ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:11.687346Z",
     "start_time": "2024-07-19T06:31:11.684723Z"
    }
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import os"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:12.411574Z",
     "start_time": "2024-07-19T06:31:12.408774Z"
    }
   },
   "id": "24fbd14df5814352",
   "execution_count": 134
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:12.682886Z",
     "start_time": "2024-07-19T06:31:12.677998Z"
    }
   },
   "id": "78729b367a66be29",
   "execution_count": 135
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"end_to_end_project\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:33:14.822623Z",
     "start_time": "2024-07-19T06:33:14.818209Z"
    }
   },
   "id": "44574697a7ca822",
   "execution_count": 168
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "#get the data\n",
    "\n",
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    os.makedirs(housing_path, exist_ok=True)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:33:15.865968Z",
     "start_time": "2024-07-19T06:33:15.861582Z"
    }
   },
   "id": "3e61e976ca0214a8",
   "execution_count": 169
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'datasets\\\\housing\\\\housing.csv'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mPermissionError\u001B[0m                           Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[170], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mfetch_housing_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[169], line 16\u001B[0m, in \u001B[0;36mfetch_housing_data\u001B[1;34m(housing_url, housing_path)\u001B[0m\n\u001B[0;32m     14\u001B[0m urllib\u001B[38;5;241m.\u001B[39mrequest\u001B[38;5;241m.\u001B[39murlretrieve(housing_url, tgz_path)\n\u001B[0;32m     15\u001B[0m housing_tgz \u001B[38;5;241m=\u001B[39m tarfile\u001B[38;5;241m.\u001B[39mopen(tgz_path)\n\u001B[1;32m---> 16\u001B[0m \u001B[43mhousing_tgz\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mextractall\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpath\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhousing_path\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     17\u001B[0m housing_tgz\u001B[38;5;241m.\u001B[39mclose()\n",
      "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\tarfile.py:2269\u001B[0m, in \u001B[0;36mTarFile.extractall\u001B[1;34m(self, path, members, numeric_owner, filter)\u001B[0m\n\u001B[0;32m   2264\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m tarinfo\u001B[38;5;241m.\u001B[39misdir():\n\u001B[0;32m   2265\u001B[0m         \u001B[38;5;66;03m# For directories, delay setting attributes until later,\u001B[39;00m\n\u001B[0;32m   2266\u001B[0m         \u001B[38;5;66;03m# since permissions can interfere with extraction and\u001B[39;00m\n\u001B[0;32m   2267\u001B[0m         \u001B[38;5;66;03m# extracting contents can reset mtime.\u001B[39;00m\n\u001B[0;32m   2268\u001B[0m         directories\u001B[38;5;241m.\u001B[39mappend(tarinfo)\n\u001B[1;32m-> 2269\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_extract_one\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtarinfo\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpath\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mset_attrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mtarinfo\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misdir\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2270\u001B[0m \u001B[43m                      \u001B[49m\u001B[43mnumeric_owner\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnumeric_owner\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2272\u001B[0m \u001B[38;5;66;03m# Reverse sort directories.\u001B[39;00m\n\u001B[0;32m   2273\u001B[0m directories\u001B[38;5;241m.\u001B[39msort(key\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mlambda\u001B[39;00m a: a\u001B[38;5;241m.\u001B[39mname, reverse\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
      "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\tarfile.py:2336\u001B[0m, in \u001B[0;36mTarFile._extract_one\u001B[1;34m(self, tarinfo, path, set_attrs, numeric_owner)\u001B[0m\n\u001B[0;32m   2332\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_extract_member(tarinfo, os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(path, tarinfo\u001B[38;5;241m.\u001B[39mname),\n\u001B[0;32m   2333\u001B[0m                          set_attrs\u001B[38;5;241m=\u001B[39mset_attrs,\n\u001B[0;32m   2334\u001B[0m                          numeric_owner\u001B[38;5;241m=\u001B[39mnumeric_owner)\n\u001B[0;32m   2335\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m-> 2336\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle_fatal_error\u001B[49m\u001B[43m(\u001B[49m\u001B[43me\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2337\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ExtractError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m   2338\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_handle_nonfatal_error(e)\n",
      "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\tarfile.py:2332\u001B[0m, in \u001B[0;36mTarFile._extract_one\u001B[1;34m(self, tarinfo, path, set_attrs, numeric_owner)\u001B[0m\n\u001B[0;32m   2329\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m   2331\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 2332\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_extract_member\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtarinfo\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpath\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpath\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtarinfo\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2333\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mset_attrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mset_attrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2334\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mnumeric_owner\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnumeric_owner\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2335\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m   2336\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_handle_fatal_error(e)\n",
      "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\tarfile.py:2415\u001B[0m, in \u001B[0;36mTarFile._extract_member\u001B[1;34m(self, tarinfo, targetpath, set_attrs, numeric_owner)\u001B[0m\n\u001B[0;32m   2412\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_dbg(\u001B[38;5;241m1\u001B[39m, tarinfo\u001B[38;5;241m.\u001B[39mname)\n\u001B[0;32m   2414\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m tarinfo\u001B[38;5;241m.\u001B[39misreg():\n\u001B[1;32m-> 2415\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmakefile\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtarinfo\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtargetpath\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2416\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m tarinfo\u001B[38;5;241m.\u001B[39misdir():\n\u001B[0;32m   2417\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmakedir(tarinfo, targetpath)\n",
      "File \u001B[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\tarfile.py:2461\u001B[0m, in \u001B[0;36mTarFile.makefile\u001B[1;34m(self, tarinfo, targetpath)\u001B[0m\n\u001B[0;32m   2459\u001B[0m source\u001B[38;5;241m.\u001B[39mseek(tarinfo\u001B[38;5;241m.\u001B[39moffset_data)\n\u001B[0;32m   2460\u001B[0m bufsize \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcopybufsize\n\u001B[1;32m-> 2461\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[43mbltn_open\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtargetpath\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mwb\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m target:\n\u001B[0;32m   2462\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m tarinfo\u001B[38;5;241m.\u001B[39msparse \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m   2463\u001B[0m         \u001B[38;5;28;01mfor\u001B[39;00m offset, size \u001B[38;5;129;01min\u001B[39;00m tarinfo\u001B[38;5;241m.\u001B[39msparse:\n",
      "\u001B[1;31mPermissionError\u001B[0m: [Errno 13] Permission denied: 'datasets\\\\housing\\\\housing.csv'"
     ]
    }
   ],
   "source": [
    "\n",
    "fetch_housing_data()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:33:17.217282Z",
     "start_time": "2024-07-19T06:33:17.025034Z"
    }
   },
   "id": "d02187e7fc2f05eb",
   "execution_count": 170
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:30.273598Z",
     "start_time": "2024-07-19T06:31:30.270310Z"
    }
   },
   "id": "cdbe208e7552ec35",
   "execution_count": 140
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "house=load_housing_data()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:31.446488Z",
     "start_time": "2024-07-19T06:31:31.424425Z"
    }
   },
   "id": "da3c078d72bd5c74",
   "execution_count": 141
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -122.23     37.88                41.0        880.0           129.0   \n1    -122.22     37.86                21.0       7099.0          1106.0   \n2    -122.24     37.85                52.0       1467.0           190.0   \n3    -122.25     37.85                52.0       1274.0           235.0   \n4    -122.25     37.85                52.0       1627.0           280.0   \n\n   population  households  median_income  median_house_value ocean_proximity  \n0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n4       565.0       259.0         3.8462            342200.0        NEAR BAY  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1627.0</td>\n      <td>280.0</td>\n      <td>565.0</td>\n      <td>259.0</td>\n      <td>3.8462</td>\n      <td>342200.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:32.419134Z",
     "start_time": "2024-07-19T06:31:32.410136Z"
    }
   },
   "id": "d34441574f2a3212",
   "execution_count": 142
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    " house.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:33.925083Z",
     "start_time": "2024-07-19T06:31:33.918376Z"
    }
   },
   "id": "e00aafc3c7be8a",
   "execution_count": 143
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure latitudeVSlongitude\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "house.plot(kind=\"scatter\",x=\"latitude\",y=\"longitude\",color=\"green\")\n",
    "save_fig(\"latitudeVSlongitude\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:35.716436Z",
     "start_time": "2024-07-19T06:31:35.263666Z"
    }
   },
   "id": "88eaf1822abf3c66",
   "execution_count": 144
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure latitudeVSlongitudebetter\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "house.plot(kind=\"scatter\",x=\"latitude\",y=\"longitude\",color=\"blue\",alpha=0.2)\n",
    "save_fig(\"latitudeVSlongitudebetter\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:36.921598Z",
     "start_time": "2024-07-19T06:31:36.443919Z"
    }
   },
   "id": "e43fc6872da241d8",
   "execution_count": 145
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure latitudeVSLongitudeWithMedianHousevalue\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "house.plot(kind=\"scatter\",x=\"latitude\",y=\"longitude\",alpha=0.4,s=house[\"population\"]/100,label=\"population\",\n",
    "c=\"median_house_value\",cmap=plt.get_cmap(\"jet\"),colorbar=True,sharex=False)\n",
    "plt.legend()\n",
    "\n",
    "save_fig(\"latitudeVSLongitudeWithMedianHousevalue\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:39.580180Z",
     "start_time": "2024-07-19T06:31:38.164646Z"
    }
   },
   "id": "e881d3d67133c235",
   "execution_count": 146
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "          longitude      latitude  housing_median_age   total_rooms  \\\ncount  20640.000000  20640.000000        20640.000000  20640.000000   \nmean    -119.569704     35.631861           28.639486   2635.763081   \nstd        2.003532      2.135952           12.585558   2181.615252   \nmin     -124.350000     32.540000            1.000000      2.000000   \n25%     -121.800000     33.930000           18.000000   1447.750000   \n50%     -118.490000     34.260000           29.000000   2127.000000   \n75%     -118.010000     37.710000           37.000000   3148.000000   \nmax     -114.310000     41.950000           52.000000  39320.000000   \n\n       total_bedrooms    population    households  median_income  \\\ncount    20433.000000  20640.000000  20640.000000   20640.000000   \nmean       537.870553   1425.476744    499.539680       3.870671   \nstd        421.385070   1132.462122    382.329753       1.899822   \nmin          1.000000      3.000000      1.000000       0.499900   \n25%        296.000000    787.000000    280.000000       2.563400   \n50%        435.000000   1166.000000    409.000000       3.534800   \n75%        647.000000   1725.000000    605.000000       4.743250   \nmax       6445.000000  35682.000000   6082.000000      15.000100   \n\n       median_house_value  \ncount        20640.000000  \nmean        206855.816909  \nstd         115395.615874  \nmin          14999.000000  \n25%         119600.000000  \n50%         179700.000000  \n75%         264725.000000  \nmax         500001.000000  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20433.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.569704</td>\n      <td>35.631861</td>\n      <td>28.639486</td>\n      <td>2635.763081</td>\n      <td>537.870553</td>\n      <td>1425.476744</td>\n      <td>499.539680</td>\n      <td>3.870671</td>\n      <td>206855.816909</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.003532</td>\n      <td>2.135952</td>\n      <td>12.585558</td>\n      <td>2181.615252</td>\n      <td>421.385070</td>\n      <td>1132.462122</td>\n      <td>382.329753</td>\n      <td>1.899822</td>\n      <td>115395.615874</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.350000</td>\n      <td>32.540000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.499900</td>\n      <td>14999.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.800000</td>\n      <td>33.930000</td>\n      <td>18.000000</td>\n      <td>1447.750000</td>\n      <td>296.000000</td>\n      <td>787.000000</td>\n      <td>280.000000</td>\n      <td>2.563400</td>\n      <td>119600.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.490000</td>\n      <td>34.260000</td>\n      <td>29.000000</td>\n      <td>2127.000000</td>\n      <td>435.000000</td>\n      <td>1166.000000</td>\n      <td>409.000000</td>\n      <td>3.534800</td>\n      <td>179700.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-118.010000</td>\n      <td>37.710000</td>\n      <td>37.000000</td>\n      <td>3148.000000</td>\n      <td>647.000000</td>\n      <td>1725.000000</td>\n      <td>605.000000</td>\n      <td>4.743250</td>\n      <td>264725.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.310000</td>\n      <td>41.950000</td>\n      <td>52.000000</td>\n      <td>39320.000000</td>\n      <td>6445.000000</td>\n      <td>35682.000000</td>\n      <td>6082.000000</td>\n      <td>15.000100</td>\n      <td>500001.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:39.602252Z",
     "start_time": "2024-07-19T06:31:39.581187Z"
    }
   },
   "id": "9f13b544e0babf16",
   "execution_count": 147
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sujal\\AppData\\Local\\Temp\\ipykernel_6048\\3391225141.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  house['total_bedrooms'].fillna(house['total_bedrooms'].mean(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "house['total_bedrooms'].fillna(house['total_bedrooms'].mean(),inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:41.044105Z",
     "start_time": "2024-07-19T06:31:41.039683Z"
    }
   },
   "id": "22c0f4217d0190cb",
   "execution_count": 148
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "          longitude      latitude  housing_median_age   total_rooms  \\\ncount  20640.000000  20640.000000        20640.000000  20640.000000   \nmean    -119.569704     35.631861           28.639486   2635.763081   \nstd        2.003532      2.135952           12.585558   2181.615252   \nmin     -124.350000     32.540000            1.000000      2.000000   \n25%     -121.800000     33.930000           18.000000   1447.750000   \n50%     -118.490000     34.260000           29.000000   2127.000000   \n75%     -118.010000     37.710000           37.000000   3148.000000   \nmax     -114.310000     41.950000           52.000000  39320.000000   \n\n       total_bedrooms    population    households  median_income  \\\ncount    20640.000000  20640.000000  20640.000000   20640.000000   \nmean       537.870553   1425.476744    499.539680       3.870671   \nstd        419.266592   1132.462122    382.329753       1.899822   \nmin          1.000000      3.000000      1.000000       0.499900   \n25%        297.000000    787.000000    280.000000       2.563400   \n50%        438.000000   1166.000000    409.000000       3.534800   \n75%        643.250000   1725.000000    605.000000       4.743250   \nmax       6445.000000  35682.000000   6082.000000      15.000100   \n\n       median_house_value  \ncount        20640.000000  \nmean        206855.816909  \nstd         115395.615874  \nmin          14999.000000  \n25%         119600.000000  \n50%         179700.000000  \n75%         264725.000000  \nmax         500001.000000  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.569704</td>\n      <td>35.631861</td>\n      <td>28.639486</td>\n      <td>2635.763081</td>\n      <td>537.870553</td>\n      <td>1425.476744</td>\n      <td>499.539680</td>\n      <td>3.870671</td>\n      <td>206855.816909</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.003532</td>\n      <td>2.135952</td>\n      <td>12.585558</td>\n      <td>2181.615252</td>\n      <td>419.266592</td>\n      <td>1132.462122</td>\n      <td>382.329753</td>\n      <td>1.899822</td>\n      <td>115395.615874</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.350000</td>\n      <td>32.540000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.499900</td>\n      <td>14999.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.800000</td>\n      <td>33.930000</td>\n      <td>18.000000</td>\n      <td>1447.750000</td>\n      <td>297.000000</td>\n      <td>787.000000</td>\n      <td>280.000000</td>\n      <td>2.563400</td>\n      <td>119600.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.490000</td>\n      <td>34.260000</td>\n      <td>29.000000</td>\n      <td>2127.000000</td>\n      <td>438.000000</td>\n      <td>1166.000000</td>\n      <td>409.000000</td>\n      <td>3.534800</td>\n      <td>179700.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-118.010000</td>\n      <td>37.710000</td>\n      <td>37.000000</td>\n      <td>3148.000000</td>\n      <td>643.250000</td>\n      <td>1725.000000</td>\n      <td>605.000000</td>\n      <td>4.743250</td>\n      <td>264725.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.310000</td>\n      <td>41.950000</td>\n      <td>52.000000</td>\n      <td>39320.000000</td>\n      <td>6445.000000</td>\n      <td>35682.000000</td>\n      <td>6082.000000</td>\n      <td>15.000100</td>\n      <td>500001.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:42.699127Z",
     "start_time": "2024-07-19T06:31:42.676901Z"
    }
   },
   "id": "fdeda9935c16e155",
   "execution_count": 149
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20640 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "house.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:43.630132Z",
     "start_time": "2024-07-19T06:31:43.623131Z"
    }
   },
   "id": "b503aa83016790ed",
   "execution_count": 150
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder=OneHotEncoder(drop=\"first\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:45.589617Z",
     "start_time": "2024-07-19T06:31:45.585827Z"
    }
   },
   "id": "ac9e1e8e52a1454a",
   "execution_count": 151
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "encoded_data=encoder.fit_transform(house[['ocean_proximity']])\n",
    "encoded_df = pd.DataFrame(encoded_data.toarray(), columns=encoder.get_feature_names_out(['ocean_proximity']))\n",
    "\n",
    "# Concatenate the original DataFrame (excluding 'ocean_proximity') with the encoded DataFrame\n",
    "house = pd.concat([house.drop('ocean_proximity', axis=1), encoded_df], axis=1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:47.571290Z",
     "start_time": "2024-07-19T06:31:47.561094Z"
    }
   },
   "id": "44adc7bebb4aa995",
   "execution_count": 152
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "          longitude      latitude  housing_median_age   total_rooms  \\\ncount  20640.000000  20640.000000        20640.000000  20640.000000   \nmean    -119.569704     35.631861           28.639486   2635.763081   \nstd        2.003532      2.135952           12.585558   2181.615252   \nmin     -124.350000     32.540000            1.000000      2.000000   \n25%     -121.800000     33.930000           18.000000   1447.750000   \n50%     -118.490000     34.260000           29.000000   2127.000000   \n75%     -118.010000     37.710000           37.000000   3148.000000   \nmax     -114.310000     41.950000           52.000000  39320.000000   \n\n       total_bedrooms    population    households  median_income  \\\ncount    20640.000000  20640.000000  20640.000000   20640.000000   \nmean       537.870553   1425.476744    499.539680       3.870671   \nstd        419.266592   1132.462122    382.329753       1.899822   \nmin          1.000000      3.000000      1.000000       0.499900   \n25%        297.000000    787.000000    280.000000       2.563400   \n50%        438.000000   1166.000000    409.000000       3.534800   \n75%        643.250000   1725.000000    605.000000       4.743250   \nmax       6445.000000  35682.000000   6082.000000      15.000100   \n\n       median_house_value  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\ncount        20640.000000            20640.000000            20640.000000   \nmean        206855.816909                0.317393                0.000242   \nstd         115395.615874                0.465473                0.015563   \nmin          14999.000000                0.000000                0.000000   \n25%         119600.000000                0.000000                0.000000   \n50%         179700.000000                0.000000                0.000000   \n75%         264725.000000                1.000000                0.000000   \nmax         500001.000000                1.000000                1.000000   \n\n       ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \ncount              20640.000000                20640.000000  \nmean                   0.110950                    0.128779  \nstd                    0.314077                    0.334963  \nmin                    0.000000                    0.000000  \n25%                    0.000000                    0.000000  \n50%                    0.000000                    0.000000  \n75%                    0.000000                    0.000000  \nmax                    1.000000                    1.000000  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.569704</td>\n      <td>35.631861</td>\n      <td>28.639486</td>\n      <td>2635.763081</td>\n      <td>537.870553</td>\n      <td>1425.476744</td>\n      <td>499.539680</td>\n      <td>3.870671</td>\n      <td>206855.816909</td>\n      <td>0.317393</td>\n      <td>0.000242</td>\n      <td>0.110950</td>\n      <td>0.128779</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.003532</td>\n      <td>2.135952</td>\n      <td>12.585558</td>\n      <td>2181.615252</td>\n      <td>419.266592</td>\n      <td>1132.462122</td>\n      <td>382.329753</td>\n      <td>1.899822</td>\n      <td>115395.615874</td>\n      <td>0.465473</td>\n      <td>0.015563</td>\n      <td>0.314077</td>\n      <td>0.334963</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.350000</td>\n      <td>32.540000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.499900</td>\n      <td>14999.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.800000</td>\n      <td>33.930000</td>\n      <td>18.000000</td>\n      <td>1447.750000</td>\n      <td>297.000000</td>\n      <td>787.000000</td>\n      <td>280.000000</td>\n      <td>2.563400</td>\n      <td>119600.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.490000</td>\n      <td>34.260000</td>\n      <td>29.000000</td>\n      <td>2127.000000</td>\n      <td>438.000000</td>\n      <td>1166.000000</td>\n      <td>409.000000</td>\n      <td>3.534800</td>\n      <td>179700.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-118.010000</td>\n      <td>37.710000</td>\n      <td>37.000000</td>\n      <td>3148.000000</td>\n      <td>643.250000</td>\n      <td>1725.000000</td>\n      <td>605.000000</td>\n      <td>4.743250</td>\n      <td>264725.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.310000</td>\n      <td>41.950000</td>\n      <td>52.000000</td>\n      <td>39320.000000</td>\n      <td>6445.000000</td>\n      <td>35682.000000</td>\n      <td>6082.000000</td>\n      <td>15.000100</td>\n      <td>500001.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:48.413094Z",
     "start_time": "2024-07-19T06:31:48.383427Z"
    }
   },
   "id": "809e2bb372a3665b",
   "execution_count": 153
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   longitude                   20640 non-null  float64\n",
      " 1   latitude                    20640 non-null  float64\n",
      " 2   housing_median_age          20640 non-null  float64\n",
      " 3   total_rooms                 20640 non-null  float64\n",
      " 4   total_bedrooms              20640 non-null  float64\n",
      " 5   population                  20640 non-null  float64\n",
      " 6   households                  20640 non-null  float64\n",
      " 7   median_income               20640 non-null  float64\n",
      " 8   median_house_value          20640 non-null  float64\n",
      " 9   ocean_proximity_INLAND      20640 non-null  float64\n",
      " 10  ocean_proximity_ISLAND      20640 non-null  float64\n",
      " 11  ocean_proximity_NEAR BAY    20640 non-null  float64\n",
      " 12  ocean_proximity_NEAR OCEAN  20640 non-null  float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "house.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:49.396016Z",
     "start_time": "2024-07-19T06:31:49.388961Z"
    }
   },
   "id": "a73cd5fabd362a82",
   "execution_count": 154
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -122.23     37.88                41.0        880.0           129.0   \n1    -122.22     37.86                21.0       7099.0          1106.0   \n2    -122.24     37.85                52.0       1467.0           190.0   \n3    -122.25     37.85                52.0       1274.0           235.0   \n4    -122.25     37.85                52.0       1627.0           280.0   \n5    -122.25     37.85                52.0        919.0           213.0   \n6    -122.25     37.84                52.0       2535.0           489.0   \n7    -122.25     37.84                52.0       3104.0           687.0   \n8    -122.26     37.84                42.0       2555.0           665.0   \n9    -122.25     37.84                52.0       3549.0           707.0   \n\n   population  households  median_income  median_house_value  \\\n0       322.0       126.0         8.3252            452600.0   \n1      2401.0      1138.0         8.3014            358500.0   \n2       496.0       177.0         7.2574            352100.0   \n3       558.0       219.0         5.6431            341300.0   \n4       565.0       259.0         3.8462            342200.0   \n5       413.0       193.0         4.0368            269700.0   \n6      1094.0       514.0         3.6591            299200.0   \n7      1157.0       647.0         3.1200            241400.0   \n8      1206.0       595.0         2.0804            226700.0   \n9      1551.0       714.0         3.6912            261100.0   \n\n   ocean_proximity_INLAND  ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n0                     0.0                     0.0                       1.0   \n1                     0.0                     0.0                       1.0   \n2                     0.0                     0.0                       1.0   \n3                     0.0                     0.0                       1.0   \n4                     0.0                     0.0                       1.0   \n5                     0.0                     0.0                       1.0   \n6                     0.0                     0.0                       1.0   \n7                     0.0                     0.0                       1.0   \n8                     0.0                     0.0                       1.0   \n9                     0.0                     0.0                       1.0   \n\n   ocean_proximity_NEAR OCEAN  \n0                         0.0  \n1                         0.0  \n2                         0.0  \n3                         0.0  \n4                         0.0  \n5                         0.0  \n6                         0.0  \n7                         0.0  \n8                         0.0  \n9                         0.0  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1627.0</td>\n      <td>280.0</td>\n      <td>565.0</td>\n      <td>259.0</td>\n      <td>3.8462</td>\n      <td>342200.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>919.0</td>\n      <td>213.0</td>\n      <td>413.0</td>\n      <td>193.0</td>\n      <td>4.0368</td>\n      <td>269700.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-122.25</td>\n      <td>37.84</td>\n      <td>52.0</td>\n      <td>2535.0</td>\n      <td>489.0</td>\n      <td>1094.0</td>\n      <td>514.0</td>\n      <td>3.6591</td>\n      <td>299200.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-122.25</td>\n      <td>37.84</td>\n      <td>52.0</td>\n      <td>3104.0</td>\n      <td>687.0</td>\n      <td>1157.0</td>\n      <td>647.0</td>\n      <td>3.1200</td>\n      <td>241400.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-122.26</td>\n      <td>37.84</td>\n      <td>42.0</td>\n      <td>2555.0</td>\n      <td>665.0</td>\n      <td>1206.0</td>\n      <td>595.0</td>\n      <td>2.0804</td>\n      <td>226700.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-122.25</td>\n      <td>37.84</td>\n      <td>52.0</td>\n      <td>3549.0</td>\n      <td>707.0</td>\n      <td>1551.0</td>\n      <td>714.0</td>\n      <td>3.6912</td>\n      <td>261100.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:50.420587Z",
     "start_time": "2024-07-19T06:31:50.406669Z"
    }
   },
   "id": "60778c1af5b3b6ff",
   "execution_count": 155
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_data,test_data=train_test_split(house,test_size=0.2,random_state=42)\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:51.646473Z",
     "start_time": "2024-07-19T06:31:51.640930Z"
    }
   },
   "id": "866a3d41b87b5c0c",
   "execution_count": 156
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "x=house.drop('median_house_value',axis=1)\n",
    "y=house['median_house_value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:52.467934Z",
     "start_time": "2024-07-19T06:31:52.460753Z"
    }
   },
   "id": "ff81d314c2f66381",
   "execution_count": 157
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "ed1c2e31ebad0293"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:54.019541Z",
     "start_time": "2024-07-19T06:31:54.016269Z"
    }
   },
   "id": "4080e128c7a0aa86",
   "execution_count": 158
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "exclude_columns = ['latitude', 'longitude']\n",
    "columns_to_scale = [col for col in X_train.columns if col not in exclude_columns]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:54.751690Z",
     "start_time": "2024-07-19T06:31:54.749069Z"
    }
   },
   "id": "ce722b3b17e4d17e",
   "execution_count": 159
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "X_train_scaled = X_train.copy()\n",
    "X_train_scaled[columns_to_scale] = scaler.fit_transform(X_train[columns_to_scale])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:55.513835Z",
     "start_time": "2024-07-19T06:31:55.504441Z"
    }
   },
   "id": "ec106b3fe89d81de",
   "execution_count": 160
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "X_test_scaled = X_test.copy()\n",
    "X_test_scaled[columns_to_scale] = scaler.transform(X_test[columns_to_scale])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:56.313495Z",
     "start_time": "2024-07-19T06:31:56.307677Z"
    }
   },
   "id": "9dc7e99ec0666731",
   "execution_count": 161
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n14196    -117.03     32.71            0.348490     0.222569        0.211228   \n8267     -118.16     33.77            1.618118     0.340293        0.593094   \n17445    -120.48     34.66           -1.952710    -0.342597       -0.495226   \n14265    -117.11     32.69            0.586545    -0.561490       -0.409306   \n2271     -119.80     36.78            1.142008    -0.119565       -0.256559   \n...          ...       ...                 ...          ...             ...   \n11284    -117.96     33.78            0.507194    -0.603337       -0.805492   \n11964    -117.43     34.02            0.348490     0.203255        0.075188   \n5390     -118.38     34.03            0.586545    -0.248786        0.072801   \n860      -121.96     37.58           -1.079841     0.429046        0.139628   \n15795    -122.42     37.77            1.856173     0.728414        1.853254   \n\n       population  households  median_income  ocean_proximity_INLAND  \\\n14196    0.768276    0.322906      -0.326196               -0.680574   \n8267    -0.098901    0.672027      -0.035843               -0.680574   \n17445   -0.449818   -0.430461       0.144701               -0.680574   \n14265   -0.007434   -0.380587      -1.017864               -0.680574   \n2271    -0.485877   -0.314962      -0.171488                1.469348   \n...           ...         ...            ...                     ...   \n11284   -0.675847   -0.742833       1.307215               -0.680574   \n11964    0.287195   -0.133839      -0.436266                1.469348   \n5390     0.289833    0.070909      -0.496973               -0.680574   \n860      0.308303    0.154908       0.965450               -0.680574   \n15795    1.048834    1.947764      -0.685448               -0.680574   \n\n       ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n14196               -0.015566                 -0.355646   \n8267                -0.015566                 -0.355646   \n17445               -0.015566                 -0.355646   \n14265               -0.015566                 -0.355646   \n2271                -0.015566                 -0.355646   \n...                       ...                       ...   \n11284               -0.015566                 -0.355646   \n11964               -0.015566                 -0.355646   \n5390                -0.015566                 -0.355646   \n860                 -0.015566                 -0.355646   \n15795               -0.015566                  2.811787   \n\n       ocean_proximity_NEAR OCEAN  \n14196                    2.629758  \n8267                     2.629758  \n17445                    2.629758  \n14265                    2.629758  \n2271                    -0.380263  \n...                           ...  \n11284                   -0.380263  \n11964                   -0.380263  \n5390                    -0.380263  \n860                     -0.380263  \n15795                   -0.380263  \n\n[16512 rows x 12 columns]",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14196</th>\n      <td>-117.03</td>\n      <td>32.71</td>\n      <td>0.348490</td>\n      <td>0.222569</td>\n      <td>0.211228</td>\n      <td>0.768276</td>\n      <td>0.322906</td>\n      <td>-0.326196</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>8267</th>\n      <td>-118.16</td>\n      <td>33.77</td>\n      <td>1.618118</td>\n      <td>0.340293</td>\n      <td>0.593094</td>\n      <td>-0.098901</td>\n      <td>0.672027</td>\n      <td>-0.035843</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>17445</th>\n      <td>-120.48</td>\n      <td>34.66</td>\n      <td>-1.952710</td>\n      <td>-0.342597</td>\n      <td>-0.495226</td>\n      <td>-0.449818</td>\n      <td>-0.430461</td>\n      <td>0.144701</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>14265</th>\n      <td>-117.11</td>\n      <td>32.69</td>\n      <td>0.586545</td>\n      <td>-0.561490</td>\n      <td>-0.409306</td>\n      <td>-0.007434</td>\n      <td>-0.380587</td>\n      <td>-1.017864</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>2271</th>\n      <td>-119.80</td>\n      <td>36.78</td>\n      <td>1.142008</td>\n      <td>-0.119565</td>\n      <td>-0.256559</td>\n      <td>-0.485877</td>\n      <td>-0.314962</td>\n      <td>-0.171488</td>\n      <td>1.469348</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>11284</th>\n      <td>-117.96</td>\n      <td>33.78</td>\n      <td>0.507194</td>\n      <td>-0.603337</td>\n      <td>-0.805492</td>\n      <td>-0.675847</td>\n      <td>-0.742833</td>\n      <td>1.307215</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>11964</th>\n      <td>-117.43</td>\n      <td>34.02</td>\n      <td>0.348490</td>\n      <td>0.203255</td>\n      <td>0.075188</td>\n      <td>0.287195</td>\n      <td>-0.133839</td>\n      <td>-0.436266</td>\n      <td>1.469348</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>5390</th>\n      <td>-118.38</td>\n      <td>34.03</td>\n      <td>0.586545</td>\n      <td>-0.248786</td>\n      <td>0.072801</td>\n      <td>0.289833</td>\n      <td>0.070909</td>\n      <td>-0.496973</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>860</th>\n      <td>-121.96</td>\n      <td>37.58</td>\n      <td>-1.079841</td>\n      <td>0.429046</td>\n      <td>0.139628</td>\n      <td>0.308303</td>\n      <td>0.154908</td>\n      <td>0.965450</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>15795</th>\n      <td>-122.42</td>\n      <td>37.77</td>\n      <td>1.856173</td>\n      <td>0.728414</td>\n      <td>1.853254</td>\n      <td>1.048834</td>\n      <td>1.947764</td>\n      <td>-0.685448</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>2.811787</td>\n      <td>-0.380263</td>\n    </tr>\n  </tbody>\n</table>\n<p>16512 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:57.276618Z",
