Model_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mp\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Carpet Area</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Builder</th>\n",
       "      <th>Paid User</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Prime Location</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Link</th>\n",
       "      <th>Prop ID</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.3 Lac</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>ELV Projects Private Limited</td>\n",
       "      <td>Y</td>\n",
       "      <td>Adibatla</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>RANJITH DORA</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_54364457</td>\n",
       "      <td>***super area*1285 sqft*****transaction*New Pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.9 Lac</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Manikonda, Outer Ring Road</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Pavan  Kumar</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_56459889</td>\n",
       "      <td>***super area*1268 sqft****status*Ready to Mov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86.3 Lac</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Silversand Infratech Pvt. Ltd</td>\n",
       "      <td>Y</td>\n",
       "      <td>Hitech City</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Shegur</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_54835677</td>\n",
       "      <td>***super area*1150 sqft****status*Ready to Mov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.9 Lac</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Aminpur</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Kumaran A</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_55539835</td>\n",
       "      <td>***super area*918 sqft****status**\\t\\t\\t\\t\\t\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.35 Cr</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>Sri Aditya Homes Pvt. Ltd.</td>\n",
       "      <td>Y</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Ranjani</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/3-...</td>\n",
       "      <td>domcache_srp_50279054</td>\n",
       "      <td>***super area*1895 sqft****status**\\t\\t\\t\\t\\t\\...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price Carpet Area Bedrooms                        Builder Paid User  \\\n",
       "0  49.3 Lac           N        2   ELV Projects Private Limited         Y   \n",
       "1  79.9 Lac         NaN        2                            NaN         Y   \n",
       "2  86.3 Lac         NaN        2  Silversand Infratech Pvt. Ltd         Y   \n",
       "3  40.9 Lac           N        2                            NaN         Y   \n",
       "4   1.35 Cr           N        3     Sri Aditya Homes Pvt. Ltd.         Y   \n",
       "\n",
       "                     Locality Property Type User Type Prime Location  \\\n",
       "0                    Adibatla     Apartment   Builder              Y   \n",
       "1  Manikonda, Outer Ring Road     Apartment   Builder              Y   \n",
       "2                 Hitech City     Apartment   Builder              Y   \n",
       "3                     Aminpur     Apartment   Builder              Y   \n",
       "4                    Shaikpet     Apartment   Builder              Y   \n",
       "\n",
       "          Agent                                               Link  \\\n",
       "0  RANJITH DORA  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "1  Pavan  Kumar  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "2        Shegur  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "3     Kumaran A  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "4       Ranjani  https://www.magicbricks.com/propertyDetails/3-...   \n",
       "\n",
       "                 Prop ID                                              Other  \n",
       "0  domcache_srp_54364457  ***super area*1285 sqft*****transaction*New Pr...  \n",
       "1  domcache_srp_56459889  ***super area*1268 sqft****status*Ready to Mov...  \n",
       "2  domcache_srp_54835677  ***super area*1150 sqft****status*Ready to Mov...  \n",
       "3  domcache_srp_55539835  ***super area*918 sqft****status**\\t\\t\\t\\t\\t\\t...  \n",
       "4  domcache_srp_50279054  ***super area*1895 sqft****status**\\t\\t\\t\\t\\t\\...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('hyderabad_first_type_000.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Carpet Area</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Builder</th>\n",
       "      <th>Paid User</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Prime Location</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Link</th>\n",
       "      <th>Prop ID</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.3 Lac</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>ELV Projects Private Limited</td>\n",
       "      <td>Y</td>\n",
       "      <td>Adibatla</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>RANJITH DORA</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_54364457</td>\n",
       "      <td>***super area*1285 sqft*****transaction*New Pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.9 Lac</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Manikonda, Outer Ring Road</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Pavan  Kumar</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_56459889</td>\n",
       "      <td>***super area*1268 sqft****status*Ready to Mov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86.3 Lac</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Silversand Infratech Pvt. Ltd</td>\n",
       "      <td>Y</td>\n",
       "      <td>Hitech City</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Shegur</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_54835677</td>\n",
       "      <td>***super area*1150 sqft****status*Ready to Mov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.9 Lac</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Aminpur</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Kumaran A</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_55539835</td>\n",
       "      <td>***super area*918 sqft****status**\\t\\t\\t\\t\\t\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.4 Lac</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>BEL</td>\n",
