Second hand Car Price Prediction - Prateek Agrawal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Price Prediction of Second hand cars by using Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data from kaggle\n",
    "data = pd.read_csv('second hand car details dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b14c9cd5f8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "sns.heatmap(data.isnull(), cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we can see, this dataset does not have any null values !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(data['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b14ce59390>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dropping Car name column as it is not of much use in analysis\n",
    "\n",
    "data.Car_Name.value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of different car names used in this dataset and so, it is of not much use..!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, Chanhing year to number of years old <br>\n",
    " i.e., substracting each value with 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Present Year']=2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Years old']=df['Present Year']-df['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, dropping the year column\n",
    "df.drop(['Year'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Present Year'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Years old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>9.50</td>\n",
       "      <td>11.6</td>\n",
       "      <td>33988</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.9</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>3.35</td>\n",
       "      <td>11.0</td>\n",
       "      <td>87934</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>11.50</td>\n",
       "      <td>12.5</td>\n",
       "      <td>9000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5.30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5464</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "296           9.50           11.6       33988    Diesel      Dealer   \n",
       "297           4.00            5.9       60000    Petrol      Dealer   \n",
       "298           3.35           11.0       87934    Petrol      Dealer   \n",
       "299          11.50           12.5        9000    Diesel      Dealer   \n",
