House Prediction task.ipynb

{
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1660882786923,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "IK5V-N-ajx-J"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "Z27VrrSij2ej",
    "outputId": "d9716232-2c21-404e-c30c-95eb8167ad5c"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
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       "      <th>floors</th>\n",
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       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
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       "      <td>770</td>\n",
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       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
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       "      <td>2487200875</td>\n",
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       "      <td>7503</td>\n",
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       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing data\n",
    "df = pd.read_csv('house_data - house_data.csv.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "user_tz": -60
    },
    "id": "xqF-ibQmoNSO"
   },
   "outputs": [],
   "source": [
    "df.drop(columns=[\"id\", \"date\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "id": "Jcehjr-jodmg",
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      "text/plain": [
       "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "0  221900.0         3       1.00         1180      5650     1.0           0   \n",
       "1  538000.0         3       2.25         2570      7242     2.0           0   \n",
       "2  180000.0         2       1.00          770     10000     1.0           0   \n",
       "3  604000.0         4       3.00         1960      5000     1.0           0   \n",
       "4  510000.0         3       2.00         1680      8080     1.0           0   \n",
       "\n",
       "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "0     0          3      7        1180              0      1955             0   \n",
       "1     0          3      7        2170            400      1951          1991   \n",
       "2     0          3      6         770              0      1933             0   \n",
       "3     0          5      7        1050            910      1965             0   \n",
       "4     0          3      8        1680              0      1987             0   \n",
       "\n",
       "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0    98178  47.5112 -122.257           1340        5650  \n",
       "1    98125  47.7210 -122.319           1690        7639  \n",
       "2    98028  47.7379 -122.233           2720        8062  \n",
       "3    98136  47.5208 -122.393           1360        5000  \n",
       "4    98074  47.6168 -122.045           1800        7503  "
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     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
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   "source": [
    "DATA ANALYSIS AND VISUALIZATIONS"
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    {
     "data": {
      "text/plain": [
       "(21613, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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    "df.shape"
   ]
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    {
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       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.401822e+05</td>\n",
       "      <td>3.370842</td>\n",
       "      <td>2.114757</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>3.409430</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.673622e+05</td>\n",
       "      <td>0.930062</td>\n",
       "      <td>0.770163</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>0.650743</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  2.161300e+04   \n",
       "mean   5.401822e+05      3.370842      2.114757   2079.899736  1.510697e+04   \n",
       "std    3.673622e+05      0.930062      0.770163    918.440897  4.142051e+04   \n",
       "min    7.500000e+04      0.000000      0.000000    290.000000  5.200000e+02   \n",
       "25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n",
       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \n",
       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
       "\n",
       "             floors    waterfront          view     condition         grade  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean       1.494309      0.007542      0.234303      3.409430      7.656873   \n",
       "std        0.539989      0.086517      0.766318      0.650743      1.175459   \n",
       "min        1.000000      0.000000      0.000000      1.000000      1.000000   \n",
       "25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n",
       "50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n",
       "75%        2.000000      0.000000      0.000000      4.000000      8.000000   \n",
       "max        3.500000      1.000000      4.000000      5.000000     13.000000   \n",
       "\n",
       "         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\n",
       "count  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \n",
       "std      828.090978     442.575043     29.373411    401.679240     53.505026   \n",
       "min      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n",
       "25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n",
       "50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n",
       "75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \n",
       "max     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n",
       "\n",
       "                lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean      47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std        0.138564      0.140828     685.391304   27304.179631  \n",
       "min       47.155900   -122.519000     399.000000     651.000000  \n",
       "25%       47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%       47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max       47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 72,
     "status": "ok",
     "timestamp": 1660882787793,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "QZ0vpSDwo9B2",
    "outputId": "18d238a4-ff32-4c0a-b8e9-d0d59e8c9373"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 69,
     "status": "ok",
     "timestamp": 1660882787795,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "j4QckBiupIcs",
    "outputId": "9a1feda5-7bf1-4a13-977c-4512c2d17069"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     9824\n",
       "4     6882\n",
       "2     2760\n",
       "5     1601\n",
       "6      272\n",
       "1      199\n",
       "7       38\n",
       "8       13\n",
       "0       13\n",
       "9        6\n",
       "10       3\n",
       "11       1\n",
       "33       1\n",
       "Name: bedrooms, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bedrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 64,
     "status": "ok",
     "timestamp": 1660882787797,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "18x5oKTSp51C",
    "outputId": "d86eda87-cd15-4da3-8532-a61954b818de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19489\n",
       "2      963\n",
       "3      510\n",
       "1      332\n",
       "4      319\n",
       "Name: view, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['view'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 59,
     "status": "ok",
     "timestamp": 1660882787798,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "AGMDkVLYrcDn",
    "outputId": "58422fa7-6d14-42a5-aae8-b0c54f0ae6bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    14031\n",
       "4     5679\n",
       "5     1701\n",
       "2      172\n",
       "1       30\n",
       "Name: condition, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 709
    },
    "executionInfo": {
     "elapsed": 55,
     "status": "ok",
     "timestamp": 1660882787799,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "WHG7g55iqtBK",
    "outputId": "5969d896-c0e8-4bf0-f549-082cfa2a204f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.308338</td>\n",
       "      <td>0.525134</td>\n",
       "      <td>0.702044</td>\n",
       "      <td>0.089655</td>\n",
       "      <td>0.256786</td>\n",
       "      <td>0.266331</td>\n",
       "      <td>0.397346</td>\n",
       "      <td>0.036392</td>\n",
       "      <td>0.667463</td>\n",
       "      <td>0.605566</td>\n",
       "      <td>0.323837</td>\n",
       "      <td>0.053982</td>\n",
       "      <td>0.126442</td>\n",
       "      <td>-0.053168</td>\n",
       "      <td>0.306919</td>\n",
       "      <td>0.021571</td>\n",
       "      <td>0.585374</td>\n",
       "      <td>0.082456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.308338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.515884</td>\n",
       "      <td>0.576671</td>\n",
       "      <td>0.031703</td>\n",
       "      <td>0.175429</td>\n",
       "      <td>-0.006582</td>\n",
       "      <td>0.079532</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>0.356967</td>\n",
       "      <td>0.477600</td>\n",
       "      <td>0.303093</td>\n",
       "      <td>0.154178</td>\n",
       "      <td>0.018841</td>\n",
       "      <td>-0.152668</td>\n",
       "      <td>-0.008931</td>\n",
       "      <td>0.129473</td>\n",
       "      <td>0.391638</td>\n",
       "      <td>0.029244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.525134</td>\n",
       "      <td>0.515884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754665</td>\n",
       "      <td>0.087740</td>\n",
       "      <td>0.500653</td>\n",
       "      <td>0.063744</td>\n",
       "      <td>0.187737</td>\n",
       "      <td>-0.124982</td>\n",
       "      <td>0.664983</td>\n",
       "      <td>0.685342</td>\n",
       "      <td>0.283770</td>\n",
       "      <td>0.506019</td>\n",
       "      <td>0.050739</td>\n",
       "      <td>-0.203866</td>\n",
       "      <td>0.024573</td>\n",
       "      <td>0.223042</td>\n",
       "      <td>0.568634</td>\n",
       "      <td>0.087175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>0.702044</td>\n",
       "      <td>0.576671</td>\n",
       "      <td>0.754665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.172826</td>\n",
       "      <td>0.353949</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>-0.058753</td>\n",
       "      <td>0.762704</td>\n",
       "      <td>0.876597</td>\n",
       "      <td>0.435043</td>\n",
       "      <td>0.318049</td>\n",
       "      <td>0.055363</td>\n",
       "      <td>-0.199430</td>\n",
       "      <td>0.052529</td>\n",
       "      <td>0.240223</td>\n",
       "      <td>0.756420</td>\n",
       "      <td>0.183286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>0.089655</td>\n",
       "      <td>0.031703</td>\n",
       "      <td>0.087740</td>\n",
       "      <td>0.172826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005201</td>\n",
       "      <td>0.021604</td>\n",
       "      <td>0.074710</td>\n",
       "      <td>-0.008958</td>\n",
       "      <td>0.113621</td>\n",
       "      <td>0.183512</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>0.053080</td>\n",
       "      <td>0.007644</td>\n",
       "      <td>-0.129574</td>\n",
       "      <td>-0.085683</td>\n",
       "      <td>0.229521</td>\n",
       "      <td>0.144608</td>\n",
       "      <td>0.718557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>0.256786</td>\n",
       "      <td>0.175429</td>\n",
       "      <td>0.500653</td>\n",
       "      <td>0.353949</td>\n",
       "      <td>-0.005201</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023698</td>\n",
       "      <td>0.029444</td>\n",
       "      <td>-0.263768</td>\n",
       "      <td>0.458183</td>\n",
       "      <td>0.523885</td>\n",
       "      <td>-0.245705</td>\n",
       "      <td>0.489319</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>-0.059121</td>\n",
       "      <td>0.049614</td>\n",
       "      <td>0.125419</td>\n",
       "      <td>0.279885</td>\n",
       "      <td>-0.011269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0.266331</td>\n",
       "      <td>-0.006582</td>\n",
       "      <td>0.063744</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.021604</td>\n",
       "      <td>0.023698</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401857</td>\n",
       "      <td>0.016653</td>\n",
       "      <td>0.082775</td>\n",
       "      <td>0.072075</td>\n",
       "      <td>0.080588</td>\n",
       "      <td>-0.026161</td>\n",
       "      <td>0.092885</td>\n",
       "      <td>0.030285</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>-0.041910</td>\n",
       "      <td>0.086463</td>\n",
       "      <td>0.030703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0.397346</td>\n",
       "      <td>0.079532</td>\n",
       "      <td>0.187737</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>0.074710</td>\n",
       "      <td>0.029444</td>\n",
       "      <td>0.401857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045990</td>\n",
       "      <td>0.251321</td>\n",
       "      <td>0.167649</td>\n",
       "      <td>0.276947</td>\n",
       "      <td>-0.053440</td>\n",
       "      <td>0.103917</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>0.006157</td>\n",
       "      <td>-0.078400</td>\n",
       "      <td>0.280439</td>\n",
       "      <td>0.072575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>0.036392</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>-0.124982</td>\n",
       "      <td>-0.058753</td>\n",
       "      <td>-0.008958</td>\n",
       "      <td>-0.263768</td>\n",
       "      <td>0.016653</td>\n",
       "      <td>0.045990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144674</td>\n",
       "      <td>-0.158214</td>\n",
       "      <td>0.174105</td>\n",
       "      <td>-0.361417</td>\n",
       "      <td>-0.060618</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>-0.014941</td>\n",
       "      <td>-0.106500</td>\n",
       "      <td>-0.092824</td>\n",
       "      <td>-0.003406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0.667463</td>\n",
       "      <td>0.356967</td>\n",
       "      <td>0.664983</td>\n",
       "      <td>0.762704</td>\n",
       "      <td>0.113621</td>\n",
       "      <td>0.458183</td>\n",
       "      <td>0.082775</td>\n",
       "      <td>0.251321</td>\n",
       "      <td>-0.144674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.755923</td>\n",
       "      <td>0.168392</td>\n",
       "      <td>0.446963</td>\n",
       "      <td>0.014414</td>\n",
       "      <td>-0.184862</td>\n",
       "      <td>0.114084</td>\n",
       "      <td>0.198372</td>\n",
       "      <td>0.713202</td>\n",
       "      <td>0.119248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>0.605566</td>\n",
       "      <td>0.477600</td>\n",
       "      <td>0.685342</td>\n",
       "      <td>0.876597</td>\n",
       "      <td>0.183512</td>\n",
       "      <td>0.523885</td>\n",
       "      <td>0.072075</td>\n",
       "      <td>0.167649</td>\n",
       "      <td>-0.158214</td>\n",
       "      <td>0.755923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051943</td>\n",
       "      <td>0.423898</td>\n",
       "      <td>0.023285</td>\n",
       "      <td>-0.261190</td>\n",
       "      <td>-0.000816</td>\n",
       "      <td>0.343803</td>\n",
       "      <td>0.731870</td>\n",
       "      <td>0.194050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>0.323837</td>\n",
       "      <td>0.303093</td>\n",
       "      <td>0.283770</td>\n",
       "      <td>0.435043</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>-0.245705</td>\n",
       "      <td>0.080588</td>\n",
       "      <td>0.276947</td>\n",
       "      <td>0.174105</td>\n",
       "      <td>0.168392</td>\n",
       "      <td>-0.051943</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.133124</td>\n",
       "      <td>0.071323</td>\n",
       "      <td>0.074845</td>\n",
       "      <td>0.110538</td>\n",
       "      <td>-0.144765</td>\n",
       "      <td>0.200355</td>\n",
       "      <td>0.017276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>0.053982</td>\n",
       "      <td>0.154178</td>\n",
       "      <td>0.506019</td>\n",
       "      <td>0.318049</td>\n",
       "      <td>0.053080</td>\n",
       "      <td>0.489319</td>\n",
       "      <td>-0.026161</td>\n",
       "      <td>-0.053440</td>\n",
       "      <td>-0.361417</td>\n",
       "      <td>0.446963</td>\n",
       "      <td>0.423898</td>\n",
       "      <td>-0.133124</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.224874</td>\n",
       "      <td>-0.346869</td>\n",
       "      <td>-0.148122</td>\n",
       "      <td>0.409356</td>\n",
       "      <td>0.326229</td>\n",
       "      <td>0.070958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>0.126442</td>\n",
       "      <td>0.018841</td>\n",
       "      <td>0.050739</td>\n",
       "      <td>0.055363</td>\n",
       "      <td>0.007644</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>0.092885</td>\n",
       "      <td>0.103917</td>\n",
       "      <td>-0.060618</td>\n",
       "      <td>0.014414</td>\n",
       "      <td>0.023285</td>\n",
       "      <td>0.071323</td>\n",
       "      <td>-0.224874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.064357</td>\n",
       "      <td>0.029398</td>\n",
       "      <td>-0.068372</td>\n",
       "      <td>-0.002673</td>\n",
       "      <td>0.007854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>-0.053168</td>\n",
       "      <td>-0.152668</td>\n",
       "      <td>-0.203866</td>\n",
       "      <td>-0.199430</td>\n",
       "      <td>-0.129574</td>\n",
       "      <td>-0.059121</td>\n",
       "      <td>0.030285</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>-0.184862</td>\n",
       "      <td>-0.261190</td>\n",
       "      <td>0.074845</td>\n",
       "      <td>-0.346869</td>\n",
       "      <td>0.064357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.267048</td>\n",
       "      <td>-0.564072</td>\n",
       "      <td>-0.279033</td>\n",
       "      <td>-0.147221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>0.306919</td>\n",
       "      <td>-0.008931</td>\n",
       "      <td>0.024573</td>\n",
       "      <td>0.052529</td>\n",
       "      <td>-0.085683</td>\n",
       "      <td>0.049614</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>0.006157</td>\n",
       "      <td>-0.014941</td>\n",
       "      <td>0.114084</td>\n",
       "      <td>-0.000816</td>\n",
       "      <td>0.110538</td>\n",
       "      <td>-0.148122</td>\n",
       "      <td>0.029398</td>\n",
       "      <td>0.267048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.135512</td>\n",
       "      <td>0.048858</td>\n",
       "      <td>-0.086419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>0.021571</td>\n",
       "      <td>0.129473</td>\n",
       "      <td>0.223042</td>\n",
       "      <td>0.240223</td>\n",
       "      <td>0.229521</td>\n",
       "      <td>0.125419</td>\n",
       "      <td>-0.041910</td>\n",
       "      <td>-0.078400</td>\n",
       "      <td>-0.106500</td>\n",
       "      <td>0.198372</td>\n",
       "      <td>0.343803</td>\n",
       "      <td>-0.144765</td>\n",
       "      <td>0.409356</td>\n",
       "      <td>-0.068372</td>\n",
       "      <td>-0.564072</td>\n",
       "      <td>-0.135512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334605</td>\n",
       "      <td>0.254451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>0.585374</td>\n",
       "      <td>0.391638</td>\n",
       "      <td>0.568634</td>\n",
       "      <td>0.756420</td>\n",
       "      <td>0.144608</td>\n",
       "      <td>0.279885</td>\n",
       "      <td>0.086463</td>\n",
       "      <td>0.280439</td>\n",
       "      <td>-0.092824</td>\n",
       "      <td>0.713202</td>\n",
       "      <td>0.731870</td>\n",
       "      <td>0.200355</td>\n",
       "      <td>0.326229</td>\n",
       "      <td>-0.002673</td>\n",
       "      <td>-0.279033</td>\n",
       "      <td>0.048858</td>\n",
       "      <td>0.334605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>0.082456</td>\n",
       "      <td>0.029244</td>\n",
       "      <td>0.087175</td>\n",
       "      <td>0.183286</td>\n",
       "      <td>0.718557</td>\n",
       "      <td>-0.011269</td>\n",
       "      <td>0.030703</td>\n",
       "      <td>0.072575</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>0.119248</td>\n",
       "      <td>0.194050</td>\n",
       "      <td>0.017276</td>\n",
       "      <td>0.070958</td>\n",
       "      <td>0.007854</td>\n",
       "      <td>-0.147221</td>\n",
       "      <td>-0.086419</td>\n",
       "      <td>0.254451</td>\n",
       "      <td>0.183192</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  price  bedrooms  bathrooms  sqft_living  sqft_lot    floors  \\\n",
       "price          1.000000  0.308338   0.525134     0.702044  0.089655  0.256786   \n",
       "bedrooms       0.308338  1.000000   0.515884     0.576671  0.031703  0.175429   \n",
       "bathrooms      0.525134  0.515884   1.000000     0.754665  0.087740  0.500653   \n",
       "sqft_living    0.702044  0.576671   0.754665     1.000000  0.172826  0.353949   \n",
       "sqft_lot       0.089655  0.031703   0.087740     0.172826  1.000000 -0.005201   \n",
       "floors         0.256786  0.175429   0.500653     0.353949 -0.005201  1.000000   \n",
       "waterfront     0.266331 -0.006582   0.063744     0.103818  0.021604  0.023698   \n",
       "view           0.397346  0.079532   0.187737     0.284611  0.074710  0.029444   \n",
       "condition      0.036392  0.028472  -0.124982    -0.058753 -0.008958 -0.263768   \n",
       "grade          0.667463  0.356967   0.664983     0.762704  0.113621  0.458183   \n",
       "sqft_above     0.605566  0.477600   0.685342     0.876597  0.183512  0.523885   \n",
       "sqft_basement  0.323837  0.303093   0.283770     0.435043  0.015286 -0.245705   \n",
       "yr_built       0.053982  0.154178   0.506019     0.318049  0.053080  0.489319   \n",
       "yr_renovated   0.126442  0.018841   0.050739     0.055363  0.007644  0.006338   \n",
       "zipcode       -0.053168 -0.152668  -0.203866    -0.199430 -0.129574 -0.059121   \n",
       "lat            0.306919 -0.008931   0.024573     0.052529 -0.085683  0.049614   \n",
       "long           0.021571  0.129473   0.223042     0.240223  0.229521  0.125419   \n",
       "sqft_living15  0.585374  0.391638   0.568634     0.756420  0.144608  0.279885   \n",
       "sqft_lot15     0.082456  0.029244   0.087175     0.183286  0.718557 -0.011269   \n",
       "\n",
       "               waterfront      view  condition     grade  sqft_above  \\\n",
       "price            0.266331  0.397346   0.036392  0.667463    0.605566   \n",
       "bedrooms        -0.006582  0.079532   0.028472  0.356967    0.477600   \n",
       "bathrooms        0.063744  0.187737  -0.124982  0.664983    0.685342   \n",
       "sqft_living      0.103818  0.284611  -0.058753  0.762704    0.876597   \n",
       "sqft_lot         0.021604  0.074710  -0.008958  0.113621    0.183512   \n",
       "floors           0.023698  0.029444  -0.263768  0.458183    0.523885   \n",
       "waterfront       1.000000  0.401857   0.016653  0.082775    0.072075   \n",
       "view             0.401857  1.000000   0.045990  0.251321    0.167649   \n",
       "condition        0.016653  0.045990   1.000000 -0.144674   -0.158214   \n",
       "grade            0.082775  0.251321  -0.144674  1.000000    0.755923   \n",
       "sqft_above       0.072075  0.167649  -0.158214  0.755923    1.000000   \n",
       "sqft_basement    0.080588  0.276947   0.174105  0.168392   -0.051943   \n",
       "yr_built        -0.026161 -0.053440  -0.361417  0.446963    0.423898   \n",
       "yr_renovated     0.092885  0.103917  -0.060618  0.014414    0.023285   \n",
       "zipcode          0.030285  0.084827   0.003026 -0.184862   -0.261190   \n",
       "lat             -0.014274  0.006157  -0.014941  0.114084   -0.000816   \n",
       "long            -0.041910 -0.078400  -0.106500  0.198372    0.343803   \n",
       "sqft_living15    0.086463  0.280439  -0.092824  0.713202    0.731870   \n",
       "sqft_lot15       0.030703  0.072575  -0.003406  0.119248    0.194050   \n",
       "\n",
       "               sqft_basement  yr_built  yr_renovated   zipcode       lat  \\\n",
       "price               0.323837  0.053982      0.126442 -0.053168  0.306919   \n",
       "bedrooms            0.303093  0.154178      0.018841 -0.152668 -0.008931   \n",
       "bathrooms           0.283770  0.506019      0.050739 -0.203866  0.024573   \n",
       "sqft_living         0.435043  0.318049      0.055363 -0.199430  0.052529   \n",
       "sqft_lot            0.015286  0.053080      0.007644 -0.129574 -0.085683   \n",
       "floors             -0.245705  0.489319      0.006338 -0.059121  0.049614   \n",
       "waterfront          0.080588 -0.026161      0.092885  0.030285 -0.014274   \n",
       "view                0.276947 -0.053440      0.103917  0.084827  0.006157   \n",
       "condition           0.174105 -0.361417     -0.060618  0.003026 -0.014941   \n",
       "grade               0.168392  0.446963      0.014414 -0.184862  0.114084   \n",
       "sqft_above         -0.051943  0.423898      0.023285 -0.261190 -0.000816   \n",
       "sqft_basement       1.000000 -0.133124      0.071323  0.074845  0.110538   \n",
       "yr_built           -0.133124  1.000000     -0.224874 -0.346869 -0.148122   \n",
       "yr_renovated        0.071323 -0.224874      1.000000  0.064357  0.029398   \n",
       "zipcode             0.074845 -0.346869      0.064357  1.000000  0.267048   \n",
       "lat                 0.110538 -0.148122      0.029398  0.267048  1.000000   \n",
       "long               -0.144765  0.409356     -0.068372 -0.564072 -0.135512   \n",
       "sqft_living15       0.200355  0.326229     -0.002673 -0.279033  0.048858   \n",
       "sqft_lot15          0.017276  0.070958      0.007854 -0.147221 -0.086419   \n",
       "\n",
       "                   long  sqft_living15  sqft_lot15  \n",
       "price          0.021571       0.585374    0.082456  \n",
       "bedrooms       0.129473       0.391638    0.029244  \n",
       "bathrooms      0.223042       0.568634    0.087175  \n",
       "sqft_living    0.240223       0.756420    0.183286  \n",
       "sqft_lot       0.229521       0.144608    0.718557  \n",
       "floors         0.125419       0.279885   -0.011269  \n",
       "waterfront    -0.041910       0.086463    0.030703  \n",
       "view          -0.078400       0.280439    0.072575  \n",
       "condition     -0.106500      -0.092824   -0.003406  \n",
       "grade          0.198372       0.713202    0.119248  \n",
       "sqft_above     0.343803       0.731870    0.194050  \n",
       "sqft_basement -0.144765       0.200355    0.017276  \n",
       "yr_built       0.409356       0.326229    0.070958  \n",
       "yr_renovated  -0.068372      -0.002673    0.007854  \n",
       "zipcode       -0.564072      -0.279033   -0.147221  \n",
       "lat           -0.135512       0.048858   -0.086419  \n",
       "long           1.000000       0.334605    0.254451  \n",
       "sqft_living15  0.334605       1.000000    0.183192  \n",
       "sqft_lot15     0.254451       0.183192    1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 54,
     "status": "ok",
     "timestamp": 1660882787800,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "8w2q5kO3rCcu",
    "outputId": "8d921e29-4109-4198-9bf8-2340bd836523"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 19 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   price          21613 non-null  float64\n",
      " 1   bedrooms       21613 non-null  int64  \n",
      " 2   bathrooms      21613 non-null  float64\n",
      " 3   sqft_living    21613 non-null  int64  \n",
      " 4   sqft_lot       21613 non-null  int64  \n",
      " 5   floors         21613 non-null  float64\n",
      " 6   waterfront     21613 non-null  int64  \n",
      " 7   view           21613 non-null  int64  \n",
      " 8   condition      21613 non-null  int64  \n",
      " 9   grade          21613 non-null  int64  \n",
      " 10  sqft_above     21613 non-null  int64  \n",
      " 11  sqft_basement  21613 non-null  int64  \n",
      " 12  yr_built       21613 non-null  int64  \n",
      " 13  yr_renovated   21613 non-null  int64  \n",
      " 14  zipcode        21613 non-null  int64  \n",
      " 15  lat            21613 non-null  float64\n",
      " 16  long           21613 non-null  float64\n",
      " 17  sqft_living15  21613 non-null  int64  \n",
      " 18  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(5), int64(14)\n",
      "memory usage: 3.1 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 52,
     "status": "ok",
     "timestamp": 1660882787801,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "IIdPW4hYtQkU",
    "outputId": "f146e1c5-536a-4367-d3fc-84c571ecf83c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.50    5380\n",
       "1.00    3852\n",
       "1.75    3048\n",
       "2.25    2047\n",
       "2.00    1930\n",
       "1.50    1446\n",
       "2.75    1185\n",
       "3.00     753\n",
       "3.50     731\n",
       "3.25     589\n",
       "3.75     155\n",
       "4.00     136\n",
       "4.50     100\n",
       "4.25      79\n",
       "0.75      72\n",
       "4.75      23\n",
       "5.00      21\n",
       "5.25      13\n",
       "0.00      10\n",
       "5.50      10\n",
       "1.25       9\n",
       "6.00       6\n",
       "0.50       4\n",
       "5.75       4\n",
       "8.00       2\n",
       "6.25       2\n",
       "6.50       2\n",
       "6.75       2\n",
       "7.50       1\n",
       "7.75       1\n",
       "Name: bathrooms, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bathrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 835
    },
    "executionInfo": {
     "elapsed": 2763,
     "status": "ok",
     "timestamp": 1660882790521,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "bBOXVC0z8YQQ",
    "outputId": "8bf86e15-59c7-4ec3-f88d-203206467c22"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for outliers\n",
    "sns.set_style('darkgrid')\n",
    "fig, axes = plt.subplots(nrows = 3, ncols = 2, figsize = (15, 10))\n",
    "fig.suptitle('Box plots showing outliers', y= 0.93, fontsize = 15)\n",
    "\n",
    "for ax, data, name in zip(axes.flatten(), df, ['price',\t'sqft_living',\t'sqft_lot', 'long', 'lat']):\n",
    "  sns.boxplot(df[name], ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 800
    },
    "executionInfo": {
     "elapsed": 4808,
     "status": "ok",
     "timestamp": 1660882795322,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "lRTNSApzOs7i",
    "outputId": "66249577-2d22-4f68-d388-347587ff1f5a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking distribution of the top 4 features\n",
    "sns.set_style('darkgrid')\n",
    "fig, axes = plt.subplots(nrows = 2, ncols = 2, figsize = (15, 10))\n",
    "fig.suptitle('Distribution plots', y= 0.93, fontsize = 15)\n",
    "\n",
    "for ax, data, name in zip(axes.flatten(), df, ['price',\t'sqft_lot', 'long', 'lat']):\n",
    "  sns.distplot(df[name], ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "executionInfo": {
     "elapsed": 4056,
     "status": "ok",
     "timestamp": 1660882799365,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "Vv8_iyN_xPvC",
    "outputId": "614f95cd-5d59-4438-e5a3-aab894317b83"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.price\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(x)\n",
    "plt.show()\n",
    "\n",
    "df['Price_log'] = np.log(df.price)\n",
    "x = df.Price_log\n",
    "sns.distplot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "executionInfo": {
     "elapsed": 3852,
     "status": "ok",
     "timestamp": 1660882803187,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "KpaSRgxeO3_6",
    "outputId": "7d8cd21c-5750-4479-cf7c-2000978228fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xIhaTods5i9XKpImTAHAofmi8gCHVzrjMcUzJuHrQNfW3tkSS2gYuWesCqW2whnNt0QqriFN2+fn51NXVAdDQ0EBubm74XF5eHvX19SiKgtPppLW1ldzcXPLz89m/fz8AdXV1zJ07Nypte+NyuViyZAlutxtN03jzzTeZNWtWr5+baOEpuyvcGAtgNRnQ6+QakhBidIk4Qlq8eDEHDhxg2bJlaJrGxo0b2bFjB9nZ2SxatIiSkhKKi4vRNI01a9ZgNpspLS2lvLyc2tpaMjIyqKysxGq1DrltbxwOB2vWrGHFihWYTCa++MUvcvPNN3PjjTdSXl7O8uXLSUlJobKyMuqdN1Aen4peBwbdlQNJr9Nhl5tjhRCjTMRA0uv1bNiwoduxnJyc8NdFRUUUFRV1O5+Zmcn27dt7fNZQ24YUFhZ2e7106VKWLl3a7ZjFYuHZZ5/t9f2J4vEFMBsN6CIEEgRX2smiBiHEaCI3xsZRly+AOaV/XS67NQghRhsJpDjy+FTMEbYNCnGkpshD+oQQo4oEUhx5fAFSjYbIDQmOkDoVP35VdmsQQowOEkhx5BnglJ0GnHfLdSQhxOgggRRHXZcWNfRH2qXtg865lFiWJIQQSUMCKY6Cq+z61+V2c3AB5Dm3N5YlCSFE0pBAiiOPT+33lJ390n525yWQhBCjhARSHA1kUUNohNTRKYEkhBgdJJDiaCBTdikGPWajng4ZIQkhRgkJpDgK3hjbvxESBFfadcgqOyHEKCGBFEcDuTEWgtN2MkISQowWEkhxomkaXb4Aqf1c1ADBQJJFDUKI0UICKU4Uf3DHhf7ehwTBlXayqEEIMVpIIMWJ4gsF0kBGSCk4PX4UfyBWZQkhRNKQQIqTrksP5xvQoobQzbEuGSUJIUY+CaQ4CT0tdkAjpEs3x7bL9kFCiFFAAilOQiOkgS5qAAkkIcToIIEUJ5+MkAawqOFSILU5JZCEECNfxEeYq6rK+vXrOXLkCCaTiYqKCqZOnRo+X1tbS3V1NUajkdLSUhYuXEhHRwdlZWV4PB6ysrLYtGkTFotlyG1Ddu7cSXt7O2VlZQD86le/4uWXX8ZgMJCbm8v69evR6/UsXboUh8MBwJQpU9i0aVO0+6/fPJctavAF+rdI4ZMpO7mGJIQY+SIG0r59+/B6vdTU1NDQ0MDmzZt54YUXAGhra6Oqqoo9e/agKArFxcXMmzePrVu3smTJEgoLC9m2bRs1NTXceeedQ26rqipPPPEEjY2N3HbbbQB4PB7+6Z/+iV/+8pdYLBYeeeQRXnvtNebPnw9AVVVVDLuv/0Ir5VIMeqB/gZRi0GMzGWSEJIQYFSJO2dXX17NgwQIAZs+eTVNTU/hcY2Mjc+bMwWQy4XA4yM7OpqWlpdt7CgoKOHjwYFTaKorC0qVLWb16dbgGk8lEdXU1FosFAL/fj9lspqWlha6uLu677z5WrFhBQ0ND9HptELyX7kMyDWBRA0CGzSTXkIQQo0LEEZLL5cJut4dfGwwG/H4/RqMRl8sVnhIDsNlsuFyubsdtNhtOpzMqbdPT05k/fz579+4Nn9Pr9WRmZgLB0VBnZyfz5s3jvffeY+XKldx999188MEHrFq1iv/+7//GaOz7R1YUhebm5oid1hePx9Pn+4996AKg03mRU6c7epy/LtPEqdOnehxPM+n48Oz5IdUVqbZEk9oGLlnrAqltsKS2fgSS3W7H7XaHX6uqGv6l/ulzbrcbh8MRPp6amorb7SYtLS0qbfuiqio/+tGPOHbsGM899xw6nY5p06YxderU8Ndjxoyhra2NSZMm9fk5ZrOZGTNmROqSPjU3N/f5/uauE8BZxmdm4NTMPc5brFYmTexZW1bHaf7a0TmkuiLVlmhS28Ala10gtQ3WcK4tWmEVcf4oPz+furo6ABoaGsjNzQ2fy8vLo76+HkVRcDqdtLa2kpubS35+Pvv37wegrq6OuXPnRqVtX773ve+hKApbt24NT93t3r2bzZs3A3DmzBlcLhfjx48fZDcNXWjKLngNqf/G2kyyqEEIMSpEHCEtXryYAwcOsGzZMjRNY+PGjezYsYPs7GwWLVpESUkJxcXFaJrGmjVrMJvNlJaWUl5eTm1tLRkZGVRWVmK1WofctjeHDx9m9+7d3HDDDdx7770ArFixgq997Ws89thjLF++HJ1Ox8aNG684XRdr3sCla0gDDSSriY+7fHj96oCvPwkhxHAS8Te0Xq9nw4YN3Y7l5OSEvy4qKqKoqKjb+czMTLZv397js4baNqSwsDD89fXXX09LS0uv7SorK3s9ngjhEdIgFjUAnHMrTEq3RL0uIYRIFvJX7jhRwlN2ugG9b6wtBYB2p0zbCSFGNgmkOAkF0oCn7C6NkNpcnqjXJIQQyUQCKU68fhWTQY9ON9ARUjCQZIQkhBjpJJDiZLCLEsZaQyMkuTlWCDGySSDFiTcQGFQgmVMM2M1G2a1BCDHiSSDFSWjKbjDGO8yyn50QYsSTQIqTodxHNN5h5qwEkhBihJNAihNvYPCBlCUjJCHEKCCBFCeKTx3Q48svl+VI5exFWfYthBjZJJDiZEgjpDQzbm8Al+KPclVCCJE8JJDiRBnCooYsR3AfPxklCSFGMgmkOBnKooYsRyqALGwQQoxoEkhx4vUP4RpS2qURkgSSEGIES9zzGEaZwV5D8gdU/JceXfGXM05OnO8EwGE2kn5pFwchhBgJJJDiZLA3xnb5VN7+8AJGvY6Gv14IP4KiIDdTAkkIMaLIlF2cKP4AZqNhUO/V6XTYU404PbLKTggxckkgxclQn/jqMBtxenxRrEgIIZKLBFKcDDmQUlNkhCSEGNEi/oZUVZXvfe973HPPPZSUlHD8+PFu52trayksLKSoqIjXXnsNgI6ODu677z6Ki4v59re/TVdXV1TahuzcuZMf//jH4devvvoqd911F/fccw+1tbUAeDweHn74YYqLi1m1ahUdHR2D7aOoGMqNsQAOmbITQoxwEX9D7tu3D6/XS01NDWvXrmXz5s3hc21tbVRVVVFdXc327dt55pln8Hq9bN26lSVLlrBr1y5mzpxJTU1NVNp6PB7KysrYtWtXuAafz8emTZv42c9+RlVVVfj9v/jFL8jNzWXXrl0sXbqUrVu3xqYH+0FVNXwBbdA3xkJwhNTlC+C7tOJOCCFGmoi/Ievr61mwYAEAs2fPpqmpKXyusbGROXPmYDKZcDgcZGdn09LS0u09BQUFHDx4MCptFUVh6dKlrF69OlxDa2sr2dnZpKenYzKZmDt3Lm+99VaPzz106FD0em2AvJdCZCgjpLTU4IJIl4yShBAjVMRl3y6XC7vdHn5tMBjw+/0YjUZcLhcOhyN8zmaz4XK5uh232Ww4nc6otE1PT2f+/Pns3bu3W339/dxIFEWhubk5Yru+eDyeXt/v9gYD6cK5Ns61mzl1+lSPNtdlmq543NcVfIT5sZOn8ThSODdWh/P08R7tB1pbMpDaBi5Z6wKpbbCktn4Ekt1ux+12h1+rqorRaOz1nNvtxuFwhI+npqbidrtJS0uLStv+1Pfpzw0dS0tLi9gZZrOZGTNmRGzXl+bm5l7fH3za6wdkXzWJcZnjmDRR69HGYrUyaeKkPo9rqV3QchGTNZ1JE9MZlzmOKRlXD7m2ZCC1DVyy1gVS22AN59qiFVYR55Dy8/Opq6sDoKGhgdzc3PC5vLw86uvrURQFp9NJa2srubm55Ofns3//fgDq6uqYO3duVNr2Jicnh+PHj3PhwgW8Xi9vvfUWc+bM6fVzE0XxD33KznFpys4pO34LIUaoiCOkxYsXc+DAAZYtW4amaWzcuJEdO3aQnZ3NokWLKCkpobi4GE3TWLNmDWazmdLSUsrLy6mtrSUjI4PKykqsVuuQ2/YmJSWFRx99lJUrV6JpGnfddRcTJkxg+fLllJeXs3z5clJSUqisrIx65/WXNwqBZDMb0YHciySEGLEiBpJer2fDhg3djuXk5IS/LioqoqioqNv5zMxMtm/f3uOzhto2pLCwsNvrW265hVtuuaXbMYvFwrPPPtvr++MtHEiGwe3UAKCX3RqEECOc3BgbB9EYIUHoXiQZIQkhRiYJpDjwBgJAFALJLLs1CCFGLgmkOAgvahjCjbEQHCFdlEASQoxQEkhxEJqyM6cMrbvTLCl0Kn4Cas9l40IIMdxJIMWBN0ojpHRLChpwsUuuIwkhRh4JpDgITdkN9hHmIRmXHsh3/tKuDUIIMZJIIMVBtFbZZVhTALjglhGSEGLkkUCKg2hsrgrBKTuA850yQhJCjDwSSHEQrWtIRoOetFQj5ztlhCSEGHkkkOIgWlN2AGOsJi7ICEkIMQJJIMVBaMrObBz81kEhY6wpMmUnhBiRJJDiILTKLsWgG/JnZVhNfNzlk3uRhBAjjgRSHCj+ACajHp0uOoGkaqFnLAkhxMghgRQHXr+KeYgLGkJCS79Pf+yJyucJIUSykECKA69fjcqCBgguagA4JYEkhBhhJJDiILqBJCMkIcTIJIEUB95A9AIpxaDHYTZy+qIEkhBiZJFAigOvXx3yTbGXG2NNkSk7IcSIE/ER5qqqsn79eo4cOYLJZKKiooKpU6eGz9fW1lJdXY3RaKS0tJSFCxfS0dFBWVkZHo+HrKwsNm3ahMViiUnbDz74gI0bN4braWho4PnnnycvL4/bb7+d3NxcAG699VbuvffeGHRhZF6/OuRHT1xujNUkU3ZCiBEnYiDt27cPr9dLTU0NDQ0NbN68mRdeeAGAtrY2qqqq2LNnD4qiUFxczLx589i6dStLliyhsLCQbdu2UVNTw5133hmTtt/4xjeoqqoC4Ne//jVZWVkUFBRw8OBBlixZwpNPPhnbHuwHbyC6I6QMq4nmUxdRVQ29fuhLyYUQIhlE/C1ZX1/PggULAJg9ezZNTU3hc42NjcyZMweTyYTD4SA7O5uWlpZu7wmFQ6zahnR2dvLcc8/x3e9+F4CmpiYOHz7M17/+db71rW9x9uzZKHXZwCm+6F1DAsiwpeBXNc465V4kIcTIEXGE5HK5sNvt4dcGgwG/34/RaMTlcuFwOMLnbDYbLper23GbzYbT6YxZ25Ddu3fz5S9/mbFjxwIwffp0Zs2axZe+9CVeeeUVKioqePbZZ6/4syqKQnNzc6Qu6ZPH4+n1/R+73NhNepqbm/Ea7Zw6fapHm+syTf0+rnqCWwe93tDM9VmpQ6otGUhtA5esdYHUNlhSWz8CyW6343a7w69VVcVoNPZ6zu1243A4wsdTU1Nxu92kpaXFrG3IL3/5y26Bc9NNN2GxWABYvHhxxDACMJvNzJgxI2K7vjQ3N/f6fv1v28lItzBjxgxOnO9k0sSe2/5YrFYmTZzUr+MGqwdaLmJMy2LGjKuGVFsykNoGLlnrAqltsIZzbdEKq4jzSPn5+dTV1QHBBQOhRQIAeXl51NfXoygKTqeT1tZWcnNzyc/PZ//+/QDU1dUxd+7cmLUFcDqdeL1eJk365Bf3E088wW9+8xsADh06xPXXXx+N/hoUrz8w5KfFXi7DZkKvg6Ntrqh9phBCJFrEEdLixYs5cOAAy5YtQ9M0Nm7cyI4dO8jOzmbRokWUlJRQXFyMpmmsWbMGs9lMaWkp5eXl1NbWkpGRQWVlJVarNSZtAY4dO8ZVV3UfKaxdu5bHH3+cX/ziF1gsFioqKmLTg/0QzfuQIHgv0pQMKy2nnZEbCyHEMBExkPR6PRs2bOh2LCcnJ/x1UVERRUVF3c5nZmayffv2Hp8Vq7Z5eXls3bq127Grr746vPou0bx+NaojJICc8TaOnJFAEkKMHHJjbBxEc+ugkJzxdo6f68St+KP6uUIIkSgSSHGgRHmnBoCcLBsA78koSQgxQkggxUGsRkiAXEcSQowYEkgxpqoaflWLeiBNTE/FZjLQcupiVD9XCCESRQIpxryB4OPLox1Iep2Oz050yAhJCDFiSCDFmOK/FEhRvoYE8NmJabScdqJpPW+0FUKI4UYCKca8lwLJnGKI+mfPmOTg4y4fZy7KnnZCiOFPAinGQlN25hiMkK6bGNw6qfm0XEcSQgx/EW+MFUMTGiFF+xqSP6BiTw2Ouv7n2DmuzQquunOYjaRbTVH9Xk6AH00AABmoSURBVEIIEQ8SSDGm+ANA9AOpy6fS8OHHpFtSONTawdUZwfuSCnIzJZCEEMOSTNnFmDeGixoAJqalytNjhRAjggRSjMVqyi5kQpqZNpdCQJWVdkKI4U0CKcZiHUhZjlQCqkaH2xuTzxdCiHiRQIoxJbTKLlaBlGYG4MxFmbYTQgxvEkgxFo8REsBZpwSSEGJ4k0CKsfCNsTEKJJNRT4Y1hbNOuTlWCDG8SSDF2Cer7KK/U0NIliOVs7JbgxBimJNAijElxlN2ICvthBAjQ8QbY1VVZf369Rw5cgSTyURFRQVTp04Nn6+traW6uhqj0UhpaSkLFy6ko6ODsrIyPB4PWVlZbNq0CYvFErO2FRUV/PnPf8ZmC94cunXrVnw+X69t480boxtjL5eVFlxpd84toyQhxPAV8bfkvn378Hq91NTUsHbtWjZv3hw+19bWRlVVFdXV1Wzfvp1nnnkGr9fL1q1bWbJkCbt27WLmzJnU1NTErC3A4cOHeemll6iqqqKqqgqHw9Fn23iL1eMnLjchtLBBpu2EEMNYxN+S9fX1LFiwAIDZs2fT1NQUPtfY2MicOXMwmUw4HA6ys7NpaWnp9p6CggIOHjwYs7aqqnL8+HG+973vsWzZMnbv3t2j7lDbRIj1ogaA8Y7g0m9ZaSeEGM4iTtm5XC7sdnv4tcFgwO/3YzQacblcOByO8DmbzYbL5ep23Gaz4XQ6Y9a2s7OTr3/96/zDP/wDgUCAFStWMGvWrF7bRqIoCs3NzRHb9cXj8fR4/0enO9ABfznSgk6nw2u0c+r0qR7vvS7TNKTjaWY9x89e4Fz7OZynj/ertmQhtQ1cstYFUttgSW39CCS73Y7b7Q6/VlUVo9HY6zm3243D4QgfT01Nxe12k5aWFrO2FouFFStWhK8P3XTTTbS0tPTaNhKz2cyMGTP60W29a25u7vH+tA+aMRkvMnPmTABOnO9k0sSeiw8sViuTJk4a9PHJGQrnO72MyxzHlIyr+1VbspDaBi5Z6wKpbbCGc23RCquI80j5+fnU1dUB0NDQQG5ubvhcXl4e9fX1KIqC0+mktbWV3Nxc8vPz2b9/PwB1dXXMnTs3Zm0/+OADiouLCQQC+Hw+/vznP3P99df32jYRvH41ptePQrIcqbQ7vfgvXbMSQojhJuIIafHixRw4cIBly5ahaRobN25kx44dZGdns2jRIkpKSiguLkbTNNasWYPZbKa0tJTy8nJqa2vJyMigsrISq9Uas7Zf+cpXKCoqIiUlha9+9atce+21vbaNp487vTgVPx1uL0a9jhPnOwFQfIGYfL+sNDMBTePE+S6uybRHfoMQQiSZiIGk1+vZsGFDt2M5OTnhr4uKiigqKup2PjMzk+3bt/f4rFi1XbVqFatWrepX23hxKn7q3mvnrx1dqBrUvdcOwJzsMTH5fhPTgivtWttczL92fEy+hxBCxJLcGBtjvoCKUa+L+ffJSjNj0Ol474wr5t9LCCFiQQIpxhR/AIspdtsGhRj1erLSzPzlrASSEGJ4kkCKsS5vAEtK7AMJYFK6hb+ccaJpsoWQEGL4kUCKMY9PJTVOgTR5TCrnO320yc7fQohhSAIpxjy+QNwCaVJ68F6swycvxuX7CSFENEkgxViXL4AlJT7dPCk9uNLu8MmP4/L9hBAimiSQYsgXUPGrWtxGSKkpBq4aY+HdUzJCEkIMPxJIMeS5dBNsPFbZhVybZZcpOyHEsCSBFENdlwIpXiMkgGsn2Dl+rhOnxxe37ymEENEggRRDHl9wX7l4LfuGYCABNJ+KvLu5EEIkEwmkGOryxn+ElJsVfOTGu7KwQQgxzEggxZAnPGUXv24eZzeRaTfReEICSQgxvEggxVDoGlI8p+x0Oh1fmDaOQ0fPyY4NQohhRQIphjwJWNQA8KXPjOPUxx6OtrsjNxZCiCQhgRRDHl8Ao15HiiF+3ewPqOSMtwHwX42nOHG+k487vXH7/kIIMVgSSDEU3KUhvqOjLp9K61k3Y6wp/Obwaerea8ep+ONagxBCDIYEUgx1xXFj1cvpdDpyxttpbXOhynUkIcQwIYEUQ8GNVRPTxTnj7Xh8KicvdCXk+wshxEBFfIS5qqqsX7+eI0eOYDKZqKioYOrUqeHztbW1VFdXYzQaKS0tZeHChXR0dFBWVobH4yErK4tNmzZhsVhi1nbnzp3813/9FwA333wzDz30EJqmUVBQwDXXXAPA7NmzWbt2bWx6sQ8eXwBrHLcNulzoOlJrmyxsEEIMDxEDad++fXi9XmpqamhoaGDz5s288MILALS1tVFVVcWePXtQFIXi4mLmzZvH1q1bWbJkCYWFhWzbto2amhruvPPOmLRdtGgRr7zyCv/2b/+GTqejuLiYW2+9FYvFwvXXX89Pf/rTmHdiX7q8AcbaTAn53o7UFCakmWmVJ8gKIYaJiPNJ9fX1LFiwAAiOMpqamsLnGhsbmTNnDiaTCYfDQXZ2Ni0tLd3eU1BQwMGDB2PWduLEibz00ksYDAb0ej1+vx+z2czhw4c5c+YMJSUlrFq1iqNHj0a98yJJxKKGy+WMt/PBOTdev5qwGoQQor8ijpBcLhd2uz382mAw4Pf7MRqNuFwuHA5H+JzNZsPlcnU7brPZcDqdMWubkpLC2LFj0TSNH/7wh8ycOZNp06bR3t7O/fffzx133MFbb73FunXr2LNnzxV/VkVRaG5u7k+/9crj8YTfrxhseHwB/N4uTp0+FW5zXaap2+tYHs8wevGrGm+0nGCG3TOkny2WLu+3ZJOstSVrXSC1DZbU1o9AstvtuN2fXIdQVRWj0djrObfbjcPhCB9PTU3F7XaTlpYWs7YQDJLHH38cm83G97//fQBmzZqFwRAcndxwww2cOXMGTdPQ6XR9/qxms5kZM2b0q+N609zcHH7/X844UTUYPyadSRPHh9tYrFYmTZzU472xOJ4+1s//O9LM0Y9V5mSmDulni6XL+y3ZJGttyVoXSG2DNZxri1ZYRZyyy8/Pp66uDoCGhgZyc3PD5/Ly8qivr0dRFJxOJ62treTm5pKfn8/+/fsBqKurY+7cuTFrq2ka3/zmN/nsZz/Lhg0bwiH0k5/8hJdffhmAlpYWJk+efMUwijbXpXt/ErHsO8RqMjIxPZW3/3ohYTUIIUR/RRwhLV68mAMHDrBs2TI0TWPjxo3s2LGD7OxsFi1aRElJCcXFxWiaxpo1azCbzZSWllJeXk5tbS0ZGRlUVlZitVpj0nbfvn38z//8D16vlz/+8Y8APPLII9x///2sW7eO/fv3YzAY2LRpU8w783JOTzCQ4vlwvt5My7RRf/w8vsCYhNYhhBCRRAwkvV7Phg0buh3LyckJf11UVERRUVG385mZmWzfvr3HZ8Wi7eLFi3nnnXd6rX3btm29Ho8HpxJ8QF6i7kMKmZZp42DrOd47p5CX0EqEEOLK5MbYGHGFRkgJnLIDmDYueD9S42m5QVYIkdwkkGIkGa4hAVjNRnLG23jntCehdQghRCQSSDESGiElOpAA5mSP4d02j9yPJIRIahJIMRLaYTvRU3YAc67OQPFrvPORrLYTQiQvCaQYcXr8mAx6DPr4LTXvy+zsMRj18PLB44kuRQgh+iSBFCMujz/hK+xC0i0pLPtcBq/870nq3mtLdDlCCNGr5PiNOQK5FH9SXD8KuftzY5ieaeOJ/2gKP1pdCCGSiQRSjLgUX1JcP4LgY80xO/j2rdfyYUcnG371rjzaXAiRdCSQYsTpSZ4RUpdPZd+7J3EpAT5/TQa73vyQTf+vRR5tLoRIKhF3ahCD4/T4mZCWmugyevj7v7mKTm+A/3rnFFdlpDJzUjrvn3WRNyWdxTMnxHW/PyGEuJwEUoy4FD9Tk2SEdDmDXseyz2dT/acP2VZ3rNu5G68Zy3fvnMHfXC373gkh4k8CKQZUVcOt+LEkySq7TwuFktWkZ7wjlavGpPLrw6fZ/sdjfPX5Ayz87HjW3vZZZl2VnuhShRCjSHL+xhzm/nq+E1ULPkY8WRn0OmZOTufE+S7ePHaesVYzD99yLbfOmMCfPjjPkude5+FfvM1HF2QPPCFEfEggxcCrLWcBuDbLHqFlcklNMXDLdVnsLv0i37rlM/z28Glu+fEf+Kd978m2Q0KImJNAioFXW86SPdbKOLs50aUMSqpRT9Hnr+ZfV32B+Z/J5J/2/YU7/rmON1rbE12aEGIEk2tIUeZW/Lx5tIP/k39VoksZtC6fytutHQAsmjGByWMs/EfDRxS/9Ca3XJfF8huzGWM10XrWhRJQuW3mhKRcUSiEGF4kkKLs9ffb8QZUvpQzDrcyMnZEmDEpjWvG2figw81/N51mX/Nb3c5/7z+buGFqBnqdjjMXPWSPs1H+5c9y/WRZFCGE6D8JpCh7reUsdrORvCnpHLo0yhgJLCYDK+ddw8p51/DmsQ40gg//86sqr7ac5fX3z2E26pmWaePPH15gybOvc2feJL4wbSyTx1jQ63S4FD8acNUYC1ePtZDlkFGVEOITEQNJVVXWr1/PkSNHMJlMVFRUMHXq1PD52tpaqqurMRqNlJaWsnDhQjo6OigrK8Pj8ZCVlcWmTZuwWCxJ0TaWNE3jtSNnKcjNJMUw8i7PdflU3v7wk0dYtLa5Afja3KvJGe8IH5//mfG8duQsv3v3DL9qPNXn512bZefLsyaSa1W4TtO63ZSrqhrHOzr5sKOTLm8Av6qSPdZK7gRH0uyAIYSIroiBtG/fPrxeLzU1NTQ0NLB582ZeeOEFANra2qiqqmLPnj0oikJxcTHz5s1j69atLFmyhMLCQrZt20ZNTQ133nlnwtt+4xvfiEknqqpGm0vh9eNuzlxUWPjZrJh8n+HCYjLwd5+bxGN/dx1/fK+dC10+AMxGPRowOd3MX893cbD1HM+/9j6qBhv/eI6bpo9F8aucvODhL2ecuL09pzz1OkizpKBpwb8AaAAaaARfp6YYmJJhYUqGlSljg/+2mQwofhV/QMVo0JNi0DPOZmJieiomo56Pzndx6uMuLnb5cSp+fIHgisIUvQ6t8yKndGcwG4MhqGmgoaFDh9VswG424g9ofNzlw+MLkG5NIcNqwqDT4VdVUgx6MmwmbKbg+/2qhtevBv8JBP/tC6ikphhIs6SQYtDh8QWP63XB5fnd/tHpovZIE03TUC5bPanX6dDrgv/W6ZBdO2JE0zRULfhnORF9rGkamgYBTcOg06FPgkfkhEQMpPr6ehYsWADA7NmzaWpqCp9rbGxkzpw5mEwmTCYT2dnZtLS0UF9fzwMPPABAQUEBzzzzDFdffXXC28YqkMr+7X/Z+/ZHAKQYdPztZ7NQ/CPj+tFQ6HU60iwppFm634/1N1ePQdV03D3Xyh2zJvFGy1/5yK1jX/NZMqwmxjvMfHnWRK7NsnP1OCsOswENHSc6unj/rIuLHt+lz+dSNBD8BQp0+gKcveih8aML/Pbd0/gC2oDrTjEE/wf1By4F3htDX12YYtDhV4O/CKLnKKHfZ8E+0IW/hlCfXNYg3C4YrN6AesV6Qn0aDKpgSIX+fSWqqqLXJ+eztxJdm1/V8F3W76E+Neh0aJqGXv9Bt/Z99XX4v+unj/dyWL0UgKqqhb++XIpBx99MGcPu0i8N8KeJvoiB5HK5sNs/uZ/GYDDg9/sxGo24XC4cjk+mamw2Gy6Xq9txm82G0+lMiraRKIpCc3NzxHaftirPzKq86eHX7SeOAjAnrZfGF9zxP37Bzd/lpgHuxNfSy/EF4/saUfqAj+FStmelQ366Hoi0nD62U7NCjESRfvdd6byiKFGpIWIg2e123O5PfpGpqorRaOz1nNvtxuFwhI+npqbidrtJS0tLiraRzJ49O2IbIYQQsRHxynt+fj51dXUANDQ0kJubGz6Xl5dHfX09iqLgdDppbW0lNzeX/Px89u/fD0BdXR1z585NirZCCCGSl07TrjyrHVpl995776FpGhs3bqSuro7s7GwWLVpEbW0tNTU1aJrGAw88wO233057ezvl5eW43W4yMjKorKzEarUmRVshhBDJKWIgCSGEEPEw8m6WEUIIMSxJIAkhhEgKEkhCCCGSguxlFwWRtlcaqv/93//lxz/+MVVVVRw/fpxHH30UnU7Htddey/e//330ej0/+clP+MMf/oDRaOTxxx8nLy8vZm0BfD4fjz/+OB999BFer5fS0lI+85nPJEVtgUCAJ554gmPHjmEwGNi0aROapiVFbQDnzp2jsLCQn/3sZxiNxqSpC2Dp0qXh+/emTJnCPffcww9+8AMMBgPz58/noYce6vPPe0NDQ0zaArz44ou8+uqr+Hw+li9fzo033pgU/bZ3717+/d//HfjkPsaqqqqk6DOfz8ejjz7KRx99hF6v5+mnn066P289aGLIfvOb32jl5eWapmna22+/ra1evTpqn71t2zZtyZIl2t13361pmqY98MAD2htvvKFpmqY9+eST2m9/+1utqalJKykp0VRV1T766COtsLAwpm01TdN2796tVVRUaJqmaR0dHdrNN9+cNLX97ne/0x599FFN0zTtjTfe0FavXp00tXm9Xu2b3/ymdtttt2nvv/9+0tSlaZrm8Xi0r371q92O/f3f/712/PhxTVVV7f/+3/+rNTU19fnnPVZt33jjDe2BBx7QAoGA5nK5tGeffTap+i1k/fr1WnV1dVL0maYF/z/41re+pWmapr3++uvaQw89lJT9djkZIUXBlbZXGqrs7Gyee+45vvOd7wBw+PBhbrzxRiC4JdKBAweYNm0a8+fPR6fTMXnyZAKBAB0dHTFrO3bsWL785S9z++23h+s0GAxJU9utt97K3/7t3wJw8uRJMjMz+cMf/pAUtW3ZsoVly5axbdu2pPrvCdDS0kJXVxf33Xcffr+fhx9+GK/XS3Z2NgDz58/n0KFDtLW19fjz7nK5YtIW4PXXXyc3N5cHH3wQl8vFd77zHWpra5Om3wDeeecd3n//fdauXcvOnTsT3mcA06ZNIxAIoKoqLpcLo9FIQ0NDUvXbp8k1pCjoa3ulaLj99tvDO2NAcGPE0J5ll2+fdPn3Dx2PVdvQ13a7HZfLxbe+9S2+/e1vJ01tAEajkfLycp5++mluv/32pKht7969jB07NvwLJJn+ewKkpqaycuVKtm/fzlNPPcVjjz3WbYf8vj7HYDD0+dlDbev3+zl//jxNTU388z//M0899RRlZWVJ1W8QnFIMBWYs+mGgfQZgtVr56KOPuOOOO3jyyScpKSlJun77NBkhRcGVtleKNr3+k79DRNo+KVZtQ06dOsWDDz5IcXExX/nKV/jRj36UNLUBbNmyhbKyMoqKirrttZWo2vbs2YNOp+PQoUM0NzdTXl5OR0dHTL7XYPps2rRpTJ06FZ1Ox7Rp03A4HFy4cKHH53g8nh5/3nv77Gi0NRqNjBkzhunTp2MymZg+fTpms5nTp08nTb9dvHiRo0ePctNNN+FyuWLSDwPtM4CdO3cyf/581q5dy6lTp7j33nvx+XxJ02+9kRFSFFxpe6VomzlzJm+++SYQ3BLphhtuID8/n9dffx1VVTl58iSqqjJ27NiYtQVob2/nvvvuY926dXzta19Lqtr+4z/+gxdffBEAi8WCTqdj1qxZCa/tX//1X/n5z39OVVUVM2bMYMuWLRQUFCS8rpDdu3ezefNmAM6cOUNXVxdWq5UPP/wQTdN4/fXXw5/z6T/vdrudlJSUqLcFmDt3Ln/84x/RNC1c1xe/+MWk6bc//elPfOlLwZ2yY9UPA+0zgLS0tHAApKen4/f7k+b/0b7ITg1R0Nv2Sjk5OVH7/BMnTvDII49QW1vLsWPHePLJJ/H5fEyfPp2KigoMBgPPPfccdXV1qKrKY489xg033BCztgAVFRX8+te/Zvr0T3Y5/+53v0tFRUXCa+vs7OSxxx6jvb0dv9/PqlWryMnJSYp+CykpKWH9+vXo9fqkqcvr9fLYY49x8uRJdDodZWVl6PV6Nm7cSCAQYP78+axZs6bPP+8NDQ0xaQvwwx/+kDfffBNN01izZg1TpkxJmn576aWXMBqN4cfbxKofBtpnbrebxx9/nLa2Nnw+HytWrGDWrFlJ02+9kUASQgiRFGTKTgghRFKQQBJCCJEUJJCEEEIkBQkkIYQQSUECSQghRFKQQBIiie3du5ff//73iS5DiLiQZd9CCCGSgmwdJEQChUZALpeL8+fP8+CDD/Lcc89xzTXXYDKZmDZtGpmZmdxzzz1UVFTQ2NiIz+fj4Ycf5tZbb6WyspI//elPaJrGN77xDe64445E/0hCDJoEkhAJ1tnZyY4dO+jo6ODuu+8mEAjwzW9+k5kzZ/Lcc88B8Pvf/57z58+ze/du2tra+PnPf05KSgonTpyguroaRVEoKipi3rx5pKWlJfgnEmJwJJCESLDPf/7z6PV6MjMzSUtLo7W1lWnTpnVrc+zYMWbPng3A+PHjWbNmDf/yL//C4cOHKSkpAcDv93Py5EkJJDFsyaIGIRLs8OHDQHDDWpfLxbhx47rtngwwffp03nnnHQCcTicrV65k+vTpfOELX6CqqoqXX36ZO+64gylTpsS9fiGiRQJJiARrb2/n3nvv5f777+f73/8+BoOhR5tFixaRnp7O8uXLWblyJStWrOCWW27BarVSXFxMYWEhQLfnzwgx3MgqOyESaO/evRw9epSysrJElyJEwskISQghRFKQEZIQQoikICMkIYQQSUECSQghRFKQQBJCCJEUJJCEEEIkBQkkIYQQSeH/A1Zhe7JTotDAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.price\n",
    "sns.set_style('whitegrid')\n",
    "sns.distplot(x)\n",
    "plt.show()\n",
    "\n",
    "df['sqftlot_log'] = np.log(df.sqft_lot)\n",
    "x = df.sqftlot_log\n",
    "sns.distplot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_KrNIKP-Smx0"
   },
   "source": [
    "MODEL BUILDING AND TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 69,
     "status": "ok",
     "timestamp": 1660882803189,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "KsJIhUf1zcTX"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 69,
     "status": "ok",
     "timestamp": 1660882803191,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "QZBITb5z0s0r"
   },
   "outputs": [],
   "source": [
    "features = df.columns.drop(['price', 'Price_log', 'zipcode', 'sqft_lot'])\n",
    "\n",
    "#features\n",
    "X = df[features]\n",
    "#label\n",
    "y = df['Price_log']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "executionInfo": {
     "elapsed": 69,
     "status": "ok",
     "timestamp": 1660882803192,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "IjR3rYLv4mGm",
    "outputId": "b90b1cce-7d42-4910-8fff-bf54120caf94"
   },
   "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>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>sqftlot_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "      <td>8.639411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "      <td>8.887653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "      <td>9.210340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "      <td>8.517193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "      <td>8.997147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living  floors  waterfront  view  condition  \\\n",
       "0         3       1.00         1180     1.0           0     0          3   \n",
       "1         3       2.25         2570     2.0           0     0          3   \n",
       "2         2       1.00          770     1.0           0     0          3   \n",
       "3         4       3.00         1960     1.0           0     0          5   \n",
       "4         3       2.00         1680     1.0           0     0          3   \n",
       "\n",
       "   grade  sqft_above  sqft_basement  yr_built  yr_renovated      lat     long  \\\n",
       "0      7        1180              0      1955             0  47.5112 -122.257   \n",
       "1      7        2170            400      1951          1991  47.7210 -122.319   \n",
       "2      6         770              0      1933             0  47.7379 -122.233   \n",
       "3      7        1050            910      1965             0  47.5208 -122.393   \n",
       "4      8        1680              0      1987             0  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  sqftlot_log  \n",
       "0           1340        5650     8.639411  \n",
       "1           1690        7639     8.887653  \n",
       "2           2720        8062     9.210340  \n",
       "3           1360        5000     8.517193  \n",
       "4           1800        7503     8.997147  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 68,
     "status": "ok",
     "timestamp": 1660882803193,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "8ARe6Rd88onp",
    "outputId": "84e75d6b-8a2b-4ea1-d185-1fd9719ee5ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21613, 17), (21613,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 64,
     "status": "ok",
     "timestamp": 1660882803195,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "zt4PAPTaCsYK",
    "outputId": "0ca18782-8810-4600-8805-55fcbad2c4dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 60,
     "status": "ok",
     "timestamp": 1660882803196,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "WNInPO10CvML",
    "outputId": "2d8f07b3-4592-4792-a408-f261cf7d283d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 58,
     "status": "ok",
     "timestamp": 1660882803199,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "AWUuO0Ds5oJr"
   },
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 58,
     "status": "ok",
     "timestamp": 1660882803200,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "gpW7V3tYB0au",
    "outputId": "a445f89b-c4f1-4685-d6ea-5d5567ed702b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17290, 17), (17290,))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 54,
     "status": "ok",
     "timestamp": 1660882803201,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "R5NGTr0z7n65",
    "outputId": "e2fe1eb4-50f4-481b-d53c-4d49169939a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 51,
     "status": "ok",
     "timestamp": 1660882803202,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "jrIxEToA71sT"
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 50,
     "status": "ok",
     "timestamp": 1660882803202,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "WNUqH-C6ECnT"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 52,
     "status": "ok",
     "timestamp": 1660882803204,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "J4XU8qwMGp_0",
    "outputId": "c577560a-20fa-4c1b-c004-6d7a0d02ea3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25355272827196057"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 45,
     "status": "ok",
     "timestamp": 1660882803205,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "8uqs_GktLlHx",
    "outputId": "fd42117c-b7b1-4513-c9b4-40b56d49fb5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7718578820455025"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41,
     "status": "ok",
     "timestamp": 1660882803206,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "BjKNB9I9IR2f",
    "outputId": "1dfd6451-8819-4de7-f5e1-5c46e67e2eda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor()\n",
    "tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "executionInfo": {
     "elapsed": 37,
     "status": "ok",
     "timestamp": 1660882803207,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "g1Ji2YG0KGDx"
   },
   "outputs": [],
   "source": [
    "y_pred1 = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 39,
     "status": "ok",
     "timestamp": 1660882803209,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "l5Jl-_5gKLd9",
    "outputId": "6ed06e4a-e976-418e-f10c-03e741f3a575"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2522425434990844"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred1, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23899,
     "status": "ok",
     "timestamp": 1660882827074,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "TZPplqnGKOD5",
    "outputId": "8ac88d5d-0192-45d1-b3df-75c515b4a534"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "forest_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1660882827075,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "cuatLEvLSLqk"
   },
   "outputs": [],
   "source": [
    "y_pred_for = forest_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 50,
     "status": "ok",
     "timestamp": 1660882827077,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "x1P0xlFYSimG",
    "outputId": "b9aebf2d-d56c-49c1-edeb-6741d847d17d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1798919530893984"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred_for, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1660882827079,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "HdaOB65UXJ6P"
   },
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1660882827081,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "yMbV_aQjSoRg"
   },
   "outputs": [],
   "source": [
    "n_splits = 10\n",
    "kfold = KFold(n_splits, shuffle= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5PBS3apxTH7L"
   },
   "source": [
    "BUILDING RANDOM FOREST MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 146588,
     "status": "ok",
     "timestamp": 1660882973653,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "vzLHALX3TKy7",
    "outputId": "918e00ec-1a99-46c2-f2c4-5be964c76739"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.15297500228774844\n"
     ]
    }
   ],
   "source": [
    "for i, (tr_ind, test_ind) in enumerate(kfold.split(X,y)):\n",
    "  X_tr, y_tr = X.iloc[tr_ind], y.iloc[tr_ind]\n",
    "  X_vl, y_vl = X.iloc[test_ind], y.iloc[test_ind]\n",
    "\n",
    "  forest_reg = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "  forest_reg.fit(X_tr,y_tr)\n",
    "\n",
    "y_pred = forest_reg.predict(X_vl)\n",
    "print(f'RMSE: {mean_squared_error(y_vl, y_pred, squared=False)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SibGkmrMTOP0"
   },
   "source": [
    "BUILDING LIGHTGBM MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1660882973654,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "LpBNY6rYXaMA"
   },
   "outputs": [],
   "source": [
    "kwargs ={'learning_rate':0.06,\n",
    "         'n_estimators':1000,\n",
    "         'scale_pos_weight':3,\n",
    "         'max_depth':-1, \n",
    "         'colsample_bytree':0.7, \n",
    "         'subsample':0.85,\n",
    "         'reg_alpha':3, \n",
    "         'reg_lambda':20\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31550,
     "status": "ok",
     "timestamp": 1660883005190,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "_Ng_rpGoV_-d",
    "outputId": "06349c84-e769-4cb5-a82d-71657e8d44fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.14045520208202183\n"
     ]
    }
   ],
   "source": [
    "for i, (tr_ind, test_ind) in enumerate(kfold.split(X,y)):\n",
    "  X_tr, y_tr = X.iloc[tr_ind], y.iloc[tr_ind]\n",
    "  X_vl, y_vl = X.iloc[test_ind], y.iloc[test_ind]\n",
    "\n",
    "\n",
    "  lgb = LGBMRegressor(**kwargs)\n",
    "  lgb.fit(X_tr, y_tr, eval_set = [(X_vl, y_vl)],\n",
    "                    verbose = 0, early_stopping_rounds=300)\n",
    "\n",
    "  y_pred = lgb.predict(X_vl)\n",
    "print(f'RMSE: {mean_squared_error(y_vl, y_pred, squared=False)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1660883005192,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "MtuDPPXr3KdL",
    "outputId": "2a3f406d-365b-4c60-a79d-0942987c04ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d4057fac88>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(lgb.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "executionInfo": {
     "elapsed": 384,
     "status": "ok",
     "timestamp": 1660883111436,
     "user": {
      "displayName": "Favour James",
      "userId": "02102722982195895289"
     },
     "user_tz": -60
    },
    "id": "c1y0Iz7IULD5"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle_out = open(\"Predictor.pkl\",\"wb\")\n",
    "pickle.dump(lgb, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Iqu0dg2TWQpy"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOVw7/3gu58sAw41/y+iyFl",
   "collapsed_sections": [],
   "name": "House Prediction task.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}