       "      <td>Y</td>\n",
       "      <td>Moti Nagar</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Brigade</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_56379877</td>\n",
       "      <td>***super area*1174 sqft****status**\\t\\t\\t\\t\\t\\...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Price Carpet Area Bedrooms                        Builder Paid User  \\\n",
       "0  49.3 Lac           N        2   ELV Projects Private Limited         Y   \n",
       "1  79.9 Lac         NaN        2                            NaN         Y   \n",
       "2  86.3 Lac         NaN        2  Silversand Infratech Pvt. Ltd         Y   \n",
       "3  40.9 Lac           N        2                            NaN         Y   \n",
       "4  78.4 Lac           N        2                            BEL         Y   \n",
       "\n",
       "                     Locality Property Type User Type Prime Location  \\\n",
       "0                    Adibatla     Apartment   Builder              Y   \n",
       "1  Manikonda, Outer Ring Road     Apartment   Builder              Y   \n",
       "2                 Hitech City     Apartment   Builder              Y   \n",
       "3                     Aminpur     Apartment   Builder              Y   \n",
       "4                  Moti Nagar     Apartment   Builder              Y   \n",
       "\n",
       "          Agent                                               Link  \\\n",
       "0  RANJITH DORA  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "1  Pavan  Kumar  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "2        Shegur  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "3     Kumaran A  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "4       Brigade  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "\n",
       "                 Prop ID                                              Other  \n",
       "0  domcache_srp_54364457  ***super area*1285 sqft*****transaction*New Pr...  \n",
       "1  domcache_srp_56459889  ***super area*1268 sqft****status*Ready to Mov...  \n",
       "2  domcache_srp_54835677  ***super area*1150 sqft****status*Ready to Mov...  \n",
       "3  domcache_srp_55539835  ***super area*918 sqft****status**\\t\\t\\t\\t\\t\\t...  \n",
       "4  domcache_srp_56379877  ***super area*1174 sqft****status**\\t\\t\\t\\t\\t\\...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crore_prices_index = df[df['Price'].str.contains('Cr')].index\n",
    "df.drop(crore_prices_index, inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Carpet Area</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Builder</th>\n",
       "      <th>Paid User</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Prime Location</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Link</th>\n",
       "      <th>Prop ID</th>\n",
       "      <th>Other</th>\n",
       "      <th>Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>ELV Projects Private Limited</td>\n",
       "      <td>Y</td>\n",
       "      <td>Adibatla</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>RANJITH DORA</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_54364457</td>\n",
       "      <td>***super area*1285 sqft*****transaction*New Pr...</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Manikonda, Outer Ring Road</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Pavan  Kumar</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_56459889</td>\n",
       "      <td>***super area*1268 sqft****status*Ready to Mov...</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Silversand Infratech Pvt. Ltd</td>\n",
       "      <td>Y</td>\n",
       "      <td>Hitech City</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Shegur</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_54835677</td>\n",
       "      <td>***super area*1150 sqft****status*Ready to Mov...</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Aminpur</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Kumaran A</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_55539835</td>\n",
       "      <td>***super area*918 sqft****status**\\t\\t\\t\\t\\t\\t...</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>BEL</td>\n",
       "      <td>Y</td>\n",
       "      <td>Moti Nagar</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Builder</td>\n",
       "      <td>Y</td>\n",
       "      <td>Brigade</td>\n",
       "      <td>https://www.magicbricks.com/propertyDetails/2-...</td>\n",
       "      <td>domcache_srp_56379877</td>\n",
       "      <td>***super area*1174 sqft****status**\\t\\t\\t\\t\\t\\...</td>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Price Carpet Area Bedrooms                        Builder Paid User  \\\n",
       "0    49           N        2   ELV Projects Private Limited         Y   \n",
       "1    79         NaN        2                            NaN         Y   \n",
       "2    86         NaN        2  Silversand Infratech Pvt. Ltd         Y   \n",
       "3    40           N        2                            NaN         Y   \n",
       "4    78           N        2                            BEL         Y   \n",
       "\n",
       "                     Locality Property Type User Type Prime Location  \\\n",
       "0                    Adibatla     Apartment   Builder              Y   \n",
       "1  Manikonda, Outer Ring Road     Apartment   Builder              Y   \n",
       "2                 Hitech City     Apartment   Builder              Y   \n",
       "3                     Aminpur     Apartment   Builder              Y   \n",
       "4                  Moti Nagar     Apartment   Builder              Y   \n",
       "\n",
       "          Agent                                               Link  \\\n",
       "0  RANJITH DORA  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "1  Pavan  Kumar  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "2        Shegur  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "3     Kumaran A  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "4       Brigade  https://www.magicbricks.com/propertyDetails/2-...   \n",
       "\n",
       "                 Prop ID                                              Other  \\\n",
       "0  domcache_srp_54364457  ***super area*1285 sqft*****transaction*New Pr...   \n",
       "1  domcache_srp_56459889  ***super area*1268 sqft****status*Ready to Mov...   \n",
       "2  domcache_srp_54835677  ***super area*1150 sqft****status*Ready to Mov...   \n",
       "3  domcache_srp_55539835  ***super area*918 sqft****status**\\t\\t\\t\\t\\t\\t...   \n",
       "4  domcache_srp_56379877  ***super area*1174 sqft****status**\\t\\t\\t\\t\\t\\...   \n",
       "\n",
       "   Sqft  \n",
       "0  1285  \n",
       "1  1268  \n",
       "2  1150  \n",
       "3   918  \n",
       "4  1174  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = r'(\\d+)-Sq-ft'\n",
    "df['Sqft'] = df['Link'].str.extract(pattern)\n",
    "pattern = r'(\\d+)'\n",
    "df['Price'] = df['Price'].str.extract(pattern)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Builder</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Property Type</th>\n",
       "      <th>Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>ELV Projects Private Limited</td>\n",
       "      <td>Adibatla</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manikonda, Outer Ring Road</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>2</td>\n",
       "      <td>Silversand Infratech Pvt. Ltd</td>\n",
       "      <td>Hitech City</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aminpur</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>BEL</td>\n",
       "      <td>Moti Nagar</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Price Bedrooms                        Builder                    Locality  \\\n",
       "0    49        2   ELV Projects Private Limited                    Adibatla   \n",
       "1    79        2                            NaN  Manikonda, Outer Ring Road   \n",
       "2    86        2  Silversand Infratech Pvt. Ltd                 Hitech City   \n",
       "3    40        2                            NaN                     Aminpur   \n",
       "4    78        2                            BEL                  Moti Nagar   \n",
       "\n",
       "  Property Type  Sqft  \n",
       "0     Apartment  1285  \n",
       "1     Apartment  1268  \n",
       "2     Apartment  1150  \n",
       "3     Apartment   918  \n",
       "4     Apartment  1174  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"Agent\",\"Prime Location\",\"Paid User\",\"Carpet Area\"], inplace=True)\n",
    "df.drop(columns=[\"User Type\",\"Link\",\"Prop ID\"], inplace=True)\n",
    "df.drop(columns=[\"Other\"], inplace=True)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>Adibatla</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79</td>\n",
       "      <td>Manikonda, Outer Ring Road</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>Hitech City</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>Aminpur</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>Moti Nagar</td>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Price                    Locality  Sqft\n",
       "0    49                    Adibatla  1285\n",
       "1    79  Manikonda, Outer Ring Road  1268\n",
       "2    86                 Hitech City  1150\n",
       "3    40                     Aminpur   918\n",
       "4    78                  Moti Nagar  1174"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"Builder\",\"Property Type\",\"Bedrooms\"], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price        0\n",
       "Locality    89\n",
       "Sqft        76\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price       0\n",
       "Locality    0\n",
       "Sqft        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1711, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Locality\n",
       "Bachupally                    57\n",
       "Miyapur, NH  9                54\n",
       "Kompally                      47\n",
       "Nizampet                      42\n",
       "Pragathi Nagar, Kukatpally    41\n",
       "                              ..\n",
       "Anand Nagar                    1\n",
       "Vinayaka Nagar                 1\n",
       "Vishali Nagar                  1\n",
       "Bharat Nagar, Moosapet         1\n",
       "Officers Colony                1\n",
       "Name: count, Length: 463, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Locality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[57, 54, 47]\n",
      "['Bachupally', 'Miyapur, NH  9', 'Kompally']\n"
     ]
    }
   ],
   "source": [
    "indices=[]\n",
    "vc=df['Locality'].value_counts()\n",
    "vals =[]\n",
    "#places=[\"Bachupally\",\"Manikonda, Outer Ring Road\",\"Patancheru\",\"Gachibowli\",\"Nizampet\"]\n",
    "for i,v in vc.items():\n",
    "  if v>=47:\n",
    "    indices.append(i)\n",
    "    vals.append(v)\n",
    "print(vals)\n",
    "print(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2],\n",
       " [Text(0, 0, 'Bachupally'),\n",
       "  Text(1, 0, 'Miyapur, NH  9'),\n",
       "  Text(2, 0, 'Kompally')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(indices,vals)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df['Locality'].isin(indices)]\n",
    "#selecting top 3 most popular locations only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price       0\n",
       "Locality    0\n",
       "Sqft        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\divya\\AppData\\Local\\Temp\\ipykernel_8076\\3028564913.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['Locality'] = enc.fit_transform(new_df['Locality'])\n"
     ]
    }
   ],
   "source": [
    "enc = LabelEncoder()\n",
    "new_df['Locality'] = enc.fit_transform(new_df['Locality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Locality\n",
       "0    57\n",
       "2    54\n",
       "1    47\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Locality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1036</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>1650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>1560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Locality  Sqft\n",
       "1036    59         0  1449\n",
       "769     95         1  1650\n",
       "953     54         2  1106\n",
       "1482    86         0  1560\n",
       "235     70         0   850"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ss = StandardScaler()\n",
    "# new_df['Sqft'] = ss.fit_transform(new_df[['Sqft']])\n",
