final assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e917450f",
   "metadata": {},
   "source": [
    "# House Sales in King County, USA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde761b7",
   "metadata": {},
   "source": [
    "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5818125",
   "metadata": {},
   "source": [
    "| Variable      | Description                                                                                                 |\n",
    "| ------------- | ----------------------------------------------------------------------------------------------------------- |\n",
    "| id            | A notation for a house                                                                                      |\n",
    "| date          | Date house was sold                                                                                         |\n",
    "| price         | Price is prediction target                                                                                  |\n",
    "| bedrooms      | Number of bedrooms                                                                                          |\n",
    "| bathrooms     | Number of bathrooms                                                                                         |\n",
    "| sqft_living   | Square footage of the home                                                                                  |\n",
    "| sqft_lot      | Square footage of the lot                                                                                   |\n",
    "| floors        | Total floors (levels) in house                                                                              |\n",
    "| waterfront    | House which has a view to a waterfront                                                                      |\n",
    "| view          | Has been viewed                                                                                             |\n",
    "| condition     | How good the condition is overall                                                                           |\n",
    "| grade         | overall grade given to the housing unit, based on King County grading system                                |\n",
    "| sqft_above    | Square footage of house apart from basement                                                                 |\n",
    "| sqft_basement | Square footage of the basement                                                                              |\n",
    "| yr_built      | Built Year                                                                                                  |\n",
    "| yr_renovated  | Year when house was renovated                                                                               |\n",
    "| zipcode       | Zip code                                                                                                    |\n",
    "| lat           | Latitude coordinate                                                                                         |\n",
    "| long          | Longitude coordinate                                                                                        |\n",
    "| sqft_living15 | Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area |\n",
    "| sqft_lot15    | LotSize area in 2015(implies-- some renovations)                                                            |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f16a47a2",
   "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,
   "id": "2c101e2c",
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "    <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv'\n",
    "df=pd.read_csv(file_name)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e21aea4b",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a9177bd",
   "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>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",
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       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21613.00000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21600.000000</td>\n",
       "      <td>21603.000000</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>...</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",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10806.00000</td>\n",
       "      <td>4.580302e+09</td>\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>...</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>6239.28002</td>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.926657</td>\n",
       "      <td>0.768996</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>...</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>0.00000</td>\n",
       "      <td>1.000102e+06</td>\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>...</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>5403.00000</td>\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>...</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>10806.00000</td>\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>...</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>16209.00000</td>\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>...</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>21612.00000</td>\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>...</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",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\n",
       "count  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \n",
       "mean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \n",
       "std     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \n",
       "min        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n",
       "25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n",
       "50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n",
       "75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \n",
       "max    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n",
       "\n",
       "        sqft_living      sqft_lot        floors    waterfront          view  \\\n",
       "count  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n",
       "std      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n",
       "min      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n",
       "25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n",
       "50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n",
       "75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n",
       "max    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n",
       "\n",
       "       ...         grade    sqft_above  sqft_basement      yr_built  \\\n",
       "count  ...  21613.000000  21613.000000   21613.000000  21613.000000   \n",
       "mean   ...      7.656873   1788.390691     291.509045   1971.005136   \n",
       "std    ...      1.175459    828.090978     442.575043     29.373411   \n",
       "min    ...      1.000000    290.000000       0.000000   1900.000000   \n",
       "25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n",
       "50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n",
       "75%    ...      8.000000   2210.000000     560.000000   1997.000000   \n",
       "max    ...     13.000000   9410.000000    4820.000000   2015.000000   \n",
       "\n",
       "       yr_renovated       zipcode           lat          long  sqft_living15  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n",
       "mean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n",
       "std      401.679240     53.505026      0.138564      0.140828     685.391304   \n",
       "min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n",
       "25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n",
       "50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n",
       "75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n",
       "max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n",
       "\n",
       "          sqft_lot15  \n",
       "count   21613.000000  \n",
       "mean    12768.455652  \n",
       "std     27304.179631  \n",
       "min       651.000000  \n",
       "25%      5100.000000  \n",
       "50%      7620.000000  \n",
       "75%     10083.000000  \n",
       "max    871200.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b408cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>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>21600.000000</td>\n",
       "      <td>21603.000000</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>0.926657</td>\n",
       "      <td>0.768996</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  21600.000000  21603.000000  21613.000000  2.161300e+04   \n",
       "mean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \n",
       "std    3.671272e+05      0.926657      0.768996    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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['id', 'Unnamed: 0'], axis = 1, inplace = True)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85822fd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column bedrooms : 13\n",
      "number of NaN values for the column bathrooms : 10\n"
     ]
    }
   ],
   "source": [
    "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n",
    "print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbd809ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column bedrooms : 0\n",
      "number of NaN values for the column bathrooms : 0\n"
     ]
    }
   ],
   "source": [
    "mean=df['bedrooms'].mean()\n",
    "mean=df['bathrooms'].mean()\n",
    "df['bedrooms'].replace(np.nan,mean, inplace=True)\n",
    "df['bathrooms'].replace(np.nan,mean, inplace=True)\n",
    "\n",
    "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n",
    "print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3112bbb",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floors_counts = df['floors'].value_counts().to_frame()\n",
    "floors_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef8b9ea8",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(x=\"waterfront\", y=\"price\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01b2b6c5",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.regplot(x=\"sqft_above\", y=\"price\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81915f32",
   "metadata": {},
   "outputs": [
    {
     "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         0.308916\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7542bf4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00046769430149029567"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['long']]\n",
    "Y = df['price']\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)\n",
    "lm.score(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c24d03ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4928532179037931"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['sqft_living']]\n",
    "Y = df['price']\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)\n",
    "lm.score(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e5e87eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6577096599511663"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(df[features],Y)\n",
    "lm.score(df[features], Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "154a98f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.751341223073162"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())] #default degree is 2\n",
    "\n",
    "pipe=Pipeline(Input)\n",
    "pipe.fit(df[features],Y)\n",
    "pipe.score(df[features],Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c6cd49d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f055b7ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples: 4323\n",
      "number of training samples: 17290\n"
     ]
    }
   ],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \n",
    "X = df[features]\n",
    "Y = df['price']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=1)\n",
    "\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": 33,
   "id": "2bcd4471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.660993027029476, 0.6459646371728454)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "RR = Ridge(alpha=0.1)\n",
    "RR.fit(x_train, y_train)\n",
    "RR.score(x_train, y_train), RR.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1466f021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7314094557328576, 0.7543634857127555)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=PolynomialFeatures()\n",
    "x_train_pr=pr.fit_transform(x_train)\n",
    "x_test_pr=pr.fit_transform(x_test)\n",
    "\n",
    "RigeModel=Ridge(alpha=0.1)\n",
    "RigeModel.fit(x_train_pr, y_train)\n",
    "RigeModel.score(x_train_pr, y_train), RigeModel.score(x_test_pr, y_test)"
   ]
  }
 ],
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