",
       "...                           ...       ...        ...          ...       ...   \n",
       "3.081667e+05  2014-07-09 00:00:00       3.0       1.75         1510      6360   \n",
       "5.343333e+05  2014-07-09 00:00:00       3.0       2.50         1460      7573   \n",
       "4.169042e+05  2014-07-09 00:00:00       3.0       2.50         3010      7014   \n",
       "2.034000e+05  2014-07-10 00:00:00       4.0       2.00         2090      6630   \n",
       "2.206000e+05  2014-07-10 00:00:00       3.0       2.50         1490      8102   \n",
       "\n",
       "              floors  waterfront  view  condition  sqft_above  sqft_basement  \\\n",
       "price                                                                          \n",
       "3.130000e+05     1.5           0     0          3        1340              0   \n",
       "2.384000e+06     2.0           0     4          5        3370            280   \n",
       "3.420000e+05     1.0           0     0          4        1930              0   \n",
       "4.200000e+05     1.0           0     0          4        1000           1000   \n",
       "5.500000e+05     1.0           0     0          4        1140            800   \n",
       "...              ...         ...   ...        ...         ...            ...   \n",
       "3.081667e+05     1.0           0     0          4        1510              0   \n",
       "5.343333e+05     2.0           0     0          3        1460              0   \n",
       "4.169042e+05     2.0           0     0          3        3010              0   \n",
       "2.034000e+05     1.0           0     0          3        1070           1020   \n",
       "2.206000e+05     2.0           0     0          4        1490              0   \n",
       "\n",
       "              yr_built  yr_renovated                    street       city  \\\n",
       "price                                                                       \n",
       "3.130000e+05      1955          2005      18810 Densmore Ave N  Shoreline   \n",
       "2.384000e+06      1921             0           709 W Blaine St    Seattle   \n",
       "3.420000e+05      1966             0  26206-26214 143rd Ave SE       Kent   \n",
       "4.200000e+05      1963             0           857 170th Pl NE   Bellevue   \n",
       "5.500000e+05      1976          1992         9105 170th Ave NE    Redmond   \n",
       "...                ...           ...                       ...        ...   \n",
       "3.081667e+05      1954          1979            501 N 143rd St    Seattle   \n",
       "5.343333e+05      1983          2009          14855 SE 10th Pl   Bellevue   \n",
       "4.169042e+05      2009             0          759 Ilwaco Pl NE     Renton   \n",
       "2.034000e+05      1974             0         5148 S Creston St    Seattle   \n",
       "2.206000e+05      1990             0         18717 SE 258th St  Covington   \n",
       "\n",
       "              statezip country  \n",
       "price                           \n",
       "3.130000e+05  WA 98133     USA  \n",
       "2.384000e+06  WA 98119     USA  \n",
       "3.420000e+05  WA 98042     USA  \n",
       "4.200000e+05  WA 98008     USA  \n",
       "5.500000e+05  WA 98052     USA  \n",
       "...                ...     ...  \n",
       "3.081667e+05  WA 98133     USA  \n",
       "5.343333e+05  WA 98007     USA  \n",
       "4.169042e+05  WA 98059     USA  \n",
       "2.034000e+05  WA 98178     USA  \n",
       "2.206000e+05  WA 98042     USA  \n",
       "\n",
       "[4600 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.set_index('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1170c51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>statezip</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>2014-06-09 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4060</td>\n",
       "      <td>19290</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4060</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>21418 SE 5th Pl</td>\n",
       "      <td>Sammamish</td>\n",
       "      <td>WA 98074</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4542</th>\n",
       "      <td>2014-06-25 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2640</td>\n",
       "      <td>6895</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2640</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>34529 SE Jay Ct</td>\n",
       "      <td>Snoqualmie</td>\n",
       "      <td>WA 98065</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4481</th>\n",
       "      <td>2014-06-11 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3240</td>\n",
       "      <td>6863</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3240</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1923</td>\n",
       "      <td>1301-1303 Monterey Ave NE</td>\n",
       "      <td>Renton</td>\n",
       "      <td>WA 98056</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4408</th>\n",
       "      <td>2014-05-21 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2880</td>\n",
       "      <td>11965</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2880</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>25437 163rd Pl SE</td>\n",
       "      <td>Covington</td>\n",
       "      <td>WA 98042</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>2014-05-22 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2000</td>\n",
       "      <td>7900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1300</td>\n",
       "      <td>700</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>3202 S 194th St</td>\n",
       "      <td>SeaTac</td>\n",
       "      <td>WA 98188</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>2014-06-18 00:00:00</td>\n",
       "      <td>4489000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6430</td>\n",
       "      <td>27517</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6430</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1149-1199 91st Ave NE</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA 98004</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>2014-06-17 00:00:00</td>\n",
       "      <td>4668000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>9640</td>\n",
       "      <td>13068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4820</td>\n",
       "      <td>4820</td>\n",
       "      <td>1983</td>\n",
       "      <td>2009</td>\n",
       "      <td>5044 Butterworth Rd</td>\n",
       "      <td>Mercer Island</td>\n",
       "      <td>WA 98040</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>2014-06-11 00:00:00</td>\n",
       "      <td>7062500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>10040</td>\n",
       "      <td>37325</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7680</td>\n",
       "      <td>2360</td>\n",
       "      <td>1940</td>\n",
       "      <td>2001</td>\n",
       "      <td>4442 95th Ave NE</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA 98004</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4346</th>\n",
       "      <td>2014-06-23 00:00:00</td>\n",
       "      <td>12899000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2190</td>\n",
       "      <td>11394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1550</td>\n",
       "      <td>640</td>\n",
       "      <td>1956</td>\n",
       "      <td>2001</td>\n",
       "      <td>5426 40th Ave W</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98199</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4350</th>\n",
       "      <td>2014-07-03 00:00:00</td>\n",
       "      <td>26590000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>7793</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>12005 SE 219th Ct</td>\n",
       "      <td>Kent</td>\n",
       "      <td>WA 98031</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4600 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date       price  bedrooms  bathrooms  sqft_living  \\\n",
       "4472  2014-06-09 00:00:00         0.0       4.0       3.75         4060   \n",
       "4542  2014-06-25 00:00:00         0.0       5.0       3.50         2640   \n",
       "4481  2014-06-11 00:00:00         0.0       5.0       2.75         3240   \n",
       "4408  2014-05-21 00:00:00         0.0       5.0       2.25         2880   \n",
       "4411  2014-05-22 00:00:00         0.0       5.0       2.25         2000   \n",
