home_price.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1a9325bb",
   "metadata": {},
   "source": [
    "# **Internship on \"Full Stack Data Science\" at InfraBIM Techno Solutions**\n",
    "\n",
    "**DS-09**: **Project -HOUSE PRICE PREDICTION**\n",
    "\n",
    "**Internship Reg. No** .:2033\n",
    "\n",
    "**Name**:T.ANUSHA\n",
    "\n",
    "**Date**:13/01/2023\n",
    "\n",
    "**Other Team Members**: (Reg.No. - Name)\n",
    "\n",
    "1.2031-K.KARNIKA\n",
    "\n",
    "2.2032-O.AKANKSHA\n",
    "\n",
    "3.1096-S.TRIVENI REDDY"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dcd8899e",
   "metadata": {},
   "source": [
    "**House_Price_Prediction**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "85510aa2",
   "metadata": {},
   "source": [
    "1. Importing the required packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1374c5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import ML Libraries\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import r2_score, confusion_matrix, accuracy_score, log_loss, classification_report, mean_squared_error, mean_absolute_error\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import ML Libraries\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.metrics import r2_score, confusion_matrix, accuracy_score, log_loss, classification_report, mean_squared_error, mean_absolute_error\n",
    "\n",
    "# from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d48240d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "60db8e3c",
   "metadata": {},
   "source": [
    "2. Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f56e16f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0    13300000  7420         4          2        3      yes        no       no   \n",
       "1    12250000  8960         4          4        4      yes        no       no   \n",
       "2    12250000  9960         3          2        2      yes        no      yes   \n",
       "3    12215000  7500         4          2        2      yes        no      yes   \n",
       "4    11410000  7420         4          1        2      yes       yes      yes   \n",
       "..        ...   ...       ...        ...      ...      ...       ...      ...   \n",
       "540   1820000  3000         2          1        1      yes        no      yes   \n",
       "541   1767150  2400         3          1        1       no        no       no   \n",
       "542   1750000  3620         2          1        1      yes        no       no   \n",
       "543   1750000  2910         3          1        1       no        no       no   \n",
       "544   1750000  3850         3          1        2      yes        no       no   \n",
       "\n",
       "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0                no             yes        2      yes        furnished  \n",
       "1                no             yes        3       no        furnished  \n",
       "2                no              no        2      yes   semi-furnished  \n",
       "3                no             yes        3      yes        furnished  \n",
       "4                no             yes        2       no        furnished  \n",
       "..              ...             ...      ...      ...              ...  \n",
       "540              no              no        2       no      unfurnished  \n",
       "541              no              no        0       no   semi-furnished  \n",
       "542              no              no        0       no      unfurnished  \n",
       "543              no              no        0       no        furnished  \n",
       "544              no              no        0       no      unfurnished  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import House price dataset\n",
    "df=pd.read_csv(\"House_Price_Prediction.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30b16307",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(545, 13)\n"
     ]
    }
   ],
   "source": [
    "#rows and columns\n",
    "print(df.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a9547ec",
   "metadata": {},
   "source": [
    "3. Exploratory Data Analysis (EDA) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e84b19e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   price             545 non-null    int64 \n",
      " 1   area              545 non-null    int64 \n",
      " 2   bedrooms          545 non-null    int64 \n",
      " 3   bathrooms         545 non-null    int64 \n",
      " 4   stories           545 non-null    int64 \n",
      " 5   mainroad          545 non-null    object\n",
      " 6   guestroom         545 non-null    object\n",
      " 7   basement          545 non-null    object\n",
      " 8   hotwaterheating   545 non-null    object\n",
      " 9   airconditioning   545 non-null    object\n",
      " 10  parking           545 non-null    int64 \n",
      " 11  prefarea          545 non-null    object\n",
      " 12  furnishingstatus  545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#dataset information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c614e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shows top 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92270d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "540  1820000  3000         2          1        1      yes        no      yes   \n",
       "541  1767150  2400         3          1        1       no        no       no   \n",
       "542  1750000  3620         2          1        1      yes        no       no   \n",
       "543  1750000  2910         3          1        1       no        no       no   \n",
       "544  1750000  3850         3          1        2      yes        no       no   \n",
       "\n",
       "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "540              no              no        2       no      unfurnished  \n",
       "541              no              no        0       no   semi-furnished  \n",
       "542              no              no        0       no      unfurnished  \n",
       "543              no              no        0       no        furnished  \n",
       "544              no              no        0       no      unfurnished  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shows last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7370c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0    13300000  7420         4          2        3      yes        no       no   \n",
       "1    12250000  8960         4          4        4      yes        no       no   \n",
       "2    12250000  9960         3          2        2      yes        no      yes   \n",
       "3    12215000  7500         4          2        2      yes        no      yes   \n",
       "4    11410000  7420         4          1        2      yes       yes      yes   \n",
       "..        ...   ...       ...        ...      ...      ...       ...      ...   \n",
       "540   1820000  3000         2          1        1      yes        no      yes   \n",
       "541   1767150  2400         3          1        1       no        no       no   \n",
       "542   1750000  3620         2          1        1      yes        no       no   \n",
       "543   1750000  2910         3          1        1       no        no       no   \n",
       "544   1750000  3850         3          1        2      yes        no       no   \n",
       "\n",
       "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0                no             yes        2      yes        furnished  \n",
       "1                no             yes        3       no        furnished  \n",
       "2                no              no        2      yes   semi-furnished  \n",
       "3                no             yes        3      yes        furnished  \n",
       "4                no             yes        2       no        furnished  \n",
       "..              ...             ...      ...      ...              ...  \n",
       "540              no              no        2       no      unfurnished  \n",
       "541              no              no        0       no   semi-furnished  \n",
       "542              no              no        0       no      unfurnished  \n",
       "543              no              no        0       no        furnished  \n",
       "544              no              no        0       no      unfurnished  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keep first row and remove other duplicate rows of the data\n",
    "df=df.drop_duplicates(keep='first')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45d26597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0\n",
       "area                0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "stories             0\n",
       "mainroad            0\n",
       "guestroom           0\n",
       "basement            0\n",
       "hotwaterheating     0\n",
       "airconditioning     0\n",
       "parking             0\n",
       "prefarea            0\n",
       "furnishingstatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding total no of rows which are missing in a column \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19f2b1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let us convert the text data into numeric with labelEncoder\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a8458b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6e28a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "      <th>mainroad_n</th>\n",
       "      <th>guestroom_n</th>\n",
       "      <th>basement_n</th>\n",
       "      <th>hotwaterheating_n</th>\n",
       "      <th>airconditioning_n</th>\n",
       "      <th>prefarea_n</th>\n",
       "      <th>furnishingstatus_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</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>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0    13300000  7420         4          2        3      yes        no       no   \n",
       "1    12250000  8960         4          4        4      yes        no       no   \n",
       "2    12250000  9960         3          2        2      yes        no      yes   \n",
       "3    12215000  7500         4          2        2      yes        no      yes   \n",
       "4    11410000  7420         4          1        2      yes       yes      yes   \n",
       "..        ...   ...       ...        ...      ...      ...       ...      ...   \n",
       "540   1820000  3000         2          1        1      yes        no      yes   \n",
       "541   1767150  2400         3          1        1       no        no       no   \n",
       "542   1750000  3620         2          1        1      yes        no       no   \n",
       "543   1750000  2910         3          1        1       no        no       no   \n",
       "544   1750000  3850         3          1        2      yes        no       no   \n",
       "\n",
       "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \\\n",
       "0                no             yes        2      yes        furnished   \n",
       "1                no             yes        3       no        furnished   \n",
       "2                no              no        2      yes   semi-furnished   \n",
       "3                no             yes        3      yes        furnished   \n",
       "4                no             yes        2       no        furnished   \n",
       "..              ...             ...      ...      ...              ...   \n",
       "540              no              no        2       no      unfurnished   \n",
       "541              no              no        0       no   semi-furnished   \n",
       "542              no              no        0       no      unfurnished   \n",
       "543              no              no        0       no        furnished   \n",
       "544              no              no        0       no      unfurnished   \n",
       "\n",
       "     mainroad_n  guestroom_n  basement_n  hotwaterheating_n  \\\n",
       "0             1            0           0                  0   \n",
       "1             1            0           0                  0   \n",
       "2             1            0           1                  0   \n",
       "3             1            0           1                  0   \n",
       "4             1            1           1                  0   \n",
       "..          ...          ...         ...                ...   \n",
       "540           1            0           1                  0   \n",
       "541           0            0           0                  0   \n",
       "542           1            0           0                  0   \n",
       "543           0            0           0                  0   \n",
       "544           1            0           0                  0   \n",
       "\n",
       "     airconditioning_n  prefarea_n  furnishingstatus_n  \n",
       "0                    1           1                   0  \n",
       "1                    1           0                   0  \n",
       "2                    0           1                   1  \n",
       "3                    1           1                   0  \n",
       "4                    1           0                   0  \n",
       "..                 ...         ...                 ...  \n",
       "540                  0           0                   2  \n",
       "541                  0           0                   1  \n",
       "542                  0           0                   2  \n",
       "543                  0           0                   0  \n",
       "544                  0           0                   2  \n",
       "\n",
       "[545 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying encoder on textual columns\n",
    "df['mainroad_n']=le.fit_transform(df['mainroad'])\n",
    "df['guestroom_n']=le.fit_transform(df['guestroom'])\n",
    "df['basement_n']=le.fit_transform(df['basement'])\n",
    "df['hotwaterheating_n']=le.fit_transform(df['hotwaterheating'])\n",
    "df['airconditioning_n']=le.fit_transform(df['airconditioning'])\n",
    "df['prefarea_n']=le.fit_transform(df['prefarea'])\n",
    "df['furnishingstatus_n']=le.fit_transform(df['furnishingstatus'])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ad49aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#deleting cols with labels(or strings)\n",
    "df=df.drop(['mainroad','guestroom','basement','hotwaterheating','airconditioning','prefarea','furnishingstatus'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35f6bf20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "      <th>mainroad_n</th>\n",
       "      <th>guestroom_n</th>\n",
       "      <th>basement_n</th>\n",
       "      <th>hotwaterheating_n</th>\n",
       "      <th>airconditioning_n</th>\n",
       "      <th>prefarea_n</th>\n",
       "      <th>furnishingstatus_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories  parking  mainroad_n  \\\n",
       "0    13300000  7420         4          2        3        2           1   \n",
       "1    12250000  8960         4          4        4        3           1   \n",
       "2    12250000  9960         3          2        2        2           1   \n",
       "3    12215000  7500         4          2        2        3           1   \n",
       "4    11410000  7420         4          1        2        2           1   \n",
       "..        ...   ...       ...        ...      ...      ...         ...   \n",
       "540   1820000  3000         2          1        1        2           1   \n",
       "541   1767150  2400         3          1        1        0           0   \n",
       "542   1750000  3620         2          1        1        0           1   \n",
       "543   1750000  2910         3          1        1        0           0   \n",
       "544   1750000  3850         3          1        2        0           1   \n",
       "\n",
       "     guestroom_n  basement_n  hotwaterheating_n  airconditioning_n  \\\n",
       "0              0           0                  0                  1   \n",
       "1              0           0                  0                  1   \n",
       "2              0           1                  0                  0   \n",
       "3              0           1                  0                  1   \n",
       "4              1           1                  0                  1   \n",
       "..           ...         ...                ...                ...   \n",
       "540            0           1                  0                  0   \n",
       "541            0           0                  0                  0   \n",
       "542            0           0                  0                  0   \n",
       "543            0           0                  0                  0   \n",
       "544            0           0                  0                  0   \n",
       "\n",
       "     prefarea_n  furnishingstatus_n  \n",
       "0             1                   0  \n",
       "1             0                   0  \n",
       "2             1                   1  \n",
       "3             1                   0  \n",
       "4             0                   0  \n",
       "..          ...                 ...  \n",
       "540           0                   2  \n",
       "541           0                   1  \n",
       "542           0                   2  \n",
       "543           0                   0  \n",
       "544           0                   2  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04983e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'price'}>,\n",
       "        <AxesSubplot:title={'center':'area'}>,\n",
       "        <AxesSubplot:title={'center':'bedrooms'}>,\n",
       "        <AxesSubplot:title={'center':'bathrooms'}>],\n",
       "       [<AxesSubplot:title={'center':'stories'}>,\n",
       "        <AxesSubplot:title={'center':'parking'}>,\n",
       "        <AxesSubplot:title={'center':'mainroad_n'}>,\n",
       "        <AxesSubplot:title={'center':'guestroom_n'}>],\n",
       "       [<AxesSubplot:title={'center':'basement_n'}>,\n",
       "        <AxesSubplot:title={'center':'hotwaterheating_n'}>,\n",
       "        <AxesSubplot:title={'center':'airconditioning_n'}>,\n",
       "        <AxesSubplot:title={'center':'prefarea_n'}>],\n",
       "       [<AxesSubplot:title={'center':'furnishingstatus_n'}>,\n",
       "        <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate Histogram - It is a graphical representation of a grouped frequency distribution with continuous classes\n",
    "df.hist(figsize=(10,10),bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90e44813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='basement_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49102950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAjIklEQVR4nO3de3CU1eH/8c+Sy4ZAdlUuIegaQqmIolwSjQQjaEmQKEqlEqvDxYJjBisDUSpIBwkyzdQqg1YBreAFlabeBVLN6jSKoFVi0PwkxRbRWNmYhrbZaDRskvP7g8l+3W4iWYgesrxfM864Z8/z7Flws2+f58muwxhjBAAAYEkv2wsAAAAnNmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVsXaXkBXtLW16cCBA0pKSpLD4bC9HAAA0AXGGDU2Nmrw4MHq1avz4x89IkYOHDggj8djexkAAOAofPbZZzrttNM6vb9HxEhSUpKkw0/G5XJZXg2A7hQIBFRWVqbc3FzFxcXZXg6AbuT3++XxeILv453pETHSfmrG5XIRI0CUCQQCSkxMlMvlIkaAKHWkSyy4gBUAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgSANdu2bVN8fLymTZum+Ph4bdu2zfaSAFgQcYy88cYbmjp1qgYPHiyHw6EXXnjhiNu8/vrrSk9PV0JCgoYOHar169cfzVoBRBGHw6HLL788ZOzyyy/nyzCBE1DEMfLVV19p1KhRuv/++7s0f//+/crLy1N2drYqKyt1++23a8GCBXr22WcjXiyA6PC/wXHOOed85/0AolvE300zZcoUTZkypcvz169fr9NPP11r1qyRJI0YMUK7du3S3XffrenTp0f68AB6uG+fiqmoqNA555yj0tJS5eXlqaqqSunp6cF5l112ma1lAvgBfe9flPfWW28pNzc3ZGzy5MnasGGDAoFAh1+M1dzcrObm5uBtv98v6fAXagUCge93wQC+V98+NXPOOecEX9OBQCDkCMnll1+uQ4cO/eDrA9B9uvqe/b3HSG1trZKTk0PGkpOT1dLSovr6eqWkpIRtU1xcrKKiorDxsrIyJSYmfm9rBfDDaT8i0s7r9UqSzjzzTP3tb3+TpJD7AfQ8TU1NXZr3vceIFH7+1xjT4Xi7pUuXqrCwMHjb7/fL4/EoNzdXLpfr+1sogB9MVVWV8vLyFAgE5PV6lZOTo7i4OE2bNi04Jy8vz94CARyz9jMbR/K9x8igQYNUW1sbMlZXV6fY2Fj169evw22cTqecTmfYeFxcXIendQD0HFu3bg2eqqmqqgqemomLi1NVVVXIPF7vQM/W1dfw9x4j48aN05YtW0LGysrKlJGRwQ8a4AT07YtS2y9WPfPMM0OOiPzvPADRLeJf7f3yyy+1e/du7d69W9LhX93dvXu3ampqJB0+xTJr1qzg/IKCAn366acqLCxUdXW1Nm7cqA0bNujWW2/tnmcAoMdpP1Xbrv0akc7uBxDdIo6RXbt2acyYMRozZowkqbCwUGPGjNHy5cslST6fLxgmkpSWlqbS0lKVl5dr9OjRuvPOO3Xffffxa73ACc4Yo61bt4aMbd26lRABTkAO0wNe+X6/X263Ww0NDVzACkSZQCAQ/JwRTt0C0aWr7998Nw0AALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALDqqGJk7dq1SktLU0JCgtLT07V9+/bvnP/kk09q1KhRSkxMVEpKiq6//nodPHjwqBYMAACiS8QxUlJSooULF2rZsmWqrKxUdna2pkyZopqamg7nv/nmm5o1a5bmzp2rDz/8UE8//bTeffddzZs375gXDwAAer6IY2T16tWaO3eu5s2bpxEjRmjNmjXyeDxat25dh/PffvttDRkyRAsWLFBaWpouvPBC3Xjjjdq1a9cxLx4AAPR8sZFMPnTokCoqKrRkyZKQ8dzcXO3cubPDbbKysrRs2TKVlpZqypQpqqur0zPPPKPLLrus08dpbm5Wc3Nz8Lbf75ckBQIBBQKBSJYM4DjX/prmtQ1En66+riOKkfr6erW2tio5OTlkPDk5WbW1tR1uk5WVpSeffFL5+fn65ptv1NLSoiuuuEK///3vO32c4uJiFRUVhY2XlZUpMTExkiUD6CG8Xq/tJQDoZk1NTV2aF1GMtHM4HCG3jTFhY+327NmjBQsWaPny5Zo8ebJ8Pp8WL16sgoICbdiwocNtli5dqsLCwuBtv98vj8ej3NxcuVyuo1kygONUIBCQ1+tVTk6O4uLibC8HQDdqP7NxJBHFSP/+/RUTExN2FKSuri7saEm74uJijR8/XosXL5YknXvuuerTp4+ys7O1atUqpaSkhG3jdDrldDrDxuPi4vhhBUQpXt9A9OnqazqiC1jj4+OVnp4edjjV6/UqKyurw22amprUq1fow8TExEg6fEQFAACc2CL+bZrCwkI9/PDD2rhxo6qrq7Vo0SLV1NSooKBA0uFTLLNmzQrOnzp1qp577jmtW7dOH3/8sXbs2KEFCxbo/PPP1+DBg7vvmQAAgB4p4mtG8vPzdfDgQa1cuVI+n08jR45UaWmpUlNTJUk+ny/kM0fmzJmjxsZG3X///brlllt00kkn6ZJLLtFvf/vb7nsWAACgx3KYHnCuxO/3y+12q6GhgQtYgSgTCARUWlqqvLw8rhkBokxX37/5bhoAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACriBEAAGAVMQIAAKwiRgAAgFXECAAAsIoYAQAAVhEjAADAKmIEAABYRYwAAACrjipG1q5dq7S0NCUkJCg9PV3bt2//zvnNzc1atmyZUlNT5XQ69aMf/UgbN248qgUDAIDoEhvpBiUlJVq4cKHWrl2r8ePH68EHH9SUKVO0Z88enX766R1uM2PGDH3xxRfasGGDhg0bprq6OrW0tBzz4gEAQM/nMMaYSDbIzMzU2LFjtW7duuDYiBEjNG3aNBUXF4fNf/nll3XNNdfo448/1imnnHJUi/T7/XK73WpoaJDL5TqqfQA4PgUCAZWWliovL09xcXG2lwOgG3X1/TuiIyOHDh1SRUWFlixZEjKem5urnTt3drjNSy+9pIyMDN11113atGmT+vTpoyuuuEJ33nmnevfu3eE2zc3Nam5uDnky0uEfWoFAIJIlAzjOtb+meW0D0aerr+uIYqS+vl6tra1KTk4OGU9OTlZtbW2H23z88cd68803lZCQoOeff1719fWaP3++/v3vf3d63UhxcbGKiorCxsvKypSYmBjJkgH0EF6v1/YSAHSzpqamLs2L+JoRSXI4HCG3jTFhY+3a2trkcDj05JNPyu12S5JWr16tn/3sZ3rggQc6PDqydOlSFRYWBm/7/X55PB7l5uZymgaIMoFAQF6vVzk5OZymAaJM+5mNI4koRvr376+YmJiwoyB1dXVhR0vapaSk6NRTTw2GiHT4GhNjjP75z3/qxz/+cdg2TqdTTqczbDwuLo4fVkCU4vUNRJ+uvqYj+tXe+Ph4paenhx1O9Xq9ysrK6nCb8ePH68CBA/ryyy+DYx999JF69eql0047LZKHBwAAUSjizxkpLCzUww8/rI0bN6q6ulqLFi1STU2NCgoKJB0+xTJr1qzg/GuvvVb9+vXT9ddfrz179uiNN97Q4sWL9Ytf/KLTC1gBAMCJI+JrRvLz83Xw4EGtXLlSPp9PI0eOVGlpqVJTUyVJPp9PNTU1wfl9+/aV1+vVzTffrIyMDPXr108zZszQqlWruu9ZAACAHivizxmxgc8ZAaIXnzMCRK+uvn/z3TQAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWHVWMrF27VmlpaUpISFB6erq2b9/epe127Nih2NhYjR49+mgeFgAARKGIY6SkpEQLFy7UsmXLVFlZqezsbE2ZMkU1NTXfuV1DQ4NmzZqln/zkJ0e9WAAAEH0ijpHVq1dr7ty5mjdvnkaMGKE1a9bI4/Fo3bp137ndjTfeqGuvvVbjxo076sUCAIDoExvJ5EOHDqmiokJLliwJGc/NzdXOnTs73e6RRx7Rvn379MQTT2jVqlVHfJzm5mY1NzcHb/v9fklSIBBQIBCIZMkAjnPtr2le20D06errOqIYqa+vV2trq5KTk0PGk5OTVVtb2+E2f//737VkyRJt375dsbFde7ji4mIVFRWFjZeVlSkxMTGSJQPoIbxer+0lAOhmTU1NXZoXUYy0czgcIbeNMWFjktTa2qprr71WRUVFOuOMM7q8/6VLl6qwsDB42+/3y+PxKDc3Vy6X62iWDOA4FQgE5PV6lZOTo7i4ONvLAdCN2s9sHElEMdK/f3/FxMSEHQWpq6sLO1oiSY2Njdq1a5cqKyv1y1/+UpLU1tYmY4xiY2NVVlamSy65JGw7p9Mpp9MZNh4XF8cPKyBK8foGok9XX9MRXcAaHx+v9PT0sMOpXq9XWVlZYfNdLpeqqqq0e/fu4D8FBQUaPny4du/erczMzEgeHgAARKGIT9MUFhZq5syZysjI0Lhx4/TQQw+ppqZGBQUFkg6fYvn888/1+OOPq1evXho5cmTI9gMHDlRCQkLYOAAAODFFHCP5+fk6ePCgVq5cKZ/Pp5EjR6q0tFSpqamSJJ/Pd8TPHAEAAGjnMMYY24s4Er/fL7fbrYaGBi5gBaJMIBBQaWmp8vLyuGYEiDJdff/mu2kAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsIkYAAIBVxAgAALCKGAEAAFYRIwAAwCpiBAAAWEWMAAAAq4gRAABgFTECAACsOqoYWbt2rdLS0pSQkKD09HRt376907nPPfeccnJyNGDAALlcLo0bN06vvPLKUS8YAABEl4hjpKSkRAsXLtSyZctUWVmp7OxsTZkyRTU1NR3Of+ONN5STk6PS0lJVVFTo4osv1tSpU1VZWXnMiwcAAD2fwxhjItkgMzNTY8eO1bp164JjI0aM0LRp01RcXNylfZx99tnKz8/X8uXLuzTf7/fL7XaroaFBLpcrkuUCOM4FAgGVlpYqLy9PcXFxtpcDoBt19f07NpKdHjp0SBUVFVqyZEnIeG5urnbu3NmlfbS1tamxsVGnnHJKp3Oam5vV3NwcvO33+yUd/qEVCAQiWTKA41z7a5rXNhB9uvq6jihG6uvr1draquTk5JDx5ORk1dbWdmkf99xzj7766ivNmDGj0znFxcUqKioKGy8rK1NiYmIkSwbQQ3i9XttLANDNmpqaujQvohhp53A4Qm4bY8LGOrJ582atWLFCL774ogYOHNjpvKVLl6qwsDB42+/3y+PxKDc3l9M0QJQJBALyer3KycnhNA0QZdrPbBxJRDHSv39/xcTEhB0FqaurCzta8r9KSko0d+5cPf3005o0adJ3znU6nXI6nWHjcXFx/LACohSvbyD6dPU1HdFv08THxys9PT3scKrX61VWVlan223evFlz5szRU089pcsuuyyShwQAAFEu4tM0hYWFmjlzpjIyMjRu3Dg99NBDqqmpUUFBgaTDp1g+//xzPf7445IOh8isWbN077336oILLggeVendu7fcbnc3PhUAANATRRwj+fn5OnjwoFauXCmfz6eRI0eqtLRUqampkiSfzxfymSMPPvigWlpadNNNN+mmm24Kjs+ePVuPPvrosT8DAADQo0X8OSM28DkjQPTic0aA6NXV92++mwYAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWESMAAMCqo4qRtWvXKi0tTQkJCUpPT9f27du/c/7rr7+u9PR0JSQkaOjQoVq/fv1RLRYAAESfiGOkpKRECxcu1LJly1RZWans7GxNmTJFNTU1Hc7fv3+/8vLylJ2drcrKSt1+++1asGCBnn322WNePAAA6PkcxhgTyQaZmZkaO3as1q1bFxwbMWKEpk2bpuLi4rD5t912m1566SVVV1cHxwoKCvT+++/rrbfe6tJj+v1+ud1uNTQ0yOVyRbJcAMe5QCCg0tJS5eXlKS4uzvZyAHSjrr5/x0ay00OHDqmiokJLliwJGc/NzdXOnTs73Oatt95Sbm5uyNjkyZO1YcMGBQKBDn/4NDc3q7m5OeTJSId/aAUCgUiWDOBbDjT49UxV5THv56vGBv3j/x37fiSpra1Ndf/6l56u+qt69Tr2y9iGjRyjPknuY95PssupK84apd6xvY95X8CJqqvv2RHFSH19vVpbW5WcnBwynpycrNra2g63qa2t7XB+S0uL6uvrlZKSErZNcXGxioqKwsbLysqUmJgYyZIBfMvLdQf0Zvza7tnZqd2zG0mSR/J1064q//Oi9J/u2dcne+frnD6Du2dnwAmoqampS/MiipF2Docj5LYxJmzsSPM7Gm+3dOlSFRYWBm/7/X55PB7l5uZymgY4BqMb/Hqm6sfHvJ/v48jIwAEDODICRJn2MxtHElGM9O/fXzExMWFHQerq6sKOfrQbNGhQh/NjY2PVr1+/DrdxOp1yOp1h43FxcZxTBo5Bav9+uuXiSd2zsyumd8tuuGYEiF5dfU1H9L8h8fHxSk9Pl9frDRn3er3KysrqcJtx48aFzS8rK1NGRgY/eAAAQOS/2ltYWKiHH35YGzduVHV1tRYtWqSamhoVFBRIOnyKZdasWcH5BQUF+vTTT1VYWKjq6mpt3LhRGzZs0K233tp9zwIAAPRYEV8zkp+fr4MHD2rlypXy+XwaOXKkSktLlZqaKkny+XwhnzmSlpam0tJSLVq0SA888IAGDx6s++67T9Ond88hXgAA0LNF/DkjNvA5I0D04poRIHp19f2b76YBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYBUxAgAArCJGAACAVcQIAACwihgBAABWRfxx8Da0f0hsV7+KGEDPEQgE1NTUJL/fzyewAlGm/X37SB/23iNipLGxUZLk8XgsrwQAAESqsbFRbre70/t7xHfTtLW16cCBA0pKSpLD4bC9HADdyO/3y+Px6LPPPuO7p4AoY4xRY2OjBg8erF69Or8ypEfECIDoxRdhAuACVgAAYBUxAgAArCJGAFjldDp1xx13yOl02l4KAEu4ZgQAAFjFkREAAGAVMQIAAKwiRgAAgFXECHAcmDhxohYuXGh7GT+oFStWaPTo0VYe+5NPPpHD4dDu3butPD6AUMQIEAV+qDd2h8OhF1544Xt/nO40Z84cTZs2LWTM4/HI5/Np5MiRdhYFIESP+G4aANElEAhYffyYmBgNGjTI6hoA/B+OjADHiba2Nv3qV7/SKaecokGDBmnFihXB+2pqanTllVeqb9++crlcmjFjhr744gtJ0qOPPqqioiK9//77cjgccjgcevTRR3XLLbdo6tSpwX2sWbNGDodD27ZtC44NHz5cDz74oCTp3XffVU5Ojvr37y+3260JEybovffeC84dMmSIJOmnP/2pHA5H8LYkbdmyRenp6UpISNDQoUNVVFSklpaW4P0Oh0Pr16/XlVdeqT59+mjVqlXB+zZt2qQhQ4bI7XbrmmuuCX4xpnT4ey3uuusuDR06VL1799aoUaP0zDPPBO9vbW3V3LlzlZaWpt69e2v48OG69957g/evWLFCjz32mF588cXgn015eXnYaZry8nI5HA699tprysjIUGJiorKysrR3796Qv6NVq1Zp4MCBSkpK0rx587RkyZIuH5FqP0Jz9913KyUlRf369dNNN91kPcyA44IBYN2ECROMy+UyK1asMB999JF57LHHjMPhMGVlZaatrc2MGTPGXHjhhWbXrl3m7bffNmPHjjUTJkwwxhjT1NRkbrnlFnP22Wcbn89nfD6faWpqMi+99JJxu92mtbXVGGPMtGnTTP/+/c3ixYuNMcb4fD4jyVRXVxtjjHnttdfMpk2bzJ49e8yePXvM3LlzTXJysvH7/cYYY+rq6owk88gjjxifz2fq6uqMMca8/PLLxuVymUcffdTs27fPlJWVmSFDhpgVK1YEn58kM3DgQLNhwwazb98+88knn5g77rjD9O3b11x11VWmqqrKvPHGG2bQoEHm9ttvD253++23mzPPPNO8/PLLZt++feaRRx4xTqfTlJeXG2OMOXTokFm+fLl55513zMcff2yeeOIJk5iYaEpKSowxxjQ2NpoZM2aYSy+9NPhn09zcbPbv328kmcrKSmOMMX/5y1+MJJOZmWnKy8vNhx9+aLKzs01WVlZwLU888YRJSEgwGzduNHv37jVFRUXG5XKZUaNGdenvePbs2cblcpmCggJTXV1ttmzZYhITE81DDz0UyX8qQFQiRoDjwIQJE8yFF14YMnbeeeeZ2267zZSVlZmYmBhTU1MTvO/DDz80ksw777xjjDHmjjvuCHtT/O9//2t69epldu3aZdra2ky/fv1McXGxOe+884wxxjz11FMmOTm50zW1tLSYpKQks2XLluCYJPP888+HzMvOzja/+c1vQsY2bdpkUlJSQrZbuHBhyJw77rjDJCYmBmPHGGMWL15sMjMzjTHGfPnllyYhIcHs3LkzZLu5c+ean//8552ue/78+Wb69OnB27NnzzZXXnllyJzOYuTVV18Nztm2bZuRZL7++mtjjDGZmZnmpptuCtnP+PHjI4qR1NRU09LSEhy7+uqrTX5+fpe2B6IZp2mA48S5554bcjslJUV1dXWqrq6Wx+ORx+MJ3nfWWWfppJNOUnV1daf7c7vdGj16tMrLy1VVVaVevXrpxhtv1Pvvv6/GxkaVl5drwoQJwfl1dXUqKCjQGWecIbfbLbfbrS+//FI1NTXfue6KigqtXLlSffv2Df5zww03yOfzqampKTgvIyMjbNshQ4YoKSkp7DlL0p49e/TNN98oJycnZN+PP/649u3bF9xm/fr1ysjI0IABA9S3b1/94Q9/OOKaO/Ptv4OUlJTgn4sk7d27V+eff37I/P+9fSRnn322YmJiQh6jff/AiYwLWIHjRFxcXMhth8OhtrY2GWPkcDjC5nc2/m0TJ05UeXm54uPjNWHCBJ188sk6++yztWPHDpWXl4f8OvGcOXP0r3/9S2vWrFFqaqqcTqfGjRunQ4cOfedjtLW1qaioSFdddVXYfQkJCcF/79OnT5efc/t+JWnbtm069dRTQ+a1f4/Nn/70Jy1atEj33HOPxo0bp6SkJP3ud7/TX//61+9cc2e+vZ72P9v2dXx7rJ2J8Ns0vuv5AicyYgQ4zp111lmqqanRZ599Fjw6smfPHjU0NGjEiBGSpPj4eLW2toZtO3HiRG3YsEGxsbGaNGmSJGnChAn64x//qI8++ijkyMj27du1du1a5eXlSZI+++wz1dfXh+wvLi4u7HHGjh2rvXv3atiwYd33pHX4eTudTtXU1ISs89u2b9+urKwszZ8/Pzj27aMmUud/NpEaPny43nnnHc2cOTM4tmvXrmPeLwBiBDjuTZo0Seeee66uu+46rVmzRi0tLZo/f74mTJgQPPUxZMgQ7d+/X7t379Zpp52mpKQkOZ1OXXTRRWpsbNSWLVuCv8EyceJETZ8+XQMGDNBZZ50VfJxhw4Zp06ZNysjIkN/v1+LFi9W7d++QtQwZMkSvvfaaxo8fL6fTqZNPPlnLly/X5ZdfLo/Ho6uvvlq9evXSBx98oKqqqpDfmolUUlKSbr31Vi1atEhtbW268MIL5ff7tXPnTvXt21ezZ8/WsGHD9Pjjj+uVV15RWlqaNm3apHfffVdpaWkha37llVe0d+9e9evXT263+6jWc/PNN+uGG25QRkaGsrKyVFJSog8++EBDhw496ucI4DCuGQGOc+0fNHbyySfroosu0qRJkzR06FCVlJQE50yfPl2XXnqpLr74Yg0YMECbN2+WdPi6kTFjxuiUU04Jhkd2drba2trCjjZs3LhR//nPfzRmzBjNnDlTCxYs0MCBA0Pm3HPPPfJ6vfJ4PBozZowkafLkydq6dau8Xq/OO+88XXDBBVq9erVSU1OP+bnfeeedWr58uYqLizVixAhNnjxZW7ZsCcZGQUGBrrrqKuXn5yszM1MHDx4MOUoiSTfccIOGDx8evK5kx44dR7WW6667TkuXLtWtt96qsWPHav/+/ZozZ07IqSgAR8dhIj3pCQCQJOXk5GjQoEHatGmT7aUAPRqnaQCgC5qamrR+/XpNnjxZMTEx2rx5s1599VV5vV7bSwN6PI6MAEAXfP3115o6daree+89NTc3a/jw4fr1r38d/C2ivn37drrtn//8Z2VnZ/9QSwV6HGIEALrBP/7xj07vO/XUU8MuBgbwf4gRAABgFb9NAwAArCJGAACAVcQIAACwihgBAABWESMAAMAqYgQAAFhFjAAAAKuIEQAAYNX/B+L5uaDtAq6mAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='hotwaterheating_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "540c38ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "725326bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc9a9493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='bedrooms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0252504c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAiWklEQVR4nO3df1DVdeLv8ddJjkcQDqYFwgqJ2bpAUQalbCq6Jl7YXL1Z/bFO1u5su27+qMhscZ1Jpm1wShuy/Eq2KCnTjzHUtUQv7BaYd2ETxbtuq7Q1Ki5CXva7chT0cMDP/aPrmc4CygHszY/nY4axz/u8P5/zhplzzrPP+cCxWZZlCQAAwJAbTC8AAAAMbsQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjAowvYCuuHz5ss6cOaOQkBDZbDbTywEAAF1gWZbOnz+vyMhI3XBD5+c/+kWMnDlzRlFRUaaXAQAAuuH06dMaM2ZMp7f3ixgJCQmR9M0343Q6Da8GQG/yeDwqLi5Wamqq7Ha76eUA6EUul0tRUVHe1/HO9IsYufLWjNPpJEaAAcbj8SgoKEhOp5MYAQaoa11iwQWsAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAIxpa2tTWVmZ9u/fr7KyMrW1tZleEgADehQj2dnZstlsevrpp686r6ysTImJiRo2bJjGjRun3NzcntwtgAFgx44dGj9+vGbNmqVXX31Vs2bN0vjx47Vjxw7TSwPwHet2jBw8eFCbNm1SQkLCVeedOHFC6enpmjp1qqqqqrRy5UotW7ZMhYWF3b1rAP3cjh079NBDD+mOO+7Qp59+qnfffVeffvqp7rjjDj300EMECTDIdCtGLly4oAULFuitt97SjTfeeNW5ubm5io6OVk5OjmJjY/WLX/xCP//5z7V27dpuLRhA/9bW1qZnn31WDzzwgHbt2qVJkyYpMDBQkyZN0q5du/TAAw9o+fLlvGUDDCLd+qC8xYsX68c//rHuv/9+/e53v7vq3PLycqWmpvqMzZ49W3l5efJ4PB1+MJbb7Zbb7fZuu1wuSd98oJbH4+nOkgH0EWVlZTp58qS2bdumtrY272P6yr/PPfecpk2bpk8++UQpKSkmlwqgh7r6mu13jLz33ns6dOiQKisruzS/vr5e4eHhPmPh4eFqbW1VQ0ODIiIi2u2TnZ2trKysduPFxcUKCgryd8kA+pD9+/dLkv75z3/qX//6l3e8pKREknTx4kVJ0t69e9