Bangalore_house_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Required Libraries :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mysql.connector as sqltor\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting with MySQL :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_connection = sqltor.connect(host = 'localhost', user = 'root', passwd = 'warmachine68', database = 'house_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_connection.is_connected()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql('Select * from bengaluru_prices',my_connection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, we infer that we have successfully connected with our MySQL Database."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using MySQL commands :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>None</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2     None       1440   2.0      3.0   62.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('Select * from bengaluru_prices',my_connection).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>None</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>18-Feb</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>None</td>\n",
       "      <td>1800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability          location       size  society  \\\n",
       "0            Plot  Area  Ready To Move  Chikka Tirupathi  4 Bedroom  Theanmp   \n",
       "1        Built-up  Area  Ready To Move       Uttarahalli      3 BHK     None   \n",
       "2  Super built-up  Area         18-Feb        Whitefield      3 BHK     None   \n",
       "\n",
       "  total_sqft  bath  balcony  price  \n",
       "0       2600   5.0      3.0  120.0  \n",
       "1       1440   2.0      3.0   62.0  \n",
       "2       1800   2.0      2.0   70.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('SELECT * FROM bengaluru_prices WHERE balcony > 1; ',my_connection).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AVG(price)</th>\n",
       "      <th>SUM(price)</th>\n",
       "      <th>COUNT(price)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112.565627</td>\n",
       "      <td>1499374.145</td>\n",
       "      <td>13320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AVG(price)   SUM(price)  COUNT(price)\n",
       "0  112.565627  1499374.145         13320"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql('SELECT AVG(price), SUM(price), COUNT(price) from bengaluru_prices',my_connection).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>None</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>None</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2     None       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4     None       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13247.000000</td>\n",
       "      <td>12711.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.692610</td>\n",
       "      <td>1.584376</td>\n",
       "      <td>112.565627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.341458</td>\n",
       "      <td>0.817263</td>\n",
       "      <td>148.971674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bath       balcony         price\n",
       "count  13247.000000  12711.000000  13320.000000\n",
       "mean       2.692610      1.584376    112.565627\n",
       "std        1.341458      0.817263    148.971674\n",
       "min        1.000000      0.000000      8.000000\n",
       "25%        2.000000      1.000000     50.000000\n",
       "50%        2.000000      2.000000     72.000000\n",
       "75%        3.000000      2.000000    120.000000\n",
       "max       40.000000      3.000000   3600.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning - "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling NULL values :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "plt.xticks(rotation = 45, fontsize = 14)\n",
    "sns.heatmap(df.isnull(), yticklabels = False, cbar = False).set_title('Heatmap showing missing/NULL values in different columns',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this heatmap, we can clearly observe that such a large amount of data is missing from the society column that we cannot extract any useful information from this so we will simply drop this column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "  total_sqft  bath  balcony   price  \n",
       "0       1056   2.0      1.0   39.07  \n",
       "1       2600   5.0      3.0  120.00  \n",
       "2       1440   2.0      3.0   62.00  \n",
       "3       1521   3.0      1.0   95.00  \n",
       "4       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns = ['society'], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.balcony = df.balcony.fillna(df.balcony.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "  total_sqft  bath  balcony   price  \n",
       "0       1056   2.0      1.0   39.07  \n",
       "1       2600   5.0      3.0  120.00  \n",
       "2       1440   2.0      3.0   62.00  \n",
       "3       1521   3.0      1.0   95.00  \n",
       "4       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type       13246\n",
       "availability    13246\n",
       "location        13246\n",
       "size            13246\n",
       "total_sqft      13246\n",
       "bath            13246\n",
       "balcony         13246\n",
       "price           13246\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "plt.xticks(rotation = 45, fontsize = 14)\n",
    "sns.heatmap(df.isnull(), yticklabels = False, cbar = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can clearly see from the above heatmap that we have completely eliminated all the null values from our Dataframe. Now we can proceed with further cleaning of our data to so as to make it more analysable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing Data Types for better extraction of Data :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On having a close look at the data, we found that the data in the column total_sqft has its sizes in various different units (such as sq. yards, acres, etc). So converting all the other units into sqft. and changing the column's datatype to float.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.total_sqft:\n",
    "    if 'Sq. Yards' in i:\n",
    "        v = float(i[0:i.index('S')]) * 9 \n",
    "        df.total_sqft.replace(i, str(v), inplace = True)\n",
    "    elif 'Sq. Meter' in i:\n",
    "        w = float(i[0:i.index('S')]) * 10.7639\n",
    "        df.total_sqft.replace(i, str(w), inplace = True)\n",
    "    elif 'Acres' in i:\n",
    "        x = float(i[0:i.index('A')]) * 43560\n",
    "        df.total_sqft.replace(i, str(x), inplace = True)\n",
    "    elif 'Perch' in i:\n",
    "        y = float(i[0:i.index('P')]) * 272.25\n",
    "        df.total_sqft.replace(i, str(y), inplace = True)\n",
    "    elif '-' in i:\n",
    "        p = float(i[0 : i.index('-') - 1 ])\n",
    "        q = float(i[i.index('-') + 2 : len(i)])\n",
    "        r = (p + q)/2\n",
    "        df.total_sqft.replace(i, str(r), inplace = True)\n",
    "    elif 'Cent' in i:\n",
    "        z = float(i[0:i.index('C')]) * 435.6\n",
    "        df.total_sqft.replace(i, str(z), inplace = True)\n",
    "    elif 'Guntha' in i:\n",
    "        s = float(i[0:i.index('G')]) * 1089\n",
    "        df.total_sqft.replace(i, str(s), inplace = True)\n",
    "    elif 'Ground' in i:\n",
    "        t = float(i[0:i.index('G')]) * 2400\n",
    "        df.total_sqft.replace(i, str(t), inplace = True)\n",
    "    else:\n",
    "        df.total_sqft.replace(i, i, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.total_sqft = df['total_sqft'].astype('float')"
   ]
  },
  {
   "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>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   total_sqft  bath  balcony   price  \n",
       "0      1056.0   2.0      1.0   39.07  \n",
       "1      2600.0   5.0      3.0  120.00  \n",
       "2      1440.0   2.0      3.0   62.00  \n",
       "3      1521.0   3.0      1.0   95.00  \n",
       "4      1200.0   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, changing records in the column 'size' to numeric values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
       "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
       "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
       "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
       "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
       "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type        object\n",
       "availability     object\n",
       "location         object\n",
       "size             object\n",
       "total_sqft      float64\n",
       "bath            float64\n",
       "balcony         float64\n",
       "price           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HERE WE HAVE CONSIDERED 1 BEDROOM = 1RK  = 1 BHK\n",
    "\n",
    "for i in df['size']:\n",
    "    if 'B' in i:\n",
    "        df['size'].replace(to_replace = i, value = i[0 : i.index('B')-1], inplace = True)\n",
    "    elif 'R' in i:\n",
    "        df['size'].replace(to_replace = i, value = i[0 : i.index('R')-1], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['size'] = df['size'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'size':'BHK'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location  BHK  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II  2.0   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4.0   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli  3.0   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli  3.0   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur  2.0   \n",
       "\n",
       "   total_sqft  bath  balcony   price  \n",
       "0      1056.0   2.0      1.0   39.07  \n",
       "1      2600.0   5.0      3.0  120.00  \n",
       "2      1440.0   2.0      3.0   62.00  \n",
       "3      1521.0   3.0      1.0   95.00  \n",
       "4      1200.0   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ####  Creating new feature as price_per_sqft -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's make a new column as price_per_sqft which tells the price of land in each record per square feet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'price_per_sqft'] = df.loc[:,'price'] * 100000/df.loc[:,'total_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13246 entries, 0 to 13319\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   area_type       13246 non-null  object \n",
      " 1   availability    13246 non-null  object \n",
      " 2   location        13246 non-null  object \n",
      " 3   BHK             13246 non-null  float64\n",
      " 4   total_sqft      13246 non-null  float64\n",
      " 5   bath            13246 non-null  float64\n",
      " 6   balcony         13246 non-null  float64\n",
      " 7   price           13246 non-null  float64\n",
      " 8   price_per_sqft  13246 non-null  float64\n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating new feature as sqft_per_BHK -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this column, we will now enter the area in square feet per BHK of each record in our database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'sqft_per_BHK'] = df.loc[:,'total_sqft'] / df.loc[:,'BHK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">BHK</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_sqft</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">price_per_sqft</th>\n",
       "      <th colspan=\"8\" halign=\"left\">sqft_per_BHK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Built-up  Area</th>\n",
       "      <td>2410.0</td>\n",
       "      <td>2.787552</td>\n",
       "      <td>1.228421</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>2107.051567</td>\n",
       "      <td>...</td>\n",
       "      <td>6801.522843</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>643.740862</td>\n",
       "      <td>2564.266435</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>550.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>124781.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carpet  Area</th>\n",
       "      <td>87.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>1.394897</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1295.648119</td>\n",
       "      <td>...</td>\n",
       "      <td>8333.961293</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>490.511683</td>\n",
       "      <td>146.051809</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>411.516667</td>\n",
       "      <td>487.5</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plot  Area</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>4.060229</td>\n",
       "      <td>1.995477</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>3565.531628</td>\n",
       "      <td>...</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>12000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1292.305234</td>\n",
       "      <td>16639.494655</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>400.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>653400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Super built-up  Area</th>\n",
       "      <td>8740.0</td>\n",
       "      <td>2.517963</td>\n",
       "      <td>0.865889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8740.0</td>\n",