     "start_time": "2024-07-19T06:31:57.264922Z"
    }
   },
   "id": "33dac71c8e35c2a8",
   "execution_count": 162
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16512 entries, 14196 to 15795\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   longitude                   16512 non-null  float64\n",
      " 1   latitude                    16512 non-null  float64\n",
      " 2   housing_median_age          16512 non-null  float64\n",
      " 3   total_rooms                 16512 non-null  float64\n",
      " 4   total_bedrooms              16512 non-null  float64\n",
      " 5   population                  16512 non-null  float64\n",
      " 6   households                  16512 non-null  float64\n",
      " 7   median_income               16512 non-null  float64\n",
      " 8   ocean_proximity_INLAND      16512 non-null  float64\n",
      " 9   ocean_proximity_ISLAND      16512 non-null  float64\n",
      " 10  ocean_proximity_NEAR BAY    16512 non-null  float64\n",
      " 11  ocean_proximity_NEAR OCEAN  16512 non-null  float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "X_train_scaled.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:58.119343Z",
     "start_time": "2024-07-19T06:31:58.112639Z"
    }
   },
   "id": "a6f13b43784a0883",
   "execution_count": 163
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4128 entries, 20046 to 3665\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   longitude                   4128 non-null   float64\n",
      " 1   latitude                    4128 non-null   float64\n",
      " 2   housing_median_age          4128 non-null   float64\n",
      " 3   total_rooms                 4128 non-null   float64\n",
      " 4   total_bedrooms              4128 non-null   float64\n",
      " 5   population                  4128 non-null   float64\n",
      " 6   households                  4128 non-null   float64\n",
      " 7   median_income               4128 non-null   float64\n",
      " 8   ocean_proximity_INLAND      4128 non-null   float64\n",
      " 9   ocean_proximity_ISLAND      4128 non-null   float64\n",
      " 10  ocean_proximity_NEAR BAY    4128 non-null   float64\n",
      " 11  ocean_proximity_NEAR OCEAN  4128 non-null   float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 419.2 KB\n"
     ]
    }
   ],
   "source": [
    "X_test_scaled.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:59.066660Z",
     "start_time": "2024-07-19T06:31:59.059780Z"
    }
   },
   "id": "a3caf64f8b51a98b",
   "execution_count": 164
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "        longitude    latitude  housing_median_age  total_rooms  \\\ncount  4128.00000  4128.00000         4128.000000  4128.000000   \nmean   -119.51936    35.58671            0.012379    -0.014352   \nstd       1.99447     2.13276            0.993349     1.015993   \nmin    -124.19000    32.54000           -2.190766    -1.212190   \n25%    -121.69000    33.93000           -0.841786    -0.561950   \n50%    -118.47000    34.23000            0.031083    -0.244647   \n75%    -117.99000    37.70000            0.665897     0.210268   \nmax    -114.58000    41.81000            1.856173    16.230712   \n\n       total_bedrooms   population   households  median_income  \\\ncount     4128.000000  4128.000000  4128.000000    4128.000000   \nmean        -0.007474    -0.004293    -0.005870      -0.026476   \nstd          1.003222     0.979773     1.017887       0.988068   \nmin         -1.280439    -1.247516    -1.307202      -1.775438   \n25%         -0.569212    -0.567670    -0.582709      -0.699627   \n50%         -0.226726    -0.234343    -0.246713      -0.199951   \n75%          0.206454     0.259931     0.257282       0.401826   \nmax         11.772241    12.924594    12.308529       5.839268   \n\n       ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\ncount             4128.000000            4.128000e+03   \nmean                 0.008984            1.721276e-18   \nstd                  1.003618            1.000121e+00   \nmin                 -0.680574           -1.556621e-02   \n25%                 -0.680574           -1.556621e-02   \n50%                 -0.680574           -1.556621e-02   \n75%                  1.469348           -1.556621e-02   \nmax                  1.469348            6.424173e+01   \n\n       ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \ncount               4128.000000                 4128.000000  \nmean                  -0.021101                    0.036823  \nstd                    0.973631                    1.040067  \nmin                   -0.355646                   -0.380263  \n25%                   -0.355646                   -0.380263  \n50%                   -0.355646                   -0.380263  \n75%                   -0.355646                   -0.380263  \nmax                    2.811787                    2.629758  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4128.00000</td>\n      <td>4128.00000</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n      <td>4.128000e+03</td>\n      <td>4128.000000</td>\n      <td>4128.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.51936</td>\n      <td>35.58671</td>\n      <td>0.012379</td>\n      <td>-0.014352</td>\n      <td>-0.007474</td>\n      <td>-0.004293</td>\n      <td>-0.005870</td>\n      <td>-0.026476</td>\n      <td>0.008984</td>\n      <td>1.721276e-18</td>\n      <td>-0.021101</td>\n      <td>0.036823</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.99447</td>\n      <td>2.13276</td>\n      <td>0.993349</td>\n      <td>1.015993</td>\n      <td>1.003222</td>\n      <td>0.979773</td>\n      <td>1.017887</td>\n      <td>0.988068</td>\n      <td>1.003618</td>\n      <td>1.000121e+00</td>\n      <td>0.973631</td>\n      <td>1.040067</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.19000</td>\n      <td>32.54000</td>\n      <td>-2.190766</td>\n      <td>-1.212190</td>\n      <td>-1.280439</td>\n      <td>-1.247516</td>\n      <td>-1.307202</td>\n      <td>-1.775438</td>\n      <td>-0.680574</td>\n      <td>-1.556621e-02</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.69000</td>\n      <td>33.93000</td>\n      <td>-0.841786</td>\n      <td>-0.561950</td>\n      <td>-0.569212</td>\n      <td>-0.567670</td>\n      <td>-0.582709</td>\n      <td>-0.699627</td>\n      <td>-0.680574</td>\n      <td>-1.556621e-02</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.47000</td>\n      <td>34.23000</td>\n      <td>0.031083</td>\n      <td>-0.244647</td>\n      <td>-0.226726</td>\n      <td>-0.234343</td>\n      <td>-0.246713</td>\n      <td>-0.199951</td>\n      <td>-0.680574</td>\n      <td>-1.556621e-02</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-117.99000</td>\n      <td>37.70000</td>\n      <td>0.665897</td>\n      <td>0.210268</td>\n      <td>0.206454</td>\n      <td>0.259931</td>\n      <td>0.257282</td>\n      <td>0.401826</td>\n      <td>1.469348</td>\n      <td>-1.556621e-02</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.58000</td>\n      <td>41.81000</td>\n      <td>1.856173</td>\n      <td>16.230712</td>\n      <td>11.772241</td>\n      <td>12.924594</td>\n      <td>12.308529</td>\n      <td>5.839268</td>\n      <td>1.469348</td>\n      <td>6.424173e+01</td>\n      <td>2.811787</td>\n      <td>2.629758</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:31:59.909803Z",
     "start_time": "2024-07-19T06:31:59.888682Z"
    }
   },
   "id": "af0c7e055fdfc8da",
   "execution_count": 165
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n14196    -117.03     32.71            0.348490     0.222569        0.211228   \n8267     -118.16     33.77            1.618118     0.340293        0.593094   \n17445    -120.48     34.66           -1.952710    -0.342597       -0.495226   \n14265    -117.11     32.69            0.586545    -0.561490       -0.409306   \n2271     -119.80     36.78            1.142008    -0.119565       -0.256559   \n17848    -121.86     37.42           -0.683082     1.099060        0.643214   \n6252     -117.97     34.04           -0.048268    -0.439627       -0.289972   \n9389     -122.53     37.91            0.665897    -0.054266       -0.335319   \n6113     -117.90     34.13           -1.873359    -0.697148       -0.531026   \n6061     -117.79     34.02           -1.873359     7.379811        5.545428   \n\n       population  households  median_income  ocean_proximity_INLAND  \\\n14196    0.768276    0.322906      -0.326196               -0.680574   \n8267    -0.098901    0.672027      -0.035843               -0.680574   \n17445   -0.449818   -0.430461       0.144701               -0.680574   \n14265   -0.007434   -0.380587      -1.017864               -0.680574   \n2271    -0.485877   -0.314962      -0.171488                1.469348   \n17848    1.115675    0.790151       1.439919               -0.680574   \n6252    -0.062842   -0.293963      -0.715013               -0.680574   \n9389    -0.375941   -0.217838       2.157529               -0.680574   \n6113    -0.534249   -0.496085      -1.250241               -0.680574   \n6061     7.036405    5.977096       1.336938               -0.680574   \n\n       ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n14196               -0.015566                 -0.355646   \n8267                -0.015566                 -0.355646   \n17445               -0.015566                 -0.355646   \n14265               -0.015566                 -0.355646   \n2271                -0.015566                 -0.355646   \n17848               -0.015566                 -0.355646   \n6252                -0.015566                 -0.355646   \n9389                -0.015566                  2.811787   \n6113                -0.015566                 -0.355646   \n6061                -0.015566                 -0.355646   \n\n       ocean_proximity_NEAR OCEAN  \n14196                    2.629758  \n8267                     2.629758  \n17445                    2.629758  \n14265                    2.629758  \n2271                    -0.380263  \n17848                   -0.380263  \n6252                    -0.380263  \n9389                    -0.380263  \n6113                    -0.380263  \n6061                    -0.380263  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14196</th>\n      <td>-117.03</td>\n      <td>32.71</td>\n      <td>0.348490</td>\n      <td>0.222569</td>\n      <td>0.211228</td>\n      <td>0.768276</td>\n      <td>0.322906</td>\n      <td>-0.326196</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>8267</th>\n      <td>-118.16</td>\n      <td>33.77</td>\n      <td>1.618118</td>\n      <td>0.340293</td>\n      <td>0.593094</td>\n      <td>-0.098901</td>\n      <td>0.672027</td>\n      <td>-0.035843</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>17445</th>\n      <td>-120.48</td>\n      <td>34.66</td>\n      <td>-1.952710</td>\n      <td>-0.342597</td>\n      <td>-0.495226</td>\n      <td>-0.449818</td>\n      <td>-0.430461</td>\n      <td>0.144701</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>14265</th>\n      <td>-117.11</td>\n      <td>32.69</td>\n      <td>0.586545</td>\n      <td>-0.561490</td>\n      <td>-0.409306</td>\n      <td>-0.007434</td>\n      <td>-0.380587</td>\n      <td>-1.017864</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>2271</th>\n      <td>-119.80</td>\n      <td>36.78</td>\n      <td>1.142008</td>\n      <td>-0.119565</td>\n      <td>-0.256559</td>\n      <td>-0.485877</td>\n      <td>-0.314962</td>\n      <td>-0.171488</td>\n      <td>1.469348</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>17848</th>\n      <td>-121.86</td>\n      <td>37.42</td>\n      <td>-0.683082</td>\n      <td>1.099060</td>\n      <td>0.643214</td>\n      <td>1.115675</td>\n      <td>0.790151</td>\n      <td>1.439919</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>6252</th>\n      <td>-117.97</td>\n      <td>34.04</td>\n      <td>-0.048268</td>\n      <td>-0.439627</td>\n      <td>-0.289972</td>\n      <td>-0.062842</td>\n      <td>-0.293963</td>\n      <td>-0.715013</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>9389</th>\n      <td>-122.53</td>\n      <td>37.91</td>\n      <td>0.665897</td>\n      <td>-0.054266</td>\n      <td>-0.335319</td>\n      <td>-0.375941</td>\n      <td>-0.217838</td>\n      <td>2.157529</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>2.811787</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>6113</th>\n      <td>-117.90</td>\n      <td>34.13</td>\n      <td>-1.873359</td>\n      <td>-0.697148</td>\n      <td>-0.531026</td>\n      <td>-0.534249</td>\n      <td>-0.496085</td>\n      <td>-1.250241</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>6061</th>\n      <td>-117.79</td>\n      <td>34.02</td>\n      <td>-1.873359</td>\n      <td>7.379811</td>\n      <td>5.545428</td>\n      <td>7.036405</td>\n      <td>5.977096</td>\n      <td>1.336938</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:32:01.208245Z",
     "start_time": "2024-07-19T06:32:01.196823Z"
    }
   },
   "id": "536e00ee3e077d48",
   "execution_count": 166
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n20046    -119.01     36.06           -0.286324    -0.522862       -0.001495   \n3024     -119.46     35.14            0.110435     0.138415       -0.001495   \n15663    -122.44     37.80            1.856173     0.546310       -0.001495   \n20484    -118.72     34.28           -0.921138     0.188080       -0.001495   \n9814     -121.93     36.62            0.427842    -0.133821       -0.001495   \n13311    -117.61     34.08           -1.317896     0.820846       -0.001495   \n7113     -118.02     33.89            0.586545    -0.582643       -0.001495   \n7668     -118.08     33.92            0.745249    -0.601038       -0.001495   \n18246    -122.08     37.39           -1.952710    -0.160953       -0.001495   \n5723     -118.23     34.18            1.300711    -0.142558       -0.001495   \n\n       population  households  median_income  ocean_proximity_INLAND  \\\n20046   -0.030301   -0.370087      -1.155085                1.469348   \n3024     0.121851    0.220532      -0.708659                1.469348   \n15663   -0.102419    1.215396      -0.210402               -0.680574   \n20484    0.244979   -0.013091       0.975113               -0.680574   \n9814    -0.319653   -0.188964      -0.081794               -0.680574   \n13311    0.856225    0.900400       0.437942                1.469348   \n7113    -0.665293   -0.732333       0.631826               -0.680574   \n7668    -0.365387   -0.606334      -0.099754               -0.680574   \n18246   -0.331087    0.220532       0.484628               -0.680574   \n5723    -0.425192   -0.422586       2.222647               -0.680574   \n\n       ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  \\\n20046               -0.015566                 -0.355646   \n3024                -0.015566                 -0.355646   \n15663               -0.015566                  2.811787   \n20484               -0.015566                 -0.355646   \n9814                -0.015566                 -0.355646   \n13311               -0.015566                 -0.355646   \n7113                -0.015566                 -0.355646   \n7668                -0.015566                 -0.355646   \n18246               -0.015566                  2.811787   \n5723                -0.015566                 -0.355646   \n\n       ocean_proximity_NEAR OCEAN  \n20046                   -0.380263  \n3024                    -0.380263  \n15663                   -0.380263  \n20484                   -0.380263  \n9814                     2.629758  \n13311                   -0.380263  \n7113                    -0.380263  \n7668                    -0.380263  \n18246                   -0.380263  \n5723                    -0.380263  ",
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>ocean_proximity_INLAND</th>\n      <th>ocean_proximity_ISLAND</th>\n      <th>ocean_proximity_NEAR BAY</th>\n      <th>ocean_proximity_NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>20046</th>\n      <td>-119.01</td>\n      <td>36.06</td>\n      <td>-0.286324</td>\n      <td>-0.522862</td>\n      <td>-0.001495</td>\n      <td>-0.030301</td>\n      <td>-0.370087</td>\n      <td>-1.155085</td>\n      <td>1.469348</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>3024</th>\n      <td>-119.46</td>\n      <td>35.14</td>\n      <td>0.110435</td>\n      <td>0.138415</td>\n      <td>-0.001495</td>\n      <td>0.121851</td>\n      <td>0.220532</td>\n      <td>-0.708659</td>\n      <td>1.469348</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>15663</th>\n      <td>-122.44</td>\n      <td>37.80</td>\n      <td>1.856173</td>\n      <td>0.546310</td>\n      <td>-0.001495</td>\n      <td>-0.102419</td>\n      <td>1.215396</td>\n      <td>-0.210402</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>2.811787</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>20484</th>\n      <td>-118.72</td>\n      <td>34.28</td>\n      <td>-0.921138</td>\n      <td>0.188080</td>\n      <td>-0.001495</td>\n      <td>0.244979</td>\n      <td>-0.013091</td>\n      <td>0.975113</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>9814</th>\n      <td>-121.93</td>\n      <td>36.62</td>\n      <td>0.427842</td>\n      <td>-0.133821</td>\n      <td>-0.001495</td>\n      <td>-0.319653</td>\n      <td>-0.188964</td>\n      <td>-0.081794</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>2.629758</td>\n    </tr>\n    <tr>\n      <th>13311</th>\n      <td>-117.61</td>\n      <td>34.08</td>\n      <td>-1.317896</td>\n      <td>0.820846</td>\n      <td>-0.001495</td>\n      <td>0.856225</td>\n      <td>0.900400</td>\n      <td>0.437942</td>\n      <td>1.469348</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>7113</th>\n      <td>-118.02</td>\n      <td>33.89</td>\n      <td>0.586545</td>\n      <td>-0.582643</td>\n      <td>-0.001495</td>\n      <td>-0.665293</td>\n      <td>-0.732333</td>\n      <td>0.631826</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>7668</th>\n      <td>-118.08</td>\n      <td>33.92</td>\n      <td>0.745249</td>\n      <td>-0.601038</td>\n      <td>-0.001495</td>\n      <td>-0.365387</td>\n      <td>-0.606334</td>\n      <td>-0.099754</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>18246</th>\n      <td>-122.08</td>\n      <td>37.39</td>\n      <td>-1.952710</td>\n      <td>-0.160953</td>\n      <td>-0.001495</td>\n      <td>-0.331087</td>\n      <td>0.220532</td>\n      <td>0.484628</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>2.811787</td>\n      <td>-0.380263</td>\n    </tr>\n    <tr>\n      <th>5723</th>\n      <td>-118.23</td>\n      <td>34.18</td>\n      <td>1.300711</td>\n      <td>-0.142558</td>\n      <td>-0.001495</td>\n      <td>-0.425192</td>\n      <td>-0.422586</td>\n      <td>2.222647</td>\n      <td>-0.680574</td>\n      <td>-0.015566</td>\n      <td>-0.355646</td>\n      <td>-0.380263</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:30:53.991011Z",
     "start_time": "2024-07-19T06:30:53.980689Z"
    }
   },
   "id": "62cd8d9abdd8604d",
   "execution_count": 132
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "d3134231911a46ae"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error,root_mean_squared_error,mean_absolute_error,r2_score"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:36:56.252939Z",
     "start_time": "2024-07-19T06:36:56.176812Z"
    }
   },
   "id": "738560d6acc9f8e5",
   "execution_count": 171
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:37:44.541906Z",
     "start_time": "2024-07-19T06:37:44.538489Z"
    }
   },
   "id": "128eeaa05bd77d76",
   "execution_count": 172
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()",
      "text/html": "<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_scaled,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:40:37.853561Z",
     "start_time": "2024-07-19T06:40:37.837276Z"
    }
   },
   "id": "7c0ef7863b9ec4f6",
   "execution_count": 173
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "y_train_predicted=model.predict(X_train_scaled)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:43:21.418580Z",
     "start_time": "2024-07-19T06:43:21.411079Z"
    }
   },
   "id": "2aeafe0f7e9c75ac",
   "execution_count": 174
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure trainVsPREDICTED\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train, y_train_predicted, color='green', label='Actual vs Predicted',alpha=0.3)\n",
    "\n",
    "plt.plot(y_train, y_train, color='red', linestyle='--', label='Perfect Fit (y=x)')\n",
    "\n",
    "plt.xlabel('Actual y_train')\n",
    "plt.ylabel('Predicted y_train')\n",
    "plt.title('Training Data: Actual vs Predicted with Perfect Fit Line')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.show()\n",
    "save_fig(\"trainVsPREDICTED\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:51:16.969662Z",
     "start_time": "2024-07-19T06:51:16.659119Z"
    }
   },
   "id": "6c79fede831160c9",
   "execution_count": 180
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "y_test_predicted = model.predict(X_test_scaled)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:59:30.352629Z",
     "start_time": "2024-07-19T06:59:30.347961Z"
    }
   },
   "id": "c7ced9251f031f91",
   "execution_count": 183
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared for training data: 0.65\n",
      "R-squared for test data: 0.63\n"
     ]
    }
   ],
   "source": [
    "r2_train = r2_score(y_train, y_train_predicted)\n",
    "\n",
    "r2_test = r2_score(y_test, y_test_predicted)\n",
    "\n",
    "print(f\"R-squared for training data: {r2_train:.2f}\")\n",
    "print(f\"R-squared for test data: {r2_test:.2f}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T06:59:31.282393Z",
     "start_time": "2024-07-19T06:59:31.276458Z"
    }
   },
   "id": "c96f4a2eeeef1eee",
   "execution_count": 184
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for training data: 68433.94\n",
      "RMSE for test data: 70031.42\n"
     ]
    }
   ],
   "source": [
    "rmse_train = np.sqrt(mean_squared_error(y_train, y_train_predicted))\n",
    "\n",
    "# Calculate RMSE for test data\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test, y_test_predicted))\n",
    "\n",
    "print(f\"RMSE for training data: {rmse_train:.2f}\")\n",
    "print(f\"RMSE for test data: {rmse_test:.2f}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-19T07:00:50.296752Z",
     "start_time": "2024-07-19T07:00:50.292138Z"
    }
   },
   "id": "b977ce2bc9f3dfa8",
   "execution_count": 185
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "#above rmse and r2 score indicated model has not performed well for linear regression"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "41eb0fe2d5a51c5"
  }
 ],
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