HousePriceprediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6dca2d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a104f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b750938f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33517bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39fbc4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
       "           37.88      , -122.23      ],\n",
       "        [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
       "           37.86      , -122.22      ],\n",
       "        [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
       "           37.85      , -122.24      ],\n",
       "        ...,\n",
       "        [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
       "           39.43      , -121.22      ],\n",
       "        [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
       "           39.43      , -121.32      ],\n",
       "        [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
       "           39.37      , -121.24      ]]),\n",
       " 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]),\n",
       " 'frame': None,\n",
       " 'target_names': ['MedHouseVal'],\n",
       " 'feature_names': ['MedInc',\n",
       "  'HouseAge',\n",
       "  'AveRooms',\n",
       "  'AveBedrms',\n",
       "  'Population',\n",
       "  'AveOccup',\n",
       "  'Latitude',\n",
       "  'Longitude'],\n",
       " 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nAn household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surpinsingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fb5bfe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame(df.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "36e1d431",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.columns = df.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d4754e7",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>1.5603</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>845.0</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
       "\n",
       "       Longitude  \n",
       "0        -122.23  \n",
       "1        -122.22  \n",
       "2        -122.24  \n",
       "3        -122.25  \n",
       "4        -122.25  \n",
       "...          ...  \n",
       "20635    -121.09  \n",
       "20636    -121.21  \n",
       "20637    -121.22  \n",
       "20638    -121.32  \n",
       "20639    -121.24  \n",
       "\n",
       "[20640 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "75cf1b01",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  Price  \n",
       "0    -122.23  4.526  \n",
       "1    -122.22  3.585  \n",
       "2    -122.24  3.521  \n",
       "3    -122.25  3.413  \n",
       "4    -122.25  3.422  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b8b10e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Independent features and independent features\n",
    "X = dataset\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5627d1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7baceb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "## train test split\n",
    "## X_train and y_train is used to train model and X_test and y_test is used to test model\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.30, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "54a64be7",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7061</th>\n",
       "      <td>4.1312</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.882353</td>\n",
       "      <td>0.975490</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>2.985294</td>\n",
       "      <td>33.93</td>\n",
       "      <td>-118.02</td>\n",
       "      <td>1.93800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14689</th>\n",
       "      <td>2.8631</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.401210</td>\n",
       "      <td>1.076613</td>\n",
       "      <td>999.0</td>\n",
       "      <td>2.014113</td>\n",
       "      <td>32.79</td>\n",
       "      <td>-117.09</td>\n",
       "      <td>1.69700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17323</th>\n",
       "      <td>4.2026</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.617544</td>\n",
       "      <td>0.989474</td>\n",
       "      <td>731.0</td>\n",
       "      <td>2.564912</td>\n",
       "      <td>34.59</td>\n",
       "      <td>-120.14</td>\n",
       "      <td>2.59800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10056</th>\n",
       "      <td>3.1094</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.869565</td>\n",
       "      <td>1.094203</td>\n",
       "      <td>302.0</td>\n",
       "      <td>2.188406</td>\n",
       "      <td>39.26</td>\n",
       "      <td>-121.00</td>\n",
       "      <td>1.36100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15750</th>\n",
       "      <td>3.3068</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.801205</td>\n",
       "      <td>1.066265</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>2.298193</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.45</td>\n",
       "      <td>5.00001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>6.3700</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.129032</td>\n",
       "      <td>0.926267</td>\n",
       "      <td>658.0</td>\n",
       "      <td>3.032258</td>\n",
       "      <td>33.78</td>\n",
       "      <td>-117.96</td>\n",
       "      <td>2.29200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>3.0500</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.868597</td>\n",
       "      <td>1.269488</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>3.904232</td>\n",
       "      <td>34.02</td>\n",
       "      <td>-117.43</td>\n",
       "      <td>0.97800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>2.9344</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.986717</td>\n",
       "      <td>1.079696</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>3.332068</td>\n",
       "      <td>34.03</td>\n",
       "      <td>-118.38</td>\n",
       "      <td>2.22100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>5.7192</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.395349</td>\n",
       "      <td>1.067979</td>\n",
       "      <td>1777.0</td>\n",
       "      <td>3.178891</td>\n",
       "      <td>37.58</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>2.83500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>2.5755</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.402576</td>\n",
       "      <td>1.058776</td>\n",
       "      <td>2619.0</td>\n",
       "      <td>2.108696</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>3.25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14448 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "7061   4.1312      35.0  5.882353   0.975490      1218.0  2.985294     33.93   \n",
       "14689  2.8631      20.0  4.401210   1.076613       999.0  2.014113     32.79   \n",
       "17323  4.2026      24.0  5.617544   0.989474       731.0  2.564912     34.59   \n",
       "10056  3.1094      14.0  5.869565   1.094203       302.0  2.188406     39.26   \n",
       "15750  3.3068      52.0  4.801205   1.066265      1526.0  2.298193     37.77   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "11284  6.3700      35.0  6.129032   0.926267       658.0  3.032258     33.78   \n",
       "11964  3.0500      33.0  6.868597   1.269488      1753.0  3.904232     34.02   \n",
       "5390   2.9344      36.0  3.986717   1.079696      1756.0  3.332068     34.03   \n",
       "860    5.7192      15.0  6.395349   1.067979      1777.0  3.178891     37.58   \n",
       "15795  2.5755      52.0  3.402576   1.058776      2619.0  2.108696     37.77   \n",
       "\n",
       "       Longitude    Price  \n",
       "7061     -118.02  1.93800  \n",
       "14689    -117.09  1.69700  \n",
       "17323    -120.14  2.59800  \n",
       "10056    -121.00  1.36100  \n",
       "15750    -122.45  5.00001  \n",
       "...          ...      ...  \n",
       "11284    -117.96  2.29200  \n",
       "11964    -117.43  0.97800  \n",
       "5390     -118.38  2.22100  \n",
       "860      -121.96  2.83500  \n",
       "15795    -122.42  3.25000  \n",
       "\n",
       "[14448 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cff2cc49",
   "metadata": {},
   "outputs": [],
   "source": [
    "## standardising the dataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "39d52794",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0592e3fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9d1fe974",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "##cross validation\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f970065c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression = LinearRegression()\n",
    "regression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a5be2955",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = cross_val_score(regression,X_train,y_train,scoring='neg_mean_squared_error',cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "290a1ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.438275102181047e-30"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0a303b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "## preddiction on test data\n",
    "reg_pred=regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d864ce79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.477  , 0.458  , 5.00001, ..., 2.184  , 1.194  , 2.098  ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9c59071d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tribhuvan/Desktop/practise/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 57,
     "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": [
    "import seaborn as sns\n",
    "sns.distplot(reg_pred-y_test,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8cfd2bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "020821a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "score=r2_score(reg_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "05d099cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b02e1e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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