Data Analysis with python coursera - House price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/kc_house_data_NaN.csv'\n",
    "df=pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>...</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>0</td>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3.0</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>...</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>1</td>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3.0</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>...</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>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2.0</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>...</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>3</td>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4.0</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>...</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>4</td>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3.0</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>...</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 × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n",
       "0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n",
       "1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n",
       "2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n",
       "3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n",
       "4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n",
       "\n",
       "   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n",
       "0         1180      5650     1.0           0  ...      7        1180   \n",
       "1         2570      7242     2.0           0  ...      7        2170   \n",
       "2          770     10000     1.0           0  ...      6         770   \n",
       "3         1960      5000     1.0           0  ...      7        1050   \n",
       "4         1680      8080     1.0           0  ...      8        1680   \n",
       "\n",
       "   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0              0      1955             0    98178  47.5112 -122.257   \n",
       "1            400      1951          1991    98125  47.7210 -122.319   \n",
       "2              0      1933             0    98028  47.7379 -122.233   \n",
       "3            910      1965             0    98136  47.5208 -122.393   \n",
       "4              0      1987             0    98074  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0           1340        5650  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         int64\n",
       "id                 int64\n",
       "date              object\n",
       "price            float64\n",
       "bedrooms         float64\n",
       "bathrooms        float64\n",
       "sqft_living        int64\n",
       "sqft_lot           int64\n",
       "floors           float64\n",
       "waterfront         int64\n",
       "view               int64\n",
       "condition          int64\n",
       "grade              int64\n",
       "sqft_above         int64\n",
       "sqft_basement      int64\n",
       "yr_built           int64\n",
       "yr_renovated       int64\n",
       "zipcode            int64\n",
       "lat              float64\n",
       "long             float64\n",
       "sqft_living15      int64\n",
       "sqft_lot15         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df['bedrooms'].mean()\n",
    "df['bedrooms'].replace(np.nan,mean, inplace=True)\n",
    "mean=df['bathrooms'].mean()\n",
    "df['bathrooms'].replace(np.nan,mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>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>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.372870</td>\n",
       "      <td>2.115736</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>3.409430</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.926378</td>\n",
       "      <td>0.768818</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>0.650743</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  2.161300e+04   \n",
       "mean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \n",
       "std    3.671272e+05      0.926378      0.768818    918.440897  4.142051e+04   \n",
       "min    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n",
       "25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n",
       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \n",
       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
       "\n",
       "             floors    waterfront          view     condition         grade  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean       1.494309      0.007542      0.234303      3.409430      7.656873   \n",
       "std        0.539989      0.086517      0.766318      0.650743      1.175459   \n",
       "min        1.000000      0.000000      0.000000      1.000000      1.000000   \n",
       "25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n",
       "50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n",
       "75%        2.000000      0.000000      0.000000      4.000000      8.000000   \n",
       "max        3.500000      1.000000      4.000000      5.000000     13.000000   \n",
       "\n",
       "         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\n",
       "count  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \n",
       "std      828.090978     442.575043     29.373411    401.679240     53.505026   \n",
       "min      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n",
       "25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n",
       "50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n",
       "75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \n",
       "max     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n",
       "\n",
       "                lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean      47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std        0.138564      0.140828     685.391304   27304.179631  \n",
       "min       47.155900   -122.519000     399.000000     651.000000  \n",
       "25%       47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%       47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max       47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['id','Unnamed: 0'], axis = 1, inplace = True)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>floors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>10680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>8241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     floors\n",
       "1.0   10680\n",
       "2.0    8241\n",
       "1.5    1910\n",
       "3.0     613\n",
       "2.5     161\n",
       "3.5       8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floors'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='waterfront', ylabel='price'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"waterfront\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sqft_above', ylabel='price'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"sqft_above\", y=\"price\", data=df, ci = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ailaty\\AppData\\Local\\Temp\\ipykernel_17464\\2183116142.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['price'].sort_values()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "zipcode         -0.053203\n",
       "long             0.021626\n",
       "condition        0.036362\n",
       "yr_built         0.054012\n",
       "sqft_lot15       0.082447\n",
       "sqft_lot         0.089661\n",
       "yr_renovated     0.126434\n",
       "floors           0.256794\n",
       "waterfront       0.266369\n",
       "lat              0.307003\n",
       "bedrooms         0.308797\n",
       "sqft_basement    0.323816\n",
       "view             0.397293\n",
       "bathrooms        0.525738\n",
       "sqft_living15    0.585379\n",
       "sqft_above       0.605567\n",
       "grade            0.667434\n",
       "sqft_living      0.702035\n",
       "price            1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price'].sort_values()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4928532179037931"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = df[['sqft_living']]\n",
    "Y1 = df['price']\n",
    "lm = LinearRegression()\n",
    "lm\n",
    "lm.fit(X1,Y1)\n",
    "lm.score(X1, Y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6576378970386156"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = df[features]\n",
    "Y2 = df['price']\n",
    "lm.fit(X2,Y2)\n",
    "lm.score(X2,Y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7513400538020479"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]\n",
    "pipe=Pipeline(Input)\n",
    "pipe\n",
    "pipe.fit(df[features],df['price'])\n",
    "pipe.score(df[features],df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples : 3242\n",
      "number of training samples: 18371\n"
     ]
    }
   ],
   "source": [
    "X = df[features ]\n",
    "Y = df['price']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n",
    "\n",
    "print(\"number of test samples :\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6478759163939111"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RigeModel=Ridge(alpha=0.1)\n",
    "RigeModel.fit(x_train, y_train)\n",
    "RigeModel.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7002744271710597"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=PolynomialFeatures(degree=2)\n",
    "x_train_pr=pr.fit_transform(x_train[features])\n",
    "x_test_pr=pr.fit_transform(x_test[features])\n",
    "RigeModel=Ridge(alpha=0.1)\n",
    "RigeModel.fit(x_train_pr, y_train)\n",
    "RigeModel.score(x_test_pr, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "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": "3.10.6 (tags/v3.10.6:9c7b4bd, Aug  1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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 },
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