       "300           5.30            5.9        5464    Petrol      Dealer   \n",
       "\n",
       "    Transmission  Owner  Years old  \n",
       "296       Manual      0          5  \n",
       "297       Manual      0          6  \n",
       "298       Manual      0         12  \n",
       "299       Manual      0          4  \n",
       "300       Manual      0          5  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Dummy variables for OneHotEncoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## dropping the first column to get rid of Dummy Variable trap\n",
    "df=pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Years old</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  Years old  \\\n",
       "0           3.35           5.59       27000      0          7   \n",
       "1           4.75           9.54       43000      0          8   \n",
       "2           7.25           9.85        6900      0          4   \n",
       "3           2.85           4.15        5200      0         10   \n",
       "4           4.60           6.87       42450      0          7   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Years old</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.552339</td>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years old</th>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "Years old                   -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                        Years old  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price           -0.236141          0.552339         -0.540571   \n",
       "Present_Price            0.047584          0.473306         -0.465244   \n",
       "Kms_Driven               0.524342          0.172515         -0.172874   \n",
       "Owner                    0.182104         -0.053469          0.055687   \n",
       "Years old                1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel        -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol         0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual   0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual     -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "Years old                             0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'                             Selling price vs years old')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df, x= \"Selling_Price\" , y = \"Years old\", color = 'purple')\n",