    "# new_df['Price'] = ss.fit_transform(new_df[['Price']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>1425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>1295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>1685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Locality  Sqft\n",
       "807     59         2  1425\n",
       "1629    72         0  1295\n",
       "830     79         1  1685\n",
       "7       72         1  1249\n",
       "849     64         0  1060"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Locality\n",
       "0    57\n",
       "2    54\n",
       "1    47\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Locality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f3507e8a40>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(new_df['Sqft'], new_df['Price'], c=new_df['Locality'],cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, accuracy_score\n",
    "from sklearn.tree import DecisionTreeRegressor, plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\divya\\AppData\\Local\\Temp\\ipykernel_8076\\60610952.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['Sqft'] = pd.to_numeric(new_df['Sqft'], errors='coerce')\n",
      "C:\\Users\\divya\\AppData\\Local\\Temp\\ipykernel_8076\\60610952.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['Price'] = pd.to_numeric(new_df['Price'], errors='coerce')\n"
     ]
    }
   ],
   "source": [
    "new_df['Sqft'] = pd.to_numeric(new_df['Sqft'], errors='coerce')\n",
    "new_df['Price'] = pd.to_numeric(new_df['Price'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(new_df.iloc[:,1:], new_df['Price'], test_size=0.2, random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.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\n",
       "clickable 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,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a: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,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a: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 */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.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",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.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>DecisionTreeRegressor()</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;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod1 = DecisionTreeRegressor()\n",
    "mod1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mod1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6373618862872354"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96.86371527777779"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f34dc85700>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(new_df['Sqft'], new_df['Price'], c=new_df['Locality'],cmap='viridis')\n",
    "# sqft_min, sqft_max = min(new_df['Sqft']), max(new_df['Sqft'])\n",
    "# price_min, price_max = min(new_df['Price']), max(new_df['Price'])\n",
    "# sqft_step = (sqft_max - sqft_min)/100 \n",
    "# price_step = (price_max - price_min)/10 \n",
    "# x = np.meshgrid(np.arange(sqft_min, sqft_max, sqft_step),np.arange(price_min, price_max, price_step))\n",
    "# #predictions = mod1.predict(np.c_[x[0].ravel(), x[1].ravel()]).reshape(x[0].shape)\n",
    "# plt.contourf(x[0], x[1], mod1.predict(np.c_[x[0].ravel(), x[1].ravel()]).reshape(x[0].shape), alpha=0.75, cmap='viridis')\n",
    "\n",
    "# #plt.contourf(x[0], x[1], predictions, alpha=0.75, cmap='viridis')\n",
    "# plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_tree(mod1, filled=True, feature_names=['Sqft', 'Locality'], rounded=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7244446500719994"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "mod2 = RandomForestRegressor(n_estimators=6, random_state=0)\n",
    "mod2.fit(x_train, y_train)\n",
    "predictions = mod2.predict(x_test)\n",
    "r2_score(y_test, predictions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "saved_model = pickle.dumps(mod2)\n",
    "with open('forest_model.pkl', 'wb') as file:\n",
    "    pickle.dump(mod2, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Locality</th>\n",
       "      <th>Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>1240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>2</td>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>2</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>2</td>\n",
       "      <td>1193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1</td>\n",
       "      <td>1812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>0</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Locality  Sqft\n",
       "84           0  1240\n",
       "1841         2  1285\n",
       "1084         1  1200\n",
       "619          0  1600\n",
       "367          2   900\n",
       "...        ...   ...\n",
       "535          2  1193\n",
       "168          2  1440\n",
       "500          0  1355\n",
       "599          1  1812\n",
       "724          0  1008\n",
       "\n",
       "[126 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\divya\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([89.])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod1.predict([[0,2000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72.        , 73.66666667, 50.        , 60.        , 80.        ,\n",
       "       73.66666667, 50.        , 85.        , 70.5       , 72.        ,\n",
       "       45.        , 48.        , 99.        , 55.        , 98.        ,\n",
       "       48.        , 48.        , 86.        , 65.        , 48.        ,\n",
       "       60.        , 88.        , 50.        , 53.5       , 50.        ,\n",
       "       65.5       , 82.        , 51.        , 82.        , 72.        ,\n",
       "       84.        , 42.        ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([76.33333333, 64.5       , 48.75277778, 57.80555556, 78.16666667,\n",
       "       74.575     , 48.75277778, 80.66666667, 70.65277778, 76.33333333,\n",
       "       60.        , 60.        , 93.        , 56.97619048, 92.33333333,\n",
       "       60.        , 60.        , 81.16666667, 64.16666667, 60.66666667,\n",
       "       57.80555556, 80.83333333, 48.75277778, 64.875     , 48.75277778,\n",
       "       65.5       , 80.33333333, 57.        , 79.33333333, 65.83333333,\n",
       "       79.16666667, 45.83333333])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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