Linear_regression1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('home_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>...</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>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900</td>\n",
       "      <td>3</td>\n",
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       "      <td>1180</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000</td>\n",
       "      <td>2</td>\n",
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       "      <td>770</td>\n",
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       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000         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]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>21608</th>\n",
       "      <td>263000018</td>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1509</td>\n",
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       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>20150223T000000</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
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       "      <th>21610</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>20140623T000000</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
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       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>291310100</td>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
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       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>20141015T000000</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1076</td>\n",
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       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
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      "text/plain": [
       "               id             date   price  bedrooms  bathrooms  sqft_living  \\\n",
       "21608   263000018  20140521T000000  360000         3       2.50         1530   \n",
       "21609  6600060120  20150223T000000  400000         4       2.50         2310   \n",
       "21610  1523300141  20140623T000000  402101         2       0.75         1020   \n",
       "21611   291310100  20150116T000000  400000         3       2.50         1600   \n",
       "21612  1523300157  20141015T000000  325000         2       0.75         1020   \n",
       "\n",
       "       sqft_lot  floors  waterfront  view  ...  grade  sqft_above  \\\n",
       "21608      1131     3.0           0     0  ...      8        1530   \n",
       "21609      5813     2.0           0     0  ...      8        2310   \n",
       "21610      1350     2.0           0     0  ...      7        1020   \n",
       "21611      2388     2.0           0     0  ...      8        1600   \n",
       "21612      1076     2.0           0     0  ...      7        1020   \n",
       "\n",
       "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "21608              0      2009             0    98103  47.6993 -122.346   \n",
       "21609              0      2014             0    98146  47.5107 -122.362   \n",
       "21610              0      2009             0    98144  47.5944 -122.299   \n",
       "21611              0      2004             0    98027  47.5345 -122.069   \n",
       "21612              0      2008             0    98144  47.5941 -122.299   \n",
       "\n",
       "       sqft_living15  sqft_lot15  \n",
       "21608           1530        1509  \n",
       "21609           1830        7200  \n",
       "21610           1020        2007  \n",
       "21611           1410        1287  \n",
       "21612           1020        1357  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      "id               21613 non-null int64\n",
      "date             21613 non-null object\n",
      "price            21613 non-null int64\n",
      "bedrooms         21613 non-null int64\n",
      "bathrooms        21613 non-null float64\n",
      "sqft_living      21613 non-null int64\n",
      "sqft_lot         21613 non-null int64\n",
      "floors           21613 non-null float64\n",
      "waterfront       21613 non-null int64\n",
      "view             21613 non-null int64\n",
      "condition        21613 non-null int64\n",
      "grade            21613 non-null int64\n",
      "sqft_above       21613 non-null int64\n",
      "sqft_basement    21613 non-null int64\n",
      "yr_built         21613 non-null int64\n",
      "yr_renovated     21613 non-null int64\n",
      "zipcode          21613 non-null int64\n",
      "lat              21613 non-null float64\n",
      "long             21613 non-null float64\n",
      "sqft_living15    21613 non-null int64\n",
      "sqft_lot15       21613 non-null int64\n",
      "dtypes: float64(4), int64(16), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>2.161300e+04</td>\n",
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       "      <th>mean</th>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+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>2.876566e+09</td>\n",
       "      <td>3.671272e+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>1.000102e+06</td>\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>2.123049e+09</td>\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>3.904930e+09</td>\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>7.308900e+09</td>\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>9.900000e+09</td>\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": [
       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
       "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
       "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
       "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
       "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
       "\n",
       "           sqft_lot        floors    waterfront          view     condition  \\\n",
       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
       "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
       "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
       "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
       "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
       "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
       "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
       "\n",
       "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
       "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
       "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
       "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
       "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
       "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
       "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
       "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
       "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
       "\n",
       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
       "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(data['sqft_living'],data['price'])\n",
    "plt.xlabel('Sqft_living')\n",
    "plt.ylabel('Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e5825beb00>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('sqft_living','price',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e58260c080>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('bedrooms','price',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e58275b588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr()) #white boxes mean the correlation is high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</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>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</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>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</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>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</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>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</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>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "0         3       1.00         1180      5650     1.0           0     0   \n",