    "plt.title(\"                             Selling price vs years old\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df.corr()\n",
    "corr_features = corrmat.index\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "g=sns.heatmap(df[corr_features].corr(),annot=True, linewidth =.9 , cmap=\"gist_heat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2b16347e5f8>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l2BU3U/yKWyl2xc0Uv+JWil1xM8Wv5IJMr/FyLnCZMWYuMAZnjZcfAicYY7yRESlTgXcj++8FTgL2GmO8QBnQEpceFf+aZOkHBziHiIiIiIiIiIiIiIhIRmR0xIu1dqm1dqq1dhpwDfCMtfY64Fngyshu1UB95O/HI9tEnn/GWmsj6dcYY3zGmFOA6cB/AS8A040xpxhjCiLneDzymv7OISIiIiIiIiIiIiIikhHDscZLMkuAbxpj9uCsx7I2kr4WGB9J/ybwHQBr7SvAo8Au4Cngq9ba7sholq8Bm4E/AY9G9h3oHCIiIiIiIiIiIiIiIhmR6anGYqy1W4Gtkb//DHwsyT4fAFf18/o7gTuTpG8CNiVJT3oOERERERERERERERGRTMnWiBcREREREREREREREZERRw0vIiIiIiIiIiIiIiIiaaKGFxERERERERERERERkTRRw4uIiIiIiIiIiIiIiEiaqOFFREREREREREREREQkTdTwIiIiIiIiIiIiIiIikiZqeBEREREREREREREREUkTNbyIiIiIiIiIiIiIiIikiTfVHY0xE4F/BKbFv85a+8X0Z0tERERERERERERERMR9Um54AeqB7cDvgO7MZEdERERERERERERERMS9htLwUmStXZKxnIiIiIiIiIiIiIiIiLjcUNZ4+XdjzNyM5URERERERERERERERMTlhtLwchNO48sHxpgjxpijxpgjmcqYiIiIiIiIiIiIiIiI26Q81Zi1dmwmMyIiIiIiIiIiIiIiIuJ2KY94MY7PG2P+JbJ9kjHmY5nLmoiIiIiIiIiIiIiIiLsMZaqxNcDHgWsj263AT9KeIxEREREREREREREREZdKeaoxYKa19qPGmP8BsNa+Z4wpyFC+REREREREREREREREXGcoI166jDF5gAUwxkwEwhnJlYiIiIiIiIiIiIiIiAsNpeHlR8BjgN8YcyfwH8D3BnqBMWaMMea/jDH/a4x5xRhzeyT9FGPMTmPMbmPMI9GRM8YYX2R7T+T5aXHHWhpJf80YMycu/eJI2h5jzHfi0pOeQ0REREREREREREREJFNSbnix1j4I/DNwF7APuNxa+6tBXhYEPmWt/QhwNnCxMWYWsAJYaa2dDrwHLIjsvwB4z1p7KrAysh/GmDOAa4AzgYuBNcaYvMgInJ8AlwBnAPMj+zLAOURERERERERERERERDIi5YYXY8wqoNxa+xNr7Y+ttX8a7DXW0RrZzI/8s8CngA2R9HXA5ZG/KyPbRJ6fbYwxkfSHrbVBa+2bwB7gY5F/e6y1f7bWdgIPA5WR1/R3DhERERERERERERERkYwYylRjfwBuiUzp9f8ZY85J5UWRkSkvAc3A08AbwPvW2lBkl73AlMjfU4C3ASLPHwbGx6f3ek1/6eMHOEfv/N1ojHnRGPPigQMHUnlLIjlBsStupvgVt1LsipspfsWtFLviZopfcSvFrriZ4ldywVCmGltnrZ2LM8rkdWCFMWZ3Cq/rttaeDUyNvPZvku0WeTT9PJeu9GT5+7m19hxr7TkTJ05MtotITlLsipspfsWtFLviZopfcSvFrriZ4lfcSrErbqb4lVwwlBEvUacCpwPTgFdTfZG19n1gKzALOMEY4408NRV4N/L3XuAkgMjzZUBLfHqv1/SXfnCAc4iIiIiIiIiIiIiIiGTEUNZ4iY5wWQa8Avyttfazg7xmojHmhMjfhcCngT8BzwJXRnarBuojfz8e2Sby/DPWWhtJv8YY4zPGnAJMB/4LeAGYbow5xRhTAFwDPB55TX/nEBERERERERERERERyQjv4LvEvAl83Fp7cAivmQysM8bk4TTyPGqt/XdjzC7gYWPMHcD/AGsj+68FHjDG7MEZ6XINgLX2FWPMo8AuIAR81VrbDWCM+RqwGcgD7rPWvhI51pJ+ziEiIiIiIiIiIiIiIpIRgza8GGNOt9a+ijPC5GRjzMnxz1tr/9Dfa621LwP/J0n6n3HWe+md/gFwVT/HuhO4M0n6JmBTqucQERERERERERERERHJlFRGvHwTuBG4O8lzFvhUWnMkIiIiIiIiIiIiIiLiUoM2vFhrbzTGeIBbrLXPD0OeREREREREREREREREXMmTyk7W2jDw/QznRURERERERERERERExNVSaniJ+K0x5gpjjMlYbkRERERERERERERERFwslTVeor4JFAPdxpgOwADWWluakZyJiIiIiIiIiIiIiIi4TMoNL9basZnMiIiIiIiIiIiIiIiIiNul1PBijPEClwCnR5J2AZuttaFMZUxERERERERERERERMRtBl3jxRhzIvAK8C3gRGAK8M/AK5HnREREREREREREREREhNRGvHwP+Km19ofxicaYrwN3AdWZyJiIiIiIiIiIiIiIiIjbpNLwMstae0PvRGvtj4wxr6U/SyIiIiIiIiIiIiIiIu406FRjQMcAz7WnKyMiIiIiIiIiIiIiIiJul8qIlzJjTFWSdAOUpjk/IiIiIiIiIiIiIiIirpVKw8s24LP9PPdcGvMiIiIiIiIiIiIiIiLiaoM2vFhra1I5kDGm2lq77vizJCIiIiIiIiIiIiIi4k6prPGSqpvSeCwRERERERERERERERHXSWWqsVSZNB4rJ4VtmOa2ZoKhID6vD3+xH49JZ9uVSOYofkVEhlk4DM3NEAyCzwd+P3hU7oq4kq5n99B3JSJupjJM3EqxK26WofhN5xVg03isnBO2YRqaG5h17yymrZrGrHtn0dDcQNiGs501kUEpfkVEhlk4DA0NMGsWTJvmPDY0OOki4i66nt1D35WIuJnKMHErxa64WQbjN50NLyN6xEtzWzOVD1XSeLgRgMbDjVQ+VElzW3OWcyYyOMWviMgwa26GykpodMpdGhud7WaVuyKuo+vZPfRdiYibqQwTt1LsiptlMH7T2fDyfO8EY8xJxphnjTF/Msa8Yoy5KZJebox52hizO/I4LpJujDE/MsbsMca8bIz5aNyxqiP77zbGVMel/60xpiHymh8ZY8xA5zhWwVAwVmkd1Xi4kWAoeDyHFRkWil8RkWEWDPbcuEU1NjrpIuIuup7dQ9+ViLiZyjBxK8WuuFkG4zflhhdjzE3GmNJI48haY8wfjDEXRZ+31n4tyctCwLestX8DzAK+aow5A/gOsMVaOx3YEtkGuASYHvl3I/DTyLnLgduAmcDHgNviGlJ+Gtk3+rqLI+n9neOY+Lw+AmWBhLRAWQCf19fva8I2TFNrE43vN9LU2qRpnSRrjiV+k1FMi4ikyOeDQGK5SyDgpIuIu7jteg6HoanJ+cHY1DS6pvlw23clmeHWa8Ct+Zb0cXMZpvgd3dwcuyIZjN+hjHj5orX2CHARMBGoAf51oBdYa/dZa/8Q+fso8CdgClAJrIvstg64PPJ3JXC/dewATjDGTAbmAE9ba1uste8BTwMXR54rtdb+3lprgft7HSvZOY6Jv9hP/fz6WOV1oCxA/fx6/MX+pPtrTQ3JJUON32QU0yIiQzBhAtTV9dzABQLO9oQJ2c2XiAyd3w/19YnXc329k55rRvsc6276riQz3HoNuDXfkl5uLcMUv+LW2BWBjP529w5h3+gaLnOBWmvt/0an9UrpxcZMA/4PsBOYZK3dB07jjDEmeiVOAd6Oe9neSNpA6XuTpDPAOXrn60acETOcfPLJ/ebfYzzM8M9gx5d2EAwF8Xl9+Iv9eEzytqv+1tTY8aUdVJRU9HsekVSlGrsw9PhNRjEt6TSU+BXJJSnH7sGDsGwZrFwJ5eXQ0uJs33MPVKjMlOxQ2XuMPB6YMQN27HCmHPD5nIoETzpnbU6T/uao3rHD1WVPyrHrpu9KMiMHr4GU4jcH8y1ZkGNlWMplr+JXcix2Qfe9MgQZ/O0+lIaX/zbG/BY4BVhqjBkLpNR8bYwpATYC37DWHhmgvSbZE/YY0lNmrf058HOAc845Z8DXeown5QpmrakhmTaU2IWhxW8yimlJp6HGr0iuSDl2g0Gnl1d9fWL6qlWZzJ7IgFT2HgePxx2VRyN0jvUhxa5bvivJjBy8BlKK3xzMt2RJDpVhQ7rvVfxKDsUu6L5XhiCDv92H0vS4AGedlL+z1rYDBTjTjQ3IGJOP0+jyoLW2LpK8PzJNGJHH5kj6XuCkuJdPBd4dJH1qkvSBzjEs0rWmhkiuUEyLiAyB5jkWkWxQ2SOjnVuvAbfmWwQUvyLibrmwxou1NgyEgE8YY6qAC4BTB3pNZCqytcCfrLU/iHvqcaA68nc1UB+Xfr1xzAIOR6YL2wxcZIwZZ4wZh7POzObIc0eNMbMi57q+17GSnWNYpGNNDZFcopgWERkCzXMsItmgskdGO7deA27NtwgofkXE3TJYhqU81Zgx5j7gLOAVeqYYs0Bdvy+Cc4EvAA3GmJciad8F/hV41BizAPgLcFXkuU04a8jsAdqJjKix1rYYY5YDL0T2W2atbYn8/RXgF0Ah8GTkHwOcY1ikY00NkVyimBYRGYIcnOdYREYBlT0y2rn1GnBrvkVA8Ssi7pbBMmwoa7zMstaeMZSDW2v/g+TrsADMTrK/Bb7az7HuA+5Lkv4i8OEk6YeSnWM4He+aGiK5RjEtIjIEOTbPsYiMEip7ZLRz6zXg1nyLgOJXRNwtQ2XYUJpufm+MGVLDi4iIiIiIiIiIiIiIyGgylBEv63AaX5qAIM5IFmutPSsjORMREREREREREREREXGZoTS83EdkvRZ61ngRERERERERERERERGRiKE0vPzFWvt4xnIiIiIiIiIiIiIiIiLickNpeHnVGLMe+A3OVGMAWGvr0p4rERERERERERERERERFxpKw0shToPLRXFpFlDDi4iIiIiIiIiIiIiICCk0vBhjplpr91pra5I899nMZEtERERERERERERERMR9UhnxssUYM8da+1Z8ojGmBrgFZ+oxAcI2THNbM8FQEJ/Xh7/Yj8d4sp0tkZyg60NEREadcBiamyEYBJ8P/H7w6P8+OU6Kq4Hp8xERN1MZJm7l1th1a77FFVKJpMXA08aY6dEEY8xS4JvABZnKmNuEbZiG5gZm3TuLaaumMeveWTQ0NxC24WxnTSTrdH2IiMioEw5DQwPMmgXTpjmPDQ1OusixUlwNTJ+PiLiZyjBxK7fGrlvzLa4xaMOLtXYT8GXgSWPMh40xPwT+AfiEtXZvpjOY68I2TFNrE28ffpvKhyppPNwIQOPhRiofqqS5rTnLORy66HtqfL+RptYmVY4LcHxx0dzWnNL1odgTkRElFIK334Y33nAeQ6Fs50iGU3MzVFZCo/N/H42Nznaz++4Nc144DE1Nzmfc1DSyfyynK65G6mem607AvfHt1nxL+ri5DFP8jm5ujV235ltcI6WxU9baLcANwFbgr4DZ1tr3Mpctd4jvxf/W+2/FKpWjGg83EgwFs5S7Y6ORCZLM8cZFMBQc9PpQ7InIiBIKwcsvw/nnw6mnOo8vv6zGl9EkGOz5ERfV2OikS/qMtp6K6YirkfyZ6boTt8a3W/Mt6eXWMkzxK26NXbfmW1xj0IYXY8xRY8wR4EmgFJgNNMeljxq9e+PH9+Jv6WghUBZI2D9QFsDn9WUpt8cm1ZEJ4l7HMqrkeOPC5/UNen0o9kRkRNm3D6qqEntPVVU56TI6+HwQSPy/j0DASZf0GW09FdMRV6l+Zm7svezxJP98NFf76OHWMsGt+Zb0cmsZpvgVt8auMcnzbUx28iPZk6H73lSmGhtrrS2NPBZYa4vjtkvTkgsXSNYb/2jwaKySeMXzK1h72dpY5XKgLED9/Hr8xf5sZnvIUhmZIO51rKNKjjcu/MV+6ufXD3h9KPZEZETp7Ezee6qzMzv5keHn90N9fc+PuUDA2fa7694w5422norpiKtUPjO39l7Oy4O1axM/n7VrnXQZHdxaJrg135Jebi3DFL/i1tg1Jnm+1fAyumTwvtd7/LkbHZL1xt/dsptAWYDGw43sfGcnNz9zM2suXcPpE06nKL8If7Efj8nx1t1eoiMT4ivA3ThyR5Lrb1TJji/toKKkot/XHW9ceIyHGf4Z7PjSDoKhID6vr8/1odgTkRElP9+5cY//ERoIOOkyOng8MGMG7NjhVDz4fE7leK73/HOb6AiQ3tfaSB1ZlI64SuUz66/38o4dUNH/PWPWdXfD6tWwciWUl0NLi7O9enW2cybDxa1lglvzLenl1jJM8StujV1wb74lfTJ436tffilK1ht/2bZlPDbvsVgv/qbWJqaUTmHaCdOoKKlwXaMLpDYyQdzrWEeVpCMuPMZDRUkFgRMCSa8PxZ6IjChjxsCGDYm9pzZscNJl9PB4nJv1QMB5VKNL+o3GkUXHG1epfGZu7b2clweLFsHixXDhhc7jokW53+NW0setZYJb8y3p5dYyTPErbo3dyZPh1lsT833rrU66jB4ZvO/ViJcUJeuN39TaxNTSqQP24nebVEYmiHsd66iS4YgLxZ6IjCjhMDzyCGza5Pzg6O6G2lr41reynTORkUUji4Yulc/Mrb2XPZ7kPVfvuSfbOZPh4tYywa35lvRyaxmm+BW3xq7XC2edBdu3O1NCFxQ4jS5eVZePKhm871UkpSjaGz86TVO0N/74ovEjrmI4OjJBRp7+4jiVUSXDEReKPREZMfx++PznYe5c5wZOPf9EMic6AkRSN9hnFu29HJ12wS1lmN8Pt9/uvnxLerm1THBrviV93FyGKX5HNzfHrtcLJ52U7VxINmXwvlcNLykayb3xwzZMc1vziHtf0leux7FiUURGDI8Hzjyzb+8p9fwTGX7hsDN3s3rhps6tvZfdmm9JL7de827Nt6SPm8swxe/optgVN8tg/GY0kowx9xljmo0xf4xLKzfGPG2M2R15HBdJN8aYHxlj9hhjXjbGfDTuNdWR/XcbY6rj0v/WGNMQec2PjDFmoHMcr8HWqHCjsA3T0NzArHtnMW3VNGbdO4uG5gbCNpztrEmG5GocKxZFZEQJh+GVV+D88+HUU53HV15x0kVk+ITD0NAAs2bBtGnOY0ODrsVUuHWNIrfmW9LDrde8W/Mt6efGMkzxK6DYFXfLUPxm+ir4BXBxr7TvAFustdOBLZFtgEuA6ZF/NwI/BacRBbgNmAl8DLgtriHlp5F9o6+7eJBzSC/Nbc2xaafAWWi98qFKmtuas5wzGW0UiyIyojQ39wxVBuexstJJF5Hho2tRZHRx6zXv1nyLgOJX3EuxKxmW0YYXa+1zQEuv5EpgXeTvdcDlcen3W8cO4ARjzGRgDvC0tbbFWvse8DRwceS5Umvt7621Fri/17GSnUN6CYaCCQutg1PhHQwFs5QjGa0UiyIyogSDiYvzgbMdVJkmMqx0LYqMLm695t2abxFQ/Ip7KXYlw7Kxxsska+0+AGvtPmNMdKWaKcDbcfvtjaQNlL43SfpA5+jDGHMjzqgZTj755H4zneraE8n2A3J23Yqi/CKeuPYJivOLaeloYcXzK2hqbcLn9WU7azKIVGM36ljWT0nHmiupHsPn9VF5WiXVZ1dTXlhOS0cL615ap1gcoYYavyK5IuXY9fngoYfg4x+Hri7Iz4ff/95JF8mSUVn2+nxOz8Xqaigvh5YWWLcue9diOuYRH+wYI3Cu8iHFblcX7NvXU/ZOnuw8yuiQa9c8KcZvDuZbsiSHyrAh3fd++9tQUwN5edDdDbW1it/RJhRyYjd+fUtv9pYWT7nsVewKZCx+s3cF9GWSpNljSB8Sa+3PgZ8DnHPOOUlfH117IjoNUqAsQP38emb4ZyRUIPfer/K0SlZevJJQOMTult0s27aMptYmHpv3GDMmzcDrye7HH7Zh3j36LgufWBh7X7WVtZxYemKswUhyVyqxG5VqDCd7zW3P3Eb12dX4i/20dbZxctnJHOo4lFJjzFDOO6FoArdeeCtVj1TF9q2bV8eEognH8vFIjhtK/IrkkpRjd9w4OOssZ12X4mJoa3O2x6Vl2TmRYzIqy94JE+DWW6GqyunBGAhAXZ2THm+4GkQaGnqmtAgEoL7eWcwz1XMNdox0nCMHpRy7XV2weze89VZP2dvaCtOnq/FltEj1mh9GKcVvDuZbsiDHyrCUy97x452K6/h819Q46TI6hELw8st9y7Czzspa40tK8Tt+PFxzDcyd25PvjRsVu6NNKOSUvW++2bfsPc74zcbd9/7INGFEHqMT5+0FTorbbyrw7iDpU5OkD3SOY5Lq2hPx+82cMpNFMxfxyXWf5LQfn8bCJxZy56fupKKkgs898jn2Ht5LU2tTVhcOb25rpvLhxPdVU19Dma8sZ0bkSHoMdf2UsA3zzpF3sGHLzZ+4mcWbF3Ne7XnMvn82L+9/mS//5stMWzWNWffOoqG5od84Hsp5D7YfjDW6RPeteqSKg+0H0/ERiIgMr0OHYP9+WLgQLrzQedy/30kXkdSFw9DU5PwYbmoa+mKnBw/2VAKA81hV5aTHn+N4F1ZN5RjpmEd8sGOM9rnKDx5MXvYe1P3kqJHKNZ+L3JpvSS+3lmFuzbekz759ycuwffuym6/BNDXBFVck5vuKK5x0GT0OHHC+8/gyrKnJST9O2ahdfxyojvxdDdTHpV9vHLOAw5HpwjYDFxljxhljxgEXAZsjzx01xswyxhjg+l7HSnaOY5Lq2hPx+y05dwkLHl+QUIm84PEFLDl3CY2HG2lqa+IP+/7A24ff5u3Db9P4fuOwN8SkY02NsA3T1NqUlfxng1vfb6rfdfT9vdHyBg3NDRxoP8BVv7oqIY6vePQKqs+ujm0P1IAzlBhLZV+3fv65QJ+dyDDr7HR6+sXfxNfUOOkikppw2Ol99oc/OL1o//AHZ3soDSKpzN2dSmNFKARvvw1vvOE8hkKJx0zlGOmYR3ywY4z2ucqDweRl72h5/+J817NnOyNOX33VeZw9O/djwK35lvRyaxnm1nxL+nR2Jr//yPXfPm7Nt6RXBsuwjDa8GGMeAn4P/LUxZq8xZgHwr8BnjDG7gc9EtgE2AX8G9gD/P7AQwFrbAiwHXoj8WxZJA/gKcG/kNW8AT0bS+zvHMfF5fQTKAglpgbIAHuOJVV6GbRiP8cT2Ky8sT1qJXF5YTqAswLgx4/j5iz9nT8sezq89P6XRA+nW3/tKdU2N6DRSs+6dlZX8Dzc3v99UvuuwDdOw33l/0VFaJ5WdlDSOT59wOlurt1J3dR0VJRX9Ntb1d95QONTncxssj27+/LNNn51IFoTDyW/ih9pbX2Q0O3QI3n03sffZu+8ObeSYz+dMGxEvEEicuzsYhIoKZ0qMrVudx4qKnh9b0ekzzj8fTj3VeXz55cTGl1QaPFLJy/G+n+g6EfHvpbJy9MxV3t2d/Hvo7s5OfmT4FRbCV77iTBtz+unO41e+4qTnMrfmW9LLrWWY7nuloCD5/UlBQXbyk6r8/OT51vSko0sGy7CMNrxYa+dbaydba/OttVOttWuttYestbOttdMjjy2Rfa219qvW2g9Za2dYa1+MO8591tpTI/9q49JftNZ+OPKar1lrbSQ96TmOlb/YT/38+lilcKAswNrL1rJo0yIamhsIhUM0NDewaNMi1l62lkBZgM7uzqSVyG1dbTxx7RM0tTZx16fvItgdpKKkAhh89EC6JXtf9fPrU17fZajTV7mdm9/vhKIJ1M2rS/iue6+fsr91f8LUcxUlFVhrk8bxm++/yYXrLmTx5sWsvmQ1YwvGAn1HVUwomsBj8x7rc+18a/O3+nxug8Wjmz//bNNnJ5IFXm/ym/gsLjAp4jrROeJ79z5ra0vcb6DpyPx+ePZZ2LXL6UW+a5ez7Y+73y0qgrvugsWLnQaexYud7aIi5/lUps9IpVHF73fWW4nuF11/xT+EtRUHO0Z0nYj493LrraNnnYh0NG6Ju3V0wB13wMqVTuPjypXOdkdHtnM2MLfmW9LLrWWY7nulosJZGyX+/mTjRic9l40ZAxs2JOZ7wwYnXUaPDJZhKgVT4DEeZvhnsL1mO385/Bea25q5+Zmb2fnOTl7a/xLba7bHKjWb2pqorazltPLT2Hj1Rq549IqEhcInFU/i8AeHqf51dSx97WVrY8cb6lRfvYVtmOa25pQWPo++rx1f2pHS/r2lY6oyN3Hz+z3YfpBlW5excs5KygvLaeloYdnWZdzz2XuoKKkgbMO0drbG3t/MKTO581N38vSep6mbV5ew4P0Dn3uAUDjE1uqttHS0cOdzd3L3nLsZ6xvLroO7YtdCtOFkytgpCeeNxvqq0KqEPA4Wj27+/LNNn51IFuTnOz3Ney8wqd5TIj0GW4w+lZ6/qSw2/957fa/Fk07qOVd3N6xa5VR0lpdDS4uzfc89zvOpTEORlwfr18O11/acZ/16Jz3K43HytWNH/+95MIMdo791InbsyP3Kj3TIy4OHH3YWyo1+Dw8/nPg9yMjm8TgNjtXVPTGwbt3QrrNscGu+Jb3cWobpvlcOHYLlyxPvpZYvd+6lcvn+4+hR+OlPYdMm5zrr7oa774bvfndoHWPE3TJYhqnhJUXRacXOqz0vIb3xcCOd3Z1UlFTQeLiRMyeeSUlBCefWnktFSQVrLl3D9PLpvHv0XRY+sZCmVqdhJrp/dO2XlXNWUvVoVWwKs3ipNqZEpxPqXfE9wz9jwMaX6IiboYpODRVfoTuUqcrcxs3vNxgKUv96PfWvJy539IOuH/Dn9/5Mnsljd8vu2Ptbcu4Sntr9FPNmzIs12PiL/UwqmUSBp4A/HvgjxfnF+Lw+vnv+dzkaPMo7R99JOqpie812Fm9enNLnNlA8uvnzzzZ9diJZkJcHpaXw5JM9lb/5+bn/w1lkhzFpCAAAIABJREFUuAzWYALONVNZ6VRERn/Er1uX+COov7VVog0N/Y1W2b7daXyJ5mXRIliwoCcva9f2jJyJTp8R3/jSe/oMa6GsDNasgeJiZ1ROWZmTHs/jyWwFxGhf4wVg7NjE72Hs2GznSIaTtU7FX3zl38qV8KMfZTtnA3NrviX93FiG6b5XgkHnPq6+1xLbq1Yl3z9XFBTAli1w3309aYEA/N//m7UsSRZksAxTw8sQjC0Yy66Fu/AYD922m7v/8262vLmF1w69xppL11DqKwXg9UOvU1FSwc53dnLp+kupPK2S7336e6z49ApaOlpYtWMVS85dQtWjVUDi2i9rL1tLnun5YqPrbtz27G1Un12Nv9hPW2cbgRMCeD2JX19/0wnt+NKOY25cGUh0aqjeDT2pTlXmNm5+v0X5RWyv2Y6/2E93uJu9R/byk//6Ca8efJVL11/K/9z4P+R78vntF35Ld7ibMd4xnF1xNoufWpzQYPPtWd/my3/3Zcp8ZTS3NbPupXXcNOsmxheN73dURdiG0/K5ufnzz7bR+tndfvvtQ37NbbfdloGcyKgUCkFXV2JaV1ffBblFRqvmZvjlLxN7GNbWwre+1dMwMWYM3HILXHllT4NI7+kfouuzxFdWrljR09CQymiV7u6eRpfo8wsWOI0zAJMnOz/E3nyzpyLslFOc9KgPPoBLL+3bOPPcc4nnHmyUz2AGa7CKTlPTOx/HMk3N8eY1Gzo74Re/cKaki4+rr30t2zmT4WