prediction.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"house_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>221900.0</td>\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>...</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>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</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",
       "      <td>0</td>\n",
       "      <td>...</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>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\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>...</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>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</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",
       "      <td>...</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>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</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  221900.0         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]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>count</th>\n",
       "      <td>2.161300e+04</td>\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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "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",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21613 non-null  int64  \n",
      " 1   date           21613 non-null  object \n",
      " 2   price          21613 non-null  float64\n",
      " 3   bedrooms       21613 non-null  int64  \n",
      " 4   bathrooms      21613 non-null  float64\n",
      " 5   sqft_living    21613 non-null  int64  \n",
      " 6   sqft_lot       21613 non-null  int64  \n",
      " 7   floors         21613 non-null  float64\n",
      " 8   waterfront     21613 non-null  int64  \n",
      " 9   view           21613 non-null  int64  \n",
      " 10  condition      21613 non-null  int64  \n",
      " 11  grade          21613 non-null  int64  \n",
      " 12  sqft_above     21613 non-null  int64  \n",
      " 13  sqft_basement  21613 non-null  int64  \n",
      " 14  yr_built       21613 non-null  int64  \n",
      " 15  yr_renovated   21613 non-null  int64  \n",
      " 16  zipcode        21613 non-null  int64  \n",
      " 17  lat            21613 non-null  float64\n",
      " 18  long           21613 non-null  float64\n",
      " 19  sqft_living15  21613 non-null  int64  \n",
      " 20  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(5), int64(15), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1e92141a50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"sqft_living vs price\")\n",
    "plt.xlabel('sqft_living')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.sqft_living,df.price,color='blue',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1e92141510>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cq5k6zMysJEX3eAL4jqQA/jUiFgIHRsQmgIjYJOmAVHYysKFq36EUaxQfqhOnhTo2VTda0nyynhRTp05t9jv3NPd0zKxoRSeet0fExvTDv0LSjxuUVZ1YtBBvJNc+KUEuBBgYGBjtmGZm1oRCL7VFxMb072bgW8AxwGOVy1vp382p+BBwSNXuU4CNo8Sn1InTQh1mZlaSwhKPpD0lvbKyDJwI/AhYClRGps0FbkjLS4Ez08izmcD2dLlsOXCipH3T6LQTgeVp29OSZqbRbGfWHKuZOszMrCRFXmo7EPhWGuE8HrgyIv5T0mrgGklnA48Ap6Xyy4BTgEHgWeAsgIjYKukzwOpU7oKI2JqWPwxcDuxBNprtxhS/uJk6zMysPMoGhNlIBgYGYs2aNZ1uhplZT5G0NiIG6m3zXG22E7/gzcyK5sRjZmal8lxtBniuNjMrj3s8ZmZWKvd4+kijXkwlNvHiiSOWMTNrB/d4zMysVO7x9IE8928qse3PbR+xjJlZO7jHY2ZmpfIDpKMYSw+Q5unFuKdjZu3gB0jNzKxruMczirHU4zEzK4t7PGZm1jWceGwnnqvNzIqWO/FIOlTSO9LyHpV37ZiZmTUj13M8kj4IzAf2A15D9ubOrwAnFNc0K5PnajOzsuTt8ZwDvB14CiAiHgQOKKpRZmY2duWdueC5iHg+vU0USeMBD4cbQyo9G/d0zKxoeXs8qyR9HNhD0juBa4F/z7OjpHGS7pb0H2n9MEl3SHpQ0tWSJqT47ml9MG2fVnWMj6X4TySdVBWfnWKDkhZUxZuuox944ICZdYO8iWcBsAW4D/hzYBnwiZz7fgRYV7X+WeDzETEdeBI4O8XPBp6MiNcCn0/lkHQ4cDrwRmA28L9TMhsHfBk4GTgcOCOVbboOG7Zy3kr3dsysUHkTzx7Aoog4LSLeAyxKsYYkTQHeBXw1rQs4HrguFVkMnJqW56R10vYTUvk5wJKIeC4ifgYMAsekz2BEPBQRzwNLgDkt1jGmVXo6q9avYtX6Ve75mFlH5U08N7FzotkD+G6O/f4F+HvgxbS+P7AtInak9SFgclqeDGwASNu3p/IvxWv2GSneSh07kTRf0hpJa7Zs2ZLja5qZWV55Bxe8PCKeqaxExDOSXtFoB0nvBjZHxFpJsyrhOkVjlG0jxeslzUblR6t/OBCxEFgI2ZQ5dfbpKX7Jm5l1k7w9nl9IOqqyIulo4Jej7PN24PclPUx2Gex4sh7QxDQqDrLngTam5SHgkHT88cA+wNbqeM0+I8Ufb6GOnteuy2e+DGdmRcubeD4KXCvpB5J+AFwNnNtoh4j4WERMiYhpZIMDbo6IDwDfA96Tis0FbkjLS9M6afvNkc1guhQ4PY1IOwyYDtwJrAampxFsE1IdS9M+zdYxplWSyfbntrP9ue1OLmbWUbkutUXEakmvB15HdrnqxxHx6xbrPA9YIulC4G7gshS/DPi6pEGyXsjpqe77JV0DPADsAM6JiBcAJJ0LLAfGkQ1+uL+VOnpZu2Yc8MwFZlaWholH0vERcbOkP6zZNF0SEXF9nkoiYiWwMi0/RDYirbbMr4DTRtj/IuCiOvFlZEO7a+NN1zGW+R6PmXWT0Xo8xwE3A79XZ1sAuRKPFaeZGQeeef6ZEbc1m5zcIzKzVjVMPBFxvqSXATdGxDUltcnarJIkXsiuUDZMGo2Sk5lZO4x6jyciXkz3Upx4etQ9P7+n4TrkT06+F2RmuyrvczwrJP0d2Wi2X1SCETEmhiKPdUf+1pHAcLKorFfLk5zMzNohb+L5U7J7On9RE391e5tjzcrTA2nn4ALPYm1muypv4jmcLOkcS5aAfkD2IjjrIfV6OrXbGvWKzMzaQXmen0zP0TwFfCOFzgAmRsR7C2xbVxgYGIg1a9Z0uhmjalcPZPwF2d8iOz65Y5SSZmYjk7Q2Igbqbcvb43ldRPxO1fr3JN27602zdslzT6ZyqW3bgm0jltlrwl5ta5OZWT15E8/dkmZGxO0AkmYAtxbXLGvWrl4aq/SYtj+3fad138Mxs3bLm3hmAGdKeiStTwXWSboPiIh4UyGts1HlGVxQ6elUkkqenk9ZnODM+k/exDO70FZYQ/5xNrOxJO8koeuLbogVp9KzadTTueWRWxqut5sfRDXrX3l7PNYB7f5xrlxqq6cyqKBSZrRBBnna4mRiZvU48fS4Zh4O3Wf3fUbcVukF6dPaab0ofhDVrH858XSxPD/OeUajtXNwQZ5emC+jmVkjTjwGDCeH2vWik4WTkVn/ceLpAY1+nPNM7lnp2VRmJajX02nnJKG+jGZmjbysqANLermkOyXdK+l+SZ9O8cMk3SHpQUlXS5qQ4run9cG0fVrVsT6W4j+RdFJVfHaKDUpaUBVvuo5e9czzz+z0Dp3adcgSwKzLZ/FCvMAL8cJL62ZmnVBkj+c54PiIeEbSbsAtkm4E/gb4fEQskfQV4Gzg0vTvkxHxWkmnA58F3ifpcOB04I3AwcB3Jf12quPLwDuBIWC1pKUR8UDaN3cdBZ6Dwh079Vhg+H5KZb1ZeScJbaY3456OmdVTWOKJbPbRyp/eu6VPAMcD70/xxcCnyJLCnLQMcB3wJUlK8SUR8RzwM0mDwDGp3GBEPAQgaQkwR9K6ZuuIPDOl9rA8l9HKfo7HzPpXofd4JI0D1gKvJeud/BTYFhGVqY+HgMlpeTKwASAidkjaDuyf4rdXHbZ6nw018Rlpn2breLym3fOB+QBTp05t5at3ldrnd+o9z1N58+hI67Xy9GZ8j8fM6ik08UTEC8CRkiYC3wLeUK9Y+lcjbBspXu/+VKPyjerYORCxEFgI2WsR6uzTNdp1I3+cxgHDCaeybmbWbqWMaouIbZJWAjOBiZLGpx7JFGBjKjYEHAIMSRoP7ANsrYpXVO9TL/54C3X0vWZ7PI34OR4za6TIUW2TUk8HSXsA7wDWAd8D3pOKzQVuSMtL0zpp+83p3stS4PQ0Iu0wYDpwJ7AamJ5GsE0gG4CwNO3TbB09b+W8lbv0wz5O43bq5dSum5m1S5E9noOAxek+z8uAayLiPyQ9ACyRdCFwN3BZKn8Z8PU0eGArWSIhIu5Pb0B9ANgBnJMu4SHpXGA5MA5YFBH3p2Od10wdY91xhx4HDPdAKuvV2jU6Dvwcj5k1VuSoth8Cb64Tf4jhUWnV8V8Bp41wrIuAi+rElwHL2lHHWFb54a/Mw+ZEYGad5JkL+kAl4dSux/nFXmV0gjOzepx4xohdvaxVucQ20nq76zOz/uXE0wc8VNrMuokTT4/LM3Q5z0ve8gxAyFufmVkjTjx9IM/MBWZmZXHi6XGVnkbllQet9jzyztXWzFBp94bMrB4nnh5X+XGv3L/xj72ZdTsnnh7Xrhe4ecocMyuLE0+Py/senXbblTeUmll/c+Lpce18ZXUeld5LZYBCvd5MZXnixRN/Y5uZmROPAbDP7vsAwwmlsl6rmURX+wpuMzNw4ul52xZsA4anwamsNyvvJbs85TzgwcwaceLpcZUf9dr1on7k8wzfLvvyn5n1FieeHtfsHGsjyfscTx7NDHhwb8is/zjxGJB/OHWey2geXGBmjTjxWFOauYyWp6fjZ33M+o8TjwHD7+YZ7V09zVxGcxIxs3oKSzySDgGuAH4LeBFYGBFfkLQfcDUwDXgYeG9EPClJwBeAU4BngXkRcVc61lzgE+nQF0bE4hQ/Grgc2IPsTaQfiYhopY5u1qg3kHcYdN46RquzXXPD+fXYZv2ryB7PDuBvI+IuSa8E1kpaAcwDboqIiyUtABYA5wEnA9PTZwZwKTAjJZHzgQEg0nGWRsSTqcx84HayxDMbuDEdM3cdBZ6DwrVrOHU7BxdU5EkqHvFm1n8KSzwRsQnYlJaflrQOmAzMAWalYouBlWRJYQ5wRUQEcLukiZIOSmVXRMRWgJS8ZktaCewdEbel+BXAqWSJp6k6Ulu7Tp77IO16rXU7Bxc0o6wpfsyse5Ryj0fSNODNwB3AgZUf+ojYJOmAVGwysKFqt6EUaxQfqhOnhTp2SjyS5pP1pJg6dWpzX3aMyzO4IE/C9OACs/5VeOKRtBfwTeCjEfFUdpulftE6sWgh3rA5efaJiIXAQoCBgYHmug5tlGdYctmvte7UpKRmNnYUmngk7UaWdL4REden8GOVy1vpUtrmFB8CDqnafQqwMcVn1cRXpviUOuVbqaMr5ZmQs52vM2iXPAMHPLjArH+9rKgDpxFklwHrIuKfqzYtBeam5bnADVXxM5WZCWxPl8uWAydK2lfSvsCJwPK07WlJM1NdZ9Ycq5k6LKd7fn7PTpfXatfNzEZTZI/n7cCfAPdJqvwyfRy4GLhG0tnAI8BpadsysmHOg2RDnc8CiIitkj4DrE7lLqgMNAA+zPBw6hvTh2br6Fbd2Cto93M83fCdzKxcRY5qu4X691QATqhTPoBzRjjWImBRnfga4Ig68SearaObdVOPwhOAmtmu8swFPWAs38Dvpt6cmZXDiaeLecixmY1FTjzWlNq3irb6llEnVbP+5cTTxbrx9QJ7TdgLGB7iXVk3M8vLiacHtNqrKEK7HiDtxhF7ZlYOJ54u1u550czMuoETTxfrxqHL7XrVdoWTqFn/ceLpYu26kd/r3NMzG1uceLpYN87DdtyhxwHDPZ3KuplZXk48BRmrf6WXefnPQ67NxiYnni5W9isPzMzK4MTTZs3+ld5rf8VXnt8Zab3arn63bnyOycx2nRNPF+vGezzN2NXLcHneR2RmvceJp83yPhjZq/cv8lz+61TC6JVzaNbvnHgK0g3P3BQhTy+sXQMQPLuB2djkxFOQ0aaSGcs/qu2aVievXu09mvUrJ542a+eP4D677wMMX7KqrHe7dg8KcAIxG1sKSzySFgHvBjZHxBEpth9wNTANeBh4b0Q8KUnAF8heS/0sMC8i7kr7zAU+kQ57YUQsTvGjGX7t9TLgIxERrdTRSY1+VJsZQdbPxnLv0WwselmBx74cmF0TWwDcFBHTgZvSOsDJwPT0mQ9cCi8lqvOBGcAxwPmS9k37XJrKVvab3Uod7bZy3kpWzlvJcYcex3GHHvfSupmZZQrr8UTE9yVNqwnPAWal5cXASuC8FL8iIgK4XdJESQelsisiYiuApBXAbEkrgb0j4rYUvwI4Fbix2ToiYlM7v7e1f1Rb3v2d4M16Q9n3eA6s/NBHxCZJB6T4ZGBDVbmhFGsUH6oTb6WO30g8kuaT9YqYOnVqk18x088/gs2MavPlMbP+0y2DC1QnFi3EW6njN4MRC4GFAAMDA6Md1wri0WpmY1PZieexyuWtdCltc4oPAYdUlZsCbEzxWTXxlSk+pU75VuqwDnBSMetfZSeepcBc4OL07w1V8XMlLSEbSLA9JY7lwD9UDSg4EfhYRGyV9LSkmcAdwJnAF1upo8Dv2rfaNRrPo9XMxqYih1NfRdZbeZWkIbLRaRcD10g6G3gEOC0VX0Y2zHmQbKjzWQApwXwGWJ3KXVAZaAB8mOHh1DemD83W0Wn+UTWzflPkqLYzRth0Qp2yAZwzwnEWAYvqxNcAR9SJP9FsHZ00FqfW8esczKyRbhlc0HfG8szLeeZzq3zP8ReM32l9V1RmSti2YNsuH8us3xX5m+TE0yFlvsmzbHmm+qkkiUpSqpc0PADBbGxy4umQsTwdTiV56NPaab0olaRVOYfu+Zi1row/+Jx4rO0ql89q13d8csdLsUpSqGwrK0m412TWeU481nZ57vFUEkBl264khErSck/HbNeV8RiDE491rXa+XsH3i8y6hxOPtV2nhlO7p2PWPkX+UebEUxD/Rb3r2jnk3LMgmHUPJ56C3PLILZ1uQsfkucdjZv3LiafN2nnTvFfleY4nz3NMRfRS+ul/B7Nu5cTTZrU9nX7s+Rz5W0cCwzfyK+tmZuDE03a+zJQv+TaTnNxLMRtbnHjazBNk5tPOodJm1luceNrMPR44duqxwHBvprJejy/DmfUfJ542c4+nuZmn3dMx6z9OPG2214S9gOERXZX1saKZxNqop2Nm/cuJx4B8Q6BheKLPyszT1RN/1nJvxszq6bvEI2k28AVgHPDViLi4ncfvxgkrjzv0OGD4nktlvVqzrzLoxzlgi3oAAAaoSURBVEuIZtYefZV4JI0Dvgy8ExgCVktaGhEPdLZlxar0PCpJpVFPZKSeTq1GPR0zs0b6KvEAxwCDEfEQgKQlwByg7YlntB5DnB/AcDKorBdRpqJeT6dWN/TQzGxs67fEMxnYULU+BMyoLSRpPjAfYOrUqeW0rAS+52Jm3aDfEo/qxH6jixARC4GFAAMDAyN3IdqgUQ+l3WXMzLrByzrdgJINAYdUrU8BNnaoLWZmfanfEs9qYLqkwyRNAE4Hlna4TWZmfaWvLrVFxA5J5wLLyYZTL4qI+zvcLDOzvtJXiQcgIpYByzrdDjOzftVvl9rMzKzDnHjMzKxUivAw3EYkbQHWt7j7q4DH29icsvRqu6F32+52l8vtLt6hETGp3gYnngJJWhMRA51uR7N6td3Qu213u8vldneWL7WZmVmpnHjMzKxUTjzFWtjpBrSoV9sNvdt2t7tcbncH+R6PmZmVyj0eMzMrlROPmZmVyomnIJJmS/qJpEFJCzrdnrwkPSzpPkn3SFrT6faMRNIiSZsl/agqtp+kFZIeTP/u28k21jNCuz8l6dF0zu+RdEon21iPpEMkfU/SOkn3S/pIinf1OW/Q7q4+55JeLulOSfemdn86xQ+TdEc631enyY57ju/xFCC9Yvu/qHrFNnBGL7xiW9LDwEBEdPVDapJ+F3gGuCIijkixfwS2RsTFKdnvGxHndbKdtUZo96eAZyLinzrZtkYkHQQcFBF3SXolsBY4FZhHF5/zBu1+L118ziUJ2DMinpG0G3AL8BHgb4DrI2KJpK8A90bEpZ1sayvc4ynGS6/Yjojngcortq1NIuL7wNaa8BxgcVpeTPYD01VGaHfXi4hNEXFXWn4aWEf2Rt+uPucN2t3VIvNMWt0tfQI4HrguxbvufOflxFOMeq/Y7vr/2JMAviNpbXoFeC85MCI2QfaDAxzQ4fY041xJP0yX4rrqclUtSdOANwN30EPnvKbd0OXnXNI4SfcAm4EVwE+BbRGxIxXppd+VnTjxFCPXK7a71Nsj4ijgZOCcdGnIinUp8BrgSGAT8L8625yRSdoL+Cbw0Yh4qtPtyatOu7v+nEfECxFxJNmbko8B3lCvWLmtag8nnmL07Cu2I2Jj+ncz8C2y/+B7xWPpmn7l2v7mDrcnl4h4LP3IvAj8G116ztO9hm8C34iI61O46895vXb3yjkHiIhtwEpgJjBRUuU9aj3zu1LLiacYPfmKbUl7phuwSNoTOBH4UeO9uspSYG5angvc0MG25Fb54U7+gC485+lm92XAuoj456pNXX3OR2p3t59zSZMkTUzLewDvILs/9T3gPalY153vvDyqrSBpeOa/MPyK7Ys63KRRSXo1WS8HsrfTXtmt7ZZ0FTCLbJr4x4DzgW8D1wBTgUeA0yKiq27kj9DuWWSXfAJ4GPjzyn2TbiHpWOAHwH3Aiyn8cbL7JV17zhu0+wy6+JxLehPZ4IFxZB2EayLigvT/0SXAfsDdwB9HxHOda2lrnHjMzKxUvtRmZmalcuIxM7NSOfGYmVmpnHjMzKxUTjxmZlYqJx6zgkiaVj0LdVn7mnU7Jx6zHlL11LpZz3LiMSvWeEmL02SU10l6haSjJa1KE7Eur5py5uj0/pXbgHMqB5A0T9K1kv6dbAJXSfqcpB8pe3fS+1K5keKzUn3XSPovSRdL+kB638t9kl6Typ2W9r1X0vfLP1XWL/zXk1mxXgecHRG3SlpEllD+AJgTEVtScrgI+FPga8BfRsQqSZ+rOc5bgTdFxFZJf0T21P3vkM2AsDolireNECfF3kD2SoaHgK9GxDHKXoz2l8BHgU8CJ0XEo5XpWsyK4B6PWbE2RMStafn/ACcBRwAr0pT3nwCmSNoHmBgRq1LZr9ccZ0XVVDTHAlelSS4fA1YBb2kQB1id3k3zHNn0+t9J8fuAaWn5VuBySR8km6rFrBDu8ZgVq3ZOqqeB+yPirdXB1MNoNH/VL6qLj1BmpDhA9XxeL1atv0j6HYiID0maAbwLuEfSkRHxRINjmrXEPR6zYk2VVEkyZwC3A5MqMUm7SXpjmvp+e5rUEuADDY75feB96UVhk4DfBe5sEM9F0msi4o6I+CTwODu/2sOsbdzjMSvWOmCupH8FHgS+CCwHLkmX18aTzWJ+P3AWsEjSs6nMSL5Fds/nXrJe0t9HxM8ljRR/fc62fk7SdLKe003pOGZt59mpzcysVL7UZmZmpXLiMTOzUjnxmJlZqZx4zMysVE48ZmZWKiceMzMrlROPmZmV6v8DlogYCa2J98kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"bedrooms vs price\")\n",
    "plt.xlabel('bedrooms')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.bedrooms,df.price,color='green',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1e90d56610>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"sqft_living+ sqft_basement vs price\")\n",
    "plt.xlabel('sqft_living+ sqft_basement')\n",
    "plt.ylabel('price')\n",
    "plt.scatter((df['sqft_living']+df['sqft_basement']),df['price'], color='red',marker='+')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1e90e00c90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"bedrooms + bathrooms vs price\")\n",
    "plt.xlabel('bedrooms + bathrooms ')\n",
    "plt.ylabel('price')\n",
    "plt.scatter((df['bedrooms']+df['bathrooms']),df['price'], color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1e8eb2c190>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"floors vs price\")\n",
    "plt.xlabel('floors ')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.floors,df.price, color='green',marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1e8eb35690>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fd6c2qzATR03kusev29Or7Yy/OKPUKZW1+SfM54pTr6ChvoEzjziT9a+tL3VKVc8TwXXBE8FZOZl10yyWPb2MHbt3sKtlFwP6DWBg/4FMP2Q61/3NdaVOz2yPkk0EJ2mdpMclPSJpZYqNkLRc0pr0c68Ul6TLJa2V9JikY7KOMydtv0bSnKz4sen4a9O+6mkbZpXAN8utLyjGPZ7JEXFUVuU7G7gzIiYAd6b3AKcCE9JrLnAVZIoIcB7wfuA44LzWQpK2mZu137SetGFWKXyz3PqCUnQumAFck5avAU7Pii+NjN8BwyXtB5wCLI+ILRHxCrAcmJbWDY2I+yJzvXBpu2Pl04ZZxfDNcqt0he5cEMAvJQXwvYhYBDRExCaAiNgkad