       "1         3       2.25         2570      7242     2.0           0     0   \n",
       "2         2       1.00          770     10000     1.0           0     0   \n",
       "3         4       3.00         1960      5000     1.0           0     0   \n",
       "4         3       2.00         1680      8080     1.0           0     0   \n",
       "\n",
       "   condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "0          3      7        1180              0      1955             0   \n",
       "1          3      7        2170            400      1951          1991   \n",
       "2          3      6         770              0      1933             0   \n",
       "3          5      7        1050            910      1965             0   \n",
       "4          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  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = data[['bedrooms','bathrooms','sqft_living','sqft_lot','floors','waterfront','view','condition','grade','sqft_above','sqft_basement','yr_built','yr_renovated','zipcode','lat','long','sqft_living15','sqft_lot15']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>221900</td>\n",
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       "      <td>538000</td>\n",
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       "      <td>180000</td>\n",
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       "      <th>3</th>\n",
       "      <td>604000</td>\n",
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       "      <th>4</th>\n",
       "      <td>510000</td>\n",
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       "      <th>5</th>\n",
       "      <td>1225000</td>\n",
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       "      <th>6</th>\n",
       "      <td>257500</td>\n",
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       "      <td>291850</td>\n",
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       "      <td>229500</td>\n",
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       "      <th>9</th>\n",
       "      <td>323000</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>662500</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>468000</td>\n",
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       "      <th>12</th>\n",
       "      <td>310000</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>400000</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>530000</td>\n",
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       "      <th>15</th>\n",
       "      <td>650000</td>\n",
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       "      <th>16</th>\n",
       "      <td>395000</td>\n",
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       "      <th>17</th>\n",
       "      <td>485000</td>\n",
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       "      <th>18</th>\n",
       "      <td>189000</td>\n",
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       "      <th>19</th>\n",
       "      <td>230000</td>\n",
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       "      <td>385000</td>\n",
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       "      <th>21</th>\n",
       "      <td>2000000</td>\n",
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       "      <th>22</th>\n",
       "      <td>285000</td>\n",
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       "      <th>23</th>\n",
       "      <td>252700</td>\n",
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       "      <th>24</th>\n",
       "      <td>329000</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>233000</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>937000</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>667000</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>438000</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>719000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>21583</th>\n",
       "      <td>399950</td>\n",
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       "    <tr>\n",
       "      <th>21584</th>\n",
       "      <td>380000</td>\n",
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       "    <tr>\n",
       "      <th>21585</th>\n",
       "      <td>270000</td>\n",
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       "    <tr>\n",
       "      <th>21586</th>\n",
       "      <td>505000</td>\n",
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       "    <tr>\n",
       "      <th>21587</th>\n",
       "      <td>385000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21588</th>\n",
       "      <td>414500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21589</th>\n",
       "      <td>347500</td>\n",
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       "    <tr>\n",
       "      <th>21590</th>\n",
       "      <td>1222500</td>\n",
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       "    <tr>\n",
       "      <th>21591</th>\n",
       "      <td>572000</td>\n",
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       "    <tr>\n",
       "      <th>21592</th>\n",
       "      <td>475000</td>\n",
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       "    <tr>\n",
       "      <th>21593</th>\n",
       "      <td>1088000</td>\n",
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       "    <tr>\n",
       "      <th>21594</th>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21595</th>\n",
       "      <td>520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21596</th>\n",
       "      <td>679950</td>\n",
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       "      <th>21597</th>\n",
       "      <td>1575000</td>\n",
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       "    <tr>\n",
       "      <th>21598</th>\n",
       "      <td>541800</td>\n",
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       "      <th>21599</th>\n",
       "      <td>810000</td>\n",
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       "      <td>1537000</td>\n",
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       "      <td>467000</td>\n",
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       "      <td>224000</td>\n",
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       "      <td>429000</td>\n",
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       "      <th>21605</th>\n",
       "      <td>610685</td>\n",
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       "      <th>21606</th>\n",
       "      <td>1007500</td>\n",
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       "    <tr>\n",
       "      <th>21607</th>\n",
       "      <td>475000</td>\n",
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       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>360000</td>\n",
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       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>402101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>325000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         price\n",
       "0       221900\n",
       "1       538000\n",
       "2       180000\n",
       "3       604000\n",
       "4       510000\n",
       "5      1225000\n",
       "6       257500\n",