       "      <td>1480.105325</td>\n",
       "      <td>...</td>\n",
       "      <td>6489.243775</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>8740.0</td>\n",
       "      <td>584.137643</td>\n",
       "      <td>208.035046</td>\n",
       "      <td>89.285714</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>564.5</td>\n",
       "      <td>626.5</td>\n",
       "      <td>9000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         BHK                                                \\\n",
       "                       count      mean       std  min  25%  50%  75%   max   \n",
       "area_type                                                                    \n",
       "Built-up  Area        2410.0  2.787552  1.228421  1.0  2.0  3.0  3.0  10.0   \n",
       "Carpet  Area            87.0  2.666667  1.394897  1.0  2.0  2.0  3.0   9.0   \n",
       "Plot  Area            2009.0  4.060229  1.995477  1.0  3.0  4.0  5.0  43.0   \n",
       "Super built-up  Area  8740.0  2.517963  0.865889  1.0  2.0  2.0  3.0  27.0   \n",
       "\n",
       "                     total_sqft               ... price_per_sqft              \\\n",
       "                          count         mean  ...            75%         max   \n",
       "area_type                                     ...                              \n",
       "Built-up  Area           2410.0  2107.051567  ...    6801.522843   2300000.0   \n",
       "Carpet  Area               87.0  1295.648119  ...    8333.961293    200000.0   \n",
       "Plot  Area               2009.0  3565.531628  ...   15000.000000  12000000.0   \n",
       "Super built-up  Area     8740.0  1480.105325  ...    6489.243775     56000.0   \n",
       "\n",
       "                     sqft_per_BHK                                        \\\n",
       "                            count         mean           std        min   \n",
       "area_type                                                                 \n",
       "Built-up  Area             2410.0   643.740862   2564.266435   0.714286   \n",
       "Carpet  Area                 87.0   490.511683    146.051809  15.000000   \n",
       "Plot  Area                 2009.0  1292.305234  16639.494655   0.250000   \n",
       "Super built-up  Area       8740.0   584.137643    208.035046  89.285714   \n",
       "\n",
       "                                                           \n",
       "                             25%    50%    75%        max  \n",
       "area_type                                                  \n",
       "Built-up  Area        475.000000  550.0  630.0  124781.25  \n",
       "Carpet  Area          411.516667  487.5  550.0    1125.00  \n",
       "Plot  Area            240.000000  400.0  600.0  653400.00  \n",
       "Super built-up  Area  500.000000  564.5  626.5    9000.00  \n",
       "\n",
       "[4 rows x 56 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area_type').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysing Data from area_type -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = df.groupby('area_type').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type\n",
       "Built-up  Area          2.649378\n",
       "Carpet  Area            2.505747\n",
       "Plot  Area              3.893479\n",
       "Super built-up  Area    2.430320\n",
       "Name: (bath, mean), dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = b.iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Super built-up  Area    8740\n",
       "Built-up  Area          2410\n",
       "Plot  Area              2009\n",
       "Carpet  Area              87\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['area_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.xticks(fontsize = 13)\n",
    "sns.barplot(x = list(c.index),y = list(c[0:4])).set_title('Mean prices of different area types', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that mean prices of different area types are nearly equal (except for Plot Area type) and also the value counts of different area types vary over a wide range which will cause the model to overfit. So removing area_type from aur Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['area_type'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "      <th>sqft_per_BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>3699.810606</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4615.384615</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>4305.555556</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>6245.890861</td>\n",
       "      <td>507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4250.000000</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    availability                  location  BHK  total_sqft  bath  balcony  \\\n",
       "0         19-Dec  Electronic City Phase II  2.0      1056.0   2.0      1.0   \n",
       "1  Ready To Move          Chikka Tirupathi  4.0      2600.0   5.0      3.0   \n",
       "2  Ready To Move               Uttarahalli  3.0      1440.0   2.0      3.0   \n",
       "3  Ready To Move        Lingadheeranahalli  3.0      1521.0   3.0      1.0   \n",
       "4  Ready To Move                  Kothanur  2.0      1200.0   2.0      1.0   \n",
       "\n",
       "    price  price_per_sqft  sqft_per_BHK  \n",
       "0   39.07     3699.810606         528.0  \n",
       "1  120.00     4615.384615         650.0  \n",
       "2   62.00     4305.555556         480.0  \n",
       "3   95.00     6245.890861         507.0  \n",
       "4   51.00     4250.000000         600.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Analysing Data from availability -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['19-Dec', 'Ready To Move', '18-May', '18-Feb', '18-Nov', '20-Dec',\n",
       "       '17-Oct', '21-Dec', '19-Sep', '20-Sep', '18-Mar', '18-Apr',\n",
       "       '20-Aug', '19-Mar', '17-Sep', '18-Dec', '17-Aug', '19-Apr',\n",
       "       '18-Jun', '22-Dec', '22-Jan', '18-Aug', '19-Jan', '17-Jul',\n",
       "       '18-Jul', '21-Jun', '20-May', '19-Aug', '18-Sep', '17-May',\n",
       "       '17-Jun', '18-Oct', '21-May', '18-Jan', '20-Mar', '17-Dec',\n",
       "       '16-Mar', '19-Jun', '22-Jun', '19-Jul', '21-Feb', '19-May',\n",
       "       '17-Nov', '20-Oct', '20-Jun', '19-Feb', '21-Oct', '21-Jan',\n",
       "       '17-Mar', '17-Apr', '22-May', '19-Oct', '21-Jul', '21-Nov',\n",
       "       '21-Mar', '16-Dec', '22-Mar', '20-Jan', '21-Sep', '21-Aug',\n",
       "       '14-Nov', '19-Nov', '15-Nov', '16-Jul', '15-Jun', '17-Feb',\n",
       "       '20-Nov', '20-Jul', '16-Sep', '15-Oct', '20-Feb', '15-Dec',\n",
       "       '16-Oct', '22-Nov', '15-Aug', '17-Jan', '16-Nov', '20-Apr',\n",
       "       '16-Jan', '14-Jul'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['availability'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst1 = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
    "\n",
    "for i in df['availability']:\n",
    "    for j in range(0, len(lst1)):\n",
    "        if i[3:6] == lst1[j]:\n",
    "            df.availability.replace(i, str(j+1),inplace = True)\n",
    "        elif i[0:2] == 'Re':\n",
    "            df.availability.replace(i, str(0),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "      <th>sqft_per_BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>3699.810606</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4615.384615</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>4305.555556</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>6245.890861</td>\n",
       "      <td>507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4250.000000</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  availability                  location  BHK  total_sqft  bath  balcony  \\\n",
       "0           12  Electronic City Phase II  2.0      1056.0   2.0      1.0   \n",
       "1            0          Chikka Tirupathi  4.0      2600.0   5.0      3.0   \n",
       "2            0               Uttarahalli  3.0      1440.0   2.0      3.0   \n",
       "3            0        Lingadheeranahalli  3.0      1521.0   3.0      1.0   \n",
       "4            0                  Kothanur  2.0      1200.0   2.0      1.0   \n",
       "\n",
       "    price  price_per_sqft  sqft_per_BHK  \n",
       "0   39.07     3699.810606         528.0  \n",
       "1  120.00     4615.384615         650.0  \n",
       "2   62.00     4305.555556         480.0  \n",
       "3   95.00     6245.890861         507.0  \n",
       "4   51.00     4250.000000         600.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">BHK</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_sqft</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">price_per_sqft</th>\n",
       "      <th colspan=\"8\" halign=\"left\">sqft_per_BHK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>availability</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10564.0</td>\n",
       "      <td>2.868232</td>\n",
       "      <td>1.365601</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10564.0</td>\n",
       "      <td>1868.890308</td>\n",
       "      <td>...</td>\n",
       "      <td>7548.730678</td>\n",
       "      <td>1.200000e+07</td>\n",
       "      <td>10564.0</td>\n",
       "      <td>648.086184</td>\n",
       "      <td>2977.055604</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>466.666667</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>217800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.0</td>\n",
       "      <td>2.523810</td>\n",
       "      <td>1.078623</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>1752.495169</td>\n",
       "      <td>...</td>\n",
       "      <td>7060.593804</td>\n",
       "      <td>3.499579e+04</td>\n",
       "      <td>126.0</td>\n",
       "      <td>638.774750</td>\n",
       "      <td>296.960910</td>\n",
       "      <td>166.666667</td>\n",
       "      <td>498.375000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>643.750000</td>\n",
       "      <td>2080.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>90.0</td>\n",
       "      <td>2.644444</td>\n",
       "      <td>0.708077</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1469.258333</td>\n",
       "      <td>...</td>\n",
       "      <td>5863.882212</td>\n",
       "      <td>3.633333e+05</td>\n",
       "      <td>90.0</td>\n",
       "      <td>551.216667</td>\n",
       "      <td>142.338456</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>483.416667</td>\n",
       "      <td>538.500000</td>\n",
       "      <td>615.750000</td>\n",
       "      <td>1065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2.405063</td>\n",
       "      <td>0.742811</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1327.417722</td>\n",
       "      <td>...</td>\n",
       "      <td>5743.458029</td>\n",
       "      <td>1.128571e+04</td>\n",
       "      <td>79.0</td>\n",
       "      <td>550.361814</td>\n",
       "      <td>149.960972</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>447.500000</td>\n",
       "      <td>529.333333</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>711.0</td>\n",
       "      <td>2.520394</td>\n",
       "      <td>0.842075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>1467.756281</td>\n",
       "      <td>...</td>\n",
       "      <td>6734.110393</td>\n",
       "      <td>6.727273e+05</td>\n",
       "      <td>711.0</td>\n",
       "      <td>577.866568</td>\n",
       "      <td>161.191608</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>474.666667</td>\n",
       "      <td>561.250000</td>\n",
       "      <td>642.500000</td>\n",
       "      <td>1520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.0</td>\n",
       "      <td>2.986486</td>\n",
       "      <td>1.485023</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1606.851351</td>\n",
       "      <td>...</td>\n",
       "      <td>7532.909317</td>\n",
       "      <td>4.848485e+04</td>\n",
       "      <td>74.0</td>\n",
       "      <td>565.610736</td>\n",
       "      <td>206.320368</td>\n",
       "      <td>133.333333</td>\n",
       "      <td>483.000000</td>\n",
       "      <td>561.583333</td>\n",
       "      <td>622.666667</td>\n",
       "      <td>1283.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>239.0</td>\n",
       "      <td>2.556485</td>\n",
       "      <td>0.923520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>8015.638807</td>\n",
       "      <td>...</td>\n",
       "      <td>7042.328255</td>\n",
       "      <td>4.799465e+04</td>\n",
       "      <td>239.0</td>\n",
       "      <td>4310.604600</td>\n",
       "      <td>44725.603155</td>\n",
       "      <td>131.250000</td>\n",
       "      <td>506.666667</td>\n",
       "      <td>587.500000</td>\n",
       "      <td>668.750000</td>\n",
       "      <td>653400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>296.0</td>\n",
       "      <td>2.621622</td>\n",
       "      <td>1.327188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>1464.878007</td>\n",
       "      <td>...</td>\n",
       "      <td>6489.864865</td>\n",
       "      <td>2.861446e+04</td>\n",
       "      <td>296.0</td>\n",
       "      <td>576.975705</td>\n",
       "      <td>237.949869</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>630.250000</td>\n",
       "      <td>2750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>331.0</td>\n",
       "      <td>2.640483</td>\n",
       "      <td>0.853189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>1556.369456</td>\n",
       "      <td>...</td>\n",
       "      <td>6795.790416</td>\n",
       "      <td>2.716667e+04</td>\n",
       "      <td>331.0</td>\n",
       "      <td>578.256991</td>\n",
       "      <td>200.483435</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>478.750000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>625.250000</td>\n",
       "      <td>2750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>196.0</td>\n",
       "      <td>2.464286</td>\n",
       "      <td>0.806226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1457.557143</td>\n",
       "      <td>...</td>\n",
       "      <td>7150.713480</td>\n",
       "      <td>1.712121e+04</td>\n",
       "      <td>196.0</td>\n",
       "      <td>587.440136</td>\n",
       "      <td>133.129309</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>513.916667</td>\n",
       "      <td>565.500000</td>\n",
       "      <td>625.500000</td>\n",
       "      <td>1320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>205.0</td>\n",
       "      <td>2.507317</td>\n",
       "      <td>0.718246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1617.867463</td>\n",
       "      <td>...</td>\n",
       "      <td>6666.666667</td>\n",
       "      <td>2.340411e+04</td>\n",
       "      <td>205.0</td>\n",
       "      <td>622.745528</td>\n",
       "      <td>499.801270</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>524.333333</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>648.000000</td>\n",
       "      <td>7500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>249.0</td>\n",
       "      <td>2.365462</td>\n",
       "      <td>0.734361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>1292.092529</td>\n",
       "      <td>...</td>\n",
       "      <td>6315.789474</td>\n",
       "      <td>2.296296e+04</td>\n",
       "      <td>249.0</td>\n",
       "      <td>551.697342</td>\n",
       "      <td>103.983486</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>563.500000</td>\n",
       "      <td>615.000000</td>\n",
       "      <td>966.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>86.0</td>\n",
       "      <td>2.418605</td>\n",
       "      <td>0.774067</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1430.950581</td>\n",
       "      <td>...</td>\n",
       "      <td>5912.226735</td>\n",
       "      <td>7.653061e+04</td>\n",
       "      <td>86.0</td>\n",
       "      <td>586.321202</td>\n",
       "      <td>171.185196</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>496.208333</td>\n",
       "      <td>562.583333</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BHK                                                \\\n",
       "                count      mean       std  min  25%  50%  75%   max   \n",
       "availability                                                          \n",
       "0             10564.0  2.868232  1.365601  1.0  2.0  3.0  3.0  43.0   \n",
       "1               126.0  2.523810  1.078623  1.0  2.0  2.0  3.0   7.0   \n",
       "10               90.0  2.644444  0.708077  1.0  2.0  3.0  3.0   4.0   \n",
       "11               79.0  2.405063  0.742811  1.0  2.0  2.0  3.0   4.0   \n",
       "12              711.0  2.520394  0.842075  1.0  2.0  2.0  3.0   6.0   \n",
       "2                74.0  2.986486  1.485023  1.0  2.0  3.0  3.0   9.0   \n",
       "3               239.0  2.556485  0.923520  1.0  2.0  3.0  3.0   8.0   \n",
       "4               296.0  2.621622  1.327188  1.0  2.0  2.0  3.0  18.0   \n",
       "5               331.0  2.640483  0.853189  1.0  2.0  3.0  3.0   8.0   \n",
       "6               196.0  2.464286  0.806226  1.0  2.0  2.0  3.0   5.0   \n",
       "7               205.0  2.507317  0.718246  1.0  2.0  2.0  3.0   5.0   \n",
       "8               249.0  2.365462  0.734361  1.0  2.0  2.0  3.0   5.0   \n",
       "9                86.0  2.418605  0.774067  1.0  2.0  2.0  3.0   4.0   \n",
       "\n",
       "             total_sqft               ... price_per_sqft                \\\n",
       "                  count         mean  ...            75%           max   \n",
       "availability                          ...                                \n",
       "0               10564.0  1868.890308  ...    7548.730678  1.200000e+07   \n",
       "1                 126.0  1752.495169  ...    7060.593804  3.499579e+04   \n",
       "10                 90.0  1469.258333  ...    5863.882212  3.633333e+05   \n",
       "11                 79.0  1327.417722  ...    5743.458029  1.128571e+04   \n",
       "12                711.0  1467.756281  ...    6734.110393  6.727273e+05   \n",
       "2                  74.0  1606.851351  ...    7532.909317  4.848485e+04   \n",
       "3                 239.0  8015.638807  ...    7042.328255  4.799465e+04   \n",
       "4                 296.0  1464.878007  ...    6489.864865  2.861446e+04   \n",
       "5                 331.0  1556.369456  ...    6795.790416  2.716667e+04   \n",
       "6                 196.0  1457.557143  ...    7150.713480  1.712121e+04   \n",
       "7                 205.0  1617.867463  ...    6666.666667  2.340411e+04   \n",
       "8                 249.0  1292.092529  ...    6315.789474  2.296296e+04   \n",
       "9                  86.0  1430.950581  ...    5912.226735  7.653061e+04   \n",
       "\n",
       "             sqft_per_BHK                                                     \\\n",
       "                    count         mean           std         min         25%   \n",
       "availability                                                                   \n",
       "0                 10564.0   648.086184   2977.055604    0.250000  466.666667   \n",
       "1                   126.0   638.774750    296.960910  166.666667  498.375000   \n",
       "10                   90.0   551.216667    142.338456   15.000000  483.416667   \n",
       "11                   79.0   550.361814    149.960972  337.000000  447.500000   \n",
       "12                  711.0   577.866568    161.191608    3.666667  474.666667   \n",
       "2                    74.0   565.610736    206.320368  133.333333  483.000000   \n",
       "3                   239.0  4310.604600  44725.603155  131.250000  506.666667   \n",
       "4                   296.0   576.975705    237.949869   66.666667  475.000000   \n",
       "5                   331.0   578.256991    200.483435  200.000000  478.750000   \n",
       "6                   196.0   587.440136    133.129309  400.000000  513.916667   \n",
       "7                   205.0   622.745528    499.801270  395.000000  524.333333   \n",
       "8                   249.0   551.697342    103.983486  240.000000  497.000000   \n",
       "9                    86.0   586.321202    171.185196  354.000000  496.208333   \n",
       "\n",
       "                                                     \n",
       "                     50%         75%            max  \n",
       "availability                                         \n",
       "0             550.000000  625.000000  217800.000000  \n",
       "1             600.000000  643.750000    2080.250000  \n",
       "10            538.500000  615.750000    1065.000000  \n",
       "11            529.333333  600.000000    1166.666667  \n",
       "12            561.250000  642.500000    1520.000000  \n",
       "2             561.583333  622.666667    1283.333333  \n",
       "3             587.500000  668.750000  653400.000000  \n",
       "4             564.000000  630.250000    2750.000000  \n",
       "5             550.000000  625.250000    2750.000000  \n",
       "6             565.500000  625.500000    1320.000000  \n",
       "7             570.000000  648.000000    7500.000000  \n",
       "8             563.500000  615.000000     966.666667  \n",
       "9             562.583333  645.000000    1800.000000  \n",
       "\n",
       "[13 rows x 56 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('availability').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability\n",
       "0     2.741670\n",
       "1     2.515873\n",
       "10    2.466667\n",
       "11    2.240506\n",
       "12    2.506329\n",
       "2     3.094595\n",
       "3     2.527197\n",
       "4     2.533784\n",
       "5     2.580060\n",
       "6     2.448980\n",
       "7     2.517073\n",
       "8     2.289157\n",
       "9     2.348837\n",
       "Name: (bath, mean), dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('availability').describe().iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = df.groupby('availability').describe().iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.index = p.index.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = p.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.xticks(fontsize = 13)\n",
    "sns.barplot(x = list(p.index), y = list(p[0:13])).set_title('Mean prices for different months', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that prices don't fluctuate much in different months. So it would be better to drop this column as this won't provide us any useful insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['availability'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Outlier Removal :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "      <th>sqft_per_BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13246.000000</td>\n",
       "      <td>1.324600e+04</td>\n",
       "      <td>13246.000000</td>\n",
       "      <td>13246.000000</td>\n",
       "      <td>13246.000000</td>\n",
       "      <td>1.324600e+04</td>\n",
       "      <td>13246.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.801902</td>\n",
       "      <td>1.909255e+03</td>\n",
       "      <td>2.692586</td>\n",
       "      <td>1.584344</td>\n",
       "      <td>112.389392</td>\n",
       "      <td>7.915281e+03</td>\n",
       "      <td>701.773683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.295758</td>\n",
       "      <td>1.732484e+04</td>\n",
       "      <td>1.341506</td>\n",
       "      <td>0.800579</td>\n",
       "      <td>149.076587</td>\n",
       "      <td>1.065452e+05</td>\n",
       "      <td>6577.438478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.257423e+00</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.100000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>4.262295e+03</td>\n",
       "      <td>473.196667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.275000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>5.433101e+03</td>\n",
       "      <td>552.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.678750e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>7.317073e+03</td>\n",
       "      <td>625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.306800e+06</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>1.200000e+07</td>\n",
       "      <td>653400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BHK    total_sqft          bath       balcony         price  \\\n",
       "count  13246.000000  1.324600e+04  13246.000000  13246.000000  13246.000000   \n",
       "mean       2.801902  1.909255e+03      2.692586      1.584344    112.389392   \n",
       "std        1.295758  1.732484e+04      1.341506      0.800579    149.076587   \n",
       "min        1.000000  1.000000e+00      1.000000      0.000000      8.000000   \n",
       "25%        2.000000  1.100000e+03      2.000000      1.000000     50.000000   \n",
       "50%        3.000000  1.275000e+03      2.000000      2.000000     72.000000   \n",
       "75%        3.000000  1.678750e+03      3.000000      2.000000    120.000000   \n",
       "max       43.000000  1.306800e+06     40.000000      3.000000   3600.000000   \n",
       "\n",
       "       price_per_sqft   sqft_per_BHK  \n",
       "count    1.324600e+04   13246.000000  \n",
       "mean     7.915281e+03     701.773683  \n",
       "std      1.065452e+05    6577.438478  \n",
       "min      2.257423e+00       0.250000  \n",
       "25%      4.262295e+03     473.196667  \n",
       "50%      5.433101e+03     552.000000  \n",
       "75%      7.317073e+03     625.000000  \n",
       "max      1.200000e+07  653400.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, dropping all the rows where area in sq.ft. per BHK is less than 300sq.ft. as it would be impractical to have one like that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['sqft_per_BHK'] > 300]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the column sqft_per_BHK as it has performed its task of removing the some outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['sqft_per_BHK'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 12316 entries, 0 to 13319\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   location        12316 non-null  object \n",
      " 1   BHK             12316 non-null  float64\n",
      " 2   total_sqft      12316 non-null  float64\n",
      " 3   bath            12316 non-null  float64\n",
      " 4   balcony         12316 non-null  float64\n",
      " 5   price           12316 non-null  float64\n",