       "...                   ...         ...       ...        ...          ...   \n",
       "2761  2014-06-18 00:00:00   4489000.0       4.0       3.00         6430   \n",
       "2654  2014-06-17 00:00:00   4668000.0       5.0       6.75         9640   \n",
       "2286  2014-06-11 00:00:00   7062500.0       5.0       4.50        10040   \n",
       "4346  2014-06-23 00:00:00  12899000.0       3.0       2.50         2190   \n",
       "4350  2014-07-03 00:00:00  26590000.0       3.0       2.00         1180   \n",
       "\n",
       "      sqft_lot  floors  waterfront  view  condition  sqft_above  \\\n",
       "4472     19290     2.0           0     0          3        4060   \n",
       "4542      6895     2.0           0     0          3        2640   \n",
       "4481      6863     2.0           0     0          3        3240   \n",
       "4408     11965     2.0           0     0          4        2880   \n",
       "4411      7900     1.0           0     0          4        1300   \n",
       "...        ...     ...         ...   ...        ...         ...   \n",
       "2761     27517     2.0           0     0          3        6430   \n",
       "2654     13068     1.0           1     4          3        4820   \n",
       "2286     37325     2.0           1     2          3        7680   \n",
       "4346     11394     1.0           0     0          3        1550   \n",
       "4350      7793     1.0           0     0          4        1180   \n",
       "\n",
       "      sqft_basement  yr_built  yr_renovated                     street  \\\n",
       "4472              0      2002             0            21418 SE 5th Pl   \n",
       "4542              0      2001             0            34529 SE Jay Ct   \n",
       "4481              0      2013          1923  1301-1303 Monterey Ave NE   \n",
       "4408              0      1990             0          25437 163rd Pl SE   \n",
       "4411            700      1986             0            3202 S 194th St   \n",
       "...             ...       ...           ...                        ...   \n",
       "2761              0      2001             0      1149-1199 91st Ave NE   \n",
       "2654           4820      1983          2009        5044 Butterworth Rd   \n",
       "2286           2360      1940          2001           4442 95th Ave NE   \n",
       "4346            640      1956          2001            5426 40th Ave W   \n",
       "4350              0      1992             0          12005 SE 219th Ct   \n",
       "\n",
       "               city  statezip country  \n",
       "4472      Sammamish  WA 98074     USA  \n",
       "4542     Snoqualmie  WA 98065     USA  \n",
       "4481         Renton  WA 98056     USA  \n",
       "4408      Covington  WA 98042     USA  \n",
       "4411         SeaTac  WA 98188     USA  \n",
       "...             ...       ...     ...  \n",
       "2761       Bellevue  WA 98004     USA  \n",
       "2654  Mercer Island  WA 98040     USA  \n",
       "2286       Bellevue  WA 98004     USA  \n",
       "4346        Seattle  WA 98199     USA  \n",
       "4350           Kent  WA 98031     USA  \n",
       "\n",
       "[4600 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.sort_values('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfc2f1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
       "       'floors', 'waterfront', 'view', 'condition', 'sqft_above',\n",
       "       'sqft_basement', 'yr_built', 'yr_renovated', 'street', 'city',\n",
       "       'statezip', 'country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf6a1efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0          49\n",
       "300000.0     42\n",
       "400000.0     31\n",
       "440000.0     29\n",
       "450000.0     29\n",
       "             ..\n",
       "684680.0      1\n",
       "609900.0      1\n",
       "1635000.0     1\n",
       "1339000.0     1\n",
       "220600.0      1\n",
       "Name: price, Length: 1741, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['price'].value_counts(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a53b1cf",
   "metadata": {},
   "outputs": [
    {
     "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.600000e+03</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4.600000e+03</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>4600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.519630e+05</td>\n",
       "      <td>3.400870</td>\n",
       "      <td>2.160815</td>\n",
       "      <td>2139.346957</td>\n",
       "      <td>1.485252e+04</td>\n",
       "      <td>1.512065</td>\n",
       "      <td>0.007174</td>\n",
       "      <td>0.240652</td>\n",
       "      <td>3.451739</td>\n",
       "      <td>1827.265435</td>\n",
       "      <td>312.081522</td>\n",
       "      <td>1970.786304</td>\n",
       "      <td>808.608261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.638347e+05</td>\n",
       "      <td>0.908848</td>\n",
       "      <td>0.783781</td>\n",
       "      <td>963.206916</td>\n",
       "      <td>3.588444e+04</td>\n",
       "      <td>0.538288</td>\n",
       "      <td>0.084404</td>\n",
       "      <td>0.778405</td>\n",
       "      <td>0.677230</td>\n",
       "      <td>862.168977</td>\n",
       "      <td>464.137228</td>\n",
       "      <td>29.731848</td>\n",
       "      <td>979.414536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>6.380000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.228750e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>5.000750e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.609435e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>7.683000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1590.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.549625e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2620.000000</td>\n",
       "      <td>1.100125e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.659000e+07</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.074218e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price     bedrooms    bathrooms   sqft_living      sqft_lot  \\\n",
       "count  4.600000e+03  4600.000000  4600.000000   4600.000000  4.600000e+03   \n",
       "mean   5.519630e+05     3.400870     2.160815   2139.346957  1.485252e+04   \n",
       "std    5.638347e+05     0.908848     0.783781    963.206916  3.588444e+04   \n",
       "min    0.000000e+00     0.000000     0.000000    370.000000  6.380000e+02   \n",
       "25%    3.228750e+05     3.000000     1.750000   1460.000000  5.000750e+03   \n",
       "50%    4.609435e+05     3.000000     2.250000   1980.000000  7.683000e+03   \n",
       "75%    6.549625e+05     4.000000     2.500000   2620.000000  1.100125e+04   \n",
       "max    2.659000e+07     9.000000     8.000000  13540.000000  1.074218e+06   \n",
       "\n",
       "            floors   waterfront         view    condition   sqft_above  \\\n",
       "count  4600.000000  4600.000000  4600.000000  4600.000000  4600.000000   \n",
       "mean      1.512065     0.007174     0.240652     3.451739  1827.265435   \n",
       "std       0.538288     0.084404     0.778405     0.677230   862.168977   \n",
       "min       1.000000     0.000000     0.000000     1.000000   370.000000   \n",
       "25%       1.000000     0.000000     0.000000     3.000000  1190.000000   \n",
       "50%       1.500000     0.000000     0.000000     3.000000  1590.000000   \n",
       "75%       2.000000     0.000000     0.000000     4.000000  2300.000000   \n",
       "max       3.500000     1.000000     4.000000     5.000000  9410.000000   \n",
       "\n",
       "       sqft_basement     yr_built  yr_renovated  \n",
       "count    4600.000000  4600.000000   4600.000000  \n",
       "mean      312.081522  1970.786304    808.608261  \n",
       "std       464.137228    29.731848    979.414536  \n",
       "min         0.000000  1900.000000      0.000000  \n",
       "25%         0.000000  1951.000000      0.000000  \n",
       "50%         0.000000  1976.000000      0.000000  \n",
       "75%       610.000000  1997.000000   1999.000000  \n",
       "max      4820.000000  2014.000000   2014.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e40d3104",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab4e847e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40ae7b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'price'}>,\n",
       "        <AxesSubplot:title={'center':'bedrooms'}>,\n",
       "        <AxesSubplot:title={'center':'bathrooms'}>,\n",
       "        <AxesSubplot:title={'center':'sqft_living'}>],\n",
       "       [<AxesSubplot:title={'center':'sqft_lot'}>,\n",
       "        <AxesSubplot:title={'center':'floors'}>,\n",
       "        <AxesSubplot:title={'center':'waterfront'}>,\n",
       "        <AxesSubplot:title={'center':'view'}>],\n",
       "       [<AxesSubplot:title={'center':'condition'}>,\n",
       "        <AxesSubplot:title={'center':'sqft_above'}>,\n",
       "        <AxesSubplot:title={'center':'sqft_basement'}>,\n",
       "        <AxesSubplot:title={'center':'yr_built'}>],\n",
       "       [<AxesSubplot:title={'center':'yr_renovated'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins =50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2739877",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_nw = housing.iloc[:,1:14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a232bc2a",
   "metadata": {},
   "outputs": [
    {
     "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.130000e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1340</td>\n",
       "      <td>7912</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1340</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.384000e+06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3650</td>\n",
       "      <td>9050</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3370</td>\n",
       "      <td>280</td>\n",
       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.420000e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1930</td>\n",
       "      <td>11947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1930</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.200000e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2000</td>\n",
       "      <td>8030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.500000e+05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1940</td>\n",
       "      <td>10500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1140</td>\n",
       "      <td>800</td>\n",
       "      <td>1976</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4595</th>\n",
       "      <td>3.081667e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1510</td>\n",
       "      <td>6360</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1510</td>\n",
       "      <td>0</td>\n",
       "      <td>1954</td>\n",
       "      <td>1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>5.343333e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1460</td>\n",
       "      <td>7573</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1460</td>\n",
       "      <td>0</td>\n",
       "      <td>1983</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4597</th>\n",
       "      <td>4.169042e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3010</td>\n",
       "      <td>7014</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3010</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>2.034000e+05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2090</td>\n",
       "      <td>6630</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1070</td>\n",
       "      <td>1020</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>2.206000e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1490</td>\n",
       "      <td>8102</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1490</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4600 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "0     3.130000e+05       3.0       1.50         1340      7912     1.5   \n",
       "1     2.384000e+06       5.0       2.50         3650      9050     2.0   \n",
       "2     3.420000e+05       3.0       2.00         1930     11947     1.0   \n",
       "3     4.200000e+05       3.0       2.25         2000      8030     1.0   \n",
       "4     5.500000e+05       4.0       2.50         1940     10500     1.0   \n",
       "...            ...       ...        ...          ...       ...     ...   \n",
       "4595  3.081667e+05       3.0       1.75         1510      6360     1.0   \n",
       "4596  5.343333e+05       3.0       2.50         1460      7573     2.0   \n",
       "4597  4.169042e+05       3.0       2.50         3010      7014     2.0   \n",
       "4598  2.034000e+05       4.0       2.00         2090      6630     1.0   \n",
       "4599  2.206000e+05       3.0       2.50         1490      8102     2.0   \n",
       "\n",
       "      waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
       "0              0     0          3        1340              0      1955   \n",
       "1              0     4          5        3370            280      1921   \n",
       "2              0     0          4        1930              0      1966   \n",
       "3              0     0          4        1000           1000      1963   \n",
       "4              0     0          4        1140            800      1976   \n",
       "...          ...   ...        ...         ...            ...       ...   \n",
       "4595           0     0          4        1510              0      1954   \n",
       "4596           0     0          3        1460              0      1983   \n",
       "4597           0     0          3        3010              0      2009   \n",
       "4598           0     0          3        1070           1020      1974   \n",
       "4599           0     0          4        1490              0      1990   \n",
       "\n",
       "      yr_renovated  \n",
       "0             2005  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4             1992  \n",
       "...            ...  \n",
       "4595          1979  \n",
       "4596          2009  \n",
       "4597             0  \n",
       "4598             0  \n",
       "4599             0  \n",
       "\n",
       "[4600 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_nw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545dfc2e",
   "metadata": {},
   "source": [
    "## train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69773d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for learning purpose\n",
    "#def split_train_test(data,test_ratio):\n",
    "    #np.random.seed(42)\n",
    "    #shuffled = np.random.permutation(len(data))\n",
    "    #print(shuffled)\n",
    "    #test_set_size = int(len(data)* test_ratio)\n",
    "    #test_indices = shuffled[:test_set_size]\n",
    "    #train_indices = shuffled[test_set_size:]\n",
    "    #return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b90baf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b47afd74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(f\"rows in train set: {len(train_set)}\\n rows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cfde965f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing_nw, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86a4634b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows in train_set : 3680\n",
      " rows in test_set : 920\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"rows in train_set : {len(train_set)}\\n rows in test_set : {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23c8c54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size = 0.2, random_state = 1)\n",
    "for train_index, test_index in split.split(housing_nw, housing_nw['view']):\n",
    "    strat_train_set = housing_nw.loc[train_index]\n",
    "    strat_test_set = housing_nw.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8dd9c091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3312\n",
       "2     164\n",
       "3      93\n",
       "4      56\n",
       "1      55\n",
       "Name: view, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set['view'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "860f8752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    828\n",
       "2     41\n",
       "3     23\n",
       "1     14\n",
       "4     14\n",
       "Name: view, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['view'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "325fef57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.195121951219512"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3312/164"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ffbda559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.195121951219512"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "828/41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "62fcd2b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_nw = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ba661f",
   "metadata": {},
   "source": [
    "## correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "566ed580",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "edac5f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price            1.000000\n",
       "sqft_living      0.430410\n",
       "sqft_above       0.367570\n",
       "bathrooms        0.327110\n",
       "view             0.228504\n",
       "sqft_basement    0.210427\n",
       "bedrooms         0.200336\n",
       "floors           0.151461\n",
       "waterfront       0.135648\n",
       "sqft_lot         0.050451\n",
       "condition        0.034915\n",
       "yr_built         0.021857\n",
       "yr_renovated    -0.028774\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['price'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bed23ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "da9166c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = ['price', 'bedrooms', 'bathrooms', 'floors',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2661e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='price', ylabel='price'>,\n",
       "        <AxesSubplot:xlabel='bedrooms', ylabel='price'>,\n",
       "        <AxesSubplot:xlabel='bathrooms', ylabel='price'>,\n",
       "        <AxesSubplot:xlabel='floors', ylabel='price'>],\n",
       "       [<AxesSubplot:xlabel='price', ylabel='bedrooms'>,\n",
       "        <AxesSubplot:xlabel='bedrooms', ylabel='bedrooms'>,\n",
       "        <AxesSubplot:xlabel='bathrooms', ylabel='bedrooms'>,\n",
       "        <AxesSubplot:xlabel='floors', ylabel='bedrooms'>],\n",
       "       [<AxesSubplot:xlabel='price', ylabel='bathrooms'>,\n",
       "        <AxesSubplot:xlabel='bedrooms', ylabel='bathrooms'>,\n",
       "        <AxesSubplot:xlabel='bathrooms', ylabel='bathrooms'>,\n",
       "        <AxesSubplot:xlabel='floors', ylabel='bathrooms'>],\n",
       "       [<AxesSubplot:xlabel='price', ylabel='floors'>,\n",
       "        <AxesSubplot:xlabel='bedrooms', ylabel='floors'>,\n",
       "        <AxesSubplot:xlabel='bathrooms', ylabel='floors'>,\n",
       "        <AxesSubplot:xlabel='floors', ylabel='floors'>]], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(housing_nw[attributes], figsize = (12,8), alpha = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f6a30544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bedrooms', ylabel='price'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_nw.plot(kind = \"scatter\", x= \"bedrooms\", y = \"price\", alpha = 0.8 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b577320",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_nw = strat_train_set.drop(\"price\", axis = 1)\n",
    "housing_labels = strat_train_set[\"price\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b31b2bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2290</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2290</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2009</td>\n",
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       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2690</td>\n",
       "      <td>4000</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2120</td>\n",
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       "      <td>1989</td>\n",
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       "    <tr>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>620</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
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       "      <td>3000</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1190</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3680 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "1460       3.0       2.50         2060      4030     2.0           0     0   \n",
       "654        4.0       2.50         2700      4004     2.0           0     0   \n",
       "238        3.0       1.75         1840     10723     1.0           0     0   \n",
       "680        3.0       2.25         1970      3716     2.0           0     0   \n",
       "4271       3.0       2.50         1790      3078     2.0           0     0   \n",
       "...        ...        ...          ...       ...     ...         ...   ...   \n",
       "1043       2.0       2.50         1590      2656     2.0           0     0   \n",
       "692        4.0       2.00         2290     11137     1.0           0     0   \n",
       "2293       4.0       3.75         2690      4000     2.0           0     3   \n",
       "3444       2.0       1.00          620      2475     1.0           0     0   \n",
       "364        3.0       2.50         1190      3000     2.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  yr_renovated  \n",
       "1460          3        2060              0      1999             0  \n",
       "654           3        2700              0      2004          2003  \n",
       "238           4        1220            620      1974             0  \n",
       "680           3        1970              0      1997             0  \n",
       "4271          3        1790              0      2010             0  \n",
       "...         ...         ...            ...       ...           ...  \n",
       "1043          3        1220            370      2009             0  \n",
       "692           4        2290              0      1955          2009  \n",
       "2293          4        2120            570      1909          1989  \n",
       "3444          5         620              0      1911          1984  \n",
       "364           3        1190              0      2002             0  \n",
       "\n",
       "[3680 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_nw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "be8daa18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4346    12899000.0\n",
       "2286     7062500.0\n",
       "2761     4489000.0\n",
       "1637     3710000.0\n",
       "252      3200000.0\n",
       "           ...    \n",
       "4383           0.0\n",
       "4542           0.0\n",
       "4588           0.0\n",
       "4472           0.0\n",
       "4575           0.0\n",
       "Name: price, Length: 3680, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_labels.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c1ba6b",
   "metadata": {},
   "source": [
    "## missing attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5797833b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# three methods to deal with missing attributes:\n",
    "# 1. get rid of missing data points.\n",
    "# 2. get rid of whole attributes.\n",
    "# 3. set the value to some value either 0, mean or median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "83e9078c",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = housing_nw.dropna(subset = [\"yr_renovated\"]) #option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "74c75096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3680, 12)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6076cc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1790</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>1590</td>\n",
       "      <td>2656</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1220</td>\n",
       "      <td>370</td>\n",
       "      <td>2009</td>\n",
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       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2290</td>\n",
       "      <td>11137</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2290</td>\n",
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       "      <td>1955</td>\n",
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       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2690</td>\n",
       "      <td>4000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>2120</td>\n",
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       "      <td>1909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>620</td>\n",
       "      <td>2475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1190</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3680 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "1460       3.0       2.50         2060      4030     2.0           0     0   \n",
       "654        4.0       2.50         2700      4004     2.0           0     0   \n",
       "238        3.0       1.75         1840     10723     1.0           0     0   \n",
       "680        3.0       2.25         1970      3716     2.0           0     0   \n",
       "4271       3.0       2.50         1790      3078     2.0           0     0   \n",
       "...        ...        ...          ...       ...     ...         ...   ...   \n",
       "1043       2.0       2.50         1590      2656     2.0           0     0   \n",
       "692        4.0       2.00         2290     11137     1.0           0     0   \n",
       "2293       4.0       3.75         2690      4000     2.0           0     3   \n",
       "3444       2.0       1.00          620      2475     1.0           0     0   \n",
       "364        3.0       2.50         1190      3000     2.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  \n",
       "1460          3        2060              0      1999  \n",
       "654           3        2700              0      2004  \n",
       "238           4        1220            620      1974  \n",
       "680           3        1970              0      1997  \n",
       "4271          3        1790              0      2010  \n",
       "...         ...         ...            ...       ...  \n",
       "1043          3        1220            370      2009  \n",
       "692           4        2290              0      1955  \n",
       "2293          4        2120            570      1909  \n",
       "3444          5         620              0      1911  \n",
       "364           3        1190              0      2002  \n",
       "\n",
       "[3680 rows x 11 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_nw.drop(\"yr_renovated\", axis = 1) #option 2 note that there is no yr_renovated column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1194bf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2060</td>\n",
       "      <td>4030</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2060</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2700</td>\n",
       "      <td>4004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2700</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1840</td>\n",
       "      <td>10723</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1220</td>\n",
       "      <td>620</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1970</td>\n",
       "      <td>3716</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4271</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1790</td>\n",
       "      <td>3078</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1790</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1590</td>\n",
       "      <td>2656</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1220</td>\n",
       "      <td>370</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2290</td>\n",
       "      <td>11137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2290</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2690</td>\n",
       "      <td>4000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2120</td>\n",
       "      <td>570</td>\n",
       "      <td>1909</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>620</td>\n",
       "      <td>2475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>620</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1190</td>\n",
       "      <td>3000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1190</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3680 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "1460       3.0       2.50         2060      4030     2.0           0     0   \n",
       "654        4.0       2.50         2700      4004     2.0           0     0   \n",
       "238        3.0       1.75         1840     10723     1.0           0     0   \n",
       "680        3.0       2.25         1970      3716     2.0           0     0   \n",
       "4271       3.0       2.50         1790      3078     2.0           0     0   \n",
       "...        ...        ...          ...       ...     ...         ...   ...   \n",
       "1043       2.0       2.50         1590      2656     2.0           0     0   \n",
       "692        4.0       2.00         2290     11137     1.0           0     0   \n",
       "2293       4.0       3.75         2690      4000     2.0           0     3   \n",
       "3444       2.0       1.00          620      2475     1.0           0     0   \n",
       "364        3.0       2.50         1190      3000     2.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  yr_renovated  \n",
       "1460          3        2060              0      1999             0  \n",
       "654           3        2700              0      2004          2003  \n",
       "238           4        1220            620      1974             0  \n",
       "680           3        1970              0      1997             0  \n",
       "4271          3        1790              0      2010             0  \n",
       "...         ...         ...            ...       ...           ...  \n",
       "1043          3        1220            370      2009             0  \n",
       "692           4        2290              0      1955          2009  \n",
       "2293          4        2120            570      1909          1989  \n",
       "3444          5         620              0      1911          1984  \n",
       "364           3        1190              0      2002             0  \n",
       "\n",
       "[3680 rows x 12 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_nw # note that original dataframe is unchanged untill inplace = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7cfdfd9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "median = housing_nw[\"yr_renovated\"].median() # option 3 method median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3981ee97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460       0\n",
       "654     2003\n",
       "238        0\n",
       "680        0\n",
       "4271       0\n",
       "        ... \n",
       "1043       0\n",
       "692     2009\n",
       "2293    1989\n",
       "3444    1984\n",
       "364        0\n",
       "Name: yr_renovated, Length: 3680, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_nw[\"yr_renovated\"].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5d56e21c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c20d356",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy = \"constant\", fill_value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d490b6e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(fill_value=0, strategy='constant')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(housing_nw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "45ac04c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c3822beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = imputer.transform(housing_nw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "70fe1acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.000e+00, 2.500e+00, 2.060e+03, ..., 0.000e+00, 1.999e+03,\n",
       "        0.000e+00],\n",
       "       [4.000e+00, 2.500e+00, 2.700e+03, ..., 0.000e+00, 2.004e+03,\n",
       "        2.003e+03],\n",
       "       [3.000e+00, 1.750e+00, 1.840e+03, ..., 6.200e+02, 1.974e+03,\n",
       "        0.000e+00],\n",
       "       ...,\n",
       "       [4.000e+00, 3.750e+00, 2.690e+03, ..., 5.700e+02, 1.909e+03,\n",
       "        1.989e+03],\n",
       "       [2.000e+00, 1.000e+00, 6.200e+02, ..., 0.000e+00, 1.911e+03,\n",
       "        1.984e+03],\n",
       "       [3.000e+00, 2.500e+00, 1.190e+03, ..., 0.000e+00, 2.002e+03,\n",
       "        0.000e+00]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d1a1158a",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(x, columns = housing_nw.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3e43f634",
   "metadata": {},
   "outputs": [
    {
     "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3.680000e+03</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "      <td>3680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.400272</td>\n",
       "      <td>2.158967</td>\n",
       "      <td>2135.501359</td>\n",
       "      <td>1.513790e+04</td>\n",
       "      <td>1.511549</td>\n",
       "      <td>0.007337</td>\n",
       "      <td>0.240761</td>\n",
       "      <td>3.451087</td>\n",
       "      <td>1828.441576</td>\n",
       "      <td>307.059783</td>\n",
       "      <td>1970.788043</td>\n",
       "      <td>800.939402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.908329</td>\n",
       "      <td>0.782492</td>\n",
       "      <td>957.098412</td>\n",
       "      <td>3.802964e+04</td>\n",
       "      <td>0.536075</td>\n",
       "      <td>0.085353</td>\n",
       "      <td>0.778672</td>\n",
       "      <td>0.679605</td>\n",
       "      <td>869.302138</td>\n",
       "      <td>453.396429</td>\n",
       "      <td>29.775287</td>\n",
       "      <td>977.852390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>6.380000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>5.001000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1180.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>7.621000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1590.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2620.000000</td>\n",
       "      <td>1.104550e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2310.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.074218e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4130.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bedrooms    bathrooms   sqft_living      sqft_lot       floors  \\\n",
       "count  3680.000000  3680.000000   3680.000000  3.680000e+03  3680.000000   \n",
       "mean      3.400272     2.158967   2135.501359  1.513790e+04     1.511549   \n",
       "std       0.908329     0.782492    957.098412  3.802964e+04     0.536075   \n",
       "min       0.000000     0.000000    420.000000  6.380000e+02     1.000000   \n",
       "25%       3.000000     1.750000   1460.000000  5.001000e+03     1.000000   \n",
       "50%       3.000000     2.250000   1970.000000  7.621000e+03     1.500000   \n",
       "75%       4.000000     2.500000   2620.000000  1.104550e+04     2.000000   \n",
       "max       9.000000     8.000000  13540.000000  1.074218e+06     3.500000   \n",
       "\n",
       "        waterfront         view    condition   sqft_above  sqft_basement  \\\n",
       "count  3680.000000  3680.000000  3680.000000  3680.000000    3680.000000   \n",
       "mean      0.007337     0.240761     3.451087  1828.441576     307.059783   \n",
       "std       0.085353     0.778672     0.679605   869.302138     453.396429   \n",
       "min       0.000000     0.000000     1.000000   420.000000       0.000000   \n",
       "25%       0.000000     0.000000     3.000000  1180.000000       0.000000   \n",
       "50%       0.000000     0.000000     3.000000  1590.000000       0.000000   \n",
       "75%       0.000000     0.000000     4.000000  2310.000000     600.000000   \n",
       "max       1.000000     4.000000     5.000000  9410.000000    4130.000000   \n",
       "\n",
       "          yr_built  yr_renovated  \n",
       "count  3680.000000   3680.000000  \n",
       "mean   1970.788043    800.939402  \n",
       "std      29.775287    977.852390  \n",
       "min    1900.000000      0.000000  \n",
       "25%    1951.000000      0.000000  \n",
       "50%    1975.000000      0.000000  \n",
       "75%    1997.000000   1999.000000  \n",
       "max    2014.000000   2014.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b22c6d",
   "metadata": {},
   "source": [
    "## create pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1827938b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e18b7267",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ec97feb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4609f358",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pipeline = Pipeline([\n",
    "       ('imputer', SimpleImputer(strategy = \"constant\", fill_value = 0)),\n",
    "       #('std_scaler', StandardScaler()),\n",
    "   \n",
    "]    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aea373dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('imputer', SimpleImputer(fill_value=0, strategy='constant'))])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b6a256bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing_nw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c71672fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3680, 12)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84659cc9",
   "metadata": {},
   "source": [
    "## selecting desired model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a40d93d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9fa4f967",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "deb7ded9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "24b799a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5be0d956",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = housing_nw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8ab88a83",
   "metadata": {},
   "outputs": [
    {
     "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2060</td>\n",
       "      <td>4030</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2060</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2700</td>\n",
       "      <td>4004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2700</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1840</td>\n",
       "      <td>10723</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1220</td>\n",
       "      <td>620</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1970</td>\n",
       "      <td>3716</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4271</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1790</td>\n",
       "      <td>3078</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1790</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1590</td>\n",
       "      <td>2656</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1220</td>\n",
       "      <td>370</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2290</td>\n",
       "      <td>11137</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2290</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2690</td>\n",
       "      <td>4000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2120</td>\n",
       "      <td>570</td>\n",
       "      <td>1909</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>620</td>\n",
       "      <td>2475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>620</td>\n",
       "      <td>0</td>\n",
       "      <td>1911</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1190</td>\n",
       "      <td>3000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1190</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3680 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "1460       3.0       2.50         2060      4030     2.0           0     0   \n",
       "654        4.0       2.50         2700      4004     2.0           0     0   \n",
       "238        3.0       1.75         1840     10723     1.0           0     0   \n",
       "680        3.0       2.25         1970      3716     2.0           0     0   \n",
       "4271       3.0       2.50         1790      3078     2.0           0     0   \n",
       "...        ...        ...          ...       ...     ...         ...   ...   \n",
       "1043       2.0       2.50         1590      2656     2.0           0     0   \n",
       "692        4.0       2.00         2290     11137     1.0           0     0   \n",
       "2293       4.0       3.75         2690      4000     2.0           0     3   \n",
       "3444       2.0       1.00          620      2475     1.0           0     0   \n",
       "364        3.0       2.50         1190      3000     2.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  yr_renovated  \n",
       "1460          3        2060              0      1999             0  \n",
       "654           3        2700              0      2004          2003  \n",
       "238           4        1220            620      1974             0  \n",
       "680           3        1970              0      1997             0  \n",
       "4271          3        1790              0      2010             0  \n",
       "...         ...         ...            ...       ...           ...  \n",
       "1043          3        1220            370      2009             0  \n",
       "692           4        2290              0      1955          2009  \n",
       "2293          4        2120            570      1909          1989  \n",
       "3444          5         620              0      1911          1984  \n",
       "364           3        1190              0      2002             0  \n",
       "\n",
       "[3680 rows x 12 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "87a6903a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = housing_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9bb1aed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460     244615.0\n",
       "654      723000.0\n",
       "238      310000.0\n",
       "680      340000.0\n",
       "4271     530000.0\n",
       "          ...    \n",
       "1043     305000.0\n",
       "692     1058000.0\n",
       "2293     800000.0\n",
       "3444     402000.0\n",
       "364      229000.0\n",
       "Name: price, Length: 3680, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bbee2c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fe96ca53",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = x.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7bd5f018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>bedrooms</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
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       "    <tr>\n",
       "      <th>654</th>\n",
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       "      <td>2700</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2700</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2003</td>\n",
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       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>1840</td>\n",
       "      <td>10723</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1220</td>\n",
       "      <td>620</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1970</td>\n",
       "      <td>3716</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4271</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1790</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "1460       3.0       2.50         2060      4030     2.0           0     0   \n",
       "654        4.0       2.50         2700      4004     2.0           0     0   \n",
       "238        3.0       1.75         1840     10723     1.0           0     0   \n",
       "680        3.0       2.25         1970      3716     2.0           0     0   \n",
       "4271       3.0       2.50         1790      3078     2.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  yr_renovated  \n",
       "1460          3        2060              0      1999             0  \n",
       "654           3        2700              0      2004          2003  \n",
       "238           4        1220            620      1974             0  \n",
       "680           3        1970              0      1997             0  \n",
       "4271          3        1790              0      2010             0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4cc6fc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2ec11488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460    244615.0\n",
       "654     723000.0\n",
       "238     310000.0\n",
       "680     340000.0\n",
       "4271    530000.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "71952caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "94e0a929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.0000e+00, 2.5000e+00, 2.0600e+03, 4.0300e+03, 2.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 3.0000e+00, 2.0600e+03, 0.0000e+00,\n",
       "        1.9990e+03, 0.0000e+00],\n",
       "       [4.0000e+00, 2.5000e+00, 2.7000e+03, 4.0040e+03, 2.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 3.0000e+00, 2.7000e+03, 0.0000e+00,\n",
       "        2.0040e+03, 2.0030e+03],\n",
       "       [3.0000e+00, 1.7500e+00, 1.8400e+03, 1.0723e+04, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 4.0000e+00, 1.2200e+03, 6.2000e+02,\n",
       "        1.9740e+03, 0.0000e+00],\n",
       "       [3.0000e+00, 2.2500e+00, 1.9700e+03, 3.7160e+03, 2.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 3.0000e+00, 1.9700e+03, 0.0000e+00,\n",
       "        1.9970e+03, 0.0000e+00],\n",
       "       [3.0000e+00, 2.5000e+00, 1.7900e+03, 3.0780e+03, 2.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 3.0000e+00, 1.7900e+03, 0.0000e+00,\n",
       "        2.0100e+03, 0.0000e+00]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c24218fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([359601.05      , 670785.55      , 356473.16666666, 380219.75      ,\n",
       "       501003.26      ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "480723a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[244615.0, 723000.0, 310000.0, 340000.0, 530000.0]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2842e96",
   "metadata": {},
   "source": [
    "## evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e9d59414",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c16f6ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9d855e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(housing_nw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4e52caae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([359601.05      , 670785.55      , 356473.16666666, ...,\n",
       "       902722.        , 327061.6       , 272433.4604762 ])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f7b06307",
   "metadata": {},
   "outputs": [],
   "source": [
    "mae = mean_absolute_error(housing_labels, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a816a817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62635.83793867706"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "aa719689",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmae = np.sqrt(mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d9c573aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250.27152842198623"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "de92ed76",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mse = mean_squared_error(housing_labels, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ddbef608",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "809161ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "cc2af7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7d50805d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460     244615.0\n",
       "654      723000.0\n",
       "238      310000.0\n",
       "680      340000.0\n",
       "4271     530000.0\n",
       "          ...    \n",
       "1043     305000.0\n",
       "692     1058000.0\n",
       "2293     800000.0\n",
       "3444     402000.0\n",
       "364      229000.0\n",
       "Name: price, Length: 3680, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40daec74",
   "metadata": {},
   "source": [
    "## cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "21080e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f82f4b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scores = cross_val_score(model, housing_nw, housing_labels, scoring = \"neg_mean_squared_error\", cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b5ae45fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d80adb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "bddf8a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ada80a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores1 = cross_val_score(model, housing_nw, housing_labels, scoring = \"neg_mean_absolute_error\", cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8cd4051a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-152669.64834144, -157935.7509523 , -183321.07249687,\n",
       "       -152759.83436135, -177765.90993005, -155340.36597418,\n",
       "       -197095.88954993, -194851.18973607, -155666.42912004,\n",
       "       -161723.88686918])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1eeab4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmae_scores = np.sqrt(-scores1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f1cde10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([390.72963586, 397.41131206, 428.16010148, 390.84502602,\n",
       "       421.62294758, 394.13242188, 443.95482828, 441.41951671,\n",
       "       394.54585173, 402.14908538])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmae_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "24d324be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_scores(scores):\n",
    "    print(\"scores: \", scores)\n",
    "    print(\"mean: \", scores.mean())\n",
    "    print(\"standard_deviation: \", scores.std())\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "33d77454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores:  [390.72963586 397.41131206 428.16010148 390.84502602 421.62294758\n",
      " 394.13242188 443.95482828 441.41951671 394.54585173 402.14908538]\n",
      "mean:  410.49707269761063\n",
      "standard_deviation:  20.12836406255488\n"
     ]
    }
   ],
   "source": [
    "print_scores(rmae_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c075720f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b79cc19",
   "metadata": {},
   "source": [
    "## testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b516ce6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = strat_test_set.drop(\"price\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5a27e101",
   "metadata": {},
   "outputs": [
    {
     "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1650</td>\n",
       "      <td>13816</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1650</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1250</td>\n",
       "      <td>1103</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1250</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4357</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3200</td>\n",
       "      <td>9200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>1953</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>720</td>\n",
       "      <td>6687</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>720</td>\n",
       "      <td>0</td>\n",
       "      <td>1942</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2763</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1970</td>\n",
       "      <td>8200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1420</td>\n",
       "      <td>550</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2450</td>\n",
       "      <td>42180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2450</td>\n",
       "      <td>0</td>\n",
       "      <td>1978</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3783</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2970</td>\n",
       "      <td>12750</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2130</td>\n",
       "      <td>840</td>\n",
       "      <td>1918</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>980</td>\n",
       "      <td>895</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>670</td>\n",
       "      <td>310</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>700</td>\n",
       "      <td>10540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1250</td>\n",
       "      <td>8100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1250</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "173        3.0       2.50         1650     13816     2.0           0     0   \n",
       "2383       3.0       2.50         1250      1103     3.0           0     2   \n",
       "4357       6.0       2.75         3200      9200     1.0           0     2   \n",
       "158        2.0       1.00          720      6687     1.0           0     0   \n",
       "2763       3.0       1.75         1970      8200     1.0           0     0   \n",
       "...        ...        ...          ...       ...     ...         ...   ...   \n",
       "599        3.0       2.25         2450     42180     1.0           0     0   \n",
       "3783       4.0       2.75         2970     12750     1.5           0     1   \n",
       "539        2.0       2.50          980       895     2.0           0     0   \n",
       "522        2.0       1.00          700     10540     1.0           0     0   \n",
       "1902       2.0       1.00         1250      8100     1.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  yr_renovated  \n",
       "173           3        1650              0      1998          2006  \n",
       "2383          3        1250              0      2005             0  \n",
       "4357          4        1600           1600      1953          1983  \n",
       "158           3         720              0      1942          1999  \n",
       "2763          5        1420            550      1963             0  \n",
       "...         ...         ...            ...       ...           ...  \n",
       "599           4        2450              0      1978          2000  \n",
       "3783          4        2130            840      1918          1986  \n",
       "539           3         670            310      2009             0  \n",
       "522           3         700              0      1953             0  \n",
       "1902          4        1250              0      1947          1988  \n",
       "\n",
       "[920 rows x 12 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8aa56ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = strat_test_set[\"price\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "07db4ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_prepared = my_pipeline.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b0c0ad96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.000e+00, 2.500e+00, 1.650e+03, ..., 0.000e+00, 1.998e+03,\n",
       "        2.006e+03],\n",
       "       [3.000e+00, 2.500e+00, 1.250e+03, ..., 0.000e+00, 2.005e+03,\n",
       "        0.000e+00],\n",
       "       [6.000e+00, 2.750e+00, 3.200e+03, ..., 1.600e+03, 1.953e+03,\n",
       "        1.983e+03],\n",
       "       ...,\n",
       "       [2.000e+00, 2.500e+00, 9.800e+02, ..., 3.100e+02, 2.009e+03,\n",
       "        0.000e+00],\n",
       "       [2.000e+00, 1.000e+00, 7.000e+02, ..., 0.000e+00, 1.953e+03,\n",
       "        0.000e+00],\n",
       "       [2.000e+00, 1.000e+00, 1.250e+03, ..., 0.000e+00, 1.947e+03,\n",
       "        1.988e+03]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "fe2de9e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "final_predictions = model.predict(x_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "38ed7bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([410818.46047623, 458544.        , 709381.82      , 310253.145     ,\n",
       "       470178.61      ])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_predictions[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "94460b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_mse = mean_squared_error(y_test, final_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d25ce67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "55869883",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_rmse = np.sqrt(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "969cd571",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "bfa1c1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_mae = mean_absolute_error(y_test, final_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d743d8f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188274.38099439695"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dcd280b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rmae = np.sqrt(final_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "02377bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "433.90595869888324"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5562c1",
   "metadata": {},
   "source": [
    "## saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "20b5cbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f327d3f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['house_data_2.joblib']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(model, 'house_data_2.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "888e99b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.000e+00, 2.500e+00, 2.060e+03, 4.030e+03, 2.000e+00, 0.000e+00,\n",
       "       0.000e+00, 3.000e+00, 2.060e+03, 0.000e+00, 1.999e+03, 0.000e+00])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43de93f",
   "metadata": {},
   "source": [
    "## using the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1917b89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load('house_data_2.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "56594904",
   "metadata": {},
   "outputs": [],
   "source": [
    "features= np.array([[3.000e+00, 2.500e+00, 2.060e+03, 4.030e+03, 2.000e+00, 0.000e+00,\n",
    "       0.000e+00, 3.000e+00, 2.060e+03, 0.000e+00, 1.999e+03, 0.000e+00]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "76bb9325",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([359601.05])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9faaed38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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