+07Sgg+67fhhTrLL4hR5wetLEpO2lJc8l8I2Ls2LH5fmazgvLNcqt0hS48J0bExvSHf7mkpzrZVjli0YN4Z7q1TyqQiyDTuaCLY5oV1cRRE/csN9Q30FDfUMJszPJX0EttEbEx/XwJuJnMPZoXWy9vpZ8vpc03AGOydh8NbOwiPjpHnB60YWZmRVKwwiOpTtKQ1mXgZOAJYBnQ2jNtDnBrWl4GzE49z44HtqbLZXcAJ0vaK3UqOBm4I63bJun41Jttdrtj5dOGmZkVSSEvtTUAN6cezgOA6yLiF5JWAE2SzgKeAz6etr8N+DCwFtgOfAYgIrZIugBYkbZbGBFb0vLngauBQcDt6QVwUT5tmJlZ8fgB0i74AVIzs/yV7AFSMysMT2xmlcyFx6wCeWIzq2QuPGYVxBObWV/gwmNWQTxWm/UFLjxmFaR1rLadu3cCsHP3To/VZhXHhceswjStaqKfMr+6/dTPY7VZxXHhMasg4y4bxw2rb6C5pRmA5pZmmlY3Me6ycaVNzCwPLjxmFWTx9MXU9q9tE6vtX8uSGUtKlJFZ/lx4zCrI1IOmMu+4eW1i846bx5QDp5QoI7P8ufCYVZimJ5oAOG3CaW3em1WKQk+LYGa97BtTvsGx+x3L+xrexxMvPsFDLzxU6pTM8uKx2rrgsdrMzPLnsdrM+hiP1WaVrNuFR9IBkk5Ky4Na59oxs+LzWG1WybpVeCR9DrgR+F4KjQZuKVRSZpabx2qzvqC733i+AJwIvAYQEWuAfQuVlJnl5rHarC/obuHZERE7W99IGgC4V4JZkbWO1dbc0kxdTR3NLc0eq80qTncLz72SvgYMkvQh4Abgp93ZUVJ/SQ9L+ll6f6Ck+yWtkfRjSbUpPjC9X5vWj8s6xjkp/rSkU7Li01JsraSzs+J5t2FWKZpWNTG4ZjD1NfUMrhnssdqs4nS38JwNbAYeB/4euA34927u+0/Ak1nvLwYujYgJwCvAWSl+FvBKRIwHLk3bIekwYCZwODAN+N+pmPUHvgOcChwGnJG2zbsNs0oy/4T5XDT1Il7c/iIXT72Y+SfOL3VKZnnp1nM8kuqANyNid3rfHxgYEdu72G80cA3wTeBfgL8mU8DeHRG7JH0AOD8iTpF0R1q+L13KewEYSaboERHfSse8Azg/NXF+RJyS4uek2EX5thGdnAQ/x2PlZNZNs1j29DJ27N7BrpZdDOg3gIH9BzL9kOlc9zfXlTo9sz164zmeO4FBWe8HAb/qxn6XAV8FWtL7vYFXI2JXer8BGJWWRwHrAdL6rWn7PfF2+3QU70kbbUiaK2mlpJWbN2/uxsc0Kw53LrC+oLuF510R8Xrrm7Q8uLMdJJ0GvBQRD2aHc2waXazrrXhX7b8ViFgUEY0R0Thy5Mgcu5iVhjsX9IwfuC0v3S08f5Z0TOsbSccCb3Sxz4nAdEnrgOuBKWS+AQ1Pl7kg8zzQxrS8ARiTjj8AGAZsyY6326ej+Ms9aMOsYjStamLwgMHU19YzeIA7F3THDatvYPXLq7lx9Y2lTsXofuH5MnCDpP8j6f8APwbmdbZDRJwTEaMjYhyZzgF3RcSngLuBj6XN5gC3puVl6T1p/V3p3ssyYGbqkXYgMAF4AFgBTEg92GpTG8vSPvm2YVYx5p8wn4tOuogX//wiF5/kzgWdaX3g9u9/9vcAzP3ZXD9wWwa6NTp1RKyQdChwCJnLVU9FRHMP2/xX4HpJ3wAeBhan+GLgWklryXwLmZnaXiWpCVgN7AK+kNXJYR5wB9AfWBIRq3rShlmlyO5cAPDFX3zRnQs6sW3nNrY3byfSFfWWaGF783a27dxW4syqW6e92iRNiYi7JH001/qI+EnBMisT7tVm5WTtlrVM/9F01r26jjd2vcGgAYM4cK8DWTZzme/z5LB2y1omXT2J57c9vyc2euho7plzj89Xgb2TXm1/lX7+dY7Xab2WoZl1S3bnAiF3LujC+BHjGTtsbJvYmKFjfL66UOjOGJ0Wnog4T1I/4PaI+Ey712cLkpGZdappVRMDNIAgGKAB7lzQiVk3zeL+5+9vE7v/+ft9j6cLhR79vMvOBRHRQhcdCcysOGbdNIufPv1T3tz9JgBv7n6TZU8v8x/SDmzbsY32txMigm07fI8nl2KNft7dXm3LJf1/ksZIGtH66tVMzKxL23Zu29OxoNWO3Tt8s7wDl067lP2H7N8mNmroKC6bdlmJMipvxXpAubuF57PAPwL3AiuzXmZWRA9tfGhPD61WQfDQxodKlFF5Gz9iPGOGZR73q+1XC/geT2eK9YBydwvPYWQG5HwUeAS4gsygnWZWREs/shS1G4BDiGs/em2JMip/dTV1DKkdwoVTL2RI7RDqaupKnVJZa1rVRF1NHQsmLaCupq4g9xC7O0hoE5lJ4H6YQmcAwyPiE72eUZlxd2orN2MvHcv6194apnDs0LE8+8/PljCj8rbi+RWMHTaWhvoGXnz9Rda/tp7G/XP28jV673x11p26Ww+QAodExJFZ7++W9GjemZjZO7ZjV9t7PG/uerNEmVSGiaMm7lluqG+gob6hhNmUv2Kcr+5eantY0vGtbyS9H/hNr2djZp2addMsXt3xapvYqzteda82qyjdLTzvB34raV0a9PM+4K8kPS7psYJlZ2Zt3PzUzezcvbNNbOfundz81M0lyqgyPLf1OQZ+YyDPbX2u1KlUhJI+QJplGnAgmZEM/iotf5jM6AV/XZDMzOxtjmo4Knf83bnjlnHxby5m5+6dfPs33y51KhWh0A+QdneQUN+5NCsD+wzeJ3d8UO54tRt32Tie3frWn68rV1zJlSuu5IBhB7Duy+tKl1iZaj8I7exbZvO5n36u1weh7e43HjOzirN4+mJq+9e2idX2r2XJjCUlyqi8ldsDpGZWBi6ddin91b9NbIAG+En8Dkw9aCrzjms74te84+Yx5cApJcqovLU+QNr6jWfn7p0lfYDUzMrAkd89kt2Z6aj22BW7OOK7R5Qoo/I266ZZXHLfJW1il9x3iXsBdmDWTbOYeeNMWqIFgN2xm5k3zizZWG1mVgb+Yt+/yBk/Yl8XnlwWTl7IfvX70S/9qetHP/av37/XLx31FQsnL2TM0DEM7DcQgIH9BjJ22FhfajOrZuu3rs8Zdzfh3E5aehKbXt9EC5n/g2+hhY2vb2Tq0qklzqw8jR8xnm+f/G2a0wTTzdHM//rQ/6qcS22S3iXpAUmPSlolaUGKHyjpfklrJP1YUm2KD0zv16b147KOdU6KPy3plKz4tBRbK+nsrHjebZhVgkP2PiSveLU7aPhBecWrXftLbS3RUnGX2nYAU9JQO0cB09LoBxcDl0bEBOAV4Ky0/VnAKxExHrg0bYekw4CZZAYlnQb8b0n9JfUnM3DpqWQGMT0jbUu+bZhVikXTF+25bNSqH/34r+n/VaKMytui6YsYWju0TWzowKE+Xx1YOHkhY4aNYYAyT9oM0IDKutQWGa+ntzXpFcAU4MYUvwY4PS3PSO9J66dKUopfHxE7IuKPwFrguPRaGxHPRMRO4HpgRton3zbMKsKhVx6657JRqxZaOORKf+PJ5dy7z+W1na+1ib224zW+fvfXS5RReTv37nPZtG0Tu2IXkOm4snHbxl4/XwW9x5O+mTwCvAQsB/4AvBqRPhVsAEal5VHAeoC0fiuwd3a83T4dxffuQRvt854raaWklZs3b+7ZhzcrgPY92rqKV7uFkxcysP/ANrF39X+XOxd0oFgTDRa08ETE7og4ChhN5hvKe3Ntln7m+uYRvRjvrI22gYhFEdEYEY0jR47MsYtZaXjInPyMHzGe8yed3yZ2/qTzPRFcRzqaJafr2XPyUpRebRHxKnAPcDwwXFLrUD2jgY1peQMwBiCtHwZsyY6326ej+Ms9aMOsIjzy4iO54y/kjle7ugvrOOfOc9rEzr7zbOou9GRwuVw67VLeNeBdbWKDBgzq9QeUC9mrbaSk4Wl5EHAS8CRwN/CxtNkc4Na0vCy9J62/KzKz1C0DZqYeaQcCE4AHgBXAhNSDrZZMB4RlaZ982zCrCF/9wFdzxs8+8eyc8Wp31Yevyhn/3mnfK3ImleHcu8/NOd9TJd3j2Y/MhHGPkSkSyyPiZ8C/Av8iaS2Z+yuL0/aLgb1T/F+AswEiYhXQBKwGfgF8IV3C2wXMA+4gU9Ca0rbk24ZZpbj45IvfPmROvwF866RvlSij8jb7qNmc9p7T2sROe89pnHnEmSXKqLxt27mNaHddLYhev8fTramvq5mnvrZyowVvv1UZ5/n3uCODvzGYN3a/sef9oAGD2P5v20uYUflau2UtJyw+gc3b3+pUtW/dvvz2s7/N+75YZ1Nfe+QCswoy7rJxecWrXd2FdW2KDsAbu97wPZ4OjB8xnvEjxgPsGdX74L0OrpyRC8ys92XPLdOdeLXz2Hb5G1wzmCG1Q7hwyoUMqR1CXW3vF+luTQRnZuVhSM0QtjW//Xr7kJohJcim/P3goz/g6O8dzes7X98Tq6+t5wcf/UEJsypv35r6LcYOG0tDfQNnHnEm61/LPT7gO+FvPFY2Cj3Pe1/QOCrnJfMO49XupKUntSk6AK/vfN2DhHZi4qiJbN6+GS0Qm7dvpnH/3v+35cJjZaPQ87z3BfdtuC+veLXzIKE988XbvgjAl27/UkGO78JjJTfrplnUX1jPnFsyj1jNvmU29RfWe7KuHI7c98ic8aP29cgFudz3fAeFuoN4tau7sA4tEPc8ew8Ad6+7Gy1Qr3fGcOGxkivWPO99wYpNK3LGH9j0QJEzqQyDBwzOK17titUZw4XHSq51nvfmlmbqaupobmkuyDzvfUHrPCndjVe7Ixtyf0PsKF7tRg7OPTblPoP36dV2XHisLDStaqKupo4FkxZQV1PHDatuKHVKZan9SMtdxavdoumLcsY9H09ul067NGe8YsZqM8vH/BPm8/S8p/nKCV/h6XlPM//E+aVOqSy1H7K+q3i1O2npSTnj7tWWW7HOlwuPlYWJoybSUN8AQEN9Q0G6cPYF7Wcf7Spe7dyrLT+Lpy9G7WaPEWLJjCW92o7/tZpVkPazj3YVr3Z1A3P3xuooXu0WP7yYfmo3tbr68f2Hvt+r7bjwmFWQ2n61ecWr3UMbH8orXu0WTl6I1O4bj9TrPUxdeMwqyM6WnXnFq92f3vhTXvFqd+iVh7KrZVeb2K6WXRxy5SG92o4Lj5n1WYtOy92r7fvTe/fSUV9RrO76Ljxm1mede8+5OeP/fte/FzmTytDhc0/v7t3nnlx4zCrIe/d+b+74Prnj1e65rc/lFa92N3wi9/NzN378xl5tp2CFR9IYSXdLelLSKkn/lOIjJC2XtCb93CvFJelySWslPSbpmKxjzUnbr5E0Jyt+rKTH0z6XK90V60kbZpVg647cI3d7RO/cjmjIPdSLRy7IrXUSuPYqaSK4XcBXIuK9wPHAFyQdBpwN3BkRE4A703uAU4EJ6TUXuAoyRQQ4D3g/cBxwXmshSdvMzdpvWorn1YZZpThkn9w3eTuKV7sxw8bkjI8eNrrImVSGjgYDrZhBQiNiU0Q8lJa3AU8Co4AZwDVps2uA09PyDGBpZPwOGC5pP+AUYHlEbImIV4DlwLS0bmhE3BcRASxtd6x82jCrCHevuzuveLVzd+r87Nq9K694TxXlHo+kccDRwP1AQ0RsgkxxAvZNm40Csqe625BincU35IjTgzba5ztX0kpJKzdv3pt8vJIAAA6hSURBVJzPRzWzMrLx9Y15xavd0fsdnTN+zH69e1ei4IVHUj1wE/DliHits01zxKIH8U7T6c4+EbEoIhojonHkyNyjtZqZ9TUdTQne21OFF7TwSKohU3R+GBE/SeEXWy9vpZ8vpfgGIPuC7GhgYxfx0TniPWnDzKzqVfwgoamH2WLgyYi4JGvVMqC1Z9oc4Nas+OzU8+x4YGu6THYHcLKkvVKngpOBO9K6bZKOT23NbnesfNowsz5oWO2wvOLV7oXXX8gr3lMDevVobZ0IfBp4XNIjKfY14CKgSdJZwHPAx9O624APA2uB7cBnACJii6QLgNapFxdGxJa0/HngamAQcHt6kW8bZtY3bd3ZQffzDuLV7uh3H83vnv/d2+Md3PvpqYIVnoj4H3LfUwF42/e21DPtCx0cawnwtnG5I2Il8L4c8T/l24aZWbW79qPX8p4r3kNk3foW4gcfqaB7PGZmVjkOvfLQNkUHIAgPEmpmZoWxO3bnFe8pFx4zMysqFx4zMwOgRjV5xXvKhaeAtr65lcO/c7gHcDSzitAczXnFe8qFp4B+vubnrH55Nbetua3UqZiZlQ0XngKYddMs6i+sZ84tmWdYZ98ym/oL65l106wSZ2ZmVnouPAWwcPJCxg4bS02/zHXRmn41HDD8AC6YfEGJMzMzKz0XngIYP2I8CycvpLmlmbqaOppbmlkwaUGvT6ZkZp0bNGBQzvjgAYOLnIllc+EpkKZVTdTV1LFg0gLqauq4YVXuKWXNrHDe2PVGzvj2XduLnIllK+RYbVVt/gnzueLUK2iob+DMI85k/Wvru97JzKwKuPAUyMRRE/csN9Q30FDfUMJszMzKhy+1Wdnwc09m1cGFx8qGn3syqw4uPFZyfu7JrLq48FjJ+bkns+riwmMl5+eezKpLwQqPpCWSXpL0RFZshKTlktakn3uluCRdLmmtpMckHZO1z5y0/RpJc7Lix0p6PO1zuST1tA0rPT/3ZFY9CvmN52pgWrvY2cCdETEBuDO9BzgVmJBec4GrIFNEgPOA9wPHAee1FpK0zdys/ab1pA0rD/NPmM9ds+/ia3d9jbtm38X8E+eXOiUzK5CCFZ6I+DWwpV14BnBNWr4GOD0rvjQyfgcMl7QfcAqwPCK2RMQrwHJgWlo3NCLui4gAlrY7Vj5tWBmYOGoiix9ZzM7dO/nvR/6bxv0bS52SmRVIsR8gbYiITQARsUnSvik+Csh+tH9DinUW35Aj3pM2NrVPUtJcMt+KGDt2bJ4f0fI17rJxPLv12T3vr1xxJVeuuJIDhh3Aui+vK11iZlYQ5dK5QDli0YN4T9p4ezBiUUQ0RkTjyJEjuzisvVOLpy+mtn9tm1ht/1qWzFhSoozMrJCKXXhebL28lX6+lOIbgDFZ240GNnYRH50j3pM2rMSmHjSVecfNaxObd9w8phw4pUQZmVkhFbvwLANae6bNAW7Nis9OPc+OB7amy2V3ACdL2it1KjgZuCOt2ybp+NSbbXa7Y+XThpXYrJtmccl9l7SJXXLfJX6A1KyPKtg9Hkk/AiYB+0jaQKZ32kVAk6SzgOeAj6fNbwM+DKwFtgOfAYiILZIuAFak7RZGRGuHhc+T6Tk3CLg9vci3DSu9bTu25RU3s8pWsMITEWd0sGpqjm0D+EIHx1kCvO1if0SsBN6XI/6nfNuw0rp02qU8/MLDPL/t+T2x0UNHc9m0y0qYlZkVSrl0LuiTPNpy95x797lser3tVc+N2zby9bu/XqKMzKyQXHgKyKMtd8+2HdtoiZY2sZZo8aU2sz7KhacAWkdbnn3zbAA+ffOnPdpyZ3J1dO8sbmYVzYWnAFpHW+6nzOntp34ebbkTv3rmV3nFzayyufAUwLl3n8szrzxDc0szAM0tzfxhyx98z6IDb+56M6+4mVU2F54CuHfdvezYvaNNbMfuHdy77t4SZVTehtQOyStuZpXNhacAln5k6Z7LbK36qR/XfvTaEmVU3rbt7OA5ng7iZlbZXHgK4KxlZ+XspfXZWz9boozMzMqHC08BvNH8Rl5xM7Nq4sJjZmZF5cJTAIfve3hecTOzauLCUwBP/+npvOJmZtXEhacAXt7+cl5xM7Nq4sJTCB3NhdrVHKlmZlXAhacA6gfW5xU3M6smLjwF8Oedf84rbmZWTVx4CqD9w6Ndxc3MykF9Te6rMkNqenf4qqorPJKmSXpa0lpJZxeiDY89lp8BHUyE21HcrLvUwdwaHcWr3cP/8HBe8Z6qqsIjqT/wHeBU4DDgDEmH9XY7TR9vyhm/4RM39HZTfcIvPv2LnPE7Zt9R5EzK33tGvCdn/NARhxY5k8qw/NPLc8Z/NdtTbuQyfsT4nPGDRxzcq+1UVeEBjgPWRsQzEbETuB6Y0duNTD1oas74lAOn9HZTfYLPV/c9/cXcz4I9+cUni5xJZfC/rfz1V3/605//+NB/0J/+DFDvX3motsIzClif9X5DirUhaa6klZJWbt68uUcNtV5WO23CaW3eW+daz5dZb/O/re6576z7eP4rz/OVE77C8195nvv+7r5eb6PaLqLnurD7tqdrImIRsAigsbGxR0/fXHHqFRy737G8r+F9PPHiEzz0wkM9OUzVuHrG1T5f3TS0diiTD5jMLbNu4fTrTueeZ+8pdUplzf+28jNx1MQ9yw31DTTUN/R6G4qonqcaJX0AOD8iTknvzwGIiG91tE9jY2OsXLmySBmamfUNkh6MiMZc66rtUtsKYIKkAyXVAjOBZSXOycysqlTVpbaI2CVpHnAH0B9YEhGrSpyWmVlVqarCAxARtwG3lToPM7NqVW2X2szMrMRceMzMrKiqqldbT0jaDDz7Dg6xD1COE/E4r/yUY17lmBM4r3z11bwOiIiRuVa48BSYpJUddSksJeeVn3LMqxxzAueVr2rMy5fazMysqFx4zMysqFx4Cm9RqRPogPPKTznmVY45gfPKV9Xl5Xs8ZmZWVP7GY2ZmReXCY2ZmReXC0wskLZH0kqQnOlgvSZen6bYfk3RMmeQ1SdJWSY+k17lFymuMpLslPSlplaR/yrFNUc9ZN3Mq+vmS9C5JD0h6NOW1IMc2AyX9OJ2r+yWNK5O8/lbS5qzz9XeFziur7f6SHpb0sxzrin6+uplXSc6XpHWSHk9tvm0o/oL8LkaEX+/wBXwQOAZ4ooP1HwZuJzMf0PHA/WWS1yTgZyU4X/sBx6TlIcDvgcNKec66mVPRz1f6/PVpuQa4Hzi+3Tb/CHw3Lc8Eflwmef0tcGWx/32ltv8FuC7Xf69SnK9u5lWS8wWsA/bpZH2v/y76G08viIhfA1s62WQGsDQyfgcMl7RfGeRVEhGxKSIeSsvbgCd5+0ywRT1n3cyp6NLnfz29rUmv9j2CZgDXpOUbgamSck16WOy8SkLSaOD/Bb7fwSZFP1/dzKtc9frvogtPcXRryu0S+UC6XHK7pMOL3Xi6zHE0mf9jzlayc9ZJTlCC85UuzzwCvAQsj4gOz1VE7AK2AnuXQV4Af5Muz9woaUyhc0ouA74KtHSwviTnqxt5QWnOVwC/lPSgpLk51vf676ILT3F0a8rtEniIzHhKRwJXALcUs3FJ9cBNwJcj4rX2q3PsUvBz1kVOJTlfEbE7Io4CRgPHSXpfu01Kcq66kddPgXERcQTwK976llEwkk4DXoqIBzvbLEesoOerm3kV/XwlJ0bEMcCpwBckfbDd+l4/Xy48xbEByP6/l9HAxhLlskdEvNZ6uSQy8xTVSNqnGG1LqiHzB/6HEfGTHJsU/Zx1lVMpz1dq81XgHmBau1V7zpWkAcAwiniJtaO8IuJPEbEjvf0v4NgipHMiMF3SOuB6YIqkH7TbphTnq8u8SnS+iIiN6edLwM3Ace026fXfRRee4lgGzE69Q44HtkbEplInJendrde2JR1H5t/Dn4rQroDFwJMRcUkHmxX1nHUnp1KcL0kjJQ1Py4OAk4Cn2m22DJiTlj8G3BXprnAp82p3H2A6mftmBRUR50TE6IgYR6bjwF0RcWa7zYp+vrqTVynOl6Q6SUNal4GTgfa9YHv9d7HqZiAtBEk/ItPjaR9JG4DzyNxsJSK+S2bG0w8Da4HtwGfKJK+PAZ+XtAt4A5hZ6F/A5ETg08Dj6R4BwNeAsVm5FfucdSenUpyv/YBrJPUnU+iaIuJnkhYCKyNiGZmCea2ktWT+z31mgXPqbl5fkjQd2JXy+tsi5JVTGZyv7uRVivPVANyc/n9qAHBdRPxC0j9A4X4XPWSOmZkVlS+1mZlZUbnwmJlZUbnwmJlZUbnwmJlZUbnwmJlZUbnwmFUgSVdL+lha/r6kw9Ly19pt99tS5GfWGXenNqtAkq4mM8Lxje3ir0dEfWmyMusef+MxKwJJs9Pgj49KulbSAZLuTLE7JY1N212d5j75raRnsr7VSNKVklZL+jmwb9ax75HUKOkiYJAy86r8MK17PWv/b0t6Qpm5Vz6Z4pPS/jdKekrSD1tHZzArFI9cYFZgyoxi/W9kBmN8WdIIMgNALo2IayR9FrgcOD3tsh/wl8ChZIYruRH4CHAI8BdknjZfDSzJbicizpY0Lw3c2d5HgaOAI4F9gBWSfp3WHQ0cTmb8rd+QGcXhf3rjs5vl4m88ZoU3BbgxIl4GiIgtwAfITAgGcC2ZQtPqlohoiYjVZIoMZCb1+1EaEXojcFeeOfxl1v4vAvcCE9O6ByJiQ0S0AI8A4/I8tlleXHjMCk90PYx89vodWcvqYJue5NCR7PZ24yshVmAuPGaFdyfwCUl7A6RLbb/lrcEpP0XXl7Z+DcxUZvK1/YDJHWzXnKZ3yLX/J9P+I8l8g3ogz89h1iv8fzZmBRYRqyR9E7hX0m7gYeBLwBJJ84HNdD3i781kLtk9DvyezKWyXBYBj0l6KCI+1W7/DwCPkvnm9NWIeEHSoT39XGY95e7UZmZWVL7UZmZmReXCY2ZmReXCY2ZmReXCY2ZmReXCY2ZmReXCY2ZmReXCY2ZmRfV/AZFL+KTbeM7PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"condition vs price\")\n",
    "plt.xlabel('condition')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.condition,df.price, color='green',marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        221900.0\n",
       "1        538000.0\n",
       "2        180000.0\n",
       "3        604000.0\n",
       "4        510000.0\n",
       "           ...   \n",
       "21608    360000.0\n",
       "21609    400000.0\n",
       "21610    402101.0\n",
       "21611    400000.0\n",
       "21612    325000.0\n",
       "Name: price, Length: 21613, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_price = df['price']\n",
    "new_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dates = [1 if values == 2014 else 0 for values in df.date ]\n",
    "df['date'] = new_dates\n",
    "new_data = df.drop(['id', 'price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train , x_test , y_train , y_test = train_test_split(new_data, new_price , test_size = 0.10, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7320342760357841"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = GradientBoostingRegressor(n_estimators = 400, max_depth= 5, min_samples_split= 2,\n",
    "          learning_rate= 0.01, loss = 'ls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
       "                          init=None, learning_rate=0.01, loss='ls', max_depth=5,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=400,\n",
       "                          n_iter_no_change=None, presort='deprecated',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9050824056136874"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'params' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-21a11ffff03b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'n_estimators'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'params' is not defined"
     ]
    }
   ],
   "source": [
    "test_score = np.zeros(( params['n_estimators']), dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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
}