      " 6   price_per_sqft  12316 non-null  float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 769.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12316.000000\n",
       "mean       6202.463047\n",
       "std        4054.430860\n",
       "min           2.257423\n",
       "25%        4199.684878\n",
       "50%        5263.157895\n",
       "75%        6826.192062\n",
       "max      176470.588235\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, on having a look at the minimum and maximum values of the column 'price_per_sqft', we infer that there is a huge difference between the minimum and maximum values. So, dropping all rows either very high or very low from mean value of price per sqft as data spread over a wide range will cause overfitting which will ultimately hinder the model's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['location', 'BHK', 'total_sqft', 'bath', 'balcony', 'price',\n",
       "       'price_per_sqft'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "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>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  BHK  total_sqft  bath  balcony   price  \\\n",
       "0  Electronic City Phase II  2.0      1056.0   2.0      1.0   39.07   \n",
       "1          Chikka Tirupathi  4.0      2600.0   5.0      3.0  120.00   \n",
       "2               Uttarahalli  3.0      1440.0   2.0      3.0   62.00   \n",
       "3        Lingadheeranahalli  3.0      1521.0   3.0      1.0   95.00   \n",
       "4                  Kothanur  2.0      1200.0   2.0      1.0   51.00   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  \n",
       "3     6245.890861  \n",
       "4     4250.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'skewness is :'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "9.698589530500305"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize = (20,5))\n",
    "plt.title('Skewness', fontsize = 15)\n",
    "sns.set(font_scale = 1)\n",
    "sns.histplot(df['price_per_sqft'], kde = True, color = 'green')\n",
    "plt.show()\n",
    "display('skewness is :', df.price_per_sqft.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = df['price_per_sqft'].mean() - (df['price_per_sqft'].std())\n",
    "a2 = df['price_per_sqft'].mean() + (df['price_per_sqft'].std())\n",
    "b1 = df.price_per_sqft[df.price_per_sqft <= a1]\n",
    "b2 = df.price_per_sqft[df.price_per_sqft >= a2]\n",
    "b1.index = list(b1.index)\n",
    "df = df.drop(b1.index, axis = 0)\n",
    "b2.index = list(b2.index)\n",
    "df = df.drop(b2.index, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,6))\n",
    "sns.set_style('whitegrid')\n",
    "sns.histplot(df.price_per_sqft, kde = True, color = 'green', bins = 80).set_title('Skewness')\n",
    "sns.set(font_scale = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6733320774553124"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price_per_sqft.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11187.000000</td>\n",
       "      <td>11187.000000</td>\n",
       "      <td>11187.000000</td>\n",
       "      <td>11187.000000</td>\n",
       "      <td>11187.000000</td>\n",
       "      <td>11187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.552784</td>\n",
       "      <td>1488.741935</td>\n",
       "      <td>2.450165</td>\n",
       "      <td>1.585553</td>\n",
       "      <td>83.278677</td>\n",
       "      <td>5335.443963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.892665</td>\n",
       "      <td>856.504494</td>\n",
       "      <td>0.970279</td>\n",
       "      <td>0.784306</td>\n",
       "      <td>66.060372</td>\n",
       "      <td>1625.246537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2149.277061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>4110.350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>5066.625917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1625.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>6323.345659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>30400.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>10256.410256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BHK    total_sqft          bath       balcony         price  \\\n",
       "count  11187.000000  11187.000000  11187.000000  11187.000000  11187.000000   \n",
       "mean       2.552784   1488.741935      2.450165      1.585553     83.278677   \n",
       "std        0.892665    856.504494      0.970279      0.784306     66.060372   \n",
       "min        1.000000    340.000000      1.000000      0.000000     10.000000   \n",
       "25%        2.000000   1100.000000      2.000000      1.000000     47.000000   \n",
       "50%        2.000000   1275.000000      2.000000      2.000000     65.000000   \n",
       "75%        3.000000   1625.000000      3.000000      2.000000     95.000000   \n",
       "max       16.000000  30400.000000     16.000000      3.000000   2100.000000   \n",
       "\n",
       "       price_per_sqft  \n",
       "count    11187.000000  \n",
       "mean      5335.443963  \n",
       "std       1625.246537  \n",
       "min       2149.277061  \n",
       "25%       4110.350076  \n",
       "50%       5066.625917  \n",
       "75%       6323.345659  \n",
       "max      10256.410256  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  BHK  total_sqft  bath  balcony   price  \\\n",
       "0  Electronic City Phase II  2.0      1056.0   2.0      1.0   39.07   \n",
       "1          Chikka Tirupathi  4.0      2600.0   5.0      3.0  120.00   \n",
       "2               Uttarahalli  3.0      1440.0   2.0      3.0   62.00   \n",
       "3        Lingadheeranahalli  3.0      1521.0   3.0      1.0   95.00   \n",
       "4                  Kothanur  2.0      1200.0   2.0      1.0   51.00   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  \n",
       "3     6245.890861  \n",
       "4     4250.000000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting data of prices of 2 BHK and 3 BHK flats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_chart(df, location):\n",
    "    a = df[(df.location == location) & (df.BHK == 2)]\n",
    "    b = df[(df.location == location) & (df.BHK == 3)]\n",
    "    sns.set(font_scale = 1)\n",
    "    plt.figure(figsize = (7,5))\n",
    "    plt.xticks(fontsize = 13)\n",
    "    plt.yticks(fontsize = 13)\n",
    "    plt.title(location)\n",
    "    plt.scatter(a.total_sqft, a.price_per_sqft, color = 'Blue', label = '2 BHK')\n",
    "    plt.scatter(b.total_sqft, b.price_per_sqft, color = 'green', label = '3 BHK')\n",
    "    plt.xlabel('Total Area in Sqft. --->', fontsize = 12)\n",
    "    plt.ylabel('Price per Sqft. --->', fontsize = 12)\n",
    "    plt.title(location, fontsize = 20)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart(df, 'Sarjapur  Road')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart(df,'Whitefield')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_chart(df, 'Electronic City')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  BHK  total_sqft  bath  balcony   price  \\\n",
       "0  Electronic City Phase II  2.0      1056.0   2.0      1.0   39.07   \n",
       "1          Chikka Tirupathi  4.0      2600.0   5.0      3.0  120.00   \n",
       "2               Uttarahalli  3.0      1440.0   2.0      3.0   62.00   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.BHK = df.BHK.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing some of the entries where price per sqft of 2 BHK flats is less than mean price per sqft of 1 BHK flats. Similarly, for 3 BHK and 4 BHK flats too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5118.580125485394,\n",
       " 4938.533702205596,\n",
       " 5597.502555146354,\n",
       " 6709.094693258667,\n",
       " 6318.853424585863,\n",
       " 5497.089904425821,\n",
       " 4705.306482667697,\n",
       " 4677.296378473915,\n",
       " 4064.253649148257,\n",
       " 3717.5925925925926,\n",
       " 4850.0,\n",
       " 5069.124423963133,\n",
       " 5500.0]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = []\n",
    "for i,j in df.groupby('BHK'):\n",
    "    mean = j['price_per_sqft'].mean()\n",
    "    lst.append(mean)\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13, 16])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(df.BHK.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = []\n",
    "for a in range(2,7):\n",
    "    for i in df[(df.BHK == a) & (df.price_per_sqft < 5122.815770781089)].index:\n",
    "        l.append(i)\n",
    "\n",
    "for a in range(3,7):\n",
    "    for i in df[(df.BHK == a) & (df.price_per_sqft < 4938.0951715637775)].index:\n",
    "        l.append(i)\n",
    "\n",
    "\n",
    "for a in range(4,7):\n",
    "    for i in df[(df.BHK == 4) & (df.price_per_sqft < 5595.669150147777)].index:\n",
    "        l.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7933"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(l, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5758 entries, 3 to 13319\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   location        5758 non-null   object \n",
      " 1   BHK             5758 non-null   int32  \n",
      " 2   total_sqft      5758 non-null   float64\n",
      " 3   bath            5758 non-null   float64\n",
      " 4   balcony         5758 non-null   float64\n",
      " 5   price           5758 non-null   float64\n",
      " 6   price_per_sqft  5758 non-null   float64\n",
      "dtypes: float64(5), int32(1), object(1)\n",
      "memory usage: 337.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking correlation between various features and dropping features less correlated with price :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OnTtZkkiSL18+k9fBwcHExcWh0+lS/B1gQkICOp2OwMDAFPeT9PxeJPnUZ86WLRvOzs7cvXuXmJgY498Kkv8tk/apD69JpYWzs7PJ66T1/Ztlvi/pu/B+Aknqzo2MjOTu3bvG7/iNGzeMf49PUSqVuLu7A4l/34cPHxIWFsb9+/fx9/cH0v43SQ//2cSUVjExMUDiRe2Pef9AcuzYMebPn288W7K2tqZUqVIUK1aMixcvYkjlY7DeP2N93/t95h/z7Nkzpk+fzsmTJzEYDFhYWFCoUCEqVqzInTt3UnxPrly5kpUlDe3+2Jnwxzg6OtKiRQtatGhBfHw8s2bNYvv27axatSpZIvjcb4aSvpTvH1zel9QHrlKpgMQvrLW1Nf7+/nTr1o2AgACKFStGnjx5KFGihLHFdO7cOeRy+Wevn0HK2zyplZX093z79i3W1tYptnZS20+fP39+rl+/TlhYWIoJLolarebFixfG1lHS3+dT2yg0NNSkTKFQfHT5H/5Nklpyjx49SrFXIcmnEmp6fS+SJH3mlE5WIPEz3717l/j4eJPt8uHf8sO/4z9hjmW+L6k35P3WcFRUFLNmzeLAgQNoNBokEgn58uWjatWqH/2Of+jKlSvMmjWL27dvA4l/92LFilGyZEmePXuWbvGnhUhMqZR0luLn5/fJkVIAf/75J8OGDSN37tz89ttvlC5dmgIFCiCRSPD29jaeHZqTwWCgf//+PHjwgAEDBtCgQQNcXFxQKBS8fv2anTt3pvi+pAP7+5KS8vtdmB/y9/fn559/pmPHjiaDQJIolUomTpyIr6/vP+raSDrQv3z5MsX5SQfNrFmzAokHiapVq3L58mXUajXXrl2jQ4cOQGKrcPXq1bx69Yrz589/tBX2T9jY2PD48WM0Go1JVxGkPrHXqlULX19fLly4QPPmzT9a78SJEwwfPpyWLVsyd+5ck22U0o9uo6KijNvnn0hafqtWrfj111/T/H5zfC/Sul98za5evQpg0p08evRozpw5Q6dOnWjVqhWurq7GfdnX1/ezy3zy5An9+vXDysqK6dOnU7FiRQoVKoRUKuXQoUP4+fmZ58N8xn/2GlNaJXXtvT8sOUnSbWNOnjwJwMGDB9Hr9UyePJlmzZrh5ORkPHtKGkr9/lnI565t/BOBgYEEBQXRsGFDRowYQenSpY1nx0nXXVI6E0rp812/fh1IuXsqiaOjI0+fPv3k8Nikz/lPRvjY2NiQP39+goODefPmTbL5SS2g94dO16lTh+joaPbs2UNcXJyxuyfpGtvOnTt5/PhxqrrxUqtkyZLodDrj2ef7/vzzz1Qto169emTNmhVfX9+P3qZKr9ezadMmAOO1q6RrjkkHsPeFhoby6tUrXFxcUhVDSpydnbG0tOT27dsp7jvr1q1j2bJlvH37NsX3m+N7kTSyLKWfNLx79467d+9SsGDBVPUwZGZarZbt27cDGE9WoqOjOXPmDKVKlWLq1KkmJ1iPHz8mISHhs9vTz8+P+Ph4hg4dSocOHShSpIixa/dTxwlzE4kplVq2bIlUKmXhwoW8evXKWK7Vapk+fTpr1qwhMjIS+LsL5PXr1ybL8Pf358CBA8b3JUm6kJ90nSI9JH0RP7xwGRkZaTzbfT+GJKtXrzY5+/zjjz/w9fWlZMmSJoMtPlS0aFGqVKnCnTt3mDZtWrL7eun1ehYuXEhcXNw/vrOCh4cHKpUKLy8vk9hv377Npk2bsLOzM7m/YdLFZ29vbywsLIyJqWLFishkMtasWQN8fph4WiR9tgULFpj8vikgICDVZ5+2trYMHz4cjUZD3759k/0uLCEhwTj8u0SJEsYDVatWrZDJZCxfvtwkocXFxTFt2jRjnX8qaTj/gwcPWLt2rcm8S5cu8euvv7J79+6P3jLHHN+LBg0aYGtry5YtW0xOBrRaLTNnzkSlUv2rz5wZJH2W+/fvU7duXWOLSS6XY2FhQXR0tMl1LJVKxfTp0wHTbZfS9vzY3+TevXts2LDBuP4vTXTlpVKhQoUYPXo0s2fPpnnz5tSrVw97e3vOnj3Lw4cPqVu3Li1btgTA3d2dtWvXMnXqVK5cuYKjoyOBgYGcP38eBwcHIiIijEkM/r6uM3v2bKpXr25yv6x/E2+ZMmW4evUqXbp0oUKFCrx9+xY/Pz/UajVKpTLFM9uYmBg8PDxo2LAh79694+jRoygUCuOO/inz58+ne/fubN68maNHj1KrVi1y5cpFVFQU/v7+hISE0KRJE7p06fKPPlO/fv04f/48vr6+BAYGUrVqVSIiIvDz88NgMLBgwQKTLrk8efLg6upKUFAQxYsXNx4wbWxsKFGiBDdv3sTJySld73tWvnx5OnXqxLZt22jdujW1atUiIiKCY8eOYWtry9u3bz862OB9nTt35uXLlyxbtozmzZtTs2ZNihQpQmRkJAEBATx9+pTChQuzbNky4wGnQIECjB071ng/wgYNGmBtbc3Zs2cJDw+nWbNmtG7d+l99vrFjx3L9+nXmzJnDiRMnKFOmDC9evODYsWPIZDK8vLywsEj5fNcc3wsbGxu8vLwYMWIEnTp1omHDhmTPnp2AgACCgoKoVKkS/fr1+1ef+Ut6f1CJRqPh9evX+Pv78/TpU0qUKMGsWbOM85VKJQ0bNuTo0aO0b9+eGjVqEBcXx6lTp3j9+jX29vbExMSg1+uxsLDAwcEBS0tLLl26xKxZs2jYsCF169Zl/vz5rFixgkePHuHk5ERoaCinTp0yXpN7/2/ypYjElAa9evWicOHCrFmzhmPHjqHX6ylQoADjxo2ja9euxgNE8eLF8fb2ZtGiRfj5+SGVSsmXLx9Dhw6lXbt21K5dmzNnzjBgwAAAunTpwh9//MHVq1d5+PAhvXr1+texWlhYsGzZMn777TcuXLjA7du3yZ07N7Vr1+bHH39k/vz5+Pn5ERYWZjLyb9KkSVy+fJmDBw9iYWFB3bp1GTZsWLIh5ylxdHRk//79bNu2jePHj3P27Fmio6OxsbEx3nyzRYsW//gzWVlZsW7dOlavXo2vry9bt27Fzs6OunXrMmDAAOMPi99Xu3ZtgoKCko02rFKlCjdv3vzoUOl/Y9KkSTg5ObFjxw62bdtGrly5GD16NK9evWLVqlWfHHDwvmHDhlGnTh02b97MrVu3uHLlCgaDgcKFC9OlSxc8PT2TLat79+4UKlSI1atXc+zYMQwGA0WKFGHAgAG0a9fuX3+2bNmysWPHDlasWMHx48eNN5GtV68eAwcO/GSr2lzfi0aNGrFlyxZ+//13zp07h1qtxsnJiTFjxtC9e/dk1/oys/cHlVhYWGBnZ0fRokXp1asXnTp1StYl6eXlRe7cufHz82PTpk04OjpSunRp+vfvz4EDB1i/fj2XLl2iWrVqWFpaMmnSJBYtWsSWLVuwtbWlUqVKrF27lt9++42AgADOnz9P3rx58fT0ZMCAATRq1Ihz585hMBjMcsnhYySGjOhAFDKdxYsXs2TJEpYuXUqDBg0yOpyv1qtXr5DL5SlebB87diz79u3jwoULmfomtoKQ0cQ1JkFIRz4+PlSpUsX4Y+skYWFhHD9+nKJFi4qkJAifIbryBCEdNWvWjOXLl/PLL79w+vRpChYsyKtXrzh27BgajYZffvklo0MUhHS3evVqQkNDjYNs3vfgwQPGjRvHgwcPKFWqFPPmzSN37tyfXJ5oMQlCOsqdOze7du2iZcuW3Lp1i7Vr13L27Flq1KjBtm3bqFq1akaHKAjpRqPRsGjRIubNm5fi/KTbc7Vr147Lly9TunRpvLy8PrtccY1JEARB+EemTZvG48ePyZs3L3q9PlmLKSgoiF69ehlvxRUXF0fVqlW5ePHiJ3/ULrryBEEQBKPo6GjjHTPeZ2dnh52dnUnZjz/+iKOjI4sXLzb5fWeS0NBQChYsaHxtbW2Ng4MDoaGhJjfd/ZBITBlA8zrlB+llZpGd//0Q9i/JZlzyp+hmdm167v18pUxmYdpugJ7hukV/HY8Wf9+Vp//+CcVpOeas33owxXshpnTja0dHx08uKzY2Ntn9PhUKRYoP13yfSEyCIAjfOv2n7zL+vh49euDh4ZGs/MPWUmoolcpkd4FRqVTJHrHxIZGYBEEQvnWG1D+6IqUuu3/K2dnZ5I72cXFxREREfPYpwWJUniAIwrdOr0/9lI5cXV2xtbVl8+bNqNVqlixZQvXq1T97N3+RmARBEL5xBoM+1dO/9fTpU8qXL8/Tp0+BxLvKJP3w/K+//krVfTdFV54gCMK3zsxPoX1/UETevHmNj8oBKFKkiPGRHaklEpMgCMK3Lh1aQl+SSEyCIAjfOl36PevtSxCJSRAE4Vtn5q689CYSkyAIwjcuPQY1fEkiMQmCIHzrRItJEARByFREi0nISLfuBDJo9GTOHtyWYTFICxfBdtgoZM6F0T1/Rsy8OWiD7iWrJ1EqsRk8DMuqNQADCWfP8G7JQtAl3j5F0cQd6y6eSOyzogt+SMz/fkMXbJ77DAY+fsnMrX4EPXlF/hxZmdKtMaUKJX9mTMiLN8zc6sfd8JdksZLTrlZZ+jVNfJRFfIKGBXvP4Hf9PgaDgQblXRnZtg5KS/M/2tu5uDNDZg3Bubgzz8KesfCnhQT9GZSsXrEKxRgweQBOLk7EvYvj0OZDbP3fVrPHlxIrN2dyTR2ClaszmsfPeP7zQlR/JY9ZkkVJrok/YlO3KmAg5uh5XkxfBtrU32bnn3IpUYRxs0fhUqIIT0KfMX3kbO78mXxfLlbalRFTh+BSvDCx7+LYv+UAqxasN873vbITewc7kh7m8Or5a9rV6mb2+I2+ssEP4ge23wiDwcAunyP0HzEBjVabcYHIZNhPnUnC6ZO89mhG3JaN2M+ehySFe2PZ/jQOSRYb3nh25E2f7shc3bDu0BkAy8pVyNJnANEzphDh0Qz1tavYTUr+ELL0oNHqGL58P40qunFu/mD6NqnCj4t38S4+IVndn9ceokqxgpydOwjv4R3Yevo6p28+AOC3PWe48fApG0d34dCMfsQnaJi2+ZhZYn6fTC5j8urJnPU9S7uS7di+eDszNs3A2sZ0m8st5UxZO4Vj24/RtkRbRrcbTXPP5lRvUt3sMSYjl5Fv2WRiDp3lfuV2RCzfTv7VM7DIknw/yTNzBBa2WXhUvyfB7v1RlHIhW592Zg9RJpcxb60Xfj6nqFvMnTWLNrB463yyfLBdrZRWLNw4Bz/fUzQo2YKBHUbQvENTPLq2AMA+mz2OuXPQqHQr6rg0oY5Lky+blCDD7vzwT/3nEtPjx49xc3OjfPnyxqlbt26EhIQA4Obmluz27QcPHsTT0xOAPXv20KdPH5P53t7e1KxZk4cPH36Rz5CSpas2smPfQfr36JRhMQDIy5YDmYz4vbtApyPh9El0ocFY1alnUs8iWzYsq9UgZsFcDHFxGCIjiZ48AdWJ4wAoW7UlbusmtEGBoNcTt20zMTOngkSS7jFfCQpHq9PTrV5F5FIpTSoVo0je7By9FpisbsiLNwAYMCAhMRxLWWLHw4kb9xnUogb5ctijtJQzok1tjl8LIiaFBJeeylQrg1QuZd/qfei0Os74nCEsKIzaLWqb1NOoNfSq3ovDWw4DYOdgh4XUgpjIGLPGlxLrymVAJuXthn2g1RFz6AwJD8KwdTeNWerogE29qrz4ZRH62Dh0b6J4MnAa0b6nzB5jxerlkclkbF21E51Wx/H9J3kUFEzDlqb7cu68Obl57TY71+5Br9cTHvyY00fOUbZyaQCKl3YlPPgxqniV2WP+KIM+9VMm8J/syrO0tDT+Mlmr1TJ//nx++eUXNm7cmOZlbdy4kQ0bNrB+/XqKFCmS3qGmWofWzRjcrzuX/7iZYTEAyJwKoQsLNSnThochdS5sWq+oC/qXL1DUa4jSoy1IZSScOEbsujWJ811cUV+5RNYFi5EWcEIbFEjM4oVghudaPnoeQeHc2UzKnHNl48HT18nq9mtalaU+F/j9wAV0egPd6lWkeolCQGKrVWn1d7edhYUErV7Pk9eRFCuQK93jTuLk4kT4/XCTsvCH4RQqVihZ3fjYxMcNbLq6iey5snNq3yn+uvSX2WL7GKsiTqgfmsasfhSOlWshkzJF8aJonr3CrkVdsnq2RCKTEe17ktf/22D2GAu7FCLkgem+HPIgjCLFTffl0IfhjOkz0fhaJpdRvV5V9m7yARK7+SQSC9YdXEFepzzcuxXEb5MWJ1u2WWWSllBq/edaTB+SyWS0bNmSe/eS9xt/zs6dO1mxYkWGJyWAnI7ZM3T9SSRKJYYPbnNvUCUgUViZ1rO1wyJ3bqTOzrzp35vIUUOxrFYD646JXXkSO1sULVsTs2AeEZ3bow0LxX76LLCQpnvM8QkaFJam52gKSzkqdfJ+eYlEwsi2dbi4YCjbf/bkxI377L1wC4D65VxYfvAiLyJjiFOp+d/ec0gtJKjU5u1aVWRRkKAy3eYJ8QlYKa0+8g7oVaMXfWr1waW0C91GfeFuJUBircCg+vx+IrW3QZ4vF5YuBQlpNZBwzzHY1K1Ktr7tzR6jMosS1QetXVW8CoVS8dH3yC3lzFw2GVW8ij0b9wOg0+u5c+MuY/pOpEXl9gT9dZ//bf71k3+f9GYw6FI9ZQb/+cSk0WjYt28ftWv/3YXQpEkTKlWqZJwmTJiQ7H0+Pj5MnjwZb2/vDE9KmYlBpUJi+UESUlhh+ODBYAaNBolURuzyZaCKR//8GfG7d2BZs1ZiBbUGlc++xNaXRk3sam+k+fMj/czt8v8JhaUclcY0eajUGpRWliZlt0Ofs/nkNbrUrYCVXIZb/pz0bPgdO87eAGBU2+8pkicHXWdvpuOsjZQqlBullSV21h8/kKUHVZwKyw9itVJaoYr9eNeRJkHD05Cn7Fq+i2qNqpk1vpQY4lVIPohZorBCH2cas16tQSKT8mrOSgxxKjSPn/N23R5sG5r/ulh8XDxWCtMYFUqFsdX5oeyO2fh950IccmRlUMcRJKjUAGxctpVJQ2bw8tkrEuITWOK1ArusdhQv7Wb2z2D0lXXl/ScTk1qtNiadcuXKsXnzZtq2bWucf+TIEa5evWqcZs6cafL+e/fuMXfuXIoUKYKvr++XDj9T04aFIC1QwKRMVsAJXahpt4UuLAwAyfu3v5f+3RrShoeZzpNIgPS/vgRQJE92Ql+8NSkLfvGGwnlMW6Ev3sag0eqMI6sSQ7ZA9v9xv4iMYWCL6vjN/gHfqX2o5FoAg8GAU04Hs8SdJOx+GPmL5DcpK1CkAGH3w0zK8hbKy+pzq03O1OVWct5FvzNrfClJeBiGpbNpzJaFC6B+aBqzOvgxABZ27+0LsvRvNackOCiEgkVMT4QKFXUiOCgkWV1nl4KsP+xNePATBnUcSUzU39u0c9/2xutNAFKpFJlMijpBbbbYk9FpUz9lAv/JxGRpaWlMOrdu3WLRokUMGjSI8PDwz7+ZxOtSq1ev5rfffmPz5s34+/ubOeKvh+bGdUCCsk17kEqx+r4eUuciJFwwfTy0LuQRmsB72Pw4GBQKLHLmQtmmPQmnTgCgOnoIZUsPpIWLgExGlr4D0IWGoAsJTveYv/v/BLLxxDU0Oh1Hrt7j/pPX1Ctb1KReuSL50OkN/H7gIlqdntCXb9ngd5UmlYoBsP74FWZuPUGCRktEdCyztp+gXc0yyKTm/ZrdvHgTiURC676tkcqk1GlZB+fizlw8ctGk3rPQZ2gSNPQY0wOpTIqTixNt+7flyJYjZo0vJXGXboJEgkOP1iCTYuteBys3Z94dN41ZHRRC/K0gco7rj0RphSxvThx6eBB98IzZY7x68ToSiYTO/dojlUlp2KoeRYsX4dRh033Z1t6GxVvnc9znJFOHe6H5oAs4T4HcjJo2lOw5s2OlsGT4lEGEPXrMvVvJh8abjV6X+ikT+E8mpvdZWFhQr149nJycuHXrVqreU6pUKVxdXXFxcWHw4MGMGzeOyMhI8wb6tdBqiZowBqtatcm+5wDWXTyJnjIBQ1QUVvUakMPnsLFq1IQxGNRqsm/YisNSb9T+F4jfswsAlc8+4rZsxO6XqWTf7YuskDNRUyZ+bK3/ilwmZengtpy4EUSdn5ay6sglFgxoRTZbaw5evku1EYsAyGZrzdLBbbgSFM73Y5bx4+JdtKpaks7flwdguEdt9Ho9DcYtp4PXBooXyMWQVrXMEvP7tBotv3T/hZpNa7Lj1g46DunItL7TiHoTRd3Wddlzbw+QODhjap+p5C+Sn203tjFp9SR2Ld/FyT0nzR5jMhotj/v/gk2jmrhc2kH2HzrydNA0dG+jsG1eF5dre4xVn/SfhEGtpvDxtRTctYh3JwJ4u36f2UPUarQM6zqaes3qcOLOQXoP7c7o3hOIfBNFE4+GnLmfmNDd2zUmV96ctO3eijP3jxinGcsmAbDEawV//XGHzcdWcfSmD/mc8jKy5zj0X3JAwlfWlScxGMwwzCkTe/z4MU2bNjVJQleuXGHAgAHs2bOHxo0bc/78eRwdHY3zDx48yLZt29i4cSN79uzh4MGDrF69GgCdTkfnzp3JnTs3ixYtSlUMmtfm+ZGoOUV27pXRIaSJzbiuGR1CmrXpuTejQ0izhVkyOoK06RYdm9EhpNmVp2c/X+kzVAGpfx6SomrHf72+f+s/OVxcrVZTvnx54+s8efIwY8YMChUqlOZlSaVSZs+ejYeHB7t37za5ViUIgpApZJKWUGr951pMmYFoMZmfaDF9GaLFZH7p0mK6sDnVdRU1Mv67859sMQmCIPyXGL6ye+WJxCQIgvCt+8ru/CASkyAIwrfuK7vGJBKTIAjCt060mARBEIRMRbSYBEEQhEwlk9xqKLVEYhIEQfjWia48QRAEIVMRiUkQBEHIVMQ1JkEQBCFTES0mQRAEIVMRLSZBEAQhUxGj8oTP+dpuiAqQdevajA4hTWL6fH3bOCQh5Ud2Z2YFD0zJ6BDSRNJ0TkaHkDFEV54gCIKQqYjEJAiCIGQqX9nTjURiEgRB+NaJFpMgCIKQqYjEJAiCIGQqYlSeIAiCkKmIa0yCIAhCpiK68gRBEIRMRSQmQRAEIVP5ym5JZJHRAQiCIAjmZdDqUj2lxdWrV2nWrBnlypVj4MCBvHv3Llmde/fu0bZtWypUqECbNm24efPmZ5crEpMgCMK3zqBP/ZRK8fHxDB06lDFjxhAQEIBCoWDp0qXJ6o0ZMwZPT0+uXbtGhw4dGD169GeXLRKTIAjCt05vSP2USgEBAeTLl486deqgUCgYPHgw+/fvT1YvPDwcg8GAwWDAwsICKyurzy470yWmN2/ekJCQ8Mk6sbGxREVFfaGIPu/p06dmW7a0cBGy/m8ZOXyO4OC9FplrsRTrSZRKbEePI/tuX7Lv9sFm2CiQSo3zFU3cybZhK9n3HybrwiVInQubLea0uHUnkNrNOmV0GEgLFcFuzjIcth/B7n9rkRZNeTujVJJl6DiybvIl60YfrH98bztbWGDdZxBZ1+0h6yZfbH6eiSRbDrPE61bSha2HVnMt+Az7T2+hVLniKdYrUaYYG/ev4NL9E5z4w4eBo/oY5ymUVkyeO46ztw7hH3icJevnkidfLrPECxAY8gTPCQup4jmWtqN+5a8HYSnWuxfyhF6TF1O9xzhaDZ/FofPXUqy33vcUTQdNM1u8AC4lirDaZxlnHhxhy4m1FC+b8n5RrLQrK/Ys4sTdA/hc2UGfET1M5rfs7M5e/62cCjrMyv1LKFr8C3//9PpUT9HR0Tx+/DjZFB0dbbLI0NBQChUqZHzt5OREREQEkZGRJvV69erF+PHjKVWqFLNmzWLGjBmfDTfTJaamTZsm2wAf8vT0JDg4+LPLcnNz49WrV+kVmtG9e/eoW7culSpV4vLly3h4eKT7OgCQybCfOpOE0yd57dGMuC0bsZ89D4m1dbKqtj+NQ5LFhjeeHXnTpzsyVzesO3QGwLJyFbL0GUD0jClEeDRDfe0qdpPM+4X+HIPBwC6fI/QfMQGNNoN//CeTYfPzTBLOn+Rtl2aodm3Eduo8UCbfzjZDErdzZL+ORA3ujqyoGwqPxO1s1aQVsmKliBrSk8jebTGo4snSf1i6hyuXy1i6fi6H9x2niks9Vixcy6odi8lik8WknkJpxfItCzi8349qbg3p3XYQrTs1p71nawBGThxMQef8tKzdme/LNifi1Rvme89M93gBNFotw+eupnH18pxf60XfNg34YeZy3sWpTOq9i1MxyGsFZVwKcmrldH77qRf/23wA/5uBJvUCQ56wbPths8SaRCaXMXftTI77nKResWasXbSRxVvnkcXGdL+wUlqxYONs/HxP0bBkSwZ1GEHzDk1o3bUFANXrVWHQzwP4ecAU6hdrxuWzV5nt/YW/f2lITOvXr6d+/frJpvXr15ssMjY2FqVSaXwtk8mQy+WoVKZ/U5lMxuLFi7lx4wY//fQTw4cP/2zjI9Mlpg+z7T+tY06nTp2iVKlSXL16lbx58xIXF2eW9cjLlgOZjPi9u0CnI+H0SXShwVjVqWdSzyJbNiyr1SBmwVwMcXEYIiOJnjwB1YnjAChbtSVu6ya0QYGg1xO3bTMxM6eCRGKWuFNj6aqN7Nh3kP49Mr61JCtVDolMRoJv4nZWnzuJLiwYq5qm21nikA155RrELp0L8XEYoiKJ8ZqA+kzidpbmcwIk/z8BegMG9ae/gP/EdzUqIpPL2OC9Da1Wx6F9x3kQ+Aj31g1M6uXOm4s/r95iy5qd6PV6QoPDOXH4DBWrlAPAysqSJXNXEvk2igRVApvX7KRMhZJI32tpp5crtx+g0erp1qwOcpmUpjUqUCR/bo5evG5S70ZgMDq9gWFdmmNlKadI/tx0bFyT3X7+xjoqtZoJSzbTqUnNdI/zfRWrl0Mmk7Ft1S50Wh3H95/kUVAwDVqa7he58ubk1rU77Fy7F71eT3jwE84cOU+5yqUB6NC7LesWbeLuzUD0ej3rFm9mwsCpSL7k989gSPXUo0cPTpw4kWzq0cO0FahUKk0SjFarRaPRmCSrmzdv4ufnR8OGDbG0tKRr165YWlpy5cqVT4abqYaLd+/eHYCGDRvi7e3NypUruX79Ojlz5mTUqFHUr1+fcePG8fTpU3r06MHvv/9Orly5mD59OoGBgajVapo0acKMGTNS/Ud/8eIFP/30E3fv3iVnzpz88MMPtGzZEo1Gw8yZM/H19SV37tzUrFmT+Ph43NzcWLp0KQaDgU6dOvHq1SvUajXly5fn0qVLWFpaptv2kDkVQhcWalKmDQ9L1g0nK+qC/uULFPUaovRoC1IZCSeOEbtuTeJ8F1fUVy6RdcFipAWc0AYFErN4YYb+GrxD62YM7tedy398foSOuUkLFEL32HQ7656EIS30wXYu7IL+9Qss6zRE0bwtyGSoTx8jfnPidk445otl1Zo4bPLBoNOhf/aY6PFD0j3eoq7OPLofYlIWfD8Ul+JFTcpCHoYxpOcY42u5XEatetXYuXEfAJN/mmVSv0HTOty/9widLm0js1Lj0eMXFM6f06TMOV9O7oc9MynTG/RYyWVYWPx9ziy1sCD02d89H79t9OX7SqUoUbgAx/xvpHusxvhcChHywHS/CH0QlqwbLuxhOGP6TDS+lsllVKtXhX2bfAEoVsaVi6cu4b1vMYWKOnH3z0B+/Xkhhi/5/UvDaDs7Ozvs7Ow+W8/Z2Zljx44ZX4eGhuLg4IC9vb2x7NmzZ2g0GpP3yWQyZLJPp55M1WLasGEDAMePH2fhwoW4ublx8eJFpk2bxtixY3n48CGzZ88mb968rF+/nurVqzNx4kS+//57Ll68iI+PDydPnvxsNn7f8uXLKVeuHFeuXGHmzJnMmDEDtVrNxo0buXXrFn5+fsyfP599+/YB0LVrVwYMGEDbtm3Ztm0b69evx9LSkuvXr6drUoLE60aGD5q8BlUCEoXpxUOJrR0WuXMjdXbmTf/eRI4aimW1Glh3TOxiktjZomjZmpgF84jo3B5tWCj202eBRfqfGadWTsfsGbbuD0kUybczCQnwwUVaiY0dFjlzIy3oTNSw3sT8PBR55Roo2iRuZ2Qy1H9c5m3vdrzt2hxt0F1sxk1P93its1gT/0EXWHy8CqVS8dH3yC3lzFsxA1W8im3r9ySb7966Ib0HezJz/Lx0jxcgTpWA4oPvh8LSEpVabVJWvlhhNFoda/adQK3R8vDxc3b5XUSlTjy4nfvjDn89CGVAu8ZmifN91lmUqOJN9wtVfAIK5ccv3sst5cxYNglVvIo9GxMHAthltaV9z9bMGj2P5hXbE3I/lPnrZ5mlZfpRZhiVV7VqVUJDQzlx4gQqlYply5bRtGlTkzrlypXj8ePH7NmzB71ez759+4iOjqZMmTKfXHamSkxJ9Ho9d+7cYejQoVhaWlKpUiUaNWrEkSNHktWdN28eXbt2JTY2ltevX2NnZ8fr169TvS47OzsCAgI4ffo0JUqUMLZ6jh07Rq9evXBwcKBYsWLmu470CQaVConlBwdHhRWGeNMnnRo0GiRSGbHLl4EqHv3zZ8Tv3oFlzVqJFdQaVD77EltfGjWxq72R5s+P1MnpS32UTM2QkHw7Y2UFH9nO8Wv+fzu/eIZq/w4sqyVu5yzDxqO+cApDxCuIjyN2xUJkxUohLZi+F7rj4+KTHRyVSgVxsSl3KefImZ11e5aRLUc2ercbTILK9GA7cFQfJv06liE9RnM14HqKy/i3lFaWJKhNz5xVajXWH5xk2VorWfpzf85fv0uDAZPxWrWLFnW+wy6LkoioGLxW72bm4K7IZeY/qMfHqbD6ID6F0oq42JSfNJzdMRvLdi4gWw4HBnccSYIqMelq1Bp2rttH8P1Q1Alqlnh541Q4PwWLfsHvnxlG5SmVSpYuXcrChQupXr06CQkJjBo1iqdPn1K+fHmePn1Krly5WLp0KevXr+e7775j69atrFixAusUrpO/L1N15SUJDQ0lW7ZsJi2QPHny8OLFi2R1g4KC6N27NyqVitKlS6PRaNLURB40aBBqtZqpU6cSFRVF165dGTlyJJGRkeTK9fcIpQIFChAYGPiJJaU/bVgIyrbtTcpkBZxQHT9mUqYLSxzdJLGxwRAXm1j43tmYNjwMiY3N32+QvHcdREAfHoJFS9PtLM3nhPqU6XbWP/n/7Zzl7+0seW87W+TIiUT+XqtArwMMGNJ5cMeDoGC6D+hsUubsUpD9Ow4lq1vE1ZmV2xdx6fxVJo3yQvNecpDJpMxZOpVS5UvQrUV/HgQ+Stc431c4f242HTxjUhb85CUt6lQyKVNrtOj1etZMHWws+9+WA5QoXAD/PwN5ExWD54SFAGh0ehLUGmr2HM/OeWPIk8MhXWMODgqhc3/T/aJgUScO7TyWrK6zS0EWbZ3H1fN/4DVmnsl2DnkQhp39398/CwvJl72+BBjMdEuiChUq4Ovra1JmY2PD9et/n+BUr149xWHkn5IpW0z58+fnzZs3qN9r5j99+hQHB9MdT61WM2LECLy8vDhz5gxLlizB5v0DcCoEBgbSv39/Tp8+zcaNG9m7dy/+/v7kyZOHZ8/+7v9++fLlv/tQ/4DmxnVAgrJNe5BKsfq+HlLnIiRcOGtSTxfyCE3gPWx+HAwKBRY5c6Fs056EUycAUB09hLKlB9LCRUAmI0vfAehCQ9CFfH5k43+B5tZ1kEiwapm4nS1r1UNWqAjqgA+2c+gjtPfvYd1nMFgpsHDMhaJle9RnE7ez5qo/ys69kGTNBpaWWPcaiC74Ifqn4eka7+XzV5FIJPQY0BmZTIp764a4lSiK36HTJvXs7G1ZtWMRh/cfZ/yQqSYHS4DxM0biVtKFTk17mzUpAVQuVRQDsPHAaTRaHYcv/MH9sKfUq2zapaPT6+k3bRl+AX+i1+u5cvsBe04E0L5hdZrXrsSlTb9yft0szq+bxawh3ciTIyvn181K96QEcPXidSQSCZ37tUcqk9KwVT2KFi/C6cOm+4WtvQ2Lts7juM9Jpg6flWw7+247RLueHriUKIJMLmPQzwN4FBjCo8Av+P0zQ4vJnDJdYpLL5ajVaooVK8aiRYtQq9Vcu3aNI0eO0KhRI2Od2NhY1Go1arUahUKBXq9nw4YNBAYGJrvY9inr16/nf//7HxqNBkdHRyQSCXZ2drRq1YrVq1fz+vVrQkND2b17d4rvt7S0RKfTfXb44z+i1RI1YQxWtWqTfc8BrLt4Ej1lAoaoKKzqNSCHz9/DZaMmjMGgVpN9w1Yclnqj9r9A/J5dAKh89hG3ZSN2v0wl+25fZIWciZoy8WNr/e/Rank3dQyW1WrjsPkAinaexHhNwBAdhWWdBjhs+3s7x0xL3M5ZvbdiN98b9aULqHwTt3Pc8gVo79/DfsEqsq7eiYVDdmK8JqT7IBONRsuAzsNo2LwuAUF+DBjei8E9xvA2IpLmbRtzNfg0AC3bNyV33lx06tGWq8GnjdO85dOxtbOhY482FCiYj+NX95nMt7HN8ukA/gG5TMbS8f3xu3ST2r0nsGqPHwtG9yGbnQ0Hz12jqudYILHLb/6oXvy+8yjVe4xn7rq9zBjUBdeCedM9ps/RarQM6zqGus1q43fnAL2GejKm9wQi30TR2KMBp+8n7hdN2zUmV96ctO3eitP3Dxun6csmAbBr3T7W/G8js1ZM5fhtX4q4OZsMlvgizHCNyZwkhi86NOTzxo8fz+HDh1m5ciXe3t5cv36dbNmyMWzYMJo1awbA4sWLWb16NbNnz+bly5csW7YMg8FA2bJlsba2pmDBgowYMQI3NzfOnz+Po6PjR9f38uVLxo0bx59//olSqaRnz5707dsXg8HAkiVL2LZtG9myZaNQoUI4ODgwbdo0Fi9ezKtXr5g2bRoGg4HevXtz48YNjh49Ss6cOT+6riSvGtZJt+31pWTdujajQ0iTmD69MjqENKvhn/K1i8zs+vEpGR1CmtRuOiejQ0izy0/PfL7SZ8RO6fz5Sv8vy5St/3p9/1amS0yZ1fvJ6N8Sicn8RGL6MkRiMr90SUyTUv97wSzTtv3r9f1bmXLwgyAIgpCOMkkXXWp984kpMDCQTp1SPlsoUKAAPj4+XzgiQRCELyyTDGpIrW8+Mbm5uZkMXfynhgxJ/1/wC4IgfAnmGi5uLt98YhIEQfjP04rEJAiCIGQm4hqTIAiCkKmIa0yCIAhCZmIQiUkQBEHIVERiEgRBEDIVMSpPEARByFTEqDxBEAQhM/na7jwnEpMgCMK3TlxjEj7HZlzXjA4hzb62m6Larv66bjoLUKD8wIwOIc0ifjDPo9jNRSH5jx7yRGISBEEQMhMxXFwQBEHIXERiEgRBEDITg1YkJkEQBCEzES0mQRAEIVP5un7GJBKTIAjCt04MfhAEQRAyF9FiEgRBEDITMfhBEARByFS+sucEisQkCILwzROJSRAEQchMRItJEARByFxEYvp2PX36lLx5837RdQY+fsnMrX4EPXlF/hxZmdKtMaUK5U5WL+TFG2Zu9eNu+EuyWMlpV6ss/ZpWBSA+QcOCvWfwu34fg8FAg/KujGxbB6Wl3CwxSwsVIcuPo5AWKozu+TNiF89B9+Be8opKJVn6DUNeuQYYDKgvniHOeyHodGBhgXWvH7GsVR9kcrR3bhK7fAGGN6/NEnNa3LoTyKDRkzl7cFuGxVC4uDNDZw2lcHFnnoU9Y/5PCwj6MyhZPZfSRflh8gCcizkT9y6Ow1uPsPl/WwBQKK3oO6EvtdxrIrGQcO7QebynrSRBlWCWmGVFC+MwZgSyooXRPX3G25lz0dwNTFZPYq3EfuQQlLWqg8FA/KmzRM5blLhfvMemc3uytPfgRZsuZokXoEiJwoyaPYIixZ15FvqM2aPmce/P5DG7lnZhyJSBFC5emLh3sRzYcpj1CzcCsP7kanLlz2WsK7WwwEppxcBWQ/jr6h2zxf6+r63FZJHRAWSES5cu0aRJkzS9Z9OmTSxfvhyAPXv20KdPH3OEZkKj1TF8+X4aVXTj3PzB9G1ShR8X7+JdfPIDx89rD1GlWEHOzh2E9/AObD19ndM3HwDw254z3Hj4lI2ju3BoRj/iEzRM23zMPEHLZNj8PJOE8yd526UZql0bsZ06D5TWyaraDBmHJIsNkf06EjW4O7Kibig8OgNg1aQVsmKliBrSk8jebTGo4snSf5h5Yk4lg8HALp8j9B8xAY1Wm2FxyOQypqyewhnfM3iUbMvWxduYtWkm1jam29hKYcX0ddM443uWdmU6MKbTOBq1b4h7l6YA9JvYl5LflWRYq+F4Vu2BQqlgxK9m2sYyGdl/nUHcidM8a9iSmHWbybHwVyTWyfcLhwljsLCx4blHF1507oW8mBs23TqaLq5oYWz7m/eO9zK5DK810znlcwr34q3YsGgL87fMSXE7z9ngxSnf07Qo5cGIjqNp2qERLbo2A6BHvT40cW1unM4fu8jR3ce/WFIC0GtTP2UG/8nE9E+8ffv2i6/zSlA4Wp2ebvUqIpdKaVKpGEXyZufoteRnbCEv3gBgwIAEkEjAUpbYID5x4z6DWtQgXw57lJZyRrSpzfFrQcSkkOD+LVmpckhkMhJ8d4FOh/rcSXRhwVjVrGdST+KQDXnlGsQunQvxcRiiIonxmoD6zHEApPmcIPGTJL5Bb8CgNs+ZfGotXbWRHfsO0r9HpwyNo2y1MsjkUvau3odOq+O0zxlCg8Ko06K2ST3HvI7c/eMuPut90ev1PA15ysVj/pT8riQANZvWZP3c9TwPf0GCKoGVM1dRu3ltrG2TJ4t/y6pC4n4Ru3036HTE+51CExyCskFdk3oW2bOhqFWdyNnzMcTFoX8bScSYicQfPfHewizJNnk8sbv2pXuc7ytfrSwymYydq/ag0+o46XOK4KAQ6rX83qReznyO3L52hz3r9qPX63kc/IRzRy9SunKpZMts6FGfEuWLMX/sQrPGnoxBkvopE/jPJiatVsv48eOpXLkynp6ehIeH8+7dO0aPHk3dunUpU6YM3bt35/Xr11y8eJEVK1awe/duxo0bB8C7d+8YOnQolSpVwsPDg9DQ0HSP8dHzCArnzmZS5pwrGw+eJu/O6te0KssPXKTy0IW0nLKGppWKU71EISDxTF9p9Xe3nYWFBK1ez5PXkekes7RAIXSPTbeF7kkY0kKFTcpkhV3Qv36BZZ2G2K/Yiv3qnSjcW6OPSPxsCcd8sciWHYdNPjhsO4LMxY24VYvTPd606NC6GTvWLKZkMdcMjcPJxYmw++EmZeEPw3Eu5mxS9vjRY6b2m258LZPL+O77Sjy8/RAAiYUEVZzKOF+v0yOTy8jjlCfdY5Y5F0QTYrpfaEPDkBcxjVnu6oLuxUuUjRuQa9cmcu3bhk271uhevTLWsR/8A/Hn/FHfup3ucb6vkGtBQh+Yxhz2IJzCH2zn8IePmdh3svG1TC6jat3vuP/XA5N6lgpLBv4ygEWTlqKKV/ElGfSpnzKD/2xiCg8Pp2TJkpw/f56KFSsyevRoVq5ciVqt5ujRo/j7+yORSNi2bRvVq1dnwIABtG3bltmzZwNw69Yt2rRpw6VLl3B2djZ286Wn+AQNCkvTy4AKSzkqtSZZXYlEwsi2dbi4YCjbf/bkxI377L1wC4D65VxYfvAiLyJjiFOp+d/ec0gtJKjU6d9ulyiUGBI+aNkkJICVlWk9GzsscuZGWtCZqGG9ifl5KPLKNVC0SezKQyZD/cdl3vZux9uuzdEG3cVm3HQyUk7H7Bm6/iTKLMpk14ES4hOwUlp95B0gt5Qzfsk4EuITOLDpIADnD13Ac2Q3sufOjsJaQd+f+6DT6rBSWKZ7zBZKJYYPYjaoEpAoTGO2sLNFmjs38sKFeOnZl9cDR6CoWR2bbomtVKtqVbAsUYyY1evTPcYPKbMoSfigV0EVr0KhVHz0PXJLOZOXTkAVn8D+jb4m85p1bMrrFxFcOO5vlng/xaCXpHrKDP6ziSlv3rx069YNS0tLBg4cyF9//UWXLl2YPj3x4Pfs2TPs7e159d6Z2vtKlSrF999/j1QqpXbt2jx79izdY1RYylFpTJOHSq1BaWV64Lgd+pzNJ6/RpW4FrOQy3PLnpGfD79hx9gYAo9p+T5E8Oeg6ezMdZ22kVKHcKK0ssbP++BfsnzIkqJBYfnCAtLKC+HjTehoNEqmM+DXLQBWP/sUzVPt3YFmtFgBZho1HfeEUhohXEB9H7IqFyIqVQlrQtOX1X6SKU2H1wT5gpbQiPjY+xfoOjg78um02WXNkZWyX8ahVagC8p3sTEhjCkgOL+f3oMgJvBBIfG09M1Lt0j1mvUiH5IGaJwgrDBy0Hg0aDRCYlatFyDPEqdE+f8W7bTpR1amHhkJWsPw3l7bRZyQZCmEN8nArLDxKnQqkgPi7l7ZzN0YGFO+aRNYcDIzqNNm7nJM06N02WrL4U0WL6SuTK9fcoGUtLS7Jmzcrbt2/58ccfqV27NtOnT+fly5cYDCnfysPW1tb4f7lcjs4MX5QiebIT+sL02lbwizcUzmN65v7ibQwarc4kVqnUAplUmjg/MoaBLarjN/sHfKf2oZJrAQwGA045HdI9Zn14CBb5CpiUSfM5oQs37RLRPwkDQJLFxlgm+f94ASxy5EQif+9AptcBBgwZOOggswi9H0b+IvlNygoUKUDY/bBkdZ1cnFhyYBFPQ54yrvN43r2XdHLkcWT9vI10rtSFXrV686f/TSQWEp4EP0n3mLXBIcicTPcLWUEnNMEhpvVCEz+Dhe3f+wX/v19YVa6ERTYHHFcuJc8xHxym/Iw0V07yHPNBmitnusccEhSK0wfb2aloAUKCknfbF3QpiPehZTwJecrITqNNtjNAzryOFC5emNMHzqR7nKmh10lSPWUG/9nE9Pr139dp4uPjefv2LV5eXtSuXRt/f3/Wr19PqVLJL15+Sd/9fwLZeOIaGp2OI1fvcf/Ja+qVLWpSr1yRfOj0Bn4/cBGtTk/oy7ds8LtKk0rFAFh//Aozt54gQaMlIjqWWdtP0K5mGWTS9P/za25dB4kEq5btQSrFslY9ZIWKoA44a1JPF/oI7f17WPcZDFYKLBxzoWjZHvXZxIvcmqv+KDv3QpI1G1haYt1rILrgh+ifhqe02v+UPy/+iUQioU1fD6QyKd+3rINz8UJcOHLRpJ6NvQ2zNs/ktO8Z5o6cj+aDLuD2P7RjqNcQ5FZysubIyuDpAzm46RB6XfqfNidcuwESCVk6tQOpFGWDusiLFkZ15rxJPe3DYNR37mE/bCAShQJp7lzYdGpH/PGTxB/141ldd541asmzRi15O8UL3YuXPGvUEt2Ll+ke8/WLN5BIJLTv1xapTEq9lnUpUrwwZw+bxmxjb8P8rXM46XMar+Fzkm1ngBIVihMaFMq76Nh0jzM1RFfeVyI8PJydO3eiVqtZtGgR1apVIy4uDisrKyQSCVeuXGHPnj1o//8M3dLSktjYL7tTyWVSlg5uy4kbQdT5aSmrjlxiwYBWZLO15uDlu1QbsQiAbLbWLB3chitB4Xw/Zhk/Lt5Fq6ol6fx9eQCGe9RGr9fTYNxyOnhtoHiBXAxpVcs8QWu1vJs6BstqtXHYfABFO09ivCZgiI7Csk4DHLYdNlaNmTYGg1pNVu+t2M33Rn3pAirfXQDELV+A9v497BesIuvqnVg4ZCfGawJ8pAX7X6LVaJnQfSI1m9Zk962ddB7Sial9pxH1Jop6reuy/95eABq0qY9jHkdaeDZn/729xmn8ksQBPKu8VmEhtWDb1S0sP7qM+389YM2ctWYKWkvEyHEov69FnqP7se3ZlYixk9BHRqFsVJ88Jw4aq0aMGo9BrSbX7k04rvkd1dkLvNu+2zxxfSpkjZbR3cZTx70WB2/vo/vQLkzoM4moN1E09KjPkaADADRu25CceRxp1b0FR4IOGKdJSycYl5U7f25ev8i43+AZDKmfMgOJ4WN9Vd+wS5cuMWfOHPLkycPFixepWLEiXl5eBAcH88svv/D69WsKFSpE1apVuX79Olu3buXu3bv07t2bihUrUq9ePQ4ePMjq1asBOHjwINu2bWPjxo2pWn/8CW9zfjyziF+0OaNDSBPb1WY6wJpRs/IDMzqENFubX/35SplI57DM0SJIi7NPTny+0meEVmiQ6roF//D71+v7t/6TiSmjicRkfiIxfRkiMZlfeiSmkHINU1230I3j/3p9/9Z/titPEAThv8JcXXlXr16lWbNmlCtXjoEDB/LuXfIRnW/evGHIkCFUqVKFxo0bc+7cuc8uVyQmQRCEb5xeZ5HqKbXi4+MZOnQoY8aMISAgAIVCwdKlS5PVGzFiBHnz5uXChQtMmjSJESNGkPDhbx0/IBKTIAjCN84cv2MKCAggX7581KlTB4VCweDBg9m/f79JnadPn3Lv3j1++uknZDIZNWrUYNOmTUgkn+5SFXcXFwRB+Mbp03APvOjoaKKjo5OV29nZYWdnZ3wdGhpKoUKFjK+dnJyIiIggMjKSrFmzAnDv3j2cnZ357bff2L9/Pzlz5uSXX37B0vLTdxcRiUkQBOEbZ0hDYlq/fj1LlixJVj548GCGDBlifB0bG4tSqTS+lslkyOVyVKq/7+YRExPDzZs3qVevHqdPn8bPz4/Bgwdz7Ngxk5sUfEgkJkEQhG9cWn4426NHDzw8PJKVv99aAlAqlSbXirRaLRqNxiRZyeVyrK2t6devHxKJBHd3d37//Xdu3rxJjRo1PhqDSEyCIAjfuLSMtvuwy+5jnJ2dOXbs7+e6hYaG4uDggL29vbGsUKFCqFQqNBqNsftOr9d/9FZvScTgB0EQhG+cTmeR6im1qlatSmhoKCdOnEClUrFs2TKaNm1qUqd48eI4OTmxYMECdDodBw8e5M2bN1SqVOmTyxaJSRAE4RtnMEhSPaWWUqlk6dKlLFy4kOrVq5OQkMCoUaN4+vQp5cuX5+nTp0gkElauXElQUBBVqlTh999/Z8mSJSgUn36ygejKEwRB+MaZ6/4+FSpUwNfX9FEeNjY2XL9+3fg6X758xtu3pZZITIIgCN+4tAwXzwxEYhIEQfjGpaWLLjMQiSkDtOm5N6NDSLOQhJSf2plZFfgKb4h68PqyjA4hzUoW75DRIaTJo6j0f9L010CXSZ6zlFoiMQmCIHzjRItJEARByFTENSZBEAQhU/naHronEpMgCMI3TrSYBEEQhExFXGMSBEEQMhUdIjEJgiAImYj+K7vIJBKTIAjCN04vWkyCIAhCZmIQiUkQBEHITPQZHUAaicQkCILwjRMtJkEQBCFT0WZ0AGkkHhT4lXEu7sxv+35jb+Belh1fhmtZ1xTrFatQjAX7F7D7zm42Xt5I52Gdv1iMbiVd2HpoNdeCz7D/9BZKlSueYr0SZYqxcf8KLt0/wYk/fBg4qo9xnkJpxeS54zh76xD+gcdZsn4uefLlMlvMhYs7s3DfAnwC97Hi+O8f3a4upYsyf9dc9vy1i00BG+g6rItJzINnDGL7H1vZcWMbQ7wGY6WwMlvMqXXrTiC1m3XK6DAoVtKF7YfWcCPkHL5ntlG6XIkU65UsU4zNPt5cfXCK09cPMGhUX+M8hdKKqXPHc/6vI1wOOsGyDfPNul+UKVOC82d9iHp7n+t/+FGpYtlP1nd2duLVi9vY2//9aPIsWaxZtfI3nj+9RcSru+zetZp8+fKYLeaUGJCkesoMRGL6isjkMiavnsxZ37O0K9mO7Yu3M2PTDKxtrE3qyS3lTFk7hWPbj9G2RFtGtxtNc8/mVG9S3ewxyuUylq6fy+F9x6niUo8VC9eyasdisthkMamnUFqxfMsCDu/3o5pbQ3q3HUTrTs1p79kagJETB1PQOT8ta3fm+7LNiXj1hvneM80Ss0wuY8rqKZzxPYNHybZsXbyNWZtmJtuuVgorpq+bxhnfs7Qr04ExncbRqH1D3LskPk6638S+lPyuJMNaDcezag8USgUjfh1mlphTw2AwsMvnCP1HTECjzdhzZrlcxrIN8zm0/ziVin7P8gVrWLNzSYr7hffWhRze70dl1/r0aPsjbTo1p6OnBwA//TKEgs75aV6rI7XKuBPxKoIF3l5milnOnl1r2LHTh+yOxZk1exGHD23B1tYmxfotWzbm9Mk9ODhkNSn3mvkzeXLnxK14DfLmL0dUVAxLFpsn5o/RS1I/ZQZfVWK6dOkSTZo0SdN7PD09OXjwoJki+rLKVCuDVC5l3+p96LQ6zvicISwojNotapvU06g19Krei8NbDgNg52CHhdSCmMgYs8f4XY2KyOQyNnhvQ6vVcWjfcR4EPsK9dQOTernz5uLPq7fYsmYner2e0OBwThw+Q8Uq5QCwsrJkydyVRL6NIkGVwOY1OylToSRSqTTdYy5brQwyuZS9/79dT/ucITQojDofbFfHvI7c/eMuPut90ev1PA15ysVj/pT8riQANZvWZP3c9TwPf0GCKoGVM1dRu3ltrG2tU1qt2S1dtZEd+w7Sv0fGt5Yq16iIXC5j/YqtaLU6Du47xoPARzRr3dCkXp68ublx5RabVu9I3C8eheP3wX6xeK63cb/YtHoHZSuWMst+8X2dasjlMhYtXoVWq2XHDh/u3AmiQ/uWyer27NGRObN+YfqMBcnmjRw1mbbt+xIVFY2tbRZsbbPw+vWbdI/3U/RIUj1lBl9VYvqvc3JxIvx+uElZ+MNwChUrlKxufGzi85M2Xd3E/w78j+vnr/PXpb/MHmNRV2ce3Q8xKQu+H4pL8aImZSEPwxjSc4zxtVwuo1a9aty9FQjA5J9m8celP43zGzStw/17j9DpdOkes5OLE2EpbFfnYs4mZY8fPWZqv+nG1zK5jO++r8TD2w8BkFhIUMWpjPP1Oj0yuYw8Tl+22yZJh9bN2LFmMSWLpdwt+SUVdSvMw6Bgk7JH90NwLWG6XwQ/DGVQz9HG13K5jNr1q3Pn//eLX0Z5cc1kv/ie+3cfmmW/KF7clXv3HpiU3Qt8QKlSxZLVPXT4BCVL1+a435lk83Q6HSqVipkzxvH08Z98V6k8s+csTvd4P8WQhikz+OoSk1arZfz48VSuXBlPT0/Cw8N59+4do0ePpm7dupQpU4bu3bvz+vXrZO8NCQnB09OT8uXL07JlS27evAnAkydP6NevH5UqVcLd3Z0TJ04A8PjxYxo2bMicOXOoVKkSDRs25Ny5cwB07dqVvXv/fuDf5s2bGTVqlFk/uyKLggRVgklZQnwCVsqPX8foVaMXfWr1waW0C91GdTNrfADWWayJf+/gDBAfr0KpVHz0PXJLOfNWzEAVr2Lb+j3J5ru3bkjvwZ7MHD8v3eMFUGZRpnm7yi3ljF8yjoT4BA5sSmyRnz90Ac+R3cieOzsKawV9f+6DTqvDSmFplrg/J6dj9gxZb0qss1gTH2+6jVOzX/zm7UV8vIqt63cnm9+sdSP6DunOtJ/npnu8ADY2WYiLN31AZlxcPNbWymR1X758jV7/6UHZ06YvwN7Bhf0+Rzh0YDMy2Zcbe6aVSFI9ZQZfXWIKDw+nZMmSnD9/nooVKzJ69GhWrlyJWq3m6NGj+Pv7I5FI2LZtW7L3DhkyhCpVqnD58mV++OEHhg8fjsFgYPTo0bi5uXHx4kWmTZvG2LFjefgw8Sw4LCyMLFmy4O/vT+fOnZk2bRoATZo04fjx48ZlHz9+PM3djGmlilNhaWV6kLNSWqGKVX3kHaBJ0PA05Cm7lu+iWqNqZo0PID4uHsUHB3SlUkFcbFyK9XPkzM66PcvIliMbvdsNTpYgBo7qw6RfxzKkx2iuBlw3S8yqOBVWKWzXpFbnhxwcHfh122yy5sjK2C7jUavUAHhP9yYkMIQlBxbz+9FlBN4IJD42npiod2aJ+2sSHxuPQpF8v4j9xH6xYe9ysudwoGfbgcn2i0Gj+jJl7jgGdf+Jq/7m2S9iY+NQKkwTp7W1knfvUo75cxISEoiPVzFm7HQKFsxP6RRaXuYiWkxmljdvXrp164alpSUDBw7kr7/+okuXLkyfntjF8uzZM+zt7Xn16pXJ+0JDQ3n8+DE//PADcrkcd3d3FixYwMuXL7lz5w5Dhw7F0tKSSpUq0ahRI44cOWJ8b69evYzvCQ9P7PJp1KgR/v7+xMfHExkZye3bt6lVq5ZZP3vY/TDyF8lvUlagSAHC7oeZlOUtlJfV51abnPHLreS8izb/AfJBUDCFihQ0KXN2KciDD7pxAIq4OrPj6DrCgh/Tu90goqP+vgYmk0mZv2IGrTo2o1uL/vifvWK2mENTuV0hsdtvyYFFPA15yrjO43n3XtLJkceR9fM20rlSF3rV6s2f/jeRWEh4EvzEbLF/LR4EPcK5qOl+UdilEA8DU94vdh9bT1hwOD3aDky2Xyzw9sKjY3M6N+/LxbOXzRbz3btBuLoWMSkr5laUu3eD0rScnTtW0q1bO+NruVyGRCIhMio6XeJMDX0apszgq0tMuXL9PTTU0tKSrFmz8vbtW3788Udq167N9OnTefnyJQaDae5/8+YN2bJlM2k+ly1blufPn5MtWzYsLf8+Y86TJw8vXrwwriNLlsSRQ1Kp1LjcXLlyUbx4cS5cuMCJEyeoUaMGCsXHuyXSw82LN5FIJLTu2xqpTEqdlnVwLu7MxSMXTeo9C32GJkFDjzE9kMqkOLk40bZ/W45sOfKRJaefy+evIpFI6DGgMzKZFPfWDXErURS/Q6dN6tnZ27JqxyIO7z/O+CFT0ag1JvPHzxiJW0kXOjXtzYPAR2aN+c+LfyKRSGjT1wOpTMr3LevgXLwQFz7Yrjb2NszaPJPTvmeYO3J+spjb/9COoV5DkFvJyZojK4OnD+TgpkPodZnl655xLp2/ikQCPQd0QSaT0qx1I9xKFOXYoVMm9ezsbVm7cwmH9h1n7OApybbxhJmjKFbShfZNe5p9vzh1+iISiYRhQ/shk8no0KElpUsXZ9/+w2lazpUrNxg7ZjD58+fF2lrJwgXTOXPGn+Dg5Cc+5iJG5ZnZ+9eO4uPjefv2LV5eXtSuXRt/f3/Wr19PqVKlkr0vZ86cvHnzBu17w2YXLFiAo6Mjb968Qa1WG8ufPn2Kg4PDZ2Np3Lgxp06d4tSpUzRu3PhffrLP02q0/NL9F2o2rcmOWzvoOKQj0/pOI+pNFHVb12XPvcTrMwaDgal9ppK/SH623djGpNWT2LV8Fyf3nDR7jBqNlgGdh9GweV0CgvwYMLwXg3uM4W1EJM3bNuZq8GkAWrZvSu68uejUoy1Xg08bp3nLp2NrZ0PHHm0oUDAfx6/uM5lvY5vl0wH8A1qNlgndJ1KzaU1239pJ5yGdmPr/27Ve67rsv5d4LbFBm/o45nGkhWdz9t/ba5zGLxkHwCqvVVhILdh2dQvLjy7j/l8PWDNnbbrH+zXSaLT07TSMxi3qcfn+SX4Y0ZuB3X/ibUQkLdo24XrIWQBadXAnd95cdO7ZjushZ43TbytmYmtnQ6cebSlQMB8nr/mYzDfHfqHRaGjeshttPNx59eI248cNpW27Prx+/YbOnT2IfJO6ltO8+b+zb99hLpzz4UFQAFKplE5dfkj3eD/laxuVJzF82LTIxC5dukT37t2ZMWMGrVq1YsGCBdy/f5/IyEiaN29Oz549uXLlCgMGDKBJkyZ4eXnh6elJp06daNasGe3ataN+/fr069cPPz8/fv31V/z8/OjSpQuVKlVi6NCh3Lp1i759+7Jp0ybs7e1p2rQpt27dAuDVq1fUrFmTwMDEEUIvXrygY8eOaLVajh07hrV16oYFNy3Q1GzbyFxCEiIyOoQ0KWCVLaNDSLOD15dldAhpVrJ4h4wOIU0eRT3L6BDSTKv+913Bm/KmfuBTt6eb/vX6/q2vrsVUsmRJTp8+TZUqVbh//z5eXl6MHj2aLVu2UKFCBWbNmkWnTp0IDk7ed71gwQIuXbpE1apV8fb2ZunSpVhYWPDbb78RGBhI9erVGT9+PDNmzKBkyZKfjSVXrlzkzp2b8uXLpzopCYIgfGlaSeqnzOCrajFlRsOGDaNx48a4u7un+j2ixWR+osX0ZYgWk/mlR4tpbb7Ut5h6PREtpq9WREQEFy9e5Pr169SrVy+jwxEEQfgoMfjhP+LMmTMMHjyYkSNHmn00niAIwr/xtQ0XF4+9+IfatGlDmzZtMjoMQRCEz8osCSe1RGISBEH4xhkySRddaonEJAiC8I372h4UKBKTIAjCN+5rG3otEpMgCMI3LrOMtkstkZgEQRC+cWLwgyAIgpCpiMQkCIIgZCo60ZUnCIIgZCaixSQIgiBkKmJUnvBZC9P/0TFmV/DAlIwOIU0ifpiX0SGk2dd2Q1SA23d3ZHQIaXKs5ISMDiFD6L+y1CTulScIgvCNM9e98q5evUqzZs0oV64cAwcO5N27dx+t++jRI0qXLs2rV68+u1yRmARBEL5xhjRMqRUfH8/QoUMZM2YMAQEBKBQKli5dmmJdvV7PxIkTTZ4U/ikiMQmCIHzjzPGgwICAAPLly0edOnVQKBQMHjyY/fv3p1h3w4YNlChRItXLFteYBEEQvnFpucYUHR1NdHR0snI7Ozvs7OyMr0NDQylUqJDxtZOTExEREURGRpI1a1ZjeXh4ODt37mTHjh1s3LgxVTGIxCQIgvCNS0sX3fr161myZEmy8sGDBzNkyBDj69jYWJRKpfG1TCZDLpejUqn+Xq/BwMSJExk7dixZsqR+1JdITIIgCN+4tAxq6NGjBx4eHsnK328tASiVShISEoyvtVotGo3GJFlt376dnDlzUrt27TTFKxKTIAjCNy4tXXkfdtl9jLOzM8eOHTO+Dg0NxcHBAXt7e2PZ8ePHuX79OpUqVTKWNWnShBUrVpiUfUgkJkEQhG+czgzLrFq1Kj///DMnTpygRo0aLFu2jKZNm5rUWb16tclrNzc3jhw5gqOj4yeXLUblCYIgfOP0GFI9pZZSqWTp0qUsXLiQ6tWrk5CQwKhRo3j69Cnly5fn6dOn/zhe0WISBEH4xpnrvg8VKlTA19fXpMzGxobr16+nWD8wMDBVyxWJSRAE4Rv3td3EVXTlfWWs3Jxx2vYbLn/spZDPMhSlXFOsJ8miJPeskRQN2EHRgO3kmjoEZNIvEmNgyBM8JyykiudY2o76lb8ehKVY717IE3pNXkz1HuNoNXwWh85fS7Heet9TNB00zZwhIytaGEfvxeQ5eZCcm1YhL+6WYj2JtZKsE8eQ5+g+8hzZS9axI0CafLvadG5Prj1bzBZvsZIubD+0hhsh5/A9s43S5VL+8WLJMsXY7OPN1QenOH39AING9TXOUyitmDp3POf/OsLloBMs2zCfPPlymS3mtLh1J5DazTpldBjYlnCi+sGpNA5eS63Tc7AvVzjFetlrlsT96WYaP1prnIqOSBzZZqG0pNSvfah/cxkN762k4vpRKPJl/5IfA0Ma/mUGIjF9TeQy8i2bTMyhs9yv3I6I5dvJv3oGFlmsk1XNM3MEFrZZeFS/J8Hu/VGUciFbn3ZmD1Gj1TJ87moaVy/P+bVe9G3TgB9mLuddnMqk3rs4FYO8VlDGpSCnVk7nt5968b/NB/C/adrUDwx5wrLth80btExG9l9nEHfiNM8atiRm3WZyLPwViXXy7eowYQwWNjY89+jCi869kBdzw6ZbR9PFFS2Mbf9eZgtXLpexbMN8Du0/TqWi37N8wRrW7FxCFhvT34kolFZ4b13I4f1+VHatT4+2P9KmU3M6eiYeMH/6ZQgFnfPTvFZHapVxJ+JVBAu8vcwWd2oYDAZ2+Ryh/4gJaLTaDI1FIpdSaf1PPN0fwDGXvjxYuI8q28cjs1Emq2tfuhDPfAM4WriXcXqwYC8AxSZ2JotzLs7WGcOJcgNRv4qiwoqhX/SzmOteeeYiEtMHli9fzsyZMzM6jBRZVy4DMilvN+wDrY6YQ2dIeBCGrbvpbwSkjg7Y1KvKi18WoY+NQ/cmiicDpxHte8rsMV65/QCNVk+3ZnWQy6Q0rVGBIvlzc/SiaZ/zjcBgdHoDw7o0x8pSTpH8uenYuCa7/fyNdVRqNROWbKZTk5pmjdmqQjkkMhmx23eDTke83yk0wSEoG9Q1qWeRPRuKWtWJnD0fQ1wc+reRRIyZSPzRE+8tzJJsk8cTu2uf2eKtXKMicrmM9Su2otXqOLjvGA8CH9GsdUOTenny5ubGlVtsWr0DvV5P6KNw/A6foWKVcomhWlmyeK43kW+jSFAlsGn1DspWLIU0hRbgl7J01UZ27DtI/x4Z31rKXr0EFnIpId6HMWh1PNvnT0zgY/K0rpasrl3ZwkT/FZricqRWcoLm7kbz9h16lYaQNcfIWqEoEumXO/zqMKR6ygzENaYP/PDDDxkdwkdZFXFC/TDcpEz9KBwr10ImZYriRdE8e4Vdi7pk9WyJRCYj2vckr/+3wewxPnr8gsL5c5qUOefLyf2wZyZleoMeK7kMC4u/v5xSCwtCn/195+HfNvryfaVSlChcgGP+N8wWs8y5IJoQ04OKNjQMeRFnkzK5qwu6Fy9RNm6ATXsPkMmIP+pHtPcaYx37wT8Qf84fzb1AlPW/N0u8Rd0K8zAo2KTs0f0QXEsUNSkLfhjKoJ6j/45fLqN2/eps35B4Jv/LKNPWUYOm33P/7kN0OnMMLk6dDq2bMbhfdy7/cTPDYkhi45afd/efmJS9e/AU2+IFktW1L10Iq+y2OPVogEQi4el+f4Jm70Cv1nLrp1UmdXM1qUTMvXAMui/XPhGPvcikLl26RKdOnRg6dCjlypWjY8eOhISEsHjxYgYNGsT3339Pv379WLx4MZMmTQLg9evX/Pjjj1SoUIFGjRpx5swZANRqNTNnzqRGjRrUq1eP7du3f5HPILFWYFAlmJQZVAlIFFYmZVJ7G+T5cmHpUpCQVgMJ9xyDTd2qZOvb3uwxxqkSUFhampQpLC1RfXBX4fLFCqPR6liz7wRqjZaHj5+zy+8iKrUGgHN/3OGvB6EMaNfY7DFbKJWp2q4WdrZIc+dGXrgQLz378nrgCBQ1q2PTLfHs3qpaFSxLFCNm9XqzxmudxZr4eNN44+NVKJWKj75HbinnN28v4uNVbF2/O9n8Zq0b0XdId6b9PDfd402LnI5f9trLp8iyKNDFm+63ung1UqXp/i2RWqB69obnh65wptZPBLSdQY7apXEdm/z5WnlaV6PI4Bbc/nmdOUNPRnTlZWLXr1+nTJkyXL58mSpVqvDTTz8BcO3aNbZv386CBQtM6k+cOJFs2bLh7+/PrFmzGDVqFO/evcPb25sHDx5w+PBh1q1bh7e3N1evXjV7/IZ4FRKrD74UCiv0H1y/0as1SGRSXs1ZiSFOhebxc96u24Ntw+pmj1FpZUnC/yeXJCq1GusPDvK21kqW/tyf89fv0mDAZLxW7aJFne+wy6IkIioGr9W7mTm4K/IvMGBDr0p5uxriTberQZO4XaMWLccQr0L39Bnvtu1EWacWFg5ZyfrTUN5OmwVmbnHEx8aj+GB7KpUKYmPjUqyfI2d2NuxdTvYcDvRsO5CED5LwoFF9mTJ3HIO6/8RV/5SH+f4X6eJUWChM9wup0hJd7AcnMTo9l9rNJHTtcfTxauJCXvBg4V5yN69sUq/oyDaUntObqz3n8ybgntnjN4lRDH7IvBwdHenduzeWlpYMGjSIe/fu8fz5cypWrEiuXLmwsbEx1lWpVJw7d45hw4ZhZWVFxYoVWbduHXK5HB8fH4YMGYKdnR1OTk507NgRHx8fs8ef8DAMS+f8JmWWhQugfmg66k0d/BgAC7u/P8+XGpFXOH9uQp6+NCkLfvKSwvlNR3upNVr0ej1rpg7m7JqZrJ4ymPgENSUKF8D/z0DeRMXgOWEhNXuOZ/ziTTx7HUnNnuN59vptusesDQ5B5mTaPSMr6IQmOMS0XmjidrawfW+7/v/1GKvKlbDI5oDjyqXkOeaDw5SfkebKSZ5jPkhzmXZt/lsPgh7hXLSgSVlhl0I8DAxOVreIqzO7j60nLDicHm0HEh0VY5wnk0lZ4O2FR8fmdG7el4tnL6drnF+7mMAn2BTJY1JmUzQvMUGPTcqscjtQfHJXJPK/v2MWljL0qsQTNIlMSvkVQ8jfsTYXW04l4uxf5g/+A6LFlInly5fPeE3DysqKrFmzEhERQfbsybsPoqOj0ev1JrfOKFWqFFZWVjx//px+/fpRqVIlKlWqxO+//87Lly+TLSO9xV26CRIJDj1ag0yKrXsdrNyceXf8okk9dVAI8beCyDmuPxKlFbK8OXHo4UH0wTNmj7FyqaIYgI0HTqPR6jh84Q/uhz2lXuUyJvV0ej39pi3DL+BP9Ho9V24/YM+JANo3rE7z2pW4tOlXzq+bxfl1s5g1pBt5cmTl/LpZ5MnhkO4xJ1y7ARIJWTq1A6kUZYO6yIsWRnXmvEk97cNg1HfuYT9sIBKFAmnuXNh0akf88ZPEH/XjWV13njVqybNGLXk7xQvdi5c8a9QS3Yv03Tcunb+KRAI9B3RBJpPSrHUj3EoU5dgh08Etdva2rN25hEP7jjN28BQ0H7RkJ8wcRbGSLrRv2pMHgY/SNcZvQcSF2yCR4DygKRKZlDytq2FbwokXh66Y1NO8fUfetjVwGeGBRGqBtXNuig734PG20wCUmN4duxIFuej+C+8CH6ewJvMTLaZM7P1H+iYkJBAZGUnOnCmfzWbPnh0LCwtev35tLFuzZg1hYWHkyJGDLVu2cPXqVa5evYqfn9+XGcmn0fK4/y/YNKqJy6UdZP+hI08HTUP3Ngrb5nVxubbHWPVJ/0kY1GoKH19LwV2LeHcigLfr95k9RLlMxtLx/fG7dJPavSewao8fC0b3IZudDQfPXaOq51ggsctv/qhe/L7zKNV7jGfuur3MGNQF14J5zR5jMlotESPHofy+FnmO7se2Z1cixk5CHxmFslF98pw4aKwaMWo8BrWaXLs34bjmd1RnL/Bue/JrNuak0Wjp22kYjVvU4/L9k/wwojcDu//E24hIWrRtwvWQswC06uBO7ry56NyzHddDzhqn31bMxNbOhk492lKgYD5OXvMxmW9jm/rHE3zLDBodl7vMJnezyjQKXEnR4a251vM31BEx5G1bg8aP1gKgT9BwpfMcslUtTsN7K6m2fxLPfC/xaPkhZHbWFOzRAGXBnNS9ssjkd04y2+TDzs1FazCkesoMJAZDJonEzC5dukT37t2ZMWMGrVu3ZtGiRdy4cYPKlSvz6tUrpk1L/AHn4sWLja+HDh2Kg4MDEyZM4NatWwwaNIijR4+yatUqQkJCmDVrFmq1mn79+tG4cWP69++fqlgCizX9fKVMpuD2IZ+vlIlE/DAvo0NIs7oPkj+cLbO7fXdHRoeQJsdKTsjoENKs2Yut/3oZ3Qq2SXXdTaF7Pl/JzP5TLaZ8+fJx/vx5qlatyq1bt5g379MHrylTpvDmzRtq1qzJxIkTWbBgAfb29gwaNAhHR0caN25M06ZNKVOmDL179/5Cn0IQBCFtzHETV3P6T/2OydLSkv/9738mZe8/kfHD19myZWPx4sXJlqNQKJg0aZJxWLkgCEJmllmuHaXWfyoxCYIg/BdlltF2qSUSkyAIwjdO95Wlpv9MYqpSpQpHjhzJ6DAEQRC+uK8rLf2HEpMgCMJ/1dc2+FokJkEQhG9cZhltl1oiMQmCIHzjRFeeIAiCkKmI4eKCIAhCpqIzfF1tJpGYBEEQvnFfV1oSiUkQBOGbJ7ryBEEQhExFjMoTBEEQMhXxOybhs7pFx2Z0CGkmaTono0NIE4Xk69u1H0U9y+gQ0uxre4xEo9tf4LlpmZBoMQmCIAiZihiVJwiCIGQqX1d7SSQmQRCEb57oyhMEQRAyFZGYBEEQhExFjMoTBEEQMhXxoEBBEAQhUxEtJkEQBCFTEdeYBEEQhExFtJgEQRCETEW0mARBEIRMRdxdXBAEQchUvrZbEllkdADfAoPBwPPnz82ybJcSRVjts4yzD46y9cQ6SpQtlmK9YqVdWbFnMSfvHsT3yk76juhhMt/3yk7OPjjKmftHOHP/CLvObTJLvO/HfObBEbacWEvxT8a8iBN3D+BzZQd9Poi5ZWd39vpv5VTQYVbuX0LR4oXNFnOREoVZ5rOYo/cPsM5vJcXKuqVYz7W0C4t3L+Dgnf3svLyFHsM9jfPWn1zNkaADxun4g0OcfXKCUpVKpHu8ZcqU4PxZH6Le3uf6H35Uqlj2k/WdnZ149eI29vZ2xrIsWaxZtfI3nj+9RcSru+zetZp8+fKke6xJbEs4Uf3gVBoHr6XW6TnYl0v575m9Zkncn26m8aO1xqnoCA8ALJSWlPq1D/VvLqPhvZVUXD8KRb7sZos5LW7dCaR2s04ZHUaK9AZDqqfMIN0S0/Lly5k5879z594pU6ZQrlw5xo4dy6+//oqPj0+6r0MmlzFvrRd+PqeoW8ydNYs2sHjrfLLYWJvUs1JasXDjHPx8T9GgZAsGdhhB8w5N8ejaAgD7bPY45s5Bo9KtqOPShDouTWhXq1u6x5sU89y1Mznuc5J6xZqxdtFGFm+dl2LMCzbOxs/3FA1LtmRQhxE079CE1v8fc/V6VRj08wB+HjCF+sWacfnsVWZ7TzNbzF5rpnPK5xTuxVuxYdEW5m+Zg/WHMSusmLPBi1O+p2lRyoMRHUfTtEMjWnRtBkCPen1o4trcOJ0/dpGju4/z19U76RqvXC5nz6417NjpQ3bH4syavYjDh7Zga2uTYv2WLRtz+uQeHByympR7zfyZPLlz4la8BnnzlyMqKoYli73SNdYkErmUSut/4un+AI659OXBwn1U2T4emY0yWV370oV45hvA0cK9jNODBXsBKDaxM1mcc3G2zhhOlBuI+lUUFVYMNUvMqWUwGNjlc4T+Iyag0WozNJaPMaThX2aQbonphx9+YMKEr+sW+P/Gzp072b59O3PmzOHt27dmWUfF6uWRyWRsXbUTnVbH8f0neRQUTMOW9Uzq5c6bk5vXbrNz7R70ej3hwY85feQcZSuXBqB4aVfCgx+jileZJU7TmMshk8nYtmqXScwNPog5V96c3Lp2h51r9/5/zE84c+Q85f4/5g6927Ju0Sbu3gxEr9ezbvFmJgycikQiSfeYy1cri0wmY+eqPei0Ok76nCI4KIR6Lb83qZcznyO3r91hz7r96PV6Hgc/4dzRi5SuXCrZMht61KdE+WLMH7sw3eP9vk415HIZixavQqvVsmOHD3fuBNGhfctkdXv26MicWb8wfcaCZPNGjppM2/Z9iYqKxtY2C7a2WXj9+k26xwuQvXoJLORSQrwPY9DqeLbPn5jAx+RpXS1ZXbuyhYn+KzTF5Uit5ATN3Y3m7Tv0Kg0ha46RtUJRJNKM6/xZumojO/YdpH+PzNlaAvO1mK5evUqzZs0oV64cAwcO5N27d8nq/PHHH7Rp04YKFSrg4eHBH3/88dnlfvaveenSJTp16sTQoUMpV64cHTt2JCQkhMWLFzNo0CC+//57+vXrx+LFi5k0aRIAr1+/5scff6RChQo0atSIM2fOAKBWq5k5cyY1atSgXr16bN++/bMBfmz9SXbu3EmDBg2oXr06M2bMQK1WA+Dp6cnYsWOpUqUKy5cv/+Q6Nm7cSK1atahevTqDBw8mMjISSNzozZs3p0KFCsydO5fatWvz6tUrateujVarpWPHjqxfvx5fX18WLVrEokWLPvt50qKwSyFCHph+QUMehFHkgy6t0IfhjOkz0fhaJpdRvV5VAv+6DyR2mUkkFqw7uIJjt3xYtGUehYoWTNdYkzinEHPog7Bk3XBhKcRcrV6Vv2Mu44pOr8d732KO/bWfeWtn8i461izDXgu5FiT0g5jDHoRTuJizSVn4w8dM7DvZJOaqdb/j/l8PTOpZKiwZ+MsAFk1aapaTgeLFXbl3z3Sd9wIfUKpU8i7TQ4dPULJ0bY77nUk2T6fToVKpmDljHE8f/8l3lcoze87idI8XwMYtP+/uPzEpe/fgKbbFCySra1+6EDlql6Lu1UXUu7aYYpO6YGGZeDn81k+reHs50Fg3V5NKxNwLx6DLuGsoHVo3Y8eaxZQs5pphMXyOOVpM8fHxDB06lDFjxhAQEIBCoWDp0qUmdVQqFYMHD2bAgAFcvXqVfv36MWjQIFSqT38vUnWacf36dcqUKcPly5epUqUKP/30EwDXrl1j+/btLFhgejY2ceJEsmXLhr+/P7NmzWLUqFG8e/cOb29vHjx4wOHDh1m3bh3e3t5cvXr1H6//ypUrLFmyBG9vb44dO8bjx49ZtWqV8X3Pnz/n9OnTdOv28W6riIgIFi9ezO7duzl9+jQ6nY5du3ahUqkYOnQoffv2JSAggNjYWF6+fAnA2bNnATh+/Dg9evSgRYsWDB06lKFD07dLQZlFiSo+waRMFa9CoVR89D1ySzkzl01GFa9iz8b9AOj0eu7cuMuYvhNpUbk9QX/d53+bf8VKaZWu8QJYpxhzAopPrEtuKWfGskkmMdtltaV9z9bMGj2P5hXbE3I/lPnrZyGVStM9ZmUWJQn/YDtPXjoBVXwC+zf6msxr1rEpr19EcOG4f7rHCmBjk4W4+HiTsri4eKytk3eLvXz5Gr3+0wftadMXYO/gwn6fIxw6sBmZLP3HRMmyKNDFq03KdPFqpEpLkzKJ1ALVszc8P3SFM7V+IqDtDHLULo3r2A7JlpmndTWKDG7B7Z/XpXu8aZHTMXNc4/oUc7SYAgICyJcvH3Xq1EGhUDB48GD2799vUufly5fUrFmTxo0bY2Fhgbu7e2IPSXj4J5edqsTk6OhI7969sbS0ZNCgQdy7d4/nz59TsWJFcuXKhY3N333bKpWKc+fOMWzYMKysrKhYsSLr1q1DLpfj4+PDkCFDsLOzw8nJiY4dO6bq2szH1r9//346d+5M4cKFsbGx4YcffjDZMPXq1UOpVJrE9yErKys0Gg27du3i6dOnLFu2jL59+3Lt2jVsbW1p3bo1lpaWjBo1KjWbKl3Fx8VjpTD94iqUCuJj41Osn90xG7/vXIhDjqwM6jiCBFXigWDjsq1MGjKDl89ekRCfwBKvFdhltaN46ZQv8P+7mFVYKUyTkEJpRdwnYl62cwHZcjgwuONIY8watYad6/YRfD8UdYKaJV7eOBXOT8GiTmaJ2TJZzAri41KOOZujAwt3zCNrDgdGdBqNWmV6wG3WuWmyZJWeYmPjUCpMk6a1tZJ37+L+0fISEhKIj1cxZux0ChbMT+kUWl7/li5OhcUH+7JUaYku1vSEwKDTc6ndTELXHkcfryYu5AUPFu4ld/PKJvWKjmxD6Tm9udpzPm8C7qV7vN8avUGX6ik6OprHjx8nm6Kjo02WGRoaSqFChYyvnZyciIiIMPY4JZX9+uuvxtc3b94kISGBAgWSt5Tfl6rElC9fPiwsEqtaWVmRNWtWIiIiyJ49+ZlCdHQ0er0eR0dHY1mpUqWwsrLi+fPn9OvXj0qVKlGpUiV+//13Yyvkn6z/+fPnLF++3Li8Pn36EBERYXxfSvF9yMbGBm9vby5fvkzz5s1p3bo19+7dIzIykly5chnr2drakjVr1s8uLz0FB4VQsIjpgbhQUSeCg0KS1XV2Kcj6w96EBz9hUMeRxET93dfbuW974/UmAKlUikwmRZ2gTrac9InZdKcrWNSJ4KDk1wycXQqy7vAKHqcQc8iDMOzs/z6hsLCQmOX6EkBIUChORfKblDkVLUBICjEXdCmI96FlPAl5yshOo3kXZdqnnjOvI4WLF+b0geRdZ+nl7t0gXF2LmJQVcyvK3btBaVrOzh0r6datnfG1XC5DIpEQGRX9iXf9MzGBT7ApYjriz6ZoXmKCHpuUWeV2oPjkrkjkf7eMLSxl6FUaACQyKeVXDCF/x9pcbDmViLN/pXus3yI9hlRP69evp379+smm9evXmywzNjYWpfLvVrpMJkMul3+0m+7Zs2cMGzaM4cOHo1B8vDcCUpmYXr16Zfx/QkICkZGR5MyZM8W62bNnx8LCgtevXxvL1qxZQ1hYGDly5GDLli1cvXqVq1ev4ufnl6qRfB9bv6OjI6NGjTIu7/z58+zdu9dYNzUHsqioKKysrFi3bh0BAQFUq1aNKVOmkDt3bpMh4AkJCcTExHx2eenp6sXrSCQSOvdrj1QmpWGrehQtXoRTh8+a1LO1t2Hx1vkc9znJ1OFeaNQak/l5CuRm1LShZM+ZHSuFJcOnDCLs0WPu3UrbgezfxHw6hZgXbZ33/zHPShaz77ZDtOvpgUuJIsjkMgb9PIBHgSE8CgxO95ivX7yBRCKhfb+2SGVS6rWsS5HihTl7+LxJPRt7G+ZvncNJn9N4DZ+TLGaAEhWKExoUyrvo2HSPM8mp0xeRSCQMG9oPmUxGhw4tKV26OPv2H07Tcq5cucHYMYPJnz8v1tZKFi6Yzpkz/gQHh6V7zBEXboNEgvOApkhkUvK0roZtCSdeHLpiUk/z9h1529bAZYQHEqkF1s65KTrcg8fbTgNQYnp37EoU5KL7L7wLfJzCmoSUGAyGVE89evTgxIkTyaYePUx/zqFUKklI+LvFq9Vq0Wg0JskqycOHD+ncuTMtW7akZ8+en403VYnpyZMn7Ny5E41Gw5IlSyhfvrxJi+h9UqmU+vXrs2TJEtRqNdeuXcPb2xt7e3uaNWvGkiVLePfuHW/evKF///7s3r37H6/f3d2dTZs2ER4ejlqtZtasWUyZMiU1H8nozZs39O3blwcPHpAlSxasra2xt7enbNmySCQStm/fblyv9iNDQS0tLYmNTf8DkVajZVjX0dRrVocTdw7Se2h3RveeQOSbKJp4NOTM/SMAuLdrTK68OWnbvZXxd0pn7h9hxrLEwShLvFbw1x932HxsFUdv+pDPKS8je4777LWHfx7zGOo2q43fnQP0GurJmP+PubFHA07fTzx4Nn0v5tP3Dxun6f8f8651+1jzv43MWjGV47d9KeLmbDJYIr1jHt1tPHXca3Hw9j66D+3ChD6TiHoTRUOP+hwJOgBA47YNyZnHkVbdW5j8XmnS0r9Ho+bOn5vXL15/bFXpQqPR0LxlN9p4uPPqxW3GjxtK23Z9eP36DZ07exD5JnUnHPPm/86+fYe5cM6HB0EBSKVSOnX5wSwxGzQ6LneZTe5mlWkUuJKiw1tzredvqCNiyNu2Bo0frQVAn6DhSuc5ZKtanIb3VlJt/ySe+V7i0fJDyOysKdijAcqCOal7ZZHJ75xktskPhsLf0tJisrOzI3/+/MkmOzs7k2U6OzsTGvp3r0JoaCgODg7Y29ub1Lt9+zZdu3alR48ejBgxIlXxSgyfGeZ06dIlxo8fT+nSpTl//jylS5dmzpw57Nixg1evXjFtWuJvSxYvXmx8/ebNGyZPnsylS5fInj07kyZNolq1aqhUKn799VeOHj2KVqvF3d2dCRMmfPJi68fWn9TNtnnzZtasWUNkZCTly5dn5syZ5MqVC09PTzp16kSzZs0+uxG2bt3KihUriIqKolSpUnh5eVGgQAEePnzIzz//TEhICB4eHqxdu5bz58/j6OiIm5ub8f9nzpxh5MiRtG3blp9//vmz6/sub+3P1slsJJinG81cFJKv76YmF199fddK9jt8Xftyo9tf328t5Tn+/Q/L8zmUTHXdJ29vp6pefHw89erVY8aMGdSoUYMJEyZgZ2fH5Ml/j1yNjo7G3d2dH3/8ka5du6Y6hlQlpsmTJ3PkyJFULzQ9ZfT63/d+Mvo3RGIyP5GYvgyRmMwvPRJT7qzFU133eeTdVNf9448/mDx5Mk+ePKF69erMnj2b6OhomjVrxsGDB7lw4QITJ07E2tr0x+rr16+nTJkyH13u1/ftFQRBENLEXI+9qFChAr6+piNQbWxsuH79OgDt27enffv2aV5upkhMtWvX/ujAgqSuwn9j5MiRnDp1KsV5M2fOxN3d/V+vQxAEIbP62h578dmuPCH9ia488xNdeV+G6Mozv/Toysthl/q7UryOTv/Rumn19X17BUEQhDTJLHcNTy2RmARBEL5xX1vHmEhMgiAI37iv7UGBIjEJgiB840RXniAIgpCpZJYHAKaWSEyCIAjfONFiEgRBEDIVMfhBEARByFT0YvCDIAiCkJmIFpMgCIKQqXxdaUnckkgQBEHIZFL1oEBBEARB+FJEYhIEQRAyFZGYBEEQhExFJCZBEAQhUxGJSRAEQchURGISBEEQMhWRmARBEIRMRSQmQRAEIVMRiUkQBEHIVERiEr4ojUaTYvnr16+/cCSCIGRWIjF9pYKDgz85f+/evV8okrSpUqVKsjK1Wk3jxo0zIJpv0+jRozlx4gRqtTqjQ0mTP/74g0mTJjFo0CDevHnDypUr0esz712xlyxZkmL57Nmzv3Ak3x5xE9evVPfu3dmwYQPOzs4m5dHR0fzyyy+cPXsWDw+PDIrO1LNnz2jdujUqlYqEhATKli1rMl+n01G5cuUMiu7zwsPDWb58Oc+ePUOr1ZrM27BhQwZF9XHly5dn06ZN/Pzzz9SoUYPGjRtTp04dFApFRof2UTt27GDlypW0b9+egwcPAnD06FFevHjBxIkTMzi6v7148QIfHx8AvL29sbKyMpn/7t07du/ezbhx4zIivG+GuInrV8rb25uNGzeaJKdLly4xduxYcufOzaxZs5IlrYx05coV8uTJQ8+ePVm/fr3JPEtLSxwdHTMoss9r06YNzs7O1K5dG7lcbjLP3d09g6L6vMjISE6ePMmpU6f4888/KVeuHE2bNqV+/fpYWlpmdHgmGjRowNq1aylQoADfffcdV65c4c2bNzRr1gx/f/+MDs/EnDlziIyMxNfXlxYtWpjMk8vlNGjQgNq1a2dQdN8G0WL6SvXv3x+lUkn37t1ZuXIlPj4+bN68mUGDBtG3b18sLDJXL+2gQYO4fPkyOXLkIF++fBkdTpqEh4eze/duJBJJRoeSJkqlEnt7e+zt7ZFKpbx69YqjR48yZ84cRo4cScuWLTM6RCOVSoWDgwOAcTtbWloilUozMqwURUZGMmvWLIoXL0737t0zOpxvkkhMXzFPT08UCgVt27alSJEi7N69m6JFi2Z0WCmysbFh6NCh/PXXX0yZMiXFOh8rz2jVqlXj3LlzX81Z8L59+zh+/Dj+/v6UKFGCxo0bM2TIEHLlygXAxYsXGTp0aKZKTE2bNmXYsGEMGTIEg8HA/fv3Wbx4caa89njkyBF69+7NggULaN68eYoP4cuePXsGRPbtEF153wBfX1/mzZvHmjVrKFKkSEaHk6LAwEBOnjzJ0qVL+eGHH1KsM3jw4C8c1af17dsXiURCTEwMf/31F6VKlcLW1takzsqVKzMouo/r1q0bTZo0oVGjRuTMmTPZ/Ddv3nDmzJlMcw0SEkdrrlixAl9fX54/f46joyPu7u4MGjQo2XWcjDZlyhR27dqV7HpjEolEwt27d79wVN8WkZi+UkkHzSR3795Fp9NRqlQpY1lmPGhu2bKFLl26ZHQYqfK5kY0SiYTWrVt/mWD+A+Li4pDJZFhaWhIeHk7WrFmTnQhkJnXq1OHMmTMZHcY3SSSmr1RqhoNnpjPiJJMnT07VtZrM1K03fvx4Zs2alax86NChLFq0KAMi+rQLFy4wbdo0wsPDjd1MBoMhU5/JnzlzhtGjR7Nu3TpKlCjB2rVrWblyJQsWLEjxJwaZwevXr1O1L4tuvbQT15i+Upkx6aSGUqlk3759eHh4kD9/fl69esXu3bspX748rq6uGR2e0bNnz1ixYgWQ2FX6YXdSbGws165dy4jQPmvatGl06tQJd3f3TDl4ICVeXl4sX76cEiVKANCrVy/KlCnDpEmTjMPHM5u2bdvy6tUrAOzt7YmJiUGn0wEYTwgy88lAZiYS01fK09Pzs2drmfE3NhcuXGDdunUUK1bMWObh4cGQIUMyVesjT548uLi48PbtWwBy5MhhMj9v3rz06dMnI0L7rDdv3tC9e/evJilBYuujdOnSJmVlypTJ1HcEadGiBbGxsYwaNQobGxsSEhJYsmQJWq2WsWPHZnR4XzXRlfeVOnToEJB4ZjZx4kRmzpyZrE5m/I1NpUqVOHnyJHZ2dsayiIgIWrVqxfnz5zMwso/z9/enWrVqGR1Gqs2ZMwdbW1t69+6dqX9U+76BAweSPXt2hg8fTvbs2YmIiGDJkiVERERkqhOW91WqVAl/f3+T37ZptVpq1qxJQEBABkb29ROJ6RtQuXJlLl++nNFhpMqUKVO4ffs2vXr1ImfOnDx//pzVq1dTv379TDcqL8mbN2/YunUrr1+/NnbRaLVagoOD2bx5cwZHl1yjRo0ICwtDIpEYf0ibdI3pzz//zODoUhYREcGECRM4f/68MdYGDRowadIksmXLltHhpahFixb079/f5Ee2O3fuZPfu3Wzbti0DI/v6icT0DfiaEpNWq2XDhg0c/7/27j8m6vqPA/jzCA6DAjp+FDfTRjVvYjeOE7lRsqYU8SNnzRqQls4ZGhEtMeSHRIggc6iNCkWKEeimE8ePKeDX6BcM5Q4k+qFgzsAzObk7AQ087pTvH43LC/DHIff+fG6vx+Yfvj/3x3PO7fV5//i8X//7HwYGBiAWi/Hqq69y+nTb6tWrAQBubm7QarXw9/fHkSNHEBsbi+TkZLbhJnHp0qUpn3H942aDwYChoSF4enpy7iPx/1KpVEhKSoKbmxsef/xxXL58GQKBAF9++SX8/PxYx+M1Kkx2gE+FCfinODk6OmJsbAwtLS1wd3eHv78/61hTCggIQEtLCy5duoRt27ahtLQUSqUS27dvR2VlJet4k7pw4QLq6+vR19cHkUiE8PBwi309rsjLy0NqauodT2ty6YTmfxkMBrS1tWFgYAC+vr6QSqW82tvjKm6/kpAp6XQ685+xsTHo9XqLMZ1OxzripGpra/H8888DAHbt2oWPP/4Y69evR2lpKeNkUxOJRHBycsKcOXPQ1dUFAAgKCkJPTw/jZJNrbGzEm2++CbVaDW9vb/T19eGtt95CXV0d62gTjC/TeXt7w8vLa9I/XDU0NIT9+/cjJCQEfn5+2LFjBxISEqBWq1lH4z2aMfHUVG+/AoGA09+sREdHIycnB/Pnz4dCoUBFRQU8PT2xYsUK/PTTT6zjTWrTpk0wmUzIzs7Ghg0bEBoaCmdnZxw4cAANDQ2s400QHR2NrKwsLFy40DzW1taGtLQ0TuYFgPj4eOzcuROurq6so9yzxMRE3Lp1C1988QVee+01KBQKuLi4QKlUcvJELJ/QcXGeOnv27IQxtVqNzs5OLFiwAHPmzGGQ6u6uXLmCgIAAfP/993B3d8f8+fNhMpkwMjLCOtqUsrKysG/fPty4cQNbtmxBbm4uRkZGsHXrVtbRJqXRaBAQEGAxJpVKodfr2QS6B93d3RgZGeFVYTp9+jS+++479PT04Ny5c6ioqICLiwvkcjnraLxHhYmnent7kZGRga6uLoSGhuL1119HQkIC5syZA7VajW3btnHyAsx58+Zh165dOHnyJCIjIzE4OIjdu3dP6NHEJVevXsWpU6fw1Vdfwd3dHQMDA+blGy6SyWQoLCxEYmIiHB0dYTKZUFhYCJlMxjralPz9/bFs2TLI5XKIRCKL/Sau7jE5ODhgaGgIx44dQ1BQEFxdXXH+/Hm4u7uzjsZ7tMfEU+np6ViwYAHKysogEokQHx+PgoICVFVVoaioCIWFhawjTqqgoADXrl2DQqFAUlISLl68iOvXryM/P591tCmlpaVBoVCgra0NTU1NUKlUePrpp5GWlsY62qSysrKgVCoRFBSEsLAwBAUFQaVSITs7m3W0KUkkEsTFxcHDw8N8mwLX95jWrVuHyMhI7Nu3DwkJCejs7ERcXNyUlxSTe0d7TDwVGBgIlUoFBwcHGI1GyGQy/Prrr+bncrmcs1fm3M3y5ctRVVXFOoZZcHAwmpub4ej47wLD6OgoFAoF2tvbGSa7M41GA41GA29vb/j6+rKOc0c9PT1ITk7GmTNn4OHhgYGBAcjlcuzYsWPSG9K5Qq/Xw9nZGa6urhgaGoJer8dTTz1lfl5XV4eIiAh2AXmKZkw8Nv6dh5OTE1xcXCye8fl9g2un3ZYuXYpDhw5ZjNXU1JhPF3JNZ2cnli5dCo1GA6lUioMHD2Lp0qXmE4VclJKSgpCQELS3t6OpqQnt7e2QSqVITU1lHe2ORCKReV/Mzc3NoigB/6xskPtHe0yEc7jSKTYiIgICgQBGoxFHjhxBSUkJnnjiCWi1WvT29lq0GOGSzMxMbNy4EVKpFADw4YcfQiKRID09HYcPH2acbnJdXV2oqKgwz0qFQiGSkpKgUCgYJ5sePr8gskSFiaeGh4ctDgwYDAbz38fGxmA0GllFsxuffvop6whW6e3tnXDw5aWXXsKWLVsYJbq70NBQlJeXY82aNeax6upqLF68mGGq6ePKSxbfUGHiqW+//ZZ1BLu3aNEi1hGs4u/vj71792L9+vVwcHDArVu3UFJSwtkZHvDPflh+fj5KS0shFouh1WqhVqshFostLiMev7yY2DcqTDzF9TvPCDs5OTlITExESUkJPD09odVq8eyzz6KgoIB1tCl99NFHrCMQDqHCRDiH1uWnZ+7cuaipqUFvby/0ej18fHwgFotZx7ojvs5O74b+L1uHChOxiXu5u2+8BfXBgwdnOo5dmmqZ66+//kJHRwcAbvbo4qvi4mKsXLlywonY2y1ZssSGiewHfcdEbEIikZjv8ZsMV+/245NVq1bd8blAIKA73B4ghUKBH3/80dzzijw4VJgIIcQKmzdvhkajwZIlS8yz/XE0M50eWsojNqdSqdDf3z+hG2xSUhLjZPaBbx13+Wq8IePx48ctxgUCARWmaaLCRGwqIyMDjY2NcHNzg9FoxCOPPILu7m66tuUBGj/hdnvH3draWsTGxjJOZl/Ky8tZR7BbdCURsamGhgYcPnwY+fn58Pf3R3V1NVJTUzE8PMw6mt3o6OhAUVERPvjgAzg7OyM9PR179uxBS0sL62h2xWQyYc+ePXjllVcQHBwMtVqNdevWQavVso7Ge1SYiE05OTlBLBbjmWeewS+//AIAWLlypfnUGJk+vnXc5av8/Hz8/PPP2L59O27evGm+LJert87zCRUmYlPz5s3DN998A2dnZwiFQnR0dKCrq4uubnmA5HI5Nm3aBIPBAD8/PxQXF6OsrGzCBj2Znrq6OhQUFCAgIAAODg5wdnZGRkYGTp8+zToa71FhIjaVkZGB+vp69Pf3Y/PmzVizZg3eeOMNJCQksI5mN7KysjB37lxzx93m5mYcPXqUsx13+erhhx82944ap9FoqFHgA0CHH4hNXblyBQcOHAAA+Pr6orW1FaOjo2hubmaczH7wreMuX7377rtYvXo1YmNjYTQasX//fpSXl2Pt2rWso/EefcdEZtzQ0BD+/PNPAMA777yDsrIyi+d///033nvvPVoCeUDefvttyOVybNiwAUKhEDdu3MBnn32G8+fPo7i4mHU8u6JUKlFTU4O+vj54eXkhKioKL7zwAutYvEeFicy44eFhrFq1ClevXsXly5cndFMVCoWIjo7G+++/zyihfeFrx12+0mg05jsJaR/vwaClPDLjXFxcUFlZCQBITU1FXl4e40T2bbzjblxcnHmMyx13+Wq8HfzZs2fh7u6OwcFBBAYGcr4dPB/QjInY3IULF1BfX4++vj6IRCKEh4dDIpGwjsV7t3fcvXjxIsRi8YSOu1ztYMtHMTExCA4ORkJCAoRCIQwGAz7//HP89ttv+Prrr1nH4zUqTMSmGhsbkZKSgpdffhm+vr64dOkSjh8/jpycHLr9YZpaW1vv+ht7bS/Bgkwmg1KptFgyNZlMUCgUUKlUDJPxHy3lEZvauXMnioqKsHDhQvPYihUrkJaWRoVpmqjo2Ja9toPnApoxEZsKCgpCS0uLxVum0WhESEgIlEolw2SE3J+YmBh0dHSYGzHe3g5+1qxZ5t9RO/j7RzMmYlMymQyFhYVITEyEo6MjTCYTCgsLIZPJWEcj5L7cSzv4/36AS+4NzZiITSmVSuzevRu///47PD09odPpIJFIsHHjRovlPULsQWBgIB3RtwLNmMiMGx0dxbVr1wD887X8iRMncPPmTfT29mL27NkwGAxYvnw5fWBL7A6991uHChOZcdevX0dUVBQGBwcxNjY24ct4JycnREdHM0pHyMyhy4mtQ4WJzDiRSISTJ08CmPxKIkIIuR3dLk5siooSIeRuqDARQgjhFCpMhBAyQx599FHWEXiJjosTQoiV2tvbUVVVBZ1Oh61bt6KyshJr166FgwO9808H/esRQogVDh06hJSUFMyePdt8uKehoQG5ubmMk/EfzZgIIcQKYWFhKC0txZNPPomgoCAolUro9XpERUWhpaWFdTxeoxkTIYRY4caNG3jssccA/Pu9klAoxEMPPcQyll2gwkQIIVaIiIhAUlISOjo6MDY2hnPnziEtLQ3h4eGso/EeLeURQogVjEYj9u7di9raWvT19cHb2xuRkZFISEiAs7Mz63i8RoWJEEKsNDw8DEdHRwiFQly8eBEeHh50RPwBoKU8Qgixwg8//IAXX3wRf/zxBwDgxIkTCA8Px6lTpxgn4z+aMRFCiBXCw8ORl5eHwMBA81hbWxsyMzNx9OhRhsn4j2ZMhBBiBa1Wi+eee85iTCqVQqvVMkpkP6gwEUKIFYKDg5GdnQ2dTgcA0Ol0yM3NRXBwMONk/EdLeYQQYgWdTof09HQ0NTVhbGwMAoEAYWFhyMzMhEgkYh2P16gwEULINBgMBgwNDcHT05PuyHtAqFEgIYTch7y8PKSmpuKTTz6ZskNtVlaWbUPZGSpMhBByH8aX6by9vRknsV+0lEcIIVaIj4/Hzp074erqyjqK3aEFUUIIsUJ3dzdGRkZYx7BLtJRHCCFW8Pf3x7JlyyCXyyESiSz2m2iPaXqoMBFCiBUkEgkkEgk0Gg36+/vh4+MDLy8v1rHsAu0xEUKIFXp6epCcnIwzZ87Aw8MDAwMDkMvl2LFjB3x8fFjH4zXaYyKEECukpKQgJCQE7e3taGpqQnt7O6RSKVJTU1lH4z2aMRFCiBVkMhmUSiUcHf/dETGZTFAoFFCpVAyT8R/NmAghxAqhoaEoLy+3GKuursbixYsZJbIfNGMihBArxMTEoKOjAz4+PhCLxdBqtVCr1RCLxZg1a5b5d8eOHWOYkp+oMBFCiBVaW1vv6XeLFi2a4ST2hwoTIYQQTqE9JkIIIZxChYkQQginUGEihBDCKVSYCCGEcAoVJkIIIZzyf9tO49/BMrcLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 1.1)\n",
    "plt.xticks(rotation = 90)\n",
    "sns.heatmap(df.corr(),annot = True).set_title('Heatmap Showing Correlation in Data', fontsize = 20)\n",
    "plt.figure(figsize = (15,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On seeing the above heatmap, we will now remove the column balcony from our dataframe as it is very less correlated with the prices. Also we will drop the column price_per_sqft as it has also done its task of removing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>3</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mysore Road</td>\n",
       "      <td>2</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  BHK  total_sqft  bath  price\n",
       "3   Lingadheeranahalli    3      1521.0   3.0   95.0\n",
       "6     Old Airport Road    4      2732.0   4.0  204.0\n",
       "14            Sarjapur    3      2250.0   3.0  148.0\n",
       "15         Mysore Road    2      1175.0   2.0   73.5\n",
       "20             Kengeri    1       600.0   1.0   15.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['balcony','price_per_sqft'], axis = 1,inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "#### Analysing data from location column -\n",
    "##### ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                              287\n",
       "Sarjapur  Road                          235\n",
       "Thanisandra                             144\n",
       "Kanakpura Road                          130\n",
       "Hebbal                                  129\n",
       "                                       ... \n",
       "ravindra nagar, T.dasarahalli peenya      1\n",
       "basaveshwarnagar                          1\n",
       "Shauhardha Layout                         1\n",
       "Banashankari 2 nd Stage                   1\n",
       "RMV                                       1\n",
       "Name: location, Length: 809, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, naming those locations which are not prime and have very few value counts('less than 15') as 'other' to prevent our Dataframe from getting overfit and making it concise and crisp. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_locations = df.location.value_counts()[df.location.value_counts() <= 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.location = df.location.apply([lambda x : 'other' if x in other_locations else x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>3</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mysore Road</td>\n",
       "      <td>2</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  BHK  total_sqft  bath  price\n",
       "3   Lingadheeranahalli    3      1521.0   3.0   95.0\n",
       "6     Old Airport Road    4      2732.0   4.0  204.0\n",
       "14            Sarjapur    3      2250.0   3.0  148.0\n",
       "15         Mysore Road    2      1175.0   2.0   73.5\n",
       "20             Kengeri    1       600.0   1.0   15.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace = True)\n",
    "df.drop(['index'], axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>3</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             location  BHK  total_sqft  bath  price\n",
       "0  Lingadheeranahalli    3      1521.0   3.0   95.0\n",
       "1    Old Airport Road    4      2732.0   4.0  204.0\n",
       "2            Sarjapur    3      2250.0   3.0  148.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other              1983\n",
       "Whitefield          287\n",
       "Sarjapur  Road      235\n",
       "Thanisandra         144\n",
       "Kanakpura Road      130\n",
       "                   ... \n",
       "Sanjay nagar         16\n",
       "Padmanabhanagar      16\n",
       "Kalena Agrahara      16\n",
       "Domlur               16\n",
       "Rachenahalli         16\n",
       "Name: location, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.get_dummies(df['location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>BHK</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>7th Phase JP Nagar</th>\n",
       "      <th>Akshaya Nagar</th>\n",
       "      <th>Ambalipura</th>\n",
       "      <th>Ambedkar Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Tumkur Road</th>\n",
       "      <th>Uttarahalli</th>\n",
       "      <th>Varthur</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BHK  total_sqft  bath  price  1st Phase JP Nagar  5th Phase JP Nagar  \\\n",
       "0    3      1521.0   3.0   95.0                   0                   0   \n",
       "1    4      2732.0   4.0  204.0                   0                   0   \n",
       "2    3      2250.0   3.0  148.0                   0                   0   \n",
       "3    2      1175.0   2.0   73.5                   0                   0   \n",
       "4    1       600.0   1.0   15.0                   0                   0   \n",
       "\n",
       "   7th Phase JP Nagar  Akshaya Nagar  Ambalipura  Ambedkar Nagar  ...  \\\n",
       "0                   0              0           0               0  ...   \n",
       "1                   0              0           0               0  ...   \n",
       "2                   0              0           0               0  ...   \n",
       "3                   0              0           0               0  ...   \n",
       "4                   0              0           0               0  ...   \n",
       "\n",
       "   Tumkur Road  Uttarahalli  Varthur  Vijayanagar  Vittasandra  Whitefield  \\\n",
       "0            0            0        0            0            0           0   \n",
       "1            0            0        0            0            0           0   \n",
       "2            0            0        0            0            0           0   \n",
       "3            0            0        0            0            0           0   \n",
       "4            0            0        0            0            0           0   \n",
       "\n",
       "   Yelahanka  Yelahanka New Town  Yeshwanthpur  other  \n",
       "0          0                   0             0      0  \n",
       "1          0                   0             0      0  \n",
       "2          0                   0             0      0  \n",
       "3          0                   0             0      0  \n",
       "4          0                   0             0      0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df,a], axis = 1)\n",
    "df.drop(['location'],axis = 1,inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a pickle file of the cleaned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(df, open(r'C:\\Users\\xyz\\Desktop\\clean_dataset.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have created dummies because in machine learning models, encoding all of the independent variables as dummy variables allows easy interpretation and calculation of the odds ratios, and increases the stability and significance of the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BHK                   5758\n",
       "total_sqft            5758\n",
       "bath                  5758\n",
       "price                 5758\n",
       "1st Phase JP Nagar    5758\n",
       "                      ... \n",
       "Whitefield            5758\n",
       "Yelahanka             5758\n",
       "Yelahanka New Town    5758\n",
       "Yeshwanthpur          5758\n",
       "other                 5758\n",
       "Length: 94, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Machine Learning model using scikit learn :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, after cleaning, analysing and processing data, we will create a Machine Learning Model which based on LINEAR REGRESSION for predicting the price of a house on the basis of the parameters on which it depends on.\n",
    "\n",
    "So here, we will now split our data into two parts of 80% and 20% for training and testing our model respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df.drop('price', axis = 1),\n",
    "                                                    df['price'], test_size = 0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, Ridge, LinearRegression\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.33808e-20): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.87837e-20): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.79812e-20): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.79984e-20): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.16781e-20): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.33828e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.87909e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.79926e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.80003e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.16843e-18): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, sym_pos=True,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = Ridge()\n",
    "params = {'alpha' : [1e-15,1e-10,1e-8,1e-5,1e-3,1e-2,0.1,1,10,20,40,50,55,70,30,90,100,125,1000,10000,100000]}\n",
    "gd = GridSearchCV(model2, params, scoring = 'r2')\n",
    "gd.fit(df.drop('price', axis = 1), df['price'])\n",
    "model2 = gd.best_estimator_\n",
    "model2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1624927.6810075375, tolerance: 2565.0821798966017\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1615217.244849717, tolerance: 3090.986628933263\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1663957.4892377008, tolerance: 3166.192027049079\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1704743.9046669994, tolerance: 3148.4222616987618\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1495651.166295515, tolerance: 2780.1754484714215\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29119.896450530738, tolerance: 2565.0821798966017\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44392.78021312831, tolerance: 3090.986628933263\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85150.29738464579, tolerance: 3166.192027049079\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104857.10813519405, tolerance: 2780.1754484714215\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29119.585950767156, tolerance: 2565.0821798966017\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44393.24890350504, tolerance: 3090.986628933263\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85149.17030408233, tolerance: 3166.192027049079\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104857.16470385855, tolerance: 2780.1754484714215\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8364.35958602978, tolerance: 2565.0821798966017\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44398.887755122036, tolerance: 3090.986628933263\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85157.40576996887, tolerance: 3166.192027049079\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\xyz\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 104866.23328095395, tolerance: 2780.1754484714215\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = Lasso()\n",
    "params = {'alpha' : [1e-15,1e-10,1e-8,1e-5,1e-3,1e-2,0.1,1,10,20,40,50,55,70,30,90,100,125,1000,10000,100000]}\n",
    "gd = GridSearchCV(model3, params, scoring = 'r2')\n",
    "gd.fit(df.drop('price', axis = 1), df['price'])\n",
    "model3 = gd.best_estimator_\n",
    "model3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Model Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.859012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge Regression</th>\n",
       "      <td>0.859073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Regression</th>\n",
       "      <td>0.859085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model Accuracy\n",
       "Linear Regression        0.859012\n",
       "Ridge Regression         0.859073\n",
       "Lasso Regression         0.859085"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'Model Accuracy' : [model.score(x_test, y_test),model2.score(x_test, y_test),model3.score(x_test, y_test)]}\n",
    "pd.DataFrame(dict, index = ['Linear Regression','Ridge Regression','Lasso Regression'])"
   ]
  }
 ],
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