XU1PTdLxBAr2lubu7SPL9i5PTp03rqqadUXFysYcOGdXm///zoYMuyOhy/IjMzUxkZGd5tl8ulqKgopaamyul0+rNkAH3M8OHD9eqrr2rMmDGaNGmSPB6PSkpKNGvWLNntdlVUVEiS0tLSODMC9HNX3tm4Fr9i5NChQzp79qwSExO9Y21tbdq/f7/eeOMNud1uDRkyxGef0aNHq76+3mfs7NmzCggI0KhRozq8H4fDIYfD0W7cbrd3+LYOgP5jxowZGjt2rF5++WXt2rXLO2632zVkyBC98soriomJ0YwZM9o9nwDoX7r6mu3XBawzZ87U0aNHdeTIEe9XUlKSFixYoCNHjnT4xJGcnOw9/XpFcXGxkpKSCAtgEBoyZIjWrVunjz76SPPmzVNFRYUuXryoiooKzZs3Tx999JHWrl1LiACDiF9nRkJCQnT77bf7jA0fPlyjRo3yjmdmZqq2tlZbt26VJC1atEhvvPGGMjIy9MQTT6i8vFx5eXl69913e+lbANDfPPjgg/rggw/07LPPatq0ad7xmJgYffDBB3rwwQcNrg7Ad61bv01zNXV1daqpqfFux8TEqKioSM8884w2bNigyMhIrV+/XvPnz+/tuwbQjzz44IOaO3euPvnkE+3du1dpaWm8NQMMUjbrytWkfZjL5VJoaKgaGxu5gBUYYDwej4qKipSens5bt8AA09XXbz6bBgAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRfsXIxo0blZCQIKfTKafTqeTkZO3du7fT+aWlpbLZbO2+jh8/3uOFAwCAgSHAn8ljxozRmjVrNH78eEnS22+/rblz56qqqkrx8fGd7lddXS2n0+ndvvnmm7u5XAAAMND4FSNz5szx2X7ppZe0ceNGVVRUXDVGwsLCNGLEiG4tEAAADGx+xci3tbW1afv27WpqalJycvJV506cOFGXLl1SXFycVq1apRkzZlx1vtvtltvt9m67XC5Jksfjkcfj6e6SAfRBVx7TPLaBgaerj2u/Y+To0aNKTk7WpUuXFBwcrJ07dyouLq7DuREREdq0aZMSExPldru1bds2zZw5U6WlpZo2bVqn95Gdna2srKx248XFxQoKCvJ3yQD6gZKSEtNLANDLmpubuzTPZlmW5c+BW1paVFNTo3PnzqmwsFC///3vVVZW1mmQ/Kc5c+bIZrNp9+7dnc7p6MxIVFSUGhoafK49AdD/eTwelZSUaNasWbLb7aaXA6AXuVwu3XTTTWpsbLzq67ffZ0aGDh3qvYA1KSlJBw8e1GuvvaY333yzS/tPnjxZBQUFV53jcDjkcDjajdvtdp6sgAGKxzcw8HT1Md3jvzNiWZbPWYxrqaqqUkRERE/vFgAADBB+nRlZuXKl0tLSFBUVpfPnz+u9995TaWmp9u3bJ0nKzMxUbW2ttm7dKknKycnR2LFjFR8fr5aWFhUUFKiwsFCFhYW9/50AAIB+ya8Y+frrr/Xoo4+qrq5OoaGhSkhI0L59+zRr1ixJUl1dnWpqarzzW1patHz5ctXW1iowMFDx8fHas2eP0tPTe/e7AAAA/ZbfF7Ca4HK5FBoaes0LYAD0Px6PR0VFRUpPT+eaEWCA6errN59NAwAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAov2Jk48aNSkhIkNPplNPpVHJysvbu3XvVfcrKypSYmKhhw4Zp3Lhxys3N7dGCAQDAwOJXjIwZM0Zr1qxRZWWlKisr9aMf/Uhz587V559/3uH8EydOKD09XVOnTlVVVZVWrlypZcuWqbCwsFcWDwAA+r8AfybPmTPHZ/ull17Sxo0bVVFRofj4+Hbzc3NzFR0drZycHElSbGysKisrtXbtWs2fP7/7qwYAAAOGXzHybW1tbdq+fbuampqUnJzc4Zzy8nKlpqb6jM2ePVt5eXnyeDyy2+0d7ud2u+V2u73bLpdLkuTxeOTxeLq7ZAC9pLm5WdXV1b1yrAsX3frz0a8UMqJCwYGOHh9vwoQJCgoK6oWVAeiprr5m+x0jR48eVXJysi5duqTg4GDt3LlTcXFxHc6tr69XeHi4z1h4eLhaW1vV0NCgiIiIDvfLzs5WVlZWu/Hi4mKeZIA+4KuvvtKzzz7bq8d8uZeOs27dOt166629dDQAPdHc3NyleX7HyIQJE3TkyBGdO3dOhYWFeuyxx1RWVtZpkNhsNp9ty7I6HP+2zMxMZWRkeLddLpeioqKUmpoqp9Pp75IB9LLm5mZNmTKlV471RV2jntv5d73yP+P0/YjQHh+PMyNA33HlnY1r8TtGhg4dqvHjx0uSkpKSdPDgQb322mt68803280dPXq06uvrfcbOnj2rgIAAjRo1qtP7cDgccjjan6612+2dvrUD4LsTGhqqe++9t1eONfTUv+Qob9Htd92tu27p/HkBQP/T1dfsHv+dEcuyfK7v+Lbk5GSVlJT4jBUXFyspKYmoAAAAkvyMkZUrV+rTTz/VyZMndfToUf32t79VaWmpFixYIOmbt1cWLlzonb9o0SKdOnVKGRkZOnbsmDZv3qy8vDwtX768d78LAADQb/n1Ns3XX3+tRx99VHV1dQoNDVVCQoL27dunWbNmSZLq6upUU1PjnR8TE6OioiI988wz2rBhgyIjI7V+/Xp+rRcAAHj5FSN5eXlXvT0/P7/dWEpKig4fPuzXogAAwODBZ9MAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMMqvGMnOztY999yjkJAQhYWFad68eaqurr7qPqWlpbLZbO2+jh8/3qOFAwCAgcGvGCkrK9PixYtVUVGhkpIStba2KjU1VU1NTdfct7q6WnV1dd6v2267rduLBgAAA0eAP5P37dvns71lyxaFhYXp0KFDmjZt2lX3DQsL04gRI/xeIAAAGNj8ipH/1NjYKEkaOXLkNedOnDhRly5dUlxcnFatWqUZM2Z0Otftdsvtdnu3XS6XJMnj8cjj8fRkyQD6mNbWVu+/PL6BgaWrj+lux4hlWcrIyNCUKVN0++23dzovIiJCmzZtUmJiotxut7Zt26aZM2eqtLS007Mp2dnZysrKajdeXFysoKCg7i4ZQB90+oIkBaiiokK1fzO9GgC9qbm5uUvzbJZlWd25g8WLF2vPnj06cOCAxowZ49e+c+bMkc1m0+7duzu8vaMzI1FRUWpoaJDT6ezOcgH0Uf+n5r/10FuV+uCJJN0Zfe2zrAD6D5fLpZtuukmNjY1Xff3u1pmRpUuXavfu3dq/f7/fISJJkydPVkFBQae3OxwOORyOduN2u112u93v+wPQdwUEBHj/5fENDCxdfUz7FSOWZWnp0qXauXOnSktLFRMT063FVVVVKSIiolv7AgCAgcWvGFm8eLHeeecd/eEPf1BISIjq6+slSaGhoQoMDJQkZWZmqra2Vlu3bpUk5eTkaOzYsYqPj1dLS4sKCgpUWFiowsLCXv5WAABAf+RXjGzcuFGSNH36dJ/xLVu26PHHH5ck1dXVqaamxntbS0uLli9frtraWgUGBio+Pl579uxRenp6z1YOAAAGBL/fprmW/Px8n+0VK1ZoxYoVfi0KAAAMHnw2DQAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACjiBEAAGAUMQIAAIwiRgAAgFHECAAAMIoYAQAARhEjAADAKGIEAAAYRYwAAACj/IqR7Oxs3XPPPQoJCVFYWJjmzZun6urqa+5XVlamxMREDRs2TOPGjVNubm63FwwAAAYWv2KkrKxMixcvVkVFhUpKStTa2qrU1FQ1NTV1us+JEyeUnp6uqVOnqqqqSitXrtSyZctUWFjY48UDAID+L8Cfyfv27fPZ3rJli8LCwnTo0CFNmzatw31yc3MVHR2tnJwcSVJsbKwqKyu1du1azZ8/v3urBgAAA4ZfMfKfGhsbJUkjR47sdE55eblSU1N9xmbPnq28vDx5PB7Z7fZ2+7jdbrndbu+2y+WSJHk8Hnk8np4sGRj0Tv6rSU3uNtPL8PqivtHn375iuGOIxo4abnoZQL/W1dfsbseIZVnKyMjQlClTdPvtt3c6r76+XuHh4T5j4eHham1tVUNDgyIiItrtk52draysrHbjxcXFCgoK6u6SgUHv7EXppSM9+n+Q62bFzmOml9DOb+9qVVig6VUA/Vdzc3OX5nX7WWnJkiX661//qgMHDlxzrs1m89m2LKvD8SsyMzOVkZHh3Xa5XIqKilJqaqqcTmd3lwwMep+fcUlHKrT2oTs0/ua+8X/9TZfc2vfpQf2Pqfdo+DCH6eVIkr78v01a/sFR3ZM8RfGRPOcA3XXlnY1r6VaMLF26VLt379b+/fs1ZsyYq84dPXq06uvrfcbOnj2rgIAAjRo1qsN9HA6HHI72T0p2u73Dt3UAdE1AwDcP+R9EhOr274UaXs03PB6PGo5L9467uc88vq/8nAICAvrMmoD+qKuPH79+m8ayLC1ZskQ7duzQxx9/rJiYmGvuk5ycrJKSEp+x4uJiJSUl8SAHAAD+xcjixYtVUFCgd955RyEhIaqvr1d9fb0uXrzonZOZmamFCxd6txctWqRTp04pIyNDx44d0+bNm5WXl6fly5f33ncBAAD6Lb9iZOPGjWpsbNT06dMVERHh/Xr//fe9c+rq6lRTU+PdjomJUVFRkUpLS3XXXXfpxRdf1Pr16/m1XgAAIMnPa0auXHh6Nfn5+e3GUlJSdPjwYX/uCgAADBJ8Ng0AADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo4gRAABgFDECAACMIkYAAIBRxAgAADCKGAEAAEYRIwAAwChiBAAAGEWMAAAAo/yOkf3792vOnDmKjIyUzWbTrl27rjq/tLRUNput3dfx48e7u2YAADCABPi7Q1NTk+6880797Gc/0/z587u8X3V1tZxOp3f75ptv9veuAQDAAOR3jKSlpSktLc3vOwoLC9OIESP83g8AAAxsfsdId02cOFGXLl1SXFycVq1apRkzZnQ61+12y+12e7ddLpckyePxyOPxXPe1AgNVa2ur99++8li6so6+sh6pb/6cgP6oq4+f6x4jERER2rRpkxITE+V2u7Vt2zbNnDlTpaWlmjZtWof7ZGdnKysrq914cXGxgoKCrveSgQHr9AVJCtCBAwd0Ktj0anyVlJSYXoJXX/45Af1Jc3Nzl+bZLMuyunsnNptNO3fu1Lx58/zab86cObLZbNq9e3eHt3d0ZiQqKkoNDQ0+150A8M/nZ1yat7FCu349WfGRfeOx5PF4VFJSolmzZslut5tejqS++XMC+iOXy6WbbrpJjY2NV339/s7epvm2yZMnq6CgoNPbHQ6HHA5Hu3G73d5nnqyA/iggIMD7b197LPWlx3df/jkB/UlXHz9G/s5IVVWVIiIiTNw1AADoY/w+M3LhwgV9+eWX3u0TJ07oyJEjGjlypKKjo5WZmana2lpt3bpVkpSTk6OxY8cqPj5eLS0tKigoUGFhoQoLC3vvuwAAAP2W3zFSWVnp85swGRkZkqTHHntM+fn5qqurU01Njff2lpYWLV++XLW1tQoMDFR8fLz27Nmj9PT0Xlg+AADo7/yOkenTp+tq17zm5+f7bK9YsUIrVqzwe2EAAGBw4LNpAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABjld4zs379fc+bMUWRkpGw2m3bt2nXNfcrKypSYmKhhw4Zp3Lhxys3N7c5aAQDAAOR3jDQ1NenOO+/UG2+80aX5J06cUHp6uqZOnaqqqiqtXLlSy5YtU2Fhod+LBQAAA0+AvzukpaUpLS2ty/Nzc3MVHR2tnJwcSVJsbKwqKyu1du1azZ8/39+7BwAAA4zfMeKv8vJypaam+ozNnj1beXl58ng8stvt7fZxu91yu93ebZfLJUnyeDzyeDzXd8HAANZwoVE3DKvV//rioL789/BuH+eS+5Lq/lnTK2tqa72sL7/8h/7R+t8aEtDzy9gixkRrmGNYj45x+t8XdcOwWjW5L8jjCerxmoDBqquv2dc9Rurr6xUeHu4zFh4ertbWVjU0NCgiIqLdPtnZ2crKymo3XlxcrKAgnhiA7tp39oyGx/yX8msk9U5L9I7R0v++0EvHOtc7hxkeI/2xok31RyN754DAINTc3Nyledc9RiTJZrP5bFuW1eH4FZmZmcrIyPBuu1wuRUVFKTU1VU6n8/otFBjg7mp06YOjt+l7Nw7TsIAh3T7O9TgzMn78bX3mzIgkDRt6g34YFavAgMAeHwsYrK68s3Et1z1GRo8erfr6ep+xs2fPKiAgQKNGjepwH4fDIYfD0W7cbrd3+LYOgK655aZRenbG/b1zsMTeOYzH41FRUZHS09N5fAMDTFcf09f974wkJyerpKTEZ6y4uFhJSUk88QAAAP9j5MKFCzpy5IiOHDki6Ztf3T1y5Ihqar45ZZuZmamFCxd65y9atEinTp1SRkaGjh07ps2bNysvL0/Lly/vne8AAAD0a36/TVNZWakZM2Z4t69c2/HYY48pPz9fdXV13jCRpJiYGBUVFemZZ57Rhg0bFBkZqfXr1/NrvQAAQFI3YmT69OneC1A7kp+f324sJSVFhw8f9veuAADAIMBn0wAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOu+6f29oYrf/G1qx9FDKD/8Hg8am5ulsvl4sMzgQHmyuv21f5yu9RPYuT8+fOSpKioKMMrAQAA/jp//rxCQ0M7vd1mXStX+oDLly/rzJkzCgkJkc1mM70cAL3I5XIpKipKp0+fltPpNL0cAL3IsiydP39ekZGRuuGGzq8M6RcxAmDgcrlcCg0NVWNjIzECDFJcwAoAAIwiRgAAgFHECACjHA6HXnjhBTkcDtNLAWAI14wAAACjODMCAACMIkYAAIBRxAgAADCKGAEGoenTp+vpp5/uN8cFMLARIwD8VlpaKpvNpnPnzpleCoABgBgBYJTH4zG9BACGESPAINXa2qolS5ZoxIgRGjVqlFatWuX9ZM2CggIlJSUpJCREo0eP1k9/+lOdPXtWknTy5EnNmDFDknTjjTfKZrPp8ccf9x738uXLWrFihUaOHKnRo0dr9erVPvdrs9mUm5uruXPnavjw4frd734nSdq4caNuvfVWDR06VBMmTNC2bdt89qupqdHcuXMVHBwsp9OpRx55RF9//bX39tWrV+uuu+7S5s2bFR0dreDgYP36179WW1ubXn75ZY0ePVphYWF66aWXfI67evVqRUdHy+FwKDIyUsuWLeuVny8AP1gABp2UlBQrODjYeuqpp6zjx49bBQUFVlBQkLVp0ybLsiwrLy/PKioqsr766iurvLzcmjx5spWWlmZZlmW1trZahYWFliSrurraqqurs86dO+c9rtPptFavXm198cUX1ttvv23ZbDaruLjYe9+SrLCwMCsvL8/66quvrJMnT1o7duyw7Ha7tWHDBqu6utpat26dNWTIEOvjjz+2LMuyLl++bE2cONGaMmWKVVlZaVVUVFh33323lZKS4j3uCy+8YAUHB1sPPfSQ9fnnn1u7d++2hg4das2ePdtaunSpdfz4cWvz5s2WJKu8vNyyLMvavn275XQ6raKiIuvUqVPWX/7yF+/PAMB3hxgBBqGUlBQrNjbWunz5snfs+eeft2JjYzuc/9lnn1mSrPPnz1uWZVmffPKJJcn697//3e64U6ZM8Rm75557rOeff967Lcl6+umnfeb88Ic/tJ544gmfsYcffthKT0+3LMuyiouLrSFDhlg1NTXe2z///HNLkvXZZ59ZlvVNjAQFBVkul8s7Z/bs2dbYsWOttrY279iECROs7Oxsy7Isa926ddb3v/99q6WlpcPvG8B3g7dpgEFq8uTJstls3u3k5GT94x//UFtbm6qqqjR37lzdcsstCgkJ0fTp0yV981bJtSQkJPhsR0REeN/iuSIpKcln+9ixY7rvvvt8xu677z4dO3bMe3tUVJSioqK8t8fFxWnEiBHeOZI0duxYhYSEeLfDw8MVFxfn89Hl4eHh3vU8/PDDunjxosaNG6cnnnhCO3fuVGtr6zW/RwC9ixgB4OPSpUtKTU1VcHCwCgoKdPDgQe3cuVOS1NLScs397Xa7z7bNZtPly5d9xoYPH95uv2+HkSRZluUd+/Z/dzans/u+2nqioqJUXV2tDRs2KDAwUE8++aSmTZvGRbXAd4wYAQapioqKdtu33Xabjh8/roaGBq1Zs0ZTp07VD37wg3ZnNoYOHSpJamtr65W1xMbG6sCBAz5jf/7znxUbGyvpm7MgNTU1On36tPf2v//972psbPTO6a7AwED95Cc/0fr161VaWqry8nIdPXq0R8cE4J8A0wsAYMbp06eVkZGhX/3qVzp8+LBef/11rVu3TtHR0Ro6dKhef/11LVq0SH/729/04osv+ux7yy23yGaz6aOPPlJ6eroCAwMVHBzc7bU899xzeuSRR3T33Xdr5syZ+vDDD7Vjxw798Y9/lCTdf//9SkhI0IIFC5STk6PW1lY9+eSTSklJafeWjz/y8/PV1tamSZMmKSgoSNu2bVNgYKBuueWWbh8TgP84MwIMUgsXLtTFixd17733avHixVq6dKl++ctf6uabb1Z+fr62b9+uuLg4rVmzRmvXrvXZ93vf+56ysrL0m9/8RuHh4VqyZEmP1jJv3jy99tpreuWVVxQfH68333xTW7Zs8V6rYrPZtGvXLt14442aNm2a7r//fo0bN07vv/9+j+53xIgReuutt3TfffcpISFBf/rTn/Thhx9q1KhRPTouAP/YLOv//2EBAAAAAzgzAgAAjCJGAACAUcQIAAAwihgBAABGESMAAMAoYgQAABhFjAAAAKOIEQAAYBQxAgAAjCJGAACAUcQIAAAwihgBAABG/T+1Qu3wa4U2zgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='bathrooms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0c4a12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgmUlEQVR4nO3df1CVdf738ddJDkdQDq4YCF8xde2H4M+gHdnSNJNWGiY37925d5y0mrZxb39UrNlgzuwyTUu7t+2ia1/IIsmctp0izUYymC2wH7ojCmVl7F2j4hBE7HflIOjhoOf+o/FMBOg5gL0Bn48Zxq7rfK7rfA4zF+fZdV1wHH6/3y8AAAAjV1lPAAAAXNmIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYCrMegLBOH/+vL766itFRUXJ4XBYTwcAAATB7/erpaVFCQkJuuqqns9/DIoY+eqrr5SYmGg9DQAA0AsnT57UuHHjenx8UMRIVFSUpG9fjNvtNp4NgP7k8/lUWlqq9PR0OZ1O6+kA6Ecej0eJiYmB9/GeDIoYuXBpxu12EyPAEOPz+RQZGSm3202MAEPUpW6x4AZWAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAZjZt2qTw8HAtXrxY4eHh2rRpk/WUABjoU4zk5ubK4XDo4Ycfvui4iooKpaSkaPjw4Zo0aZIKCgr68rQAhoDufnY8/PDDfBgmcAXqdYwcPHhQW7du1fTp0y867tixY8rIyNCcOXNUVVWl9evXa82aNSouLu7tUwMY5L4fHKNGjbro4wCGtl7FyOnTp7V06VI999xz+tGPfnTRsQUFBRo/frzy8vI0ZcoUPfDAA7r//vu1cePGXk0YwOD23UsxxcXFam9vV1FRkdrb2zv9TwqXbIArh8Pv9/tD3Wj58uUaPXq0/vKXv2jevHmaOXOm8vLyuh07d+5czZo1q9MPlp07d+qXv/yl2trauv1gLK/XK6/XG1i+8Kl/TU1NfFAeMMiFh4cH/ru9vV0+n09lZWVauHChnE5nl8cBDF4ej0djxoxRc3PzRd+/Q/7U3ldeeUWHDh1SZWVlUOMbGhoUFxfXaV1cXJw6OjrU1NSk+Pj4Ltvk5uYqJyeny/rS0lJFRkaGOmUAA9CoUaNUUlISWC4rK5MkRUVFqaWlRZI6PQ5g8GlrawtqXEgxcvLkST300EMqLS3V8OHDg97u+9d/L5yM6em6cHZ2trKysgLLF86MpKenc2YEGCJOnTqljIyMLmdGLoSIJGVkZBjOEEBfeTyeoMaFFCOHDh1SY2OjUlJSAuvOnTunffv2acuWLfJ6vRo2bFinbcaOHauGhoZO6xobGxUWFqaYmJhun8flcsnlcnVZ73Q6u72sA2DwyMvLC/wWzZtvvqnMzExJ3x7fb775ZqdxHO/A4BbsMRzSPSMtLS06ceJEp3X33XefbrjhBj322GOaOnVql20ee+wxvfnmm/rss88C637zm9+ourpa+/fvD+p5PR6PoqOjL3nNCcDg8P2zot+9NHNBL25nAzDABPv+HdKZkaioqC7BMWLECMXExATWZ2dnq66uTtu3b5ckrVixQlu2bFFWVpZ+/etfa//+/SosLNTf/va3UF8TgCHC7/d3ChJCBLiy9ftfYK2vr1dtbW1geeLEiSopKVF5eblmzpypJ554Qps3b9aSJUv6+6kBDCJ+v7/Lb+Hl5eURIsAVqFe/2vtD4zINMHT5fD6VlJQoIyODe0SAISbY928+mwYAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmAopRvLz8zV9+nS53W653W6lpaXprbfe6nF8eXm5HA5Hl6/PP/+8zxMHAABDQ1gog8eNG6ennnpKkydPliS9+OKLuuuuu1RVVaXk5OQet6upqZHb7Q4sX3311b2cLgAAGGpCipHMzMxOy08++aTy8/N14MCBi8ZIbGysRo0a1asJAgCAoS2kGPmuc+fO6dVXX1Vra6vS0tIuOnbWrFk6e/askpKStGHDBs2fP/+i471er7xeb2DZ4/FIknw+n3w+X2+nDGAAunBMc2wDQ0+wx3XIMXLkyBGlpaXp7NmzGjlypHbu3KmkpKRux8bHx2vr1q1KSUmR1+vVSy+9pAULFqi8vFxz587t8Tlyc3OVk5PTZX1paakiIyNDnTKAQaCsrMx6CgD6WVtbW1DjHH6/3x/Kjtvb21VbW6tTp06puLhYzz//vCoqKnoMku/LzMyUw+HQ7t27exzT3ZmRxMRENTU1dbr3BMDg5/P5VFZWpoULF8rpdFpPB0A/8ng8GjNmjJqbmy/6/h3ymZHw8PDADaypqak6ePCgNm3apGeffTao7WfPnq0dO3ZcdIzL5ZLL5eqy3ul08sMKGKI4voGhJ9hjus9/Z8Tv93c6i3EpVVVVio+P7+vTAgCAISKkMyPr16/XokWLlJiYqJaWFr3yyisqLy/X3r17JUnZ2dmqq6vT9u3bJUl5eXmaMGGCkpOT1d7erh07dqi4uFjFxcX9/0oAAMCgFFKMfP3117rnnntUX1+v6OhoTZ8+XXv37tXChQslSfX19aqtrQ2Mb29v19q1a1VXV6eIiAglJydrz549ysjI6N9XAQAABq2Qb2C14PF4FB0dfckbYAAMPj6fTyUlJcrIyOCeEWCICfb9m8+mAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmQoqR/Px8TZ8+XW63W263W2lpaXrrrbcuuk1FRYVSUlI0fPhwTZo0SQUFBX2aMAAAGFpCipFx48bpqaeeUmVlpSorK3Xbbbfprrvu0qefftrt+GPHjikjI0Nz5sxRVVWV1q9frzVr1qi4uLhfJg8AAAa/sFAGZ2Zmdlp+8sknlZ+frwMHDig5ObnL+IKCAo0fP155eXmSpClTpqiyslIbN27UkiVLej9rAAAwZIQUI9917tw5vfrqq2ptbVVaWlq3Y/bv36/09PRO6+644w4VFhbK5/PJ6XR2u53X65XX6w0sezweSZLP55PP5+vtlAH0k7a2NtXU1PTLvk6f8erDI18qatQBjYxw9Xl/119/vSIjI/thZgD6Ktj37JBj5MiRI0pLS9PZs2c1cuRI7dy5U0lJSd2ObWhoUFxcXKd1cXFx6ujoUFNTk+Lj47vdLjc3Vzk5OV3Wl5aW8kMGGAC+/PJL/fa3v+3Xff6pn/bz9NNP68c//nE/7Q1AX7S1tQU1LuQYuf7661VdXa1Tp06puLhYy5cvV0VFRY9B4nA4Oi37/f5u139Xdna2srKyAssej0eJiYlKT0+X2+0OdcoA+llbW5tuueWWftnXv+qb9ejOz/R/f56k6+Kj+7w/zowAA8eFKxuXEnKMhIeHa/LkyZKk1NRUHTx4UJs2bdKzzz7bZezYsWPV0NDQaV1jY6PCwsIUExPT43O4XC65XF1P1zqdzh4v7QD44URHR+snP/lJv+wr/MS/5drfrqkzb9TMa3r+uQBg8An2PbvPf2fE7/d3ur/ju9LS0lRWVtZpXWlpqVJTU4kKAAAgKcQYWb9+vd577z0dP35cR44c0eOPP67y8nItXbpU0reXV5YtWxYYv2LFCp04cUJZWVk6evSoXnjhBRUWFmrt2rX9+yoAAMCgFdJlmq+//lr33HOP6uvrFR0drenTp2vv3r1auHChJKm+vl61tbWB8RMnTlRJSYkeeeQRPfPMM0pISNDmzZv5tV4AABAQUowUFhZe9PGioqIu62699VYdPnw4pEkBAIArB59NAwAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMhRQjubm5uummmxQVFaXY2FgtXrxYNTU1F92mvLxcDoejy9fnn3/ep4kDAIChIaQYqaio0MqVK3XgwAGVlZWpo6ND6enpam1tveS2NTU1qq+vD3xde+21vZ40AAAYOsJCGbx3795Oy9u2bVNsbKwOHTqkuXPnXnTb2NhYjRo1KuQJAgCAoS2kGPm+5uZmSdLo0aMvOXbWrFk6e/askpKStGHDBs2fP7/HsV6vV16vN7Ds8XgkST6fTz6fry9TBjDAdHR0BP7l+AaGlmCP6V7HiN/vV1ZWlm655RZNnTq1x3Hx8fHaunWrUlJS5PV69dJLL2nBggUqLy/v8WxKbm6ucnJyuqwvLS1VZGRkb6cMYAA6eVqSwnTgwAHVfWI9GwD9qa2tLahxDr/f7+/NE6xcuVJ79uzR+++/r3HjxoW0bWZmphwOh3bv3t3t492dGUlMTFRTU5PcbndvpgtggPqo9n/0v56r1Gu/TtWM8Zc+ywpg8PB4PBozZoyam5sv+v7dqzMjq1ev1u7du7Vv376QQ0SSZs+erR07dvT4uMvlksvl6rLe6XTK6XSG/HwABq6wsLDAvxzfwNAS7DEdUoz4/X6tXr1aO3fuVHl5uSZOnNiryVVVVSk+Pr5X2wIAgKElpBhZuXKlXn75Zb3xxhuKiopSQ0ODJCk6OloRERGSpOzsbNXV1Wn79u2SpLy8PE2YMEHJyclqb2/Xjh07VFxcrOLi4n5+KQAAYDAKKUby8/MlSfPmzeu0ftu2bbr33nslSfX19aqtrQ081t7errVr16qurk4RERFKTk7Wnj17lJGR0beZAwCAISHkyzSXUlRU1Gl53bp1WrduXUiTAgAAVw4+mwYAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmCJGAACAKWIEAACYIkYAAIApYgQAAJgiRgAAgCliBAAAmAopRnJzc3XTTTcpKipKsbGxWrx4sWpqai65XUVFhVJSUjR8+HBNmjRJBQUFvZ4wAAAYWkKKkYqKCq1cuVIHDhxQWVmZOjo6lJ6ertbW1h63OXbsmDIyMjRnzhxVVVVp/fr1WrNmjYqLi/s8eQAAMPiFhTJ47969nZa3bdum2NhYHTp0SHPnzu12m4KCAo0fP155eXmSpClTpqiyslIbN27UkiVLejdrAAAwZIQUI9/X3NwsSRo9enSPY/bv36/09PRO6+644w4VFhbK5/PJ6XR22cbr9crr9QaWPR6PJMnn88nn8/VlysAV7UzHGX148qjOtp+3nkrAiX+f1lXD61Rx/LCOt4y0nk7A8PCr9NPEKYoIi7CeCjBoBfue3esY8fv9ysrK0i233KKpU6f2OK6hoUFxcXGd1sXFxamjo0NNTU2Kj4/vsk1ubq5ycnK6rC8tLVVkZGRvpwxc8T5p/Uqv+P7behpdjJgoPX9c0nHjiXzP/678P5o6IsF6GsCg1dbWFtS4XsfIqlWr9PHHH+v999+/5FiHw9Fp2e/3d7v+guzsbGVlZQWWPR6PEhMTlZ6eLrfb3dspA1e8sScbVbh9mB5ZMFmJPxoY/8d/pr1dHx7+TD+9MUkR4eHW05EknfzPGf3lH1/o9mV36sbEWOvpAIPWhSsbl9KrGFm9erV2796tffv2ady4cRcdO3bsWDU0NHRa19jYqLCwMMXExHS7jcvlksvl6rLe6XR2e1kHQHBGuEbq/Nn/0vxJKZr6X9HW05H07WncyJOnlDHtpwPm+P6krllP7zmjEa6RA2ZOwGAU7PET0m/T+P1+rVq1Sq+//rreeecdTZw48ZLbpKWlqaysrNO60tJSpaamcpADAIDQYmTlypXasWOHXn75ZUVFRamhoUENDQ06c+ZMYEx2draWLVsWWF6xYoVOnDihrKwsHT16VC+88IIKCwu1du3a/nsVAABg0AopRvLz89Xc3Kx58+YpPj4+8PX3v/89MKa+vl61tbWB5YkTJ6qkpETl5eWaOXOmnnjiCW3evJlf6wUAAJJCvGfkwo2nF1NUVNRl3a233qrDhw+H8lQAAOAKwWfTAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTIcfIvn37lJmZqYSEBDkcDu3ateui48vLy+VwOLp8ff75572dMwAAGELCQt2gtbVVM2bM0H333aclS5YEvV1NTY3cbndg+eqrrw71qQEAwBAUcowsWrRIixYtCvmJYmNjNWrUqJC3AwAAQ1vIMdJbs2bN0tmzZ5WUlKQNGzZo/vz5PY71er3yer2BZY/HI0ny+Xzy+XyXfa7AUNXR0RH4d6AcSxfmMVDmIw3M7xMwGAV7/Fz2GImPj9fWrVuVkpIir9erl156SQsWLFB5ebnmzp3b7Ta5ubnKycnpsr60tFSRkZGXe8rAkHXytCSF6f3339eJkdaz6aysrMx6CgED+fsEDCZtbW1BjXP4/X5/b5/E4XBo586dWrx4cUjbZWZmyuFwaPfu3d0+3t2ZkcTERDU1NXW67wRAaD79yqPF+Qe06zezlZwwMI4ln8+nsrIyLVy4UE6n03o6kgbm9wkYjDwej8aMGaPm5uaLvn//YJdpvmv27NnasWNHj4+7XC65XK4u651O54D5YQUMRmFhYYF/B9qxNJCO74H8fQIGk2CPH5O/M1JVVaX4+HiLpwYAAANMyGdGTp8+rS+++CKwfOzYMVVXV2v06NEaP368srOzVVdXp+3bt0uS8vLyNGHCBCUnJ6u9vV07duxQcXGxiouL++9VAACAQSvkGKmsrOz0mzBZWVmSpOXLl6uoqEj19fWqra0NPN7e3q61a9eqrq5OERERSk5O1p49e5SRkdEP0wcAAINdyDEyb948Xeye16Kiok7L69at07p160KeGAAAuDLw2TQAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwFTIMbJv3z5lZmYqISFBDodDu3btuuQ2FRUVSklJ0fDhwzVp0iQVFBT0Zq4AAGAICjlGWltbNWPGDG3ZsiWo8ceOHVNGRobmzJmjqqoqrV+/XmvWrFFxcXHIkwUAAENPWKgbLFq0SIsWLQp6fEFBgcaPH6+8vDxJ0pQpU1RZWamNGzdqyZIloT49AAAYYkKOkVDt379f6enpndbdcccdKiwslM/nk9Pp7LKN1+uV1+sNLHs8HkmSz+eTz+e7vBMGhrCWM98eVx/V/o86Ojp6vZ8zZ9p0/Mv/1y9zOtdxTkeOfKkWlWtY2LA+72/Cj69VRERkn/bxxTetkqSOjg5+5gB9EOzxc9ljpKGhQXFxcZ3WxcXFqaOjQ01NTYqPj++yTW5urnJycrqsLy0tVWRk337IAFey/V87JA3T42981qf9eBu+UMOLD/fLnPrb2OV5co2d3C/7Orj/fZ2I6JddAVektra2oMZd9hiRJIfD0WnZ7/d3u/6C7OxsZWVlBZY9Ho8SExOVnp4ut9t9+SYKDHGzW9s17WijJl09QhHO3p+FOHNmuo7/Ylq/zOnbMyNHNG3atAFzZkSSRriGaULMiD7vB7iSXbiycSmXPUbGjh2rhoaGTusaGxsVFhammJiYbrdxuVxyuVxd1judzm4v6wAITtwop5amTeyHPcUo7YbEftjPt6dxo9SmjIx5HN/AEBPsMX3Z/85IWlqaysrKOq0rLS1VamoqP3gAAEDoMXL69GlVV1erurpa0re/ultdXa3a2lpJ315iWbZsWWD8ihUrdOLECWVlZeno0aN64YUXVFhYqLVr1/bPKwAAAINayJdpKisrNX/+/MDyhXs7li9frqKiItXX1wfCRJImTpyokpISPfLII3rmmWeUkJCgzZs382u9AABAUi9iZN68eYEbULtTVFTUZd2tt96qw4cPh/pUAADgCsBn0wAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU5f9U3v7w4W/+BrsRxEDGDx8Pp/a2trk8Xj48ExgiLnwvn2xv9wuDZIYaWlpkSQlJvbPR5YDAIAfTktLi6Kjo3t83OG/VK4MAOfPn9dXX32lqKgoORwO6+kA6Ecej0eJiYk6efKk3G639XQA9CO/36+WlhYlJCToqqt6vjNkUMQIgKHL4/EoOjpazc3NxAhwheIGVgAAYIoYAQAApogRAKZcLpd+97vfyeVyWU8FgBHuGQEAAKY4MwIAAEwRIwAAwBQxAgAATBEjAAacCRMmKC8vz3oaAH4g3MAKoM/uvfdenTp1Srt27eqX/X3zzTcaMWKEIiMj+2V/AAa2QfHZNACuDO3t7QoPD9fVV19tPRUAPyAu0wAI2muvvaZp06YpIiJCMTExuv322/Xoo4/qxRdf1BtvvCGHwyGHw6Hy8nJJ0pEjR3TbbbcFxj/44IM6ffp0YH/33nuvFi9erNzcXCUkJOi6666T1PUyTXNzsx588EHFxsbK7Xbrtttu00cffRR4/KOPPtL8+fMVFRUlt9utlJQUVVZW/iDfEwB9x5kRAEGpr6/Xr371K/3pT3/Sz3/+c7W0tOi9997TsmXLVFtbK4/Ho23btkmSRo8erba2Nv3sZz/T7NmzdfDgQTU2NuqBBx7QqlWrVFRUFNjvP/7xD7ndbpWVlXX7MeN+v1933nmnRo8erZKSEkVHR+vZZ5/VggUL9K9//UujR4/W0qVLNWvWLOXn52vYsGGqrq6W0+n8ob41APqIGAEQlPr6enV0dOjuu+/WNddcI0maNm2aJCkiIkJer1djx44NjH/xxRd15swZbd++XSNGjJAkbdmyRZmZmfrjH/+ouLg4SdKIESP0/PPPKzw8vNvnfffdd3XkyBE1NjYG/krrxo0btWvXLr322mt68MEHVVtbq0cffVQ33HCDJOnaa6+9PN8EAJcFl2kABGXGjBlasGCBpk2bpl/84hd67rnn9J///KfH8UePHtWMGTMCISJJN998s86fP6+amprAumnTpvUYIpJ06NAhnT59WjExMRo5cmTg69ixY/ryyy8lSVlZWXrggQd0++2366mnngqsBzA4ECMAgjJs2DCVlZXprbfeUlJSkv7617/q+uuv17Fjx7od7/f75XA4un3su+u/GyvdOX/+vOLj41VdXd3pq6amRo8++qgk6fe//70+/fRT3XnnnXrnnXeUlJSknTt39vKVAvihESMAguZwOHTzzTcrJydHVVVVCg8P186dOxUeHq5z5851GpuUlKTq6mq1trYG1n3wwQe66qqrAjeqBuPGG29UQ0ODwsLCNHny5E5fY8aMCYy77rrr9Mgjj6i0tFR333134P4VAAMfMQIgKP/85z/1hz/8QZWVlaqtrdXrr7+ub775RlOmTNGECRP08ccfq6amRk1NTfL5fFq6dKmGDx+u5cuX65NPPtG7776r1atX65577gncLxKM22+/XWlpaVq8eLHefvttHT9+XB9++KE2bNigyspKnTlzRqtWrVJ5eblOnDihDz74QAcPHtSUKVMu43cDQH/iBlYAQXG73dq3b5/y8vLk8Xh0zTXX6Omnn9aiRYuUmpqq8vJypaam6vTp03r33Xc1b948vf3223rooYd00003KTIyUkuWLNGf//znkJ7X4XCopKREjz/+uO6//3598803Gjt2rObOnau4uDgNGzZM//73v7Vs2TJ9/fXXGjNmjO6++27l5ORcpu8EgP7GX2AFAACmuEwDAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADA1P8HeFy/DAok2KcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='stories')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27f06c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='guestroom_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ef8f0a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='parking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b7565fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='mainroad_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80b34726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='prefarea_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f4da5b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='airconditioning_n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a42b378d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f48e41ef70>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df['price'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b61c971a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='area', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['area'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58e6461f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bedrooms', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['bedrooms'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae9eb047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bathrooms', ylabel='Density'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['bathrooms'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac2e5b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stories', ylabel='Density'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['stories'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3310fb68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='parking', ylabel='Density'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['parking'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e07e886f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mainroad_n', ylabel='Density'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['mainroad_n'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a96e9ce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='airconditioning_n', ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGxCAYAAACeKZf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABawklEQVR4nO3deXzT9f0H8Feupnd60Yu2UCi0UO67cqkgiIqIOnU6EKco85bxU9HNY5tD5zHGFBRFGFPUaQHZOORqQaAchXJJW0opbSm9j/TO+f39kTZaetCWpN/km9fz8chD8803yftLmuSV7+eSCYIggIiIiEgi5GIXQERERGRLDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKUqxC+hpZrMZV65cgY+PD2QymdjlEBERUScIgoCamhqEh4dDLu/43IzLhZsrV64gMjJS7DKIiIioG/Lz8xEREdHhPi4Xbnx8fABY/nF8fX1FroaIiIg6o7q6GpGRkdbv8Y64XLhpbory9fVluCEiInIynelSwg7FREREJCkMN0RERCQpooabVatWYdiwYdYmooSEBGzfvr3d/ZOTkyGTyVpdMjIyerBqIiIicmSi9rmJiIjA22+/jZiYGADAv/71L8yZMwdpaWmIj49v936ZmZkt+sv06tXL7rUSERGRcxA13MyePbvF9bfeegurVq3C4cOHOww3wcHB8PPzs3N1RERE5Iwcps+NyWTC119/jbq6OiQkJHS478iRIxEWFoZp06YhKSmphyokIiIiZyD6UPAzZ84gISEBjY2N8Pb2xqZNmzB48OA29w0LC8Pq1asxevRo6HQ6/Pvf/8a0adOQnJyMKVOmtHkfnU4HnU5nvV5dXW2X4yAiIiLHIBMEQRCzAL1ej7y8PFRVVSExMRGfffYZ9u3b127Audrs2bMhk8mwZcuWNm9/44038Oabb7bartVqOc8NERGRk6iuroZGo+nU97fo4eZq06dPR//+/fHJJ590av+33noLX3zxBdLT09u8va0zN5GRkQw3RERETqQr4Ub0ZqmrCYLQIoxcS1paGsLCwtq9Xa1WQ61W26I0IiIicgKihptXXnkFs2bNQmRkJGpqavD1118jOTkZO3bsAAAsXboUBQUFWL9+PQBg+fLl6Nu3L+Lj46HX6/HFF18gMTERiYmJYh4GERERORBRw01xcTHmzZuHwsJCaDQaDBs2DDt27MAtt9wCACgsLEReXp51f71ejyVLlqCgoAAeHh6Ij4/H1q1bcdttt4l1CERERORgHK7Pjb11pc2OiIiIHENXvr8dZp4bIiIiIltguCEiIiJJcbjRUkS2tOFI3rV3cgIPjo8SuwQiIqfBMzdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCmihptVq1Zh2LBh8PX1ha+vLxISErB9+/YO77Nv3z6MHj0a7u7u6NevHz7++OMeqpaIiIicgajhJiIiAm+//TZSU1ORmpqKm2++GXPmzMFPP/3U5v45OTm47bbbMHnyZKSlpeGVV17Bs88+i8TExB6unIiIiByVTBAEQewifikgIADvvvsuHn300Va3vfTSS9iyZQvS09Ot2xYtWoRTp04hJSWlU49fXV0NjUYDrVYLX19fm9VNjmnDkTyxS7CJB8dHiV0CEZGouvL97TB9bkwmE77++mvU1dUhISGhzX1SUlIwY8aMFttmzpyJ1NRUGAyGNu+j0+lQXV3d4kJERETSJXq4OXPmDLy9vaFWq7Fo0SJs2rQJgwcPbnPfoqIihISEtNgWEhICo9GIsrKyNu+zbNkyaDQa6yUyMtLmx0BERESOQ/RwExsbi5MnT+Lw4cP43e9+h4cffhjnzp1rd3+ZTNbienOr2tXbmy1duhRardZ6yc/Pt13xRERE5HCUYhfg5uaGmJgYAMCYMWNw7Ngx/OMf/8Ann3zSat/Q0FAUFRW12FZSUgKlUonAwMA2H1+tVkOtVtu+cCIiInJIop+5uZogCNDpdG3elpCQgF27drXYtnPnTowZMwYqlaonyiMiIiIHJ2q4eeWVV/Djjz/i0qVLOHPmDF599VUkJyfjoYceAmBpUpo/f751/0WLFiE3NxeLFy9Geno6Pv/8c6xZswZLliwR6xCIiIjIwYjaLFVcXIx58+ahsLAQGo0Gw4YNw44dO3DLLbcAAAoLC5GX9/NQ3ujoaGzbtg0vvPACPvroI4SHh2PFihW45557xDoEIiIicjAON8+NvXGeG9fCeW6IiKTBKee5ISIiIrIFhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUUcPNsmXLMHbsWPj4+CA4OBh33XUXMjMzO7xPcnIyZDJZq0tGRkYPVU1ERESOTNRws2/fPjz11FM4fPgwdu3aBaPRiBkzZqCuru6a983MzERhYaH1MmDAgB6omIiIiBydUswn37FjR4vra9euRXBwMI4fP44pU6Z0eN/g4GD4+fnZsToiIiJyRg7V50ar1QIAAgICrrnvyJEjERYWhmnTpiEpKand/XQ6Haqrq1tciIiISLocJtwIgoDFixdj0qRJGDJkSLv7hYWFYfXq1UhMTMTGjRsRGxuLadOmYf/+/W3uv2zZMmg0GuslMjLSXodAREREDkAmCIIgdhEA8NRTT2Hr1q04cOAAIiIiunTf2bNnQyaTYcuWLa1u0+l00Ol01uvV1dWIjIyEVquFr6/vdddNjm3DkTyxS7CJB8dHiV0CEZGoqqurodFoOvX97RBnbp555hls2bIFSUlJXQ42ADBhwgRkZWW1eZtarYavr2+LCxEREUmXqB2KBUHAM888g02bNiE5ORnR0dHdepy0tDSEhYXZuDoiIiJyRqKGm6eeegobNmzA999/Dx8fHxQVFQEANBoNPDw8AABLly5FQUEB1q9fDwBYvnw5+vbti/j4eOj1enzxxRdITExEYmKiaMdBREREjkPUcLNq1SoAwI033thi+9q1a7FgwQIAQGFhIfLyfu43odfrsWTJEhQUFMDDwwPx8fHYunUrbrvttp4qm4iIiByYw3Qo7ild6ZBEzo8diomIpMHpOhQTERER2QrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJilLsAohsTRAE5Fc04HxxDYq0jQj2VUMuk4ldFhER9RCGG5KMRoMJ/9ybhS+P5KGq3mDd7q6SY2hvP9waHwoPN4WIFRIRUU9guCFJOJpTgRe/O4VL5fUAADeFHNFBXrhYVotGgxnHLlUgo6gac0f2Rlyor8jVEhGRPTHckNM7eKEMj6w9Br3JjBBfNV6fHY/pg0LgppTj3ym5yCmrw5ZTV1BWq8P6lFzcOyoCo/r4i102ERHZiagdipctW4axY8fCx8cHwcHBuOuuu5CZmXnN++3btw+jR4+Gu7s7+vXrh48//rgHqiVHdDy3EgvXp0JvMmP6oBDsWjwVtw0Ng5vS8qetkMsQE+yNZ26Owdi+AQCAjWmXkV5YLWbZRERkR6KGm3379uGpp57C4cOHsWvXLhiNRsyYMQN1dXXt3icnJwe33XYbJk+ejLS0NLzyyit49tlnkZiY2IOVkyPIr6jHI2uPol5vwuQBQfjooZHwdVe1ua9KIcecEeEYGekHswB8dTQP+RX1PVwxERH1BJkgCEJX75STk4Po6GibF1NaWorg4GDs27cPU6ZMaXOfl156CVu2bEF6erp126JFi3Dq1CmkpKRc8zmqq6uh0Wig1Wrh68u+F87KbBbw4GeHcfhiBYZH+uGrhePh6da6lXXDkbwW101mAV8czkVmcQ2CvN3wzM0DoFI4/owID46PErsEIiJRdeX7u1uf6jExMbjpppvwxRdfoLGxsVtFtkWr1QIAAgIC2t0nJSUFM2bMaLFt5syZSE1NhcFgaOdeJDX/SrmEwxcr4KFSYMUDI9oMNm1RyGW4b0wkfNyVKKvVY9e5YjtXSkREPa1b4ebUqVMYOXIkfv/73yM0NBRPPPEEjh49el2FCIKAxYsXY9KkSRgyZEi7+xUVFSEkJKTFtpCQEBiNRpSVlbXaX6fTobq6usWFnFtOWR3e2ZEBAHjl9kHoE+jVpft7uCkwd0RvAJbOyLnl7TeDEhGR8+lWuBkyZAg++OADFBQUYO3atSgqKsKkSZMQHx+PDz74AKWlpV1+zKeffhqnT5/GV199dc19ZVdNyNbcsnb1dsDSaVmj0VgvkZGRXa6NHMtft6Wj0WDGpJgg/KabzTVxYb4YGekHAcD3J6/A3PXWWSIiclDX1dlAqVRi7ty5+M9//oN33nkH2dnZWLJkCSIiIjB//nwUFhZ26nGeeeYZbNmyBUlJSYiIiOhw39DQUBQVFbXYVlJSAqVSicDAwFb7L126FFqt1nrJz8/v/AGSw0m9VIFd54ohlwFv3BnfZqDtrNuHhkGtlKOouhGnL2ttWCUREYnpusJNamoqnnzySYSFheGDDz7AkiVLkJ2djb1796KgoABz5szp8P6CIODpp5/Gxo0bsXfv3k51Uk5ISMCuXbtabNu5cyfGjBkDlar1SBm1Wg1fX98WF3JOgiBYm6PuGxOJmGDv63o8T7USUwb2AgDsTi+GycyzN0REUtCtcPPBBx9g6NChuOGGG3DlyhWsX78eubm5+Mtf/oLo6GhMnDgRn3zyCU6cONHh4zz11FP44osvsGHDBvj4+KCoqAhFRUVoaGiw7rN06VLMnz/fen3RokXIzc3F4sWLkZ6ejs8//xxr1qzBkiVLunMo5ET2ZpTg2KVKqJVyPD99oE0e84b+gfBSK1FRp0dqboVNHpOIiMTVrXCzatUqPPjgg8jLy8PmzZtxxx13QC5v+VBRUVFYs2bNNR9Hq9XixhtvRFhYmPXyzTffWPcpLCxEXt7Pw3mjo6Oxbds2JCcnY8SIEfjzn/+MFStW4J577unOoZCTEAQBf999HgDwyMRohGrcbfK4aqUCN8dazt7szSiB0WS2yeMSEZF4ujXPzaVLlxAVFdUq0AiCgPz8fERFOe6cHJznxjmlZJfj158ehrtKjkMvT0OAl1un7nf1PDdtMZrNeH/neWgbDLhnVARGO+DSDJznhohcnd3nuenfv3+bw64rKirsMrkf0Wc/XgQA3DMqotPBprOUcjkS+lk6ox+8UIZu5H0iInIg3Qo37X3419bWwt3dNs0FRM0ulNRiT0YJZDLg0Un2Cc9j+wZApZChqLoR2aWc94aIyJl1aVXwxYsXA7DMJ/Paa6/B09PTepvJZMKRI0cwYsQImxZItOZADgBgWlwI+vW6vhFS7fFwU2B0nwAcvliOgxfKrnskFhERiadL4SYtLQ2A5czNmTNn4Ob2c/OAm5sbhg8fzlFLZFPaegM2nrgMAFg42b5NnhP7B+LIxXJkFtegtEaHXj5quz4fERHZR5fCTVJSEgDgkUcewT/+8Q92yCW7+/5UAXRGM+JCfTAuuv01x2wh0FuN2FAfZBTVIPVSBWYNDbPr8xERkX10q8/N2rVrGWyoR3xzzDKj9P1jI69rNuLOGtvXEqBO5FdxUj8iIifV6TM3d999N9atWwdfX1/cfffdHe67cePG6y6M6GyBFj9dqYabUo65I3v3yHMODPGBj1qJGp0RGUXViA/X9MjzEhGR7XQ63Gg0GusvZ42GH/hkf18fs8xRc2t8KPw8bTv8uz0KuQwjo/yxP6sUx3MrGW6IiJxQp8PN2rVr2/x/Into0JvwfdoVAMADY3t2JfcxfSzhJrOoBtUNBvh6tF6zjIiIHFe3+tw0NDSgvr7eej03NxfLly/Hzp07bVYYubad54pQozMiMsADE/q1Xu3dnoJ81Ogb6AkBQFpeZY8+NxERXb9uhZs5c+Zg/fr1AICqqiqMGzcO77//PubMmYNVq1bZtEByTVtOWs7a3DWiN+Ry+3ckvtqoKMsSDKcua3v8uYmI6Pp0K9ycOHECkydPBgB89913CA0NRW5uLtavX48VK1bYtEByPVX1euzPKgUAzBkRLkoN8eEaKGSWGYuLqxtFqYGIiLqnW+Gmvr4ePj4+AICdO3fi7rvvhlwux4QJE5Cbm2vTAsn1bD9bBINJwKAwX8QE+4hSg4ebAgNDLLMUn75cJUoNRETUPd0KNzExMdi8eTPy8/Pxww8/YMaMGQCAkpISzn9D1+37kwUAgDuHi3PWptmwSD8AlqYpLqZJROQ8uhVuXnvtNSxZsgR9+/bF+PHjkZCQAMByFmfkyJE2LZBcS5G2EUdyKgAAs4eLO0PwoFBfqBQyVNTpUVDVIGotRETUeV1afqHZvffei0mTJqGwsBDDhw+3bp82bRrmzp1rs+LI9fzv9BUIgmU4doS/57XvYEduSjkGhfni9GUtTl/Wil4PERF1TrfO3ABAaGgoRo4cCbn854cYN24c4uLibFIYuaYdZ4sAALcPc4x1nYb19gMAnClg0xQRkbPo1pmburo6vP3229izZw9KSkpgNptb3H7x4kWbFEeupaSmEceb5pWZGR8qcjUWA0K84aaQQ9tgQEFVA8/eEBE5gW6Fm8ceewz79u3DvHnzEBYW1iMLGpL07TpXDEEAhkf6IdzPQ+xyAAAqhRwDQ32s61wx3BAROb5uhZvt27dj69atmDhxoq3rIRfW3CR1q4OctWkWH+aLswVanLtS7TBnlIiIqH3d6nPj7++PgIAAW9dCLkxbb0BKdjkAYGZ8iMjVtBQb6gOFTIbSWh1KOKEfEZHD61a4+fOf/4zXXnutxfpSRNdjb2YxjGYBA0O80a+Xt9jltOCuUqB/sBcA4FxhtcjVEBHRtXSrWer9999HdnY2QkJC0LdvX6hULVdNPnHihE2KI9fhqE1SzQaHaXC+uBY/XanGjbHBYpdDREQd6Fa4ueuuu2xcBrmyRoMJP2aVAQBmOGi4GRTmg+9PAgVVDdA2GKDxUF3zPkREJI5uhZvXX3/d1nWQCzuSU4F6vQkhvmrEhzvm8h0+7ipEBngir6IeGUXVGB8dKHZJRETUjm5P4ldVVYXPPvsMS5cuRUWFZbr8EydOoKCgwGbFkWvYm14MALg5LsShpxWIC7Us4plZVCNyJURE1JFunbk5ffo0pk+fDo1Gg0uXLmHhwoUICAjApk2bkJubi/Xr19u6TpIoQRCwO70EADAtzrH7ssSG+mDnuWJkl9bCYDJDpej2bwMiIrKjbn06L168GAsWLEBWVhbc3d2t22fNmoX9+/fbrDiSvvPFtSioaoBaKcfEmCCxy+lQqK87NB4qGEwCLpbWil0OERG1o1vh5tixY3jiiSdabe/duzeKioquuyhyHXsyLE1SE2OC4OGmELmajslkMmvTVAabpoiIHFa3wo27uzuqq1vP95GZmYlevXpdd1HkOvY2NUnd7OBNUs1+GW64kCYRkWPqVriZM2cO/vSnP8FgMACw/KLNy8vDyy+/jHvuucemBZJ0VdXrcaJpoUxnCTf9enlDpZBB22BAEWcrJiJySN0KN++99x5KS0sRHByMhoYGTJ06FTExMfDx8cFbb71l6xpJog5cKINZAGJDfBxmocxrUSnk6N80g/L5Yva7ISJyRN0aLeXr64sDBw4gKSkJx48fh9lsxqhRozB9+nRb10cStv98KQBgykDH7kh8tQHB3sgoqkFWSQ2mDmQzLBGRo+lyuDGbzVi3bh02btyIS5cuQSaTITo6GqGhoRAEwaHnKSHHIQgC9p+3zEo8eYBzBYQBwT4ACpFbXg+90Qw3JYeEExE5ki59KguCgDvvvBOPPfYYCgoKMHToUMTHxyM3NxcLFizA3Llz7VUnSUxWSS2KqhuhVsoxLtq5VpgP9HaDv6cKJrOAi2VsmiIicjRdOnOzbt067N+/H3v27MFNN93U4ra9e/firrvuwvr16zF//nybFknS09wkNb5fINxVjj0E/GoymQwxwT44dqkCWSW1iAt1zCUjiIhcVZfO3Hz11Vd45ZVXWgUbALj55pvx8ssv48svv7RZcSRd+5sWypwywLn62zQbEGzpVJzFTsVERA6nS2duTp8+jb/97W/t3j5r1iysWLHiuosiaWs0mHDkYjkAYIqTdsjt38sbchlQVqtDZb0e/p5uYpdE1C0bjuSJXYJNPDg+SuwSyIF06cxNRUUFQkJC2r09JCQElZWV110USdvRnArojGaE+rpbz4A4Gw83BSL8PQEAF3j2hojIoXQp3JhMJiiV7Z/sUSgUMBqN110USdsvh4A78+i6ASFNTVMlXIqBiMiRdKlZShAELFiwAGq1us3bdTqdTYoiafuxub+NkzZJNRsQ7IM96SW4UFoLk1mAQu68QY2ISEq6dObm4YcfRnBwMDQaTZuX4ODgLo2U2r9/P2bPno3w8HDIZDJs3ry5w/2Tk5Mhk8laXTIyMrpyGCSiIm0jMotrIJMBE/s7Z2fiZhH+HnBXydFoMKOgqkHscoiIqEmXztysXbvWpk9eV1eH4cOH45FHHunSmlSZmZnw9f15+C0X63Qe+7MsTVLDIvzg7+XcnXDlMhliennj7JVqZBXXICrAU+ySiIgI3Vx+wVZmzZqFWbNmdfl+wcHB8PPzs31BZHfN/W2mOukQ8KsNCPGxhJuSWkwb1H5neyIi6jlOOW/8yJEjERYWhmnTpiEpKanDfXU6Haqrq1tcSBwms4ADF6TR36ZZ82ivy5X1aNCbRK6GiIgAJws3YWFhWL16NRITE7Fx40bExsZi2rRp2L9/f7v3WbZsWYt+QZGRkT1YMf3S2QItquoN8FErMSLST+xybMLP0w29vNUwC0B2KYeEExE5AlGbpboqNjYWsbGx1usJCQnIz8/He++9hylTprR5n6VLl2Lx4sXW69XV1Qw4ImlukrohJhBKhVPl6g7FhHijtFaHrJJaDOmtEbscIiKX5/TfMBMmTEBWVla7t6vVavj6+ra4kDgOZjvnKuDXMrCpaeoC57shInIITh9u0tLSEBYWJnYZdA0NehNO5FYBACbGSKMzcbO+QV6Qy4DKegMq6vRil0NE5PJEbZaqra3FhQsXrNdzcnJw8uRJBAQEICoqCkuXLkVBQQHWr18PAFi+fDn69u2L+Ph46PV6fPHFF0hMTERiYqJYh0CddDy3EnqTGWEad/QNlNaQabVSgcgAT+SW1yO7tBYBXgFil0RE5NJEDTepqaktVhhv7hvz8MMPY926dSgsLERe3s+Luun1eixZsgQFBQXw8PBAfHw8tm7dittuu63Ha6euaW6SSugf6NRLLrSnfy9va7gZ25fhhohITKKGmxtvvBGCILR7+7p161pcf/HFF/Hiiy/auSqyh0PZllXAnX1W4vb07+WNvRkluFhaB0EQJBngiIichdP3uSHHV91owJnLVQAsI6WkKNLfAyqFDLU6I0pquMYaEZGYGG7I7o5crIBZAPoFeSFM4yF2OXahVMjRJ9ALAOe7ISISG8MN2d2hX/S3kbL+vSxDwrNL60SuhIjItTHckN0dumDpb3ODRPvbNOvfy3LmJqesFiZz+33JiIjIvhhuyK5Ka3TILLZMbif1Mzfhfh5wV8nRaDCjUNsgdjlERC6L4Ybs6vBFy1mbQWG+CPByE7ka+5LLZIgOamqaKmG/GyIisTDckF0197eZKPGzNs2am6ayy9jvhohILAw3ZFfN89tIdQj41Zo7FeeW18FoMotcDRGRa2K4Ibu5XFmP3PJ6KOQyl5m1N9hHDW+1EgaTgLzKerHLISJySQw3ZDfNZ22GR2jg464SuZqeIZPJ0K+5aaqETVNERGJguCG7Scl2jSHgV2tumrrIyfyIiETBcEN2IQgCDl6wdCa+wUU6EzdrDjf5lfXQGU0iV0NE5HoYbsguskvrUFKjg5tSjlF9/MUup0cFeLnB31MFswBcKmO/GyKinsZwQ3aR0jQEfEwff7irFCJX0/N+XoqBTVNERD2N4Ybs4mDTkgsTY1yrv02zfux3Q0QkGoYbsjmzWUBK08zEUl9yoT3Nk/kVahtRrzOKXA0RkWthuCGbO1dYDW2DAd5qJYb11ohdjih83FUI9lFDAHCRsxUTEfUohhuyueYlF8ZHB0CpcN0/Mfa7ISISh+t+85DdNE/e56pNUs2s60yV8swNEVFPYrghm9IbzTiaUwHAdTsTN4sO8oYMQFmtDtoGg9jlEBG5DIYbsqnTl6tQrzchwMsNsSE+YpcjKg83BcL9PABw1BQRUU9iuCGbah4CntAvEHK5TORqxPdz0xTDDRFRT2G4IZtq7kx8Q4xr97dp9nOn4joIgiByNUREroHhhmymQW9CWl4VANdbLLM9fQK9oJDLoG0woLxWL3Y5REQugeGGbCY1twJ6kxlhGnf0DfQUuxyH4KaUIyrA8m9xgU1TREQ9guGGbKZ5CPgN/YMgk7G/TTP2uyEi6lkMN2Qzhy409bdx8fltrtbfus5UHczsd0NEZHcMN2QT2gYDzhRoAbAz8dUi/D3hppSjwWBCobZR7HKIiCSP4YZs4mhOBcwC0C/IC2EaD7HLcSgKuQzRgU1NUyVsmiIisjeGG7KJg01NUq6+5EJ7+gdznSkiop7CcEM20Ty/jasvudCemKZ+N5fK62A0m0WuhohI2hhu6LqVVDfifHEtZDLLzMTUWoivGl5qJQwmAfkVDWKXQ0QkaQw3dN2ah4DHh/vC38tN5Gock0wm45BwIqIewnBD1+3ABTZJdUbzkPAL7FRMRGRXDDd0XQRBsM5vM5FLLnSoOdxcrqyHzmASuRoiIuliuKHrklNWhyvaRrgp5BjbN0DschxagJcb/D1VMAtATnmd2OUQEUkWww1dl4NN/W1G9fGDh5tC5Gocn3WVcDZNERHZDcMNXZeDWZYmqUnsb9MpP893wzM3RET2wnBD3WYyC0i52LRYJsNNpzSfuSmqbkStzihyNURE0sRwQ9320xUttA0G+KiVGNZbI3Y5TsFbrUSorzsA4CKHhBMR2YWo4Wb//v2YPXs2wsPDIZPJsHnz5mveZ9++fRg9ejTc3d3Rr18/fPzxx/YvlNp08ILlrM34foFQKpiTO4vz3RAR2Zeo30h1dXUYPnw4Pvzww07tn5OTg9tuuw2TJ09GWloaXnnlFTz77LNITEy0c6XUlub1pCZxFfAuYb8bIiL7Uor55LNmzcKsWbM6vf/HH3+MqKgoLF++HAAwaNAgpKam4r333sM999xjpyqpLY0GE45dqgDAyfu6KjrQC3IZUFGnR2WdnrM6ExHZmFO1JaSkpGDGjBktts2cOROpqakwGAwiVeWaTuRWQmc0I9hHjZimMxHUOWqVAhH+ngDYNEVEZA9OFW6KiooQEhLSYltISAiMRiPKysravI9Op0N1dXWLC12/g79YBVwmk4lcjfOxLsXAcENEZHNOFW4AtPoiFQShze3Nli1bBo1GY71ERkbavUZXcKCpMzGbpLqnf7ClU/HF0jrr3zAREdmGU4Wb0NBQFBUVtdhWUlICpVKJwMC2O7UuXboUWq3WesnPz++JUiVN22DAmctVAICJ7EzcLVH+nlApZKjVGVFcoxO7HCIiSRG1Q3FXJSQk4L///W+LbTt37sSYMWOgUqnavI9arYZare6J8lxGSnYZzALQr5cXwjQeYpfjlJQKOfoGeiGrpBbZJbXWuW+IiOj6iRpuamtrceHCBev1nJwcnDx5EgEBAYiKisLSpUtRUFCA9evXAwAWLVqEDz/8EIsXL8bChQuRkpKCNWvW4KuvvhLrEFzSvvOlAIApA3qJXIlziwn2RlZJLbJKati8R9RFeqMZF0trUVKjQ3WjAWcKtOjl7YZQjQcS+gciOshL7BJJRKKGm9TUVNx0003W64sXLwYAPPzww1i3bh0KCwuRl5dnvT06Ohrbtm3DCy+8gI8++gjh4eFYsWIFh4H3IEEQsP+8pTPx1FiGm+sxIMQH288WIaesDgaTGSpOhEh0TTlldThwoQxZxTUwmn/ur3aoaRHfZtFBXrh3dATmJ/SBj3vbZ/ZJukQNNzfeeGOHnSnXrVvXatvUqVNx4sQJO1ZFHckurUVBVQPclHJMiGZ/m+sR4qOGr7sS1Y1GXCqrw4AQH7FLInJYV6oasO1sIS7+YvJLP08VogI84eehwsgof5TX6ZBdUofU3ArklNXh3R8ysXr/RTwxtR8WTu7HHxAuxKn63JD49jWdtRkfHQAPN4XI1Tg3mUyGASE+OJ5biaySWoYbojaYzAL2Z5ViT3oxzAKgkMkwuq8/xkcHINTX3TpS9sHxUdb71DQasPOnYqxMvoDs0jr8bUcmtp8pwgf3Def7zEUwxlKX7Gd/G5sa2PRBe764RuRKiBxPvc6INQdysOucJdjEh/ti8YyBuGtEb4RpPNqdAsTHXYV7Rkdg5wtT8f6vhkPjocKZAi1u/+cBbDl1pYePgsTAcEOd1mgw4fBFS7v2lIEMN7YQ08sbMgAlNTpU1evFLofIYZTX6rBqXzYulddBrZTjV6Mj8OC4KPh7dn65EoVc1hRypmDqwF7QG8149qs0fLg3i/NLSRzDDXXa0ZwK6IxmhPq6Y2AIl1ywBQ83BSIDLEsxZJVwtmIiACiqbsTH+7JRXqeHn6cKi6b2x8go/27Phh7i647PF4zFY5OiAQDv7TyPN/97jgFHwhhuqNOsTVIDueSCLQ1oWpuLTVNEQFmNDp8fyEGd3oRwjTsWTe2PEBvMA6WQy/CHOwbjT3PiAQDrDl3Csu0ZDDgSxXBDnWad34ZNUjbV3O8mu7QWJjM/aMl1Vdbp8dmBi6jVGRGmccejk/rB18bDuOcn9MVbc4cAAFbvv4jlu7Ns+vjkGBhuqFOuVDUgq6QWchkwiRPO2VRvfw94qBRoNJhxubJe7HKIRNFoMOFfKZdQ3WhELx81HpkYbbcRmQ+N74M3Zg8GAPxjTxY2nrhsl+ch8TDcUKc0N0kNj/SDXxc69NG1yWUyxFibptjvhlyPWRDwzbF8lNTo4OOuxG8nRsNbbd+ZShZMjMbvbuwPAHg58QyOXaqw6/NRz2K4oU7Zn8Uh4PbU3DSVVcJ+N+R6fjhbhMziGijlMsyb0Acaj56ZUfj/ZsRi1pBQ6E1mPPHv4yjUNvTI85L9MdzQNRlNZhzI4pIL9tTcqbigsgF1OqPI1RD1nHNXqvHjBcvny72jIxDh79ljzy2Xy/DBfSMQH+6Lijo9ntmQBoPJ3GPPT/bDcEPXdOpyFaobjdB4qDA8wk/sciTJ10OFUF93CAAucEg4uYjKOj2+O5EPwNKXb5gIny8ebgqsfGgUfNRKpOZW4r0fMnu8BrI9hhu6puYlFyYNCIJCziHg9tI8dxCHhJMrMJkFfH0sD40GMyL9PTAjPkS0WvoEeuHdXw0DAHyy/yKSMktEq4Vsg+GGrim56Y0+lf1t7GqAtd9NLcyce4MkLvl8CfIrG+CukuOBsVFQysX9Orp1SBgW3NAXAPDid6dRUccZw50Zww11qLi6EacvayGTATfFBYtdjqT1CfCEm1KOWp0RV6rYsZGk63JlPZIyLD+a7hzeG/5ejjEC8+VZcYgJ9kZpjQ6vbjrDCf6cGMMNdWhPuuUDaHiEH3r5qEWuRtqUCjkGNnUsTi+sFrkaIvswmMz4NvUyzAIwtLcGwyM0Ypdk5a5SYPn9I6CUy7D9bBE2nywQuyTqJoYb6tDu9GIAwC2DxWsPdyWDwnwBAOmF7HdD0pSUUYLSWh181ErMGR7ucEu5DOmtwXPTBgAA/vTfc2yeclIMN9Suer0RB5uGaE4fxHDTE2JDfCCDZeHASn6oksQUVzda58y6c0Q4PO08UV93LbqxP+JCfVBZb8BbW9PFLoe6geGG2nUgqww6oxmRAR5cBbyHeKqV6BPoBQBIL2LTFEmHWRCwOa0AZgEYFOqDwU1nKR2RSiHHX+8eCpkMSDxxGYeafuSR82C4oXY1N0lNiwtxuFPHUjYozDJqKoNNUyQhxy9VIreiHm4KOWY7YHPU1UZF+eM34/sAAF7dfBaNBpPIFVFXMNxQm8xmAXubRjOwv03Pau53c7Gslh+oJAk1jQZs/6kQgOXzxFnWp/u/W2MR7KNGTlkdViZdELsc6gKGG2rTyctVKKvVw8ddiXHRAWKX41KCvNXo5a2GWeCEfiQN284UotFgRrifOyb0CxS7nE7zdVfhjTvjAQCr9mUji+9Hp8FwQ23afc7SJDV1YC+oFPwz6WnNTVMcEk7OLqu4BqcuayEDMHdEhNPNcj5rSCimxQXDYBLwyqYzMJs5940z4LcWtYlDwMXV3DSVWVwDEz9MyUkZzWZsOXUFAJDQPxC9/T1ErqjrZDIZ/nTXEHi6KXDsUiW+P8W5b5wBww21kldej/PFtVDIZbhxIGclFkNkgCc83RRoNJhxqbxO7HKIuuXwxQqU1+nhrVY69XQSvf088NRNMQCAt7dnoE5nFLkiuhaGG2ql+azNuL4B0HiqRK7GNcllMsSFWs7eZLBpipxQvc6IvRk/nwF2VylEruj6PDopGlEBniiu1mFlMjsXOzqGG2rFOgR8EM/aiKm53825wmqucUNOZ3dGCRoNZoRp3DG6j7/Y5Vw3d5UCr94+CADw6Y85yCuvF7ki6gjDDbWgbTDgaE4FAPa3EVtMsDeUchkq6w3IKqkVuxyiTiupbsTRnHIAwG1DwyB38DltOmvG4BBMjAmE3mjGX7dx5mJHxnBDLSRllMBoFjAg2Ns6Uy6JQ61UoH8vy8zQu5pGrxE5g+1niywzEYf5Wv+GpUAmk+G1O+KhkMuw46cizlzswBhuqIVtZywTbc0aEipyJQTAOkX99rOFIldC1Dnni2uQWVwDhUwmyc+R2FAf/GZ8FADgzf+eg9FkFrkiagvDDVnV6oxIPm9Z1G7W0DCRqyEAGBzuC7kMOFtQzTZ+cngms2D9gTShXwCCvNUiV2QfL9wyEH6eKmQW12DD0Tyxy6E2MNyQ1d6MEuiNZvQL8kJcqI/Y5RAAL7US0UGW5sGtZ3j2hhzbsUsVKKnRwdNNgZvjpNtnz8/TDYtvGQgA+Puu89A2GESuiK7GcENW25ubpIaGOvyidq5kSG8NADZNkWNr0Jt+sdhuMDzcnHvo97U8OC4KMcHeqKw34MO9WWKXQ1dhuCEAQL3eiKRMy0KZs4awScqRxIdrIJcBpy9rkV/BpilyTMmZJajXm9DLW41x0c6zflR3KRVy69DwdYcuIZeTbToUhhsCACRllKLRYEZUgCfiw33FLod+wVuttC42uI1NU+SAymt1OJTdPPQ71OnWj+qum2KDMWVgLxhMApZtyxC7HPoFhhsCAPzvtGX9FzZJOabmDt7sd0OOaMdPRTAJlikkBoa4Vn+9V28bBLnM8m9w5GK52OVQE4YbQnWjAXsyLE1Sc4b3FrkaasusIZZfw6cva5FTxtPf5DgultXipyvVkMEyYZ+r/TiKDfXBr8dZhob/ZWs6Vw13EAw3hB/OFkFvNGNAsLd1yn9yLEHeakyKCQIAfH+SqxKTYzALPw/9HhcdgBBfd5ErEscLtwyEj1qJMwVabErj+9MRMNwQtpyyNEnNGRHucr+6nMldI8MBAFtOXuFaU+QQ0vKqcKWqEe4qOaY58arf1yvIW42nbrasGv63HzJQr+eq4WJjuHFxJTWNONg0hfidbJJyaLcMDoW7So6LZXU4U6AVuxxycTqjCTt/KgJg6VjrrVaKXJG4FtzQFxH+Hiiu1mH1/otil+PyGG5c3NbThTALwMgoP0QFeopdDnXAW63E9KZfx9+fvCJyNeTq9p8vRY3OiAAvNyT0k/7Q72txVymwdJZlaPgn+y6iSNsockWujeHGxW1uah++c3i4yJVQZ8wZYTm79t9TV2Bix0USSVW9Hj9mWc743hofCqWCXyWAZRj8mD7+aDCY8O4PmWKX49JE/4tcuXIloqOj4e7ujtGjR+PHH39sd9/k5GTIZLJWl4wMzi/QHeeLa3DqshZKuQyzGW6cwtSBveDnqUJJjQ4/ZpWKXQ65qB9+KoLRLCA6yIvzYv2CTCbDH+8YDABIPHEZZy6z+Vgsooabb775Bs8//zxeffVVpKWlYfLkyZg1axby8jpeiCwzMxOFhYXWy4ABA3qoYmlJPH4ZAHBTXLBkF7iTGjelHHOagui3Ta8fUU/Kr6jHqctalx36fS3DI/0wd6TlDOuft55j53+RiBpuPvjgAzz66KN47LHHMGjQICxfvhyRkZFYtWpVh/cLDg5GaGio9aJQSHsNE3swmszY2NQkde/oCJGroa741ZhIAMCun4qhreeCfdRzBEGwTiQ5Msofvf08RK7IMf3fzFi4q+Q4mlOBH5o6XVPPEi3c6PV6HD9+HDNmzGixfcaMGTh06FCH9x05ciTCwsIwbdo0JCUldbivTqdDdXV1iwsB+7NKUVqjQ6CXG26OCxa7HOqC+HBfxIX6QG8yY8spzqlBPed0gRZ5FfVwU8gxY7DrDv2+lnA/Dzw+uR8AYNn2DOiMJpErcj2ihZuysjKYTCaEhLR8g4SEhKCoqO2kGxYWhtWrVyMxMREbN25EbGwspk2bhv3797f7PMuWLYNGo7FeIiMjbXoczuq7piaNOSN6Q8XOgE5FJpNZz96waYp6isFkxg9nLZ/NUwb2gq+HSuSKHNsTU/sj2EeN3PJ6rD+UK3Y5Lkf0b7Wr22sFQWi3DTc2NhYLFy7EqFGjkJCQgJUrV+L222/He++91+7jL126FFqt1nrJz8+3af3OqKJOj93nLMst/GoMm6Sc0V0jwqFsWo4hs6hG7HLIBfyYVYaqBgM0HipMHhAkdjkOz0utxJKZsQCAFXuzUFGnF7ki1yJauAkKCoJCoWh1lqakpKTV2ZyOTJgwAVlZWe3erlar4evr2+Li6r5NzYfeZMawCA0GhfHfwxkFequtc958dbTjDvhE16uqXo995y0/iG6ND+XZ3k66Z1QEBof5oqbRiOW7z4tdjksR7S/Uzc0No0ePxq5du1ps37VrF2644YZOP05aWhrCwsJsXZ5kmc0CNjR9Gf5mfB+Rq6Hr8eB4y2J9iccvc7p3sqvtZ4tgMAnoG+iFYREasctxGgq5DH+4wzKx35dH8nChhGdZe4qo8Xvx4sX47LPP8PnnnyM9PR0vvPAC8vLysGjRIgCWJqX58+db91++fDk2b96MrKws/PTTT1i6dCkSExPx9NNPi3UITudgdhlyy+vh467EHcMZCp3ZpJgg9An0RI3OiC2csZjs5GJpLc4UWIZ+zx7Ood9ddUP/INwyOAQms4C3tqaLXY7LEHUxkPvvvx/l5eX405/+hMLCQgwZMgTbtm1Dnz6WMwqFhYUt5rzR6/VYsmQJCgoK4OHhgfj4eGzduhW33XabWIfgdL48bPn3vGdUBDzdXHstGGcnl8vw0Pgo/HVbBr48kocHxkWJXRJJjMks4H+nf171O0zDod/d8cptg5CcWYKkzFLsPleM6RxpZncywcVmGKquroZGo4FWq3W5/jfF1Y244e29MJkF7HxhCgaG+Ihdkt1tOCKN/ijNTVBXq6jTY8KyPdAbzfj+qYkYHunXs4WR0+voPZJysRz/PXUFHioFfn/LQHg68OKY7b1HHMU7OzKwKjkbEf4e2PXCVHi4cX62rurK9zd7hbmQLw7nwmQWMK5vgEsEG1cQ4OWG24damhfXp3C4KdlOnc6I3eeKAQAz4kMcOtg4g2dujkG4xh2XKxuwMvmC2OVIHsONi2g0mPBl0y+0Ryb2FbcYsqn5CZZm3C2nClBSzZWIyTZ2nStGg8GEMI07xvYNELscp+fppsRrsy3rTn2y7yJyyupErkjaGG5cxKa0AlTU6dHbzwO3sL1XUkZG+WN0H38YTAL+fZhnb+j65ZXX4dilCgDAHcPCIWcnYpuYGR+KKQN7QW8y47Xvz3LdKTtiuHEBgiDg8wM5ACxnbZSco0JyHp0UDcDS9Nho4FTv1H0ms4BNJwsgABgV5Y/oIC+xS5IMmUyGN++Mh5tCjh+zyrDjLNedshd+y7mAH7PKkFVSCy83Be4by+UnpGjG4BBE+Hugst6AjSe43hR1349ZpSiu1sHTTYHbhoSKXY7kRAd5YdFUy7pTf/rfOdTpOEeVPTDcuIBP9mcDAO4bGwlfd64HI0VKhRyPTLScvfnswEWYzDzdTV1XXqvD3gzLTMS3Dw1jJ2I7+d2NMYjw90ChthF/38WZi+2B4UbiTuRV4uCFcijlMjzWtEotSdP9YyOh8VDhYmkdT3dTlwmCgM0nC2A0C4gJ9sYITitgNx5uCvx5zhAAwOcHc3Air1LkiqSH4UbiPtprGXJ496je6O3HCbikzFuttI6E++feLHZWpC45mV+F7NI6KOUyzBkezpmI7eymuGDcPbI3zALw4nen2VfOxhhuJOynK1rsySiBXGY5DUrSt+CGvvBWK5FRVIPd6SVil0NOok5nxNYzlpmIb44LRqC3WuSKXMNrswcjyFuNCyW1WLGn/QWgqesYbiTsoyTLWZvbh4VzxIOL8PN0w7ymeW949oY6QxAEfH/qCur1JoT4qjF5QC+xS3IZfp5u+MtdluapT/ZfxJnLWpErkg6GG4k6W6DFtjNFkMmAp27qL3Y51IMenRQNd5Ucpy9rsYdnb+gaTuZX4WyBFnKZZc05hZzNUT3p1iGhuGNYGExmAf/33SnojWaxS5IEhhuJeveHTADAncPDERfqWmtoubogb7V15NS7P2Ry5BS1q6CqAVtOWVaUvzkuGBH+niJX5JrevDMeAV5uyCiq4dIMNsJwI0GHL5Zj3/lSKOUyLL5loNjlkAgWTekPX3clMotrsDmN895Qa2azgN//5yR0RjMi/T0wdWCw2CW5rEBvNd68Mx4A8OHeCzh9uUrcgiSA4UZiBEHA33ZkAAAeGBeJPoHsa+OKNJ4qPHmTpRP5B7vOQ2fkSAxqac2BHBy+WAGVQob7xkSyOUpkdwwLw+1Dw2A0C3ju65Oc3O86MdxIzP9OF+JEXhXcVXI8e/MAscshET2c0BchvmoUVDXgX4cuiV0OOZCMompr0/XtQ8M5OsoByGQy/HXuUIRp3JFTVoc//fec2CU5NYYbCanXG7FsWzoA4HdTYxDs6y5yRSQmDzcFfj8jFgDwj91ZXDGcAACNBhOe//ok9CYzpsUFY2xff7FLoiYaTxX+fv8IyGTAN6n51v5Q1HUMNxLycXI2rmgb0dvPA09M5WzEBNw7KgIjIv1Qpzfh7e0ZYpdDDuC1788io6gGgV5uePueYZysz8FM6BeIp5rmJVuaeBrZpbUiV+ScGG4kIq+8Hh/vvwgA+OMdg+CuUohcETkCudyyCrFMBmxMK8Dx3AqxSyIR/edYPv6TehlyGbDi1yPRy4fNUY7o+ekDMD46AHV6E5784gQa9Owz11UMNxIgCAJe2XQGeqMZE2MCMTOeK/nSz4ZH+uG+0ZbV4F/ZeJbzaLioswVa/PH7swCAxbcMxMSYIJErovYoFXL889cjEeStRmZxDV7eeJoTcnYRw40EfJt6GQculEGtlOMvdw3laWZq5aVZcQj0ckNmcQ0+TOI8Gq6mtEaHhetToTOacVNsLzzJ5VgcXrCvOz58cCQUchm+P3kFq5vOzFPnMNw4ueLqRvx5q6VX/e9nDOQyC9SmAC83vDnHMo/GyqQLOHelWuSKqKfojCYs+uI4CrWN6NfLC8sfGAk5h307hQn9AvH67MEAgLd3ZCApkzOOdxbDjRMzmwW8lHgaNY1GDIvQ4LdNs9ISteX2oWGYGR8Co1nAkm9Pce4bFyAIAl5OPIPjuZXwcVfis/ljoPFQiV0WdcG8CX3w63GREATgmQ1p/GHSSQw3TuzzgzlIziyFWinHu/cOh1LBl5PaJ5PJ8Oe7hsDPU4VzhdV4Z3um2CWRnf3th0xsSiuAQi7Dhw+OQr9e3mKXRF0kk8nw5p1DMKFfAGp1Rjyy7igKqhrELsvh8dvQSZ0t0OKdppmI/3DHYMSG+ohcETmDYB93vHfvcACWcLwnvVjkishe1qdcwqrkbADA23cPxdSBXO3bWbkp5fhk3hgMDPFGcbUOCz4/iso6vdhlOTSGGydUVa/HUxtOwGASMDM+BL8ZHyV2SeREpg8OwSMT+wIAlnx7Cpcr68UtiGzuu+OX8dr3PwGwjIz61ZhIkSui66XxUGHdI+MQ4qtGVkkt5n9+FNWNBrHLclgMN07GaDLj6Q1pyC2vR28/D7zDSbioG16eFYehvTWorDdg4frjXMdGQv576gpe/O4UAGDBDX3xzM0cGSUV4X4e+OLR8QjwcsOZAi1+u/YY37vtYLhxMn/dloEDF8rgoVLg0/lj4OfpJnZJ5ITUSgU+njcaQd5uSC+sxuL/nITZzHk0nN2mtMt4/puTMAvAr8dF4vXZg/njR2IGhPjg34+Og6+7Eqm5lTyD0w6GGyey5kAOPj+YAwD44L7hGBzuK3JF5Mx6+3ngk3mj4aaQ44efivHWtnROFObEvjySi8X/OQWTWcC9oyPwFue8kqz4cA3WPzoevu5KHM+txIOfHkYF++C0wHDjJBKPX8af/2eZz+b/ZsZi1tAwkSsiKRjdJwDv3DsUgCU8f7iXE/w5G0EQ8I/dWXh101kIAjA/oQ/+ds8wzmUjcSMi/fD14wkI9HLD2YJq3LvqEPLK2X+uGcONE9h6uhAvJp4GADw2KRpP3thf5IpISuaOjMBrd1gmCnt/13l89iNnQnUWeqMZS749jb/vPg8AePLG/njzzngGGxcxONwX3zyRgN5+HrhYVoe5Kw/iRF6l2GU5BIYbB/ef1Hw889UJmMwCfjU6Aq/ePoinmsnmfjspGs9NGwAA+MvWdKzYk8UmKgdXXN2IX396GIknLkMhl+GtuUPw4q1x/HxwMTHB3tj05A2ID/dFeZ0eD6w+jP8cyxe7LNEx3DgoQRDw2Y8X8eJ3p2EWgAfGRuJtjowiO3p++gA8P90ScD7YdR5/2ZoOEzsZO6TDF8tx+4oDlpmH1Up89vAYPDS+j9hlkUiCfd3xnycSMH1QCPRGM15MPI2lG8+g0eC6s5Az3DggvdGMlxPP4C9b0wEAv50YjWV3D4WCp5rJjmQyGZ6fPhB/bGqiWnMgB4+vT0UNR2I4DL3RjHd2ZODXnx5GWa0OsSE+2PLMJNwUGyx2aSQyL7USq+eNxpIZAyGTAV8dzcPsfx7AT1e0YpcmCoYbB1NQ1YAHPz2Mb1LzIZcBf7h9EP54B5uiqOc8Oika/3hgBNRKOfZklODulYdwvrhG7LJc3tkCLeauPIhVydkQBODe0RHY9NQNXCyXrORyGZ6+eQDW/3YcevlYJvub+9EhrNiTBb3RLHZ5PYrhxoFsO1OIWcv3I7XpVPOaBWPx2OR+DDbU4+aM6I3/PJFgnQ119j8PYH3KJfbDEUFNowF//t853PnhAfx0pRp+niqsemgU3vvVcHi6KcUujxzQ5AG9sOO5ybhlcAj0JjM+2HUet6/4EYcvlotdWo9huHEARdpGLPr3cTz55QlUNxoxPNIP/3uWp5pJXMMj/fC/Zybjxthe0BnNeO37n/DrTw/jQkmt2KW5BIPJjH+nXMKN7yZjzYEcmAVg9vBw7HxhCqeCoGsK9FZj9bzRWPHrkQjydkNWSS0eWH0YT/w7FTlldWKXZ3cywcV+ilVXV0Oj0UCr1cLXV9xJ8Br0Jnx+MAerkrNRqzNCKZdh0dT+eG76AKi4wrdNbDiSJ3YJNvGgiOuHCYKAdYcu4Z0dGWg0mKFSyPDbSdF4cmoMNJ4q0eqSKoPJjI0nLuOjpGzkVVjmLekX5IXXZg/GjXb4wcP3iPRV1evx3s5MbDiSB7MAKOQy3DWiN56+OcapmjW78v3NcCOCBr0J/0nNx0dJF1BSowMAjIzyw7K7hyIulLMO2xI/uG0nv6Ier31/FkmZpQAAX3clHp/SD/Mm9GXIsYHKOj2+OpaHf6fkolDbCAAI9HLDs9MG4MHxUXb7wcP3iOs4X1yDt7dnYG9GCQBALgNuGRyC306MxrjoAIfvAsFw0wExw01+RT3+k5qPLw7norLeMgIlwt8Dv58xEHOG9+bEW3bAD27bEgQBezNK8Lcdmchs6mTs6abAfWMi8dD4KAwI8RG5QufSaDAhKaMEm08WICmjFHqTpdNnkLcaT0zph4cmRNm9Xw3fI67nZH4VVuzJsoYcAOgb6Ik5I3rjrpG9HfZsDsNNB3o63FTW6bHzXBH+e6oQB7PL0PyvHRXgiYWTo3Hf2EiolQq71+Gq+MFtHyazgP+euoKP92Ujo+jnkVQjo/wwZ3g4bh0ShlCNu4gVOq7qRgMOZ5dj17li7DhbhJpfrOocH+6LRyZG445hYXBX9cznAt8jriuruAafH7yEzWkFaPjFnDjDI/1wx9AwTB4YhNgQH4c5o+NU4WblypV49913UVhYiPj4eCxfvhyTJ09ud/99+/Zh8eLF+OmnnxAeHo4XX3wRixYt6vTz2TvcNBpMOJ5biZTschzKLsOpy9oWE6FNjAnEg+P6YGZ8CJTsV2N3/OC2L0EQ8GNWGf59OBd7M0pa/K0P6e2LhH6BmNAvEGOjA+Dr7ppNV9oGA84WaHH4YjkOXCjDqfwq/HJuxHCNO2aPCMddI3ojLrTnv0j4HqE6nRE7zxVhU9oVHMgqbfH32ctHjUkxQZgYE4SRUX6IDvQSrZXBacLNN998g3nz5mHlypWYOHEiPvnkE3z22Wc4d+4coqJa/6Hm5ORgyJAhWLhwIZ544gkcPHgQTz75JL766ivcc889nXpOe4Wb7NJavLLxDNLyqqynlpsNCvPF7UNDcefw3ogK9LTZc9K18YO755TUNGLLySvYfrYIx3Nbrm8jl1lWMh4c5ovYUB/EhfogNtQHgd5qkaq1vXq9EXkV9bhUVo/s0lqcLdDi7BUt8isaWu3bL8gLkwYE4fahYRjbN0DUJmm+R+iXSmoasfV0IZIyS3E0pxyNhpbfZ15uCgwO90V8uAaDwnzQN9ALfYO8EOyjtnswd5pwM378eIwaNQqrVq2ybhs0aBDuuusuLFu2rNX+L730ErZs2YL09HTrtkWLFuHUqVNISUnp1HPaK9xo6w0Y8eedEAQgxFeNG/oHIaF/IBL6BSIygIFGLPzgFkdJdSNSLpYjJbschy+W41I7qxVrPFQI9/NAbz939PbzQKjGAwFeKmg83ODvqYKfpxv8PFXQeKh6rJmmmcFkRr3OhDq9EfV6IyrrDSir0aGsVoeyWn3Tfy3/n19Rbx0c0Jbefh4Y3ccfkwZYfgH39vPowSPpGN8j1J5Ggwkn8ipx8EIZUrLLca6wulXYaeahUiAqwBPhfu4I1XggXOOOp2+OsWng6cr3t2gzQOn1ehw/fhwvv/xyi+0zZszAoUOH2rxPSkoKZsyY0WLbzJkzsWbNGhgMBqhU4p321niq8M9fj8TgMF9EB3k5TBslkRiCfd0xZ0RvzBnRGwBwpaoBJ/IqkVlUg4yiGmQW1SCvoh7aBgO0DQakF1Zf8zHdFHKoVXK4qxRQKy3/dVfJ4a5UQKmQQS6zXGQyy1ISchmatgGADIAAg0mAySzAYDLDaBZgNJl/3mY2Q2cwo15vRJ3e1K0ZXf08VegT4InoIC8MDvfFkHANBof7ws/TrcuPRSQ2d5UCN/QPwg39gwAARpMZF8vqcLZAizMFWlwoqUVueT0uV9ajwWBCZnGNdaBBLx81nmlajFcMooWbsrIymEwmhISEtNgeEhKCoqKiNu9TVFTU5v5GoxFlZWUIC2s9sZVOp4NO9/MvKq3Wss5GdfW1P0y7akpfbwBm1NRwqnpHUV8njdfCHn+vPclbbnl/WN4jlvdpnc6IQm0DrmgbUVjVgMKqRhRXN0LbaIC23gBtowHV9QZoG40wmQU0AmgE0NMr5agUMni6KeDroUKglxqB3ioEeKkR6KVGUNP/h/u5I9Lfq+0h8cZGVFc39nDVncf3CHVFqAcQGuOL6TE/nznRG824om1AfkU9irWNKK7WQSGX2fw1aX68zjQ4iT5399VnOARB6PCsR1v7t7W92bJly/Dmm2+22h4ZGdnVUolEs1DsAogcHN8jjuclOz1uTU0NNBpNh/uIFm6CgoKgUChanaUpKSlpdXamWWhoaJv7K5VKBAYGtnmfpUuXYvHixdbrZrMZFRUVCAwMdImmo+rqakRGRiI/P1/0GZl7mqseu6seN8Bj57Hz2KVMEATU1NQgPDz8mvuKFm7c3NwwevRo7Nq1C3PnzrVu37VrF+bMmdPmfRISEvDf//63xbadO3dizJgx7fa3UavVUKtbjsjw8/O7vuKdkK+vr+T/8NvjqsfuqscN8Nh57K7HVY79Wmdsmok60crixYvx2Wef4fPPP0d6ejpeeOEF5OXlWeetWbp0KebPn2/df9GiRcjNzcXixYuRnp6Ozz//HGvWrMGSJUvEOgQiIiJyMKL2ubn//vtRXl6OP/3pTygsLMSQIUOwbds29OnTBwBQWFiIvLyfhylGR0dj27ZteOGFF/DRRx8hPDwcK1as6PQcN0RERCR9oncofvLJJ/Hkk0+2edu6detabZs6dSpOnDhh56qkQ61W4/XXX2/VNOcKXPXYXfW4AR47j53HThaiL79AREREZEtc3IiIiIgkheGGiIiIJIXhhoiIiCSF4cbJVVZWYt68edBoNNBoNJg3bx6qqqra3d9gMOCll17C0KFD4eXlhfDwcMyfPx9Xrlxpsd+NN94ImUzW4vLAAw/Y+Wg6tnLlSkRHR8Pd3R2jR4/Gjz/+2OH++/btw+jRo+Hu7o5+/frh448/brVPYmIiBg8eDLVajcGDB2PTpk32Kv+6dOXYN27ciFtuuQW9evWCr68vEhIS8MMPP7TYZ926da1eX5lMhsZGx1smoCvHnpyc3OZxZWRktNhPiq/7ggUL2jz2+Ph46z7O8Lrv378fs2fPRnh4OGQyGTZv3nzN+0jlvd7VY5fae92mBHJqt956qzBkyBDh0KFDwqFDh4QhQ4YId9xxR7v7V1VVCdOnTxe++eYbISMjQ0hJSRHGjx8vjB49usV+U6dOFRYuXCgUFhZaL1VVVfY+nHZ9/fXXgkqlEj799FPh3LlzwnPPPSd4eXkJubm5be5/8eJFwdPTU3juueeEc+fOCZ9++qmgUqmE7777zrrPoUOHBIVCIfz1r38V0tPThb/+9a+CUqkUDh8+3FOH1SldPfbnnntOeOedd4SjR48K58+fF5YuXSqoVCrhxIkT1n3Wrl0r+Pr6tnh9CwsLe+qQOq2rx56UlCQAEDIzM1scl9FotO4j1de9qqqqxTHn5+cLAQEBwuuvv27dxxle923btgmvvvqqkJiYKAAQNm3a1OH+Unqvd/XYpfRetzWGGyd27tw5AUCLN2hKSooAQMjIyOj04xw9elQA0OJDc+rUqcJzzz1ny3Kvy7hx44RFixa12BYXFye8/PLLbe7/4osvCnFxcS22PfHEE8KECROs1++77z7h1ltvbbHPzJkzhQceeMBGVdtGV4+9LYMHDxbefPNN6/W1a9cKGo3GViXaTVePvTncVFZWtvuYrvK6b9q0SZDJZMKlS5es25zldW/WmS94Kb3Xf6kzx94WZ32v2xqbpZxYSkoKNBoNxo8fb902YcIEaDQaHDp0qNOPo9VqIZPJWi1L8eWXXyIoKAjx8fFYsmSJaKud6/V6HD9+HDNmzGixfcaMGe0eZ0pKSqv9Z86cidTUVBgMhg736cq/nb1159ivZjZbVqoPCAhosb22thZ9+vRBREQE7rjjDqSlpdmsblu4nmMfOXIkwsLCMG3aNCQlJbW4zVVe9zVr1mD69OnWSVGbOfrr3lVSea/bgrO+1+2B4caJFRUVITg4uNX24ODgVguMtqexsREvv/wyHnzwwRbrkjz00EP46quvkJycjD/+8Y9ITEzE3XffbbPau6KsrAwmk6nVgqohISHtHmdRUVGb+xuNRpSVlXW4T2f/7XpCd479au+//z7q6upw3333WbfFxcVh3bp12LJlC7766iu4u7tj4sSJyMrKsmn916M7xx4WFobVq1cjMTERGzduRGxsLKZNm4b9+/db93GF172wsBDbt2/HY4891mK7M7zuXSWV97otOOt73R5En6GYWnvjjTfw5ptvdrjPsWPHAKDNlc0FQejUiucGgwEPPPAAzGYzVq5c2eK2hQsXWv9/yJAhGDBgAMaMGYMTJ05g1KhRnTkMm7v6mK51nG3tf/X2rj6mWLpb51dffYU33ngD33//fYsgPGHCBEyYMMF6feLEiRg1ahT++c9/YsWKFbYr3Aa6cuyxsbGIjY21Xk9ISEB+fj7ee+89TJkypVuPKabu1rlu3Tr4+fnhrrvuarHdmV73rpDSe727pPBetyWGGwf09NNPX3NkUt++fXH69GkUFxe3uq20tLTVr5SrGQwG3HfffcjJycHevXuvuZrsqFGjoFKpkJWV1ePhJigoCAqFotWvrJKSknaPMzQ0tM39lUolAgMDO9znWv92Pak7x97sm2++waOPPopvv/0W06dP73BfuVyOsWPHOtSvues59l+aMGECvvjiC+t1qb/ugiDg888/x7x58+Dm5tbhvo74uneVVN7r18PZ3+v2wGYpBxQUFIS4uLgOL+7u7khISIBWq8XRo0et9z1y5Ai0Wi1uuOGGdh+/OdhkZWVh9+7d1g+Ajvz0008wGAwICwuzyTF2hZubG0aPHo1du3a12L5r1652jzMhIaHV/jt37sSYMWOgUqk63Kejf7ue1p1jByy/4hYsWIANGzbg9ttvv+bzCIKAkydPivL6tqe7x361tLS0Fscl5dcdsAyLvnDhAh599NFrPo8jvu5dJZX3endJ4b1uF2L0YibbufXWW4Vhw4YJKSkpQkpKijB06NBWQ8FjY2OFjRs3CoIgCAaDQbjzzjuFiIgI4eTJky2GBup0OkEQBOHChQvCm2++KRw7dkzIyckRtm7dKsTFxQkjR45sMaS2JzUPi12zZo1w7tw54fnnnxe8vLysI0FefvllYd68edb9m4eHvvDCC8K5c+eENWvWtBoeevDgQUGhUAhvv/22kJ6eLrz99tsOOTy0q8e+YcMGQalUCh999FG7Q/nfeOMNYceOHUJ2draQlpYmPPLII4JSqRSOHDnS48fXka4e+9///ndh06ZNwvnz54WzZ88KL7/8sgBASExMtO4j1de92W9+8xth/PjxbT6mM7zuNTU1QlpampCWliYAED744AMhLS3NOppTyu/1rh67lN7rtsZw4+TKy8uFhx56SPDx8RF8fHyEhx56qNUwWADC2rVrBUEQhJycHAFAm5ekpCRBEAQhLy9PmDJlihAQECC4ubkJ/fv3F5599lmhvLy8Zw/uKh999JHQp08fwc3NTRg1apSwb98+620PP/ywMHXq1Bb7JycnCyNHjhTc3NyEvn37CqtWrWr1mN9++60QGxsrqFQqIS4ursWXoCPpyrFPnTq1zdf34Ycftu7z/PPPC1FRUYKbm5vQq1cvYcaMGcKhQ4d68Ig6ryvH/s477wj9+/cX3N3dBX9/f2HSpEnC1q1bWz2mFF93QbDMdePh4SGsXr26zcdzhte9eTh/e3+/Un6vd/XYpfZetyWuCk5ERESSwj43REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEBAC4dOkSZDIZTp48KXYpberbty+WL19uvS6TybB58+YO77NgwYJWK2NfrzfeeAMjRoyw6WMSkW1xhmIiAgCYTCaUlpYiKCgISqVS7HJa6du3L55//nk8//zzAICioiL4+/tDrVbj0qVLiI6ORlpaWovgodVqIQgC/Pz8bFZHbW0tdDpdpxacJSJxON4nGBGJQqFQIDQ0tN3bBUGAyWRymODTUa3NNBqNzZ/X29sb3t7eNn9cIrIdNksRuZAdO3Zg0qRJ8PPzQ2BgIO644w5kZ2cDaN0slZycDJlMhh9++AFjxoyBWq3Gjz/+CLPZjHfeeQcxMTFQq9WIiorCW2+9ZX2OM2fO4Oabb4aHhwcCAwPx+OOPo7a21np7c1PRe++9h7CwMAQGBuKpp56CwWCw7lNSUoLZs2fDw8MD0dHR+PLLL1sdyy+bpaKjowEAI0eOhEwmw4033tjiuZrpdDo8++yzCA4Ohru7OyZNmoRjx45Zb28+5j179mDMmDHw9PTEDTfcgMzMTOs+VzdLdeZ4CgsLcfvtt1uPZ8OGDa2a2Toik8nw2WefYe7cufD09MSAAQOwZcuWTt2XyBUx3BC5kLq6OixevBjHjh3Dnj17IJfLMXfuXJjN5nbv8+KLL2LZsmVIT0/HsGHDsHTpUrzzzjv44x//iHPnzmHDhg0ICQkBANTX1+PWW2+Fv78/jh07hm+//Ra7d+/G008/3eIxk5KSkJ2djaSkJPzrX//CunXrsG7dOuvtCxYswKVLl7B371589913WLlyJUpKStqt8ejRowCA3bt3o7CwEBs3bmz3WBITE/Gvf/0LJ06cQExMDGbOnImKiooW+7366qt4//33kZqaCqVSid/+9rcd/rte63jmz5+PK1euIDk5GYmJiVi9enWHx9OWN998E/fddx9Onz6N2267DQ899FCruomoiZhLkhORuEpKSgQAwpkzZ4ScnBwBgJCWliYIgiAkJSUJAITNmzdb96+urhbUarXw6aeftvl4q1evFvz9/YXa2lrrtq1btwpyuVwoKioSBEEQHn74YaFPnz6C0Wi07vOrX/1KuP/++wVBEITMzEwBgHD48GHr7enp6QIA4e9//7t1GwBh06ZNgiAIrWpv9vDDDwtz5swRBEEQamtrBZVKJXz55ZfW2/V6vRAeHi787W9/a3HMu3fvblE/AKGhoUEQBEF4/fXXheHDh7d4jo6Op7n2Y8eOWW/PyspqdTwdASD84Q9/sF6vra0VZDKZsH379k7dn8jV8MwNkQvJzs7Ggw8+iH79+sHX19fanJOXl9fufcaMGWP9//T0dOh0OkybNq3NfdPT0zF8+HB4eXlZt02cOBFms7lF0058fDwUCoX1elhYmPVMRnp6OpRKZYvnjYuLu+5OwdnZ2TAYDJg4caJ1m0qlwrhx45Cent5i32HDhrWoDUCHZ1o6Op7MzEwolUqMGjXKentMTAz8/f27VP8va/Ly8oKPj0+Xz/4QuQrH6BlIRD1i9uzZiIyMxKefforw8HCYzWYMGTIEer2+3fv8Mqh4eHh0+PiCIEAmk7V52y+3q1SqVrc1N40JTQM423uc7mrvcduq+Zf1Nd/WUdNdZ46nvXo6q6PnIKKWeOaGyEWUl5cjPT0df/jDHzBt2jQMGjQIlZWVXXqMAQMGwMPDA3v27Gnz9sGDB+PkyZOoq6uzbjt48CDkcjkGDhzYqecYNGgQjEYjUlNTrdsyMzNRVVXV7n3c3NwAWIaztycmJgZubm44cOCAdZvBYEBqaioGDRrUqdq6Iy4uDkajEWlpadZtFy5c6PB4iOj6MNwQuQh/f38EBgZi9erVuHDhAvbu3YvFixd36THc3d3x0ksv4cUXX8T69euRnZ2Nw4cPY82aNQCAhx56CO7u7nj44Ydx9uxZJCUl4ZlnnsG8efOsnY6vJTY2FrfeeisWLlyII0eO4Pjx43jsscc6PGsUHBwMDw8P7NixA8XFxdBqta328fLywu9+9zv83//9H3bs2IFz585h4cKFqK+vx6OPPtqlf4euiIuLw/Tp0/H444/j6NGjSEtLw+OPPw4PDw+bn50iIguGGyIXIZfL8fXXX+P48eMYMmQIXnjhBbz77rtdfpw//vGP+P3vf4/XXnsNgwYNwv3332/t++Hp6YkffvgBFRUVGDt2LO69915MmzYNH374YZeeY+3atYiMjMTUqVNx99134/HHH0dwcHC7+yuVSqxYsQKffPIJwsPDMWfOnDb3e/vtt3HPPfdg3rx5GDVqFC5cuIAffvihy/1fumr9+vUICQnBlClTMHfuXCxcuBA+Pj5wd3e36/MSuSrOUExE1MMuX76MyMhI7N69u93O2UTUfQw3RER2tnfvXtTW1mLo0KEoLCzEiy++iIKCApw/f75VR2Eiun5sliIisjODwYBXXnkF8fHxmDt3Lnr16oXk5GSoVCp8+eWX1iUdrr7Ex8eLXTqRU+KZGyIiEdXU1KC4uLjN21QqFfr06dPDFRE5P4YbIiIikhQ2SxEREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpPw/BcyYEy04DTcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['airconditioning_n'].dropna())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "84b054fc",
   "metadata": {},
   "source": [
    "5. Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c60c9c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_48831_row0_col0, #T_48831_row1_col1, #T_48831_row2_col2, #T_48831_row3_col3, #T_48831_row4_col4, #T_48831_row5_col5, #T_48831_row6_col6, #T_48831_row7_col7, #T_48831_row8_col8, #T_48831_row9_col9, #T_48831_row10_col10, #T_48831_row11_col11, #T_48831_row12_col12 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row0_col1 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col2, #T_48831_row3_col2, #T_48831_row8_col7 {\n",
       "  background-color: #cbd8ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col3 {\n",
       "  background-color: #efcfbf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col4 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col5, #T_48831_row4_col2 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col6, #T_48831_row0_col11 {\n",
       "  background-color: #bfd3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col7 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col8, #T_48831_row1_col11, #T_48831_row3_col1, #T_48831_row3_col5, #T_48831_row8_col11 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col9, #T_48831_row4_col5, #T_48831_row8_col5 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row0_col10, #T_48831_row5_col0 {\n",
       "  background-color: #e2dad5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row0_col12, #T_48831_row4_col8, #T_48831_row8_col4, #T_48831_row10_col9, #T_48831_row12_col0, #T_48831_row12_col1, #T_48831_row12_col2, #T_48831_row12_col3, #T_48831_row12_col5, #T_48831_row12_col6, #T_48831_row12_col7, #T_48831_row12_col10, #T_48831_row12_col11 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row1_col0 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row1_col2, #T_48831_row4_col6 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row1_col3, #T_48831_row10_col3 {\n",
       "  background-color: #9bbcff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row1_col4, #T_48831_row4_col1, #T_48831_row9_col5 {\n",
       "  background-color: #80a3fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row1_col5, #T_48831_row2_col3 {\n",
       "  background-color: #cedaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row1_col6 {\n",
       "  background-color: #bcd2f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row1_col7, #T_48831_row2_col8, #T_48831_row3_col8, #T_48831_row5_col2, #T_48831_row10_col6, #T_48831_row10_col7 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row1_col8, #T_48831_row5_col4, #T_48831_row5_col8, #T_48831_row8_col1, #T_48831_row10_col8 {\n",
       "  background-color: #779af7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row1_col9, #T_48831_row6_col9, #T_48831_row6_col12, #T_48831_row7_col9 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row1_col10, #T_48831_row6_col5 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row1_col12, #T_48831_row6_col2, #T_48831_row9_col7 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row2_col0 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row2_col1 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row2_col4 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row2_col5, #T_48831_row2_col10, #T_48831_row5_col10 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row2_col6, #T_48831_row4_col9, #T_48831_row9_col6 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row2_col7, #T_48831_row3_col9, #T_48831_row5_col9, #T_48831_row6_col8, #T_48831_row7_col2, #T_48831_row7_col4, #T_48831_row11_col2, #T_48831_row11_col4 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row2_col9, #T_48831_row6_col3 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row2_col11 {\n",
       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row2_col12, #T_48831_row7_col12, #T_48831_row9_col1 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row3_col0 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row3_col4 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row3_col6, #T_48831_row8_col6, #T_48831_row8_col10, #T_48831_row11_col3 {\n",
       "  background-color: #7295f4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row3_col7, #T_48831_row6_col10 {\n",
       "  background-color: #84a7fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row3_col10 {\n",
       "  background-color: #9dbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row3_col11, #T_48831_row4_col12 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row3_col12 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row4_col0 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row4_col3 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row4_col7, #T_48831_row5_col7 {\n",
       "  background-color: #6788ee;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row4_col10 {\n",
       "  background-color: #bad0f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row4_col11 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row5_col1 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row5_col3, #T_48831_row6_col11 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row5_col6, #T_48831_row11_col6 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row5_col11 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row5_col12 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row6_col0, #T_48831_row7_col8 {\n",
       "  background-color: #d3dbe7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row6_col1, #T_48831_row10_col4 {\n",
       "  background-color: #bed2f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row6_col4 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row6_col7, #T_48831_row7_col5, #T_48831_row9_col3 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row7_col0 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row7_col1 {\n",
       "  background-color: #92b4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row7_col3, #T_48831_row11_col10 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row7_col6 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row7_col10, #T_48831_row10_col2, #T_48831_row11_col7 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row7_col11 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row8_col0 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row8_col2 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row8_col3, #T_48831_row9_col12, #T_48831_row10_col11 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row8_col9, #T_48831_row10_col12 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row8_col12, #T_48831_row9_col8, #T_48831_row11_col12 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row9_col0 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row9_col2 {\n",
       "  background-color: #6a8bef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row9_col4 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row9_col10 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row9_col11 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row10_col0 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row10_col1 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row10_col5 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row11_col0 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row11_col1, #T_48831_row11_col8 {\n",
       "  background-color: #adc9fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_48831_row11_col5 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row11_col9, #T_48831_row12_col4 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row12_col8 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_48831_row12_col9 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_48831\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_48831_level0_col0\" class=\"col_heading level0 col0\" >price</th>\n",
       "      <th id=\"T_48831_level0_col1\" class=\"col_heading level0 col1\" >area</th>\n",
       "      <th id=\"T_48831_level0_col2\" class=\"col_heading level0 col2\" >bedrooms</th>\n",
       "      <th id=\"T_48831_level0_col3\" class=\"col_heading level0 col3\" >bathrooms</th>\n",
       "      <th id=\"T_48831_level0_col4\" class=\"col_heading level0 col4\" >stories</th>\n",
       "      <th id=\"T_48831_level0_col5\" class=\"col_heading level0 col5\" >parking</th>\n",
       "      <th id=\"T_48831_level0_col6\" class=\"col_heading level0 col6\" >mainroad_n</th>\n",
       "      <th id=\"T_48831_level0_col7\" class=\"col_heading level0 col7\" >guestroom_n</th>\n",
       "      <th id=\"T_48831_level0_col8\" class=\"col_heading level0 col8\" >basement_n</th>\n",
       "      <th id=\"T_48831_level0_col9\" class=\"col_heading level0 col9\" >hotwaterheating_n</th>\n",
       "      <th id=\"T_48831_level0_col10\" class=\"col_heading level0 col10\" >airconditioning_n</th>\n",
       "      <th id=\"T_48831_level0_col11\" class=\"col_heading level0 col11\" >prefarea_n</th>\n",
       "      <th id=\"T_48831_level0_col12\" class=\"col_heading level0 col12\" >furnishingstatus_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row0\" class=\"row_heading level0 row0\" >price</th>\n",
       "      <td id=\"T_48831_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_48831_row0_col1\" class=\"data row0 col1\" >0.540000</td>\n",
       "      <td id=\"T_48831_row0_col2\" class=\"data row0 col2\" >0.370000</td>\n",
       "      <td id=\"T_48831_row0_col3\" class=\"data row0 col3\" >0.520000</td>\n",
       "      <td id=\"T_48831_row0_col4\" class=\"data row0 col4\" >0.420000</td>\n",
       "      <td id=\"T_48831_row0_col5\" class=\"data row0 col5\" >0.380000</td>\n",
       "      <td id=\"T_48831_row0_col6\" class=\"data row0 col6\" >0.300000</td>\n",
       "      <td id=\"T_48831_row0_col7\" class=\"data row0 col7\" >0.260000</td>\n",
       "      <td id=\"T_48831_row0_col8\" class=\"data row0 col8\" >0.190000</td>\n",
       "      <td id=\"T_48831_row0_col9\" class=\"data row0 col9\" >0.090000</td>\n",
       "      <td id=\"T_48831_row0_col10\" class=\"data row0 col10\" >0.450000</td>\n",
       "      <td id=\"T_48831_row0_col11\" class=\"data row0 col11\" >0.330000</td>\n",
       "      <td id=\"T_48831_row0_col12\" class=\"data row0 col12\" >-0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row1\" class=\"row_heading level0 row1\" >area</th>\n",
       "      <td id=\"T_48831_row1_col0\" class=\"data row1 col0\" >0.540000</td>\n",
       "      <td id=\"T_48831_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_48831_row1_col2\" class=\"data row1 col2\" >0.150000</td>\n",
       "      <td id=\"T_48831_row1_col3\" class=\"data row1 col3\" >0.190000</td>\n",
       "      <td id=\"T_48831_row1_col4\" class=\"data row1 col4\" >0.080000</td>\n",
       "      <td id=\"T_48831_row1_col5\" class=\"data row1 col5\" >0.350000</td>\n",
       "      <td id=\"T_48831_row1_col6\" class=\"data row1 col6\" >0.290000</td>\n",
       "      <td id=\"T_48831_row1_col7\" class=\"data row1 col7\" >0.140000</td>\n",
       "      <td id=\"T_48831_row1_col8\" class=\"data row1 col8\" >0.050000</td>\n",
       "      <td id=\"T_48831_row1_col9\" class=\"data row1 col9\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row1_col10\" class=\"data row1 col10\" >0.220000</td>\n",
       "      <td id=\"T_48831_row1_col11\" class=\"data row1 col11\" >0.230000</td>\n",
       "      <td id=\"T_48831_row1_col12\" class=\"data row1 col12\" >-0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row2\" class=\"row_heading level0 row2\" >bedrooms</th>\n",
       "      <td id=\"T_48831_row2_col0\" class=\"data row2 col0\" >0.370000</td>\n",
       "      <td id=\"T_48831_row2_col1\" class=\"data row2 col1\" >0.150000</td>\n",
       "      <td id=\"T_48831_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_48831_row2_col3\" class=\"data row2 col3\" >0.370000</td>\n",
       "      <td id=\"T_48831_row2_col4\" class=\"data row2 col4\" >0.410000</td>\n",
       "      <td id=\"T_48831_row2_col5\" class=\"data row2 col5\" >0.140000</td>\n",
       "      <td id=\"T_48831_row2_col6\" class=\"data row2 col6\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row2_col7\" class=\"data row2 col7\" >0.080000</td>\n",
       "      <td id=\"T_48831_row2_col8\" class=\"data row2 col8\" >0.100000</td>\n",
       "      <td id=\"T_48831_row2_col9\" class=\"data row2 col9\" >0.050000</td>\n",
       "      <td id=\"T_48831_row2_col10\" class=\"data row2 col10\" >0.160000</td>\n",
       "      <td id=\"T_48831_row2_col11\" class=\"data row2 col11\" >0.080000</td>\n",
       "      <td id=\"T_48831_row2_col12\" class=\"data row2 col12\" >-0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row3\" class=\"row_heading level0 row3\" >bathrooms</th>\n",
       "      <td id=\"T_48831_row3_col0\" class=\"data row3 col0\" >0.520000</td>\n",
       "      <td id=\"T_48831_row3_col1\" class=\"data row3 col1\" >0.190000</td>\n",
       "      <td id=\"T_48831_row3_col2\" class=\"data row3 col2\" >0.370000</td>\n",
       "      <td id=\"T_48831_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_48831_row3_col4\" class=\"data row3 col4\" >0.330000</td>\n",
       "      <td id=\"T_48831_row3_col5\" class=\"data row3 col5\" >0.180000</td>\n",
       "      <td id=\"T_48831_row3_col6\" class=\"data row3 col6\" >0.040000</td>\n",
       "      <td id=\"T_48831_row3_col7\" class=\"data row3 col7\" >0.130000</td>\n",
       "      <td id=\"T_48831_row3_col8\" class=\"data row3 col8\" >0.100000</td>\n",
       "      <td id=\"T_48831_row3_col9\" class=\"data row3 col9\" >0.070000</td>\n",
       "      <td id=\"T_48831_row3_col10\" class=\"data row3 col10\" >0.190000</td>\n",
       "      <td id=\"T_48831_row3_col11\" class=\"data row3 col11\" >0.060000</td>\n",
       "      <td id=\"T_48831_row3_col12\" class=\"data row3 col12\" >-0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row4\" class=\"row_heading level0 row4\" >stories</th>\n",
       "      <td id=\"T_48831_row4_col0\" class=\"data row4 col0\" >0.420000</td>\n",
       "      <td id=\"T_48831_row4_col1\" class=\"data row4 col1\" >0.080000</td>\n",
       "      <td id=\"T_48831_row4_col2\" class=\"data row4 col2\" >0.410000</td>\n",
       "      <td id=\"T_48831_row4_col3\" class=\"data row4 col3\" >0.330000</td>\n",
       "      <td id=\"T_48831_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_48831_row4_col5\" class=\"data row4 col5\" >0.050000</td>\n",
       "      <td id=\"T_48831_row4_col6\" class=\"data row4 col6\" >0.120000</td>\n",
       "      <td id=\"T_48831_row4_col7\" class=\"data row4 col7\" >0.040000</td>\n",
       "      <td id=\"T_48831_row4_col8\" class=\"data row4 col8\" >-0.170000</td>\n",
       "      <td id=\"T_48831_row4_col9\" class=\"data row4 col9\" >0.020000</td>\n",
       "      <td id=\"T_48831_row4_col10\" class=\"data row4 col10\" >0.290000</td>\n",
       "      <td id=\"T_48831_row4_col11\" class=\"data row4 col11\" >0.040000</td>\n",
       "      <td id=\"T_48831_row4_col12\" class=\"data row4 col12\" >-0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row5\" class=\"row_heading level0 row5\" >parking</th>\n",
       "      <td id=\"T_48831_row5_col0\" class=\"data row5 col0\" >0.380000</td>\n",
       "      <td id=\"T_48831_row5_col1\" class=\"data row5 col1\" >0.350000</td>\n",
       "      <td id=\"T_48831_row5_col2\" class=\"data row5 col2\" >0.140000</td>\n",
       "      <td id=\"T_48831_row5_col3\" class=\"data row5 col3\" >0.180000</td>\n",
       "      <td id=\"T_48831_row5_col4\" class=\"data row5 col4\" >0.050000</td>\n",
       "      <td id=\"T_48831_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_48831_row5_col6\" class=\"data row5 col6\" >0.200000</td>\n",
       "      <td id=\"T_48831_row5_col7\" class=\"data row5 col7\" >0.040000</td>\n",
       "      <td id=\"T_48831_row5_col8\" class=\"data row5 col8\" >0.050000</td>\n",
       "      <td id=\"T_48831_row5_col9\" class=\"data row5 col9\" >0.070000</td>\n",
       "      <td id=\"T_48831_row5_col10\" class=\"data row5 col10\" >0.160000</td>\n",
       "      <td id=\"T_48831_row5_col11\" class=\"data row5 col11\" >0.090000</td>\n",
       "      <td id=\"T_48831_row5_col12\" class=\"data row5 col12\" >-0.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row6\" class=\"row_heading level0 row6\" >mainroad_n</th>\n",
       "      <td id=\"T_48831_row6_col0\" class=\"data row6 col0\" >0.300000</td>\n",
       "      <td id=\"T_48831_row6_col1\" class=\"data row6 col1\" >0.290000</td>\n",
       "      <td id=\"T_48831_row6_col2\" class=\"data row6 col2\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row6_col3\" class=\"data row6 col3\" >0.040000</td>\n",
       "      <td id=\"T_48831_row6_col4\" class=\"data row6 col4\" >0.120000</td>\n",
       "      <td id=\"T_48831_row6_col5\" class=\"data row6 col5\" >0.200000</td>\n",
       "      <td id=\"T_48831_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_48831_row6_col7\" class=\"data row6 col7\" >0.090000</td>\n",
       "      <td id=\"T_48831_row6_col8\" class=\"data row6 col8\" >0.040000</td>\n",
       "      <td id=\"T_48831_row6_col9\" class=\"data row6 col9\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row6_col10\" class=\"data row6 col10\" >0.110000</td>\n",
       "      <td id=\"T_48831_row6_col11\" class=\"data row6 col11\" >0.200000</td>\n",
       "      <td id=\"T_48831_row6_col12\" class=\"data row6 col12\" >-0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row7\" class=\"row_heading level0 row7\" >guestroom_n</th>\n",
       "      <td id=\"T_48831_row7_col0\" class=\"data row7 col0\" >0.260000</td>\n",
       "      <td id=\"T_48831_row7_col1\" class=\"data row7 col1\" >0.140000</td>\n",
       "      <td id=\"T_48831_row7_col2\" class=\"data row7 col2\" >0.080000</td>\n",
       "      <td id=\"T_48831_row7_col3\" class=\"data row7 col3\" >0.130000</td>\n",
       "      <td id=\"T_48831_row7_col4\" class=\"data row7 col4\" >0.040000</td>\n",
       "      <td id=\"T_48831_row7_col5\" class=\"data row7 col5\" >0.040000</td>\n",
       "      <td id=\"T_48831_row7_col6\" class=\"data row7 col6\" >0.090000</td>\n",
       "      <td id=\"T_48831_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_48831_row7_col8\" class=\"data row7 col8\" >0.370000</td>\n",
       "      <td id=\"T_48831_row7_col9\" class=\"data row7 col9\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row7_col10\" class=\"data row7 col10\" >0.140000</td>\n",
       "      <td id=\"T_48831_row7_col11\" class=\"data row7 col11\" >0.160000</td>\n",
       "      <td id=\"T_48831_row7_col12\" class=\"data row7 col12\" >-0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row8\" class=\"row_heading level0 row8\" >basement_n</th>\n",
       "      <td id=\"T_48831_row8_col0\" class=\"data row8 col0\" >0.190000</td>\n",
       "      <td id=\"T_48831_row8_col1\" class=\"data row8 col1\" >0.050000</td>\n",
       "      <td id=\"T_48831_row8_col2\" class=\"data row8 col2\" >0.100000</td>\n",
       "      <td id=\"T_48831_row8_col3\" class=\"data row8 col3\" >0.100000</td>\n",
       "      <td id=\"T_48831_row8_col4\" class=\"data row8 col4\" >-0.170000</td>\n",
       "      <td id=\"T_48831_row8_col5\" class=\"data row8 col5\" >0.050000</td>\n",
       "      <td id=\"T_48831_row8_col6\" class=\"data row8 col6\" >0.040000</td>\n",
       "      <td id=\"T_48831_row8_col7\" class=\"data row8 col7\" >0.370000</td>\n",
       "      <td id=\"T_48831_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_48831_row8_col9\" class=\"data row8 col9\" >0.000000</td>\n",
       "      <td id=\"T_48831_row8_col10\" class=\"data row8 col10\" >0.050000</td>\n",
       "      <td id=\"T_48831_row8_col11\" class=\"data row8 col11\" >0.230000</td>\n",
       "      <td id=\"T_48831_row8_col12\" class=\"data row8 col12\" >-0.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row9\" class=\"row_heading level0 row9\" >hotwaterheating_n</th>\n",
       "      <td id=\"T_48831_row9_col0\" class=\"data row9 col0\" >0.090000</td>\n",
       "      <td id=\"T_48831_row9_col1\" class=\"data row9 col1\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row9_col2\" class=\"data row9 col2\" >0.050000</td>\n",
       "      <td id=\"T_48831_row9_col3\" class=\"data row9 col3\" >0.070000</td>\n",
       "      <td id=\"T_48831_row9_col4\" class=\"data row9 col4\" >0.020000</td>\n",
       "      <td id=\"T_48831_row9_col5\" class=\"data row9 col5\" >0.070000</td>\n",
       "      <td id=\"T_48831_row9_col6\" class=\"data row9 col6\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row9_col7\" class=\"data row9 col7\" >-0.010000</td>\n",
       "      <td id=\"T_48831_row9_col8\" class=\"data row9 col8\" >0.000000</td>\n",
       "      <td id=\"T_48831_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_48831_row9_col10\" class=\"data row9 col10\" >-0.130000</td>\n",
       "      <td id=\"T_48831_row9_col11\" class=\"data row9 col11\" >-0.060000</td>\n",
       "      <td id=\"T_48831_row9_col12\" class=\"data row9 col12\" >-0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row10\" class=\"row_heading level0 row10\" >airconditioning_n</th>\n",
       "      <td id=\"T_48831_row10_col0\" class=\"data row10 col0\" >0.450000</td>\n",
       "      <td id=\"T_48831_row10_col1\" class=\"data row10 col1\" >0.220000</td>\n",
       "      <td id=\"T_48831_row10_col2\" class=\"data row10 col2\" >0.160000</td>\n",
       "      <td id=\"T_48831_row10_col3\" class=\"data row10 col3\" >0.190000</td>\n",
       "      <td id=\"T_48831_row10_col4\" class=\"data row10 col4\" >0.290000</td>\n",
       "      <td id=\"T_48831_row10_col5\" class=\"data row10 col5\" >0.160000</td>\n",
       "      <td id=\"T_48831_row10_col6\" class=\"data row10 col6\" >0.110000</td>\n",
       "      <td id=\"T_48831_row10_col7\" class=\"data row10 col7\" >0.140000</td>\n",
       "      <td id=\"T_48831_row10_col8\" class=\"data row10 col8\" >0.050000</td>\n",
       "      <td id=\"T_48831_row10_col9\" class=\"data row10 col9\" >-0.130000</td>\n",
       "      <td id=\"T_48831_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_48831_row10_col11\" class=\"data row10 col11\" >0.120000</td>\n",
       "      <td id=\"T_48831_row10_col12\" class=\"data row10 col12\" >-0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row11\" class=\"row_heading level0 row11\" >prefarea_n</th>\n",
       "      <td id=\"T_48831_row11_col0\" class=\"data row11 col0\" >0.330000</td>\n",
       "      <td id=\"T_48831_row11_col1\" class=\"data row11 col1\" >0.230000</td>\n",
       "      <td id=\"T_48831_row11_col2\" class=\"data row11 col2\" >0.080000</td>\n",
       "      <td id=\"T_48831_row11_col3\" class=\"data row11 col3\" >0.060000</td>\n",
       "      <td id=\"T_48831_row11_col4\" class=\"data row11 col4\" >0.040000</td>\n",
       "      <td id=\"T_48831_row11_col5\" class=\"data row11 col5\" >0.090000</td>\n",
       "      <td id=\"T_48831_row11_col6\" class=\"data row11 col6\" >0.200000</td>\n",
       "      <td id=\"T_48831_row11_col7\" class=\"data row11 col7\" >0.160000</td>\n",
       "      <td id=\"T_48831_row11_col8\" class=\"data row11 col8\" >0.230000</td>\n",
       "      <td id=\"T_48831_row11_col9\" class=\"data row11 col9\" >-0.060000</td>\n",
       "      <td id=\"T_48831_row11_col10\" class=\"data row11 col10\" >0.120000</td>\n",
       "      <td id=\"T_48831_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_48831_row11_col12\" class=\"data row11 col12\" >-0.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48831_level0_row12\" class=\"row_heading level0 row12\" >furnishingstatus_n</th>\n",
       "      <td id=\"T_48831_row12_col0\" class=\"data row12 col0\" >-0.300000</td>\n",
       "      <td id=\"T_48831_row12_col1\" class=\"data row12 col1\" >-0.170000</td>\n",
       "      <td id=\"T_48831_row12_col2\" class=\"data row12 col2\" >-0.120000</td>\n",
       "      <td id=\"T_48831_row12_col3\" class=\"data row12 col3\" >-0.140000</td>\n",
       "      <td id=\"T_48831_row12_col4\" class=\"data row12 col4\" >-0.100000</td>\n",
       "      <td id=\"T_48831_row12_col5\" class=\"data row12 col5\" >-0.180000</td>\n",
       "      <td id=\"T_48831_row12_col6\" class=\"data row12 col6\" >-0.160000</td>\n",
       "      <td id=\"T_48831_row12_col7\" class=\"data row12 col7\" >-0.120000</td>\n",
       "      <td id=\"T_48831_row12_col8\" class=\"data row12 col8\" >-0.110000</td>\n",
       "      <td id=\"T_48831_row12_col9\" class=\"data row12 col9\" >-0.030000</td>\n",
       "      <td id=\"T_48831_row12_col10\" class=\"data row12 col10\" >-0.150000</td>\n",
       "      <td id=\"T_48831_row12_col11\" class=\"data row12 col11\" >-0.110000</td>\n",
       "      <td id=\"T_48831_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f490e98490>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr(method='pearson',min_periods=1).round(2)\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7404c6c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-844589.5992657607\n",
      "10378048.094678603\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_boundary=df['price'].mean()+3*df['price'].std()\n",
    "lower_boundary=df['price'].mean()-3*df['price'].std()\n",
    "print(lower_boundary),print(upper_boundary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "af433e76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1359.8817831227389\n",
      "11660.964351930079\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_boundary1=df['area'].mean()+3*df['area'].std()\n",
    "lower_boundary1=df['area'].mean()-3*df['area'].std()\n",
    "print(lower_boundary1),print(upper_boundary1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1e63753e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7509460329731743\n",