       "7       291850\n",
       "8       229500\n",
       "9       323000\n",
       "10      662500\n",
       "11      468000\n",
       "12      310000\n",
       "13      400000\n",
       "14      530000\n",
       "15      650000\n",
       "16      395000\n",
       "17      485000\n",
       "18      189000\n",
       "19      230000\n",
       "20      385000\n",
       "21     2000000\n",
       "22      285000\n",
       "23      252700\n",
       "24      329000\n",
       "25      233000\n",
       "26      937000\n",
       "27      667000\n",
       "28      438000\n",
       "29      719000\n",
       "...        ...\n",
       "21583   399950\n",
       "21584   380000\n",
       "21585   270000\n",
       "21586   505000\n",
       "21587   385000\n",
       "21588   414500\n",
       "21589   347500\n",
       "21590  1222500\n",
       "21591   572000\n",
       "21592   475000\n",
       "21593  1088000\n",
       "21594   350000\n",
       "21595   520000\n",
       "21596   679950\n",
       "21597  1575000\n",
       "21598   541800\n",
       "21599   810000\n",
       "21600  1537000\n",
       "21601   467000\n",
       "21602   224000\n",
       "21603   507250\n",
       "21604   429000\n",
       "21605   610685\n",
       "21606  1007500\n",
       "21607   475000\n",
       "21608   360000\n",
       "21609   400000\n",
       "21610   402101\n",
       "21611   400000\n",
       "21612   325000\n",
       "\n",
       "[21613 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = data[['price']]\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This will split the data. 90% goes to features_train and 10% to features_test\n",
    "features_train, features_test, label_train, label_test = train_test_split(features, label, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19451, 18)\n"
     ]
    }
   ],
   "source": [
    "print(features_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2162, 18)\n"
     ]
    }
   ],
   "source": [
    "print(features_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19451, 1)\n"
     ]
    }
   ],
   "source": [
    "print(label_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2162, 1)\n"
     ]
    }
   ],
   "source": [
    "print(label_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(features_train,label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>760000</td>\n",
       "    </tr>\n",
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       "      <td>1042500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7572</th>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3012</th>\n",
       "      <td>945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2860</th>\n",
       "      <td>929000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4639</th>\n",
       "      <td>360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18843</th>\n",
       "      <td>481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9201</th>\n",
       "      <td>272950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20510</th>\n",
       "      <td>560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5151</th>\n",
       "      <td>370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12146</th>\n",
       "      <td>195000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5715</th>\n",
       "      <td>765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21340</th>\n",
       "      <td>417000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6562</th>\n",
       "      <td>269000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7721</th>\n",
       "      <td>249900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7791</th>\n",
       "      <td>440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10632</th>\n",
       "      <td>563225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9756</th>\n",
       "      <td>374950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9350</th>\n",
       "      <td>295000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8897</th>\n",
       "      <td>392500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18001</th>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16560</th>\n",
       "      <td>310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4754</th>\n",
       "      <td>405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14238</th>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21158</th>\n",
       "      <td>1065000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8790</th>\n",
       "      <td>415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16198</th>\n",
       "      <td>81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20545</th>\n",
       "      <td>455000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19932</th>\n",
       "      <td>820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>412000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>369950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16696</th>\n",
       "      <td>405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9950</th>\n",
       "      <td>445000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10829</th>\n",
       "      <td>299000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21299</th>\n",
       "      <td>325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5058</th>\n",
       "      <td>397500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11143</th>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8357</th>\n",
       "      <td>255000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21283</th>\n",
       "      <td>988990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15755</th>\n",
       "      <td>315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5534</th>\n",
       "      <td>525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10341</th>\n",
       "      <td>785000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7365</th>\n",
       "      <td>325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>716500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14839</th>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18231</th>\n",
       "      <td>230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20963</th>\n",
       "      <td>230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11325</th>\n",
       "      <td>470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19283</th>\n",
       "      <td>435000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5406</th>\n",
       "      <td>459900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3289</th>\n",
       "      <td>193000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13619</th>\n",
       "      <td>427500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10907</th>\n",
       "      <td>650500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7565</th>\n",
       "      <td>236775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>410000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2162 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         price\n",
       "12816   760000\n",
       "4739    287000\n",
       "15479   549800\n",
       "5170    395000\n",
       "627    1042500\n",
       "7572    300000\n",
       "3012    945000\n",
       "2860    929000\n",
       "4639    360000\n",
       "18843   481000\n",
       "9201    272950\n",
       "20510   560000\n",
       "5151    370000\n",
       "12146   195000\n",
       "5715    765000\n",
       "21340   417000\n",
       "6562    269000\n",
       "7721    249900\n",
       "7791    440000\n",
       "10632   563225\n",
       "9756    374950\n",
       "9350    295000\n",
       "8897    392500\n",
       "18001   300000\n",
       "16560   310000\n",
       "4754    405000\n",
       "14238   240000\n",
       "21158  1065000\n",
       "8790    415000\n",
       "16198    81000\n",
       "...        ...\n",
       "20545   455000\n",
       "19932   820000\n",
       "1963    412000\n",
       "1140    625000\n",
       "948     369950\n",
       "16696   405000\n",
       "9950    445000\n",
       "10829   299000\n",
       "21299   325000\n",
       "5058    397500\n",
       "11143   215000\n",
       "8357    255000\n",
       "21283   988990\n",
       "15755   315000\n",
       "5534    525000\n",
       "10341   785000\n",
       "7365    325000\n",
       "9988    716500\n",
       "14839   160000\n",
       "18231   230000\n",
       "20963   230000\n",
       "11325   470000\n",
       "19283   435000\n",
       "5406    459900\n",
       "3289    193000\n",
       "13619   427500\n",
       "10907   650500\n",
       "7565    236775\n",
       "1836    260000\n",
       "693     410000\n",
       "\n",
       "[2162 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    760000\n",
       "Name: 12816, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([870392.71956437])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(features_test)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    760000\n",
       "Name: 14393, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_test.iloc[435]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([663911.44249314])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(features_test)[435] #The prediction is not closer to the target mark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predict = pd.DataFrame({\n",
    "    'bedrooms': 5,\n",
    "    'bathrooms': 2,\n",
    "    'sqft_living': 1000,\n",
    "    'sqft_lot': 20456,\n",
    "    'floors': 2,\n",
    "    'sqft_above': 1480,\n",
    "    'sqft_lot15': 6005,\n",
    "    'yr_built': 1965,\n",
    "    'condition': 4,\n",
    "    'zipcode': 98064,\n",
    "    'waterfront': 0,\n",
    "    'view': 0,\n",
    "    'grade': 8,\n",
    "    'yr_renovated': 1987,\n",
    "    'sqft_basement': 450,\n",
    "    'lat': 23.5677,\n",
    "    'long': -23.456,\n",
    "    'sqft_living15': 1200,\n",
    "}, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.45887757e+09]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    247000\n",
       "Name: 18052, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_train.iloc[15718]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bedrooms             1.0000\n",
       "bathrooms            1.0000\n",
       "sqft_living        460.0000\n",
       "sqft_lot          4120.0000\n",
       "floors               1.0000\n",
       "waterfront           0.0000\n",
       "view                 0.0000\n",
       "condition            3.0000\n",
       "grade                4.0000\n",
       "sqft_above         460.0000\n",
       "sqft_basement        0.0000\n",
       "yr_built          1937.0000\n",
       "yr_renovated         0.0000\n",
       "zipcode          98055.0000\n",
       "lat                 47.4868\n",
       "long              -122.1990\n",
       "sqft_living15      990.0000\n",
       "sqft_lot15        4120.0000\n",
       "Name: 18052, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.iloc[15718]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-50513.58737022]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([features_train.iloc[15718]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    247000\n",
       "Name: 18052, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_train.iloc[15718]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_2 = data[['sqft_living','grade','sqft_above','sqft_living15','bathrooms','bedrooms']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_2 = data[['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_2_train, features_2_test, label_2_train, label_2_test, = train_test_split(features_2,label_2,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.fit(features_2_train, label_2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19451, 6)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_2_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>sqft_living</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>1300</td>\n",
       "      <td>6</td>\n",
       "      <td>1300</td>\n",
       "      <td>1240</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10776</th>\n",
       "      <td>2420</td>\n",
       "      <td>9</td>\n",
       "      <td>2420</td>\n",
       "      <td>2390</td>\n",
       "      <td>2.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12171</th>\n",
       "      <td>1590</td>\n",
       "      <td>7</td>\n",
       "      <td>1590</td>\n",
       "      <td>1820</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15001</th>\n",
       "      <td>2970</td>\n",
       "      <td>9</td>\n",
       "      <td>1770</td>\n",
       "      <td>2990</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5534</th>\n",
       "      <td>1540</td>\n",
       "      <td>7</td>\n",
       "      <td>770</td>\n",
       "      <td>1090</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sqft_living  grade  sqft_above  sqft_living15  bathrooms  bedrooms\n",
       "827           1300      6        1300           1240       1.00         4\n",
       "10776         2420      9        2420           2390       2.25         4\n",
       "12171         1590      7        1590           1820       2.50         3\n",
       "15001         2970      9        1770           2990       2.50         3\n",
       "5534          1540      7         770           1090       2.00         4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_2_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10776</th>\n",
       "      <td>998000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12171</th>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15001</th>\n",
       "      <td>525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5534</th>\n",
       "      <td>525000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price\n",
       "827    175000\n",
       "10776  998000\n",
       "12171  300000\n",
       "15001  525000\n",
       "5534   525000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_2_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[312505.85979859]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.predict([features_2_train.loc[6548]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12738</th>\n",
       "      <td>287000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14043</th>\n",
       "      <td>744500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5103</th>\n",
       "      <td>619000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6902</th>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17279</th>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price\n",
       "12738  287000\n",
       "14043  744500\n",
       "5103   619000\n",
       "6902   250000\n",
       "17279  350000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_2_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150323.71036031])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.predict(features_2_test)[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = mean_squared_error(label_2_test, model_2.predict(features_2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248037.30069139073"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(label_test,model.predict(features_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223507.57974762825"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49955638204.6424"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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