      "5.179329196384624\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_boundary2=df['bedrooms'].mean()+3*df['bedrooms'].std()\n",
    "lower_boundary2=df['bedrooms'].mean()-3*df['bedrooms'].std()\n",
    "print(lower_boundary2),print(upper_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5dd9bafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR= df['bathrooms'].quantile(0.75) - df['bathrooms'].quantile(0.25)\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ae982a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.5\n",
      "3.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge= df['bathrooms'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge= df['bathrooms'].quantile(0.75)+(IQR*1.5)\n",
    "print(lower_bridge), print(upper_bridge)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3a134aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR1= df['stories'].quantile(0.75) - df['stories'].quantile(0.25)\n",
    "IQR1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f32b55da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.5\n",
      "3.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge1= df['stories'].quantile(0.25)-(IQR1*1.5)\n",
    "upper_bridge1= df['stories'].quantile(0.75)+(IQR1*1.5)\n",
    "print(lower_bridge1), print(upper_bridge1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7e1df42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR2= df['parking'].quantile(0.75) - df['parking'].quantile(0.25)\n",
    "IQR2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6dd1b733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.5\n",
      "2.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge2= df['parking'].quantile(0.25)-(IQR2*1.5)\n",
    "upper_bridge2= df['parking'].quantile(0.75)+(IQR2*1.5)\n",
    "print(lower_bridge2), print(upper_bridge2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6e7d02a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR3= df['mainroad_n'].quantile(0.75) - df['mainroad_n'].quantile(0.25)\n",
    "IQR3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f1dce951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge3= df['mainroad_n'].quantile(0.25)-(IQR3*1.5)\n",
    "upper_bridge3= df['mainroad_n'].quantile(0.75)+(IQR3*1.5)\n",
    "print(lower_bridge3), print(upper_bridge3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8088f593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR4= df['guestroom_n'].quantile(0.75) - df['guestroom_n'].quantile(0.25)\n",
    "IQR4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e4bcf774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge4= df['guestroom_n'].quantile(0.25)-(IQR4*1.5)\n",
    "upper_bridge4= df['guestroom_n'].quantile(0.75)+(IQR4*1.5)\n",
    "print(lower_bridge4), print(upper_bridge4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "802a63e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR5= df['prefarea_n'].quantile(0.75) - df['prefarea_n'].quantile(0.25)\n",
    "IQR5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c471b4bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Calculating the boundaries\n",
    "lower_bridge5= df['prefarea_n'].quantile(0.25)-(IQR5*1.5)\n",
    "upper_bridge5= df['prefarea_n'].quantile(0.75)+(IQR5*1.5)\n",
    "print(lower_bridge5), print(upper_bridge5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a04ac19c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "      <th>mainroad_n</th>\n",
       "      <th>guestroom_n</th>\n",
       "      <th>basement_n</th>\n",
       "      <th>hotwaterheating_n</th>\n",
       "      <th>airconditioning_n</th>\n",
       "      <th>prefarea_n</th>\n",
       "      <th>furnishingstatus_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories  parking  mainroad_n  \\\n",
       "0    13300000  7420         4          2        3        2           1   \n",
       "1    12250000  8960         4          4        4        3           1   \n",
       "2    12250000  9960         3          2        2        2           1   \n",
       "3    12215000  7500         4          2        2        3           1   \n",
       "4    11410000  7420         4          1        2        2           1   \n",
       "..        ...   ...       ...        ...      ...      ...         ...   \n",
       "540   1820000  3000         2          1        1        2           1   \n",
       "541   1767150  2400         3          1        1        0           0   \n",
       "542   1750000  3620         2          1        1        0           1   \n",
       "543   1750000  2910         3          1        1        0           0   \n",
       "544   1750000  3850         3          1        2        0           1   \n",
       "\n",
       "     guestroom_n  basement_n  hotwaterheating_n  airconditioning_n  \\\n",
       "0              0           0                  0                  1   \n",
       "1              0           0                  0                  1   \n",
       "2              0           1                  0                  0   \n",
       "3              0           1                  0                  1   \n",
       "4              1           1                  0                  1   \n",
       "..           ...         ...                ...                ...   \n",
       "540            0           1                  0                  0   \n",
       "541            0           0                  0                  0   \n",
       "542            0           0                  0                  0   \n",
       "543            0           0                  0                  0   \n",
       "544            0           0                  0                  0   \n",
       "\n",
       "     prefarea_n  furnishingstatus_n  \n",
       "0             1                   0  \n",
       "1             0                   0  \n",
       "2             1                   1  \n",
       "3             1                   0  \n",
       "4             0                   0  \n",
       "..          ...                 ...  \n",
       "540           0                   2  \n",
       "541           0                   1  \n",
       "542           0                   2  \n",
       "543           0                   0  \n",
       "544           0                   2  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "57e26e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3cd97f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['price']>=10378048,'price']=10378048\n",
    "df.loc[df['area']>=11660,'area']=11660\n",
    "df.loc[df['bedrooms']>=5,'bedrooms']=5\n",
    "df.loc[df['bathrooms']>=3,'bathrooms']=3\n",
    "df.loc[df['stories']>=3,'stories']=3\n",
    "df.loc[df['parking']>=2,'parking']=2\n",
    "df.loc[df['mainroad_n']>=1,'mainroad_n']=1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e97c5ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting dependent and independent variables and assigning x & y to them\n",
    "X=df.drop(['price'],axis='columns')\n",
    "Y=df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fbbc7a02",
   "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</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "      <th>mainroad_n</th>\n",
       "      <th>guestroom_n</th>\n",
       "      <th>basement_n</th>\n",
       "      <th>hotwaterheating_n</th>\n",
       "      <th>airconditioning_n</th>\n",
       "      <th>prefarea_n</th>\n",
       "      <th>furnishingstatus_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>544</th>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  bedrooms  bathrooms  stories  parking  mainroad_n  guestroom_n  \\\n",
       "0    7420         4          2        3        2           1            0   \n",
       "1    8960         4          3        3        2           1            0   \n",
       "2    9960         3          2        2        2           1            0   \n",
       "3    7500         4          2        2        2           1            0   \n",
       "4    7420         4          1        2        2           1            1   \n",
       "..    ...       ...        ...      ...      ...         ...          ...   \n",
       "540  3000         2          1        1        2           1            0   \n",
       "541  2400         3          1        1        0           0            0   \n",
       "542  3620         2          1        1        0           1            0   \n",
       "543  2910         3          1        1        0           0            0   \n",
       "544  3850         3          1        2        0           1            0   \n",
       "\n",
       "     basement_n  hotwaterheating_n  airconditioning_n  prefarea_n  \\\n",
       "0             0                  0                  1           1   \n",
       "1             0                  0                  1           0   \n",
       "2             1                  0                  0           1   \n",
       "3             1                  0                  1           1   \n",
       "4             1                  0                  1           0   \n",
       "..          ...                ...                ...         ...   \n",
       "540           1                  0                  0           0   \n",
       "541           0                  0                  0           0   \n",
       "542           0                  0                  0           0   \n",
       "543           0                  0                  0           0   \n",
       "544           0                  0                  0           0   \n",
       "\n",
       "     furnishingstatus_n  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     1  \n",
       "3                     0  \n",
       "4                     0  \n",
       "..                  ...  \n",
       "540                   2  \n",
       "541                   1  \n",
       "542                   2  \n",
       "543                   0  \n",
       "544                   2  \n",
       "\n",
       "[545 rows x 12 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f7959ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      10378048\n",
       "1      10378048\n",
       "2      10378048\n",
       "3      10378048\n",
       "4      10378048\n",
       "         ...   \n",
       "540     1820000\n",
       "541     1767150\n",
       "542     1750000\n",
       "543     1750000\n",
       "544     1750000\n",
       "Name: price, Length: 545, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6373e626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into 80% training data and 20% testing data.\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c14c5d5d",
   "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</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "      <th>mainroad_n</th>\n",
       "      <th>guestroom_n</th>\n",
       "      <th>basement_n</th>\n",
       "      <th>hotwaterheating_n</th>\n",
       "      <th>airconditioning_n</th>\n",
       "      <th>prefarea_n</th>\n",
       "      <th>furnishingstatus_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>9800</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6540</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>3520</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>8250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>431</th>\n",
       "      <td>3180</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  bedrooms  bathrooms  stories  parking  mainroad_n  guestroom_n  \\\n",
       "175  9800         4          2        2        2           1            1   \n",
       "25   6540         4          2        2        2           1            1   \n",
       "253  3520         2          1        2        0           1            0   \n",
       "257  8250         3          1        1        0           1            0   \n",
       "431  3180         4          1        2        0           1            0   \n",
       "\n",
       "     basement_n  hotwaterheating_n  airconditioning_n  prefarea_n  \\\n",
       "175           0                  0                  0           0   \n",
       "25            1                  0                  1           1   \n",
       "253           0                  0                  0           1   \n",
       "257           0                  0                  0           0   \n",
       "431           1                  0                  1           0   \n",
       "\n",
       "     furnishingstatus_n  \n",
       "175                   1  \n",
       "25                    0  \n",
       "253                   0  \n",
       "257                   0  \n",
       "431                   2  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4bb8685a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "175    5250000\n",
       "25     8540000\n",
       "253    4515000\n",
       "257    4480000\n",
       "431    3290000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a80e28cc",
   "metadata": {},
   "source": [
    "6. Model Selection and Evaluation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c1443f23",
   "metadata": {},
   "source": [
    "A. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "431401d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ad04b6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "84bd9ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ffb7b131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7173465725698999"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2d38ad8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7173465725698999"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,Y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "922940d4",
   "metadata": {},
   "source": [
    "B. Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9c1bab68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest:  0.7114555637749351\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "\n",
    "rf_model.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "print('Random Forest: ', r2_score(Y_test, y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "02c5cfe1",
   "metadata": {},
   "source": [
    "C. DecisionTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2aa54f4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree:  0.7114555637749351\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor()\n",
    "\n",
    "rf_model.fit(X_train, Y_train)\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "print('Decision Tree: ', r2_score(Y_test, y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "99a00ff2",
   "metadata": {},
   "source": [
    "Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5e283082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7641706.56])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = lr.predict([[9960,3,2,2,2,1,0,1,0,0,1,1]]).round(2)\n",
    "preds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a1d92547",
   "metadata": {},
   "source": [
    "7. Create Pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ff24493d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# save the model\n",
    "import pickle\n",
    "pickle.dump(lr, open(\"1024.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a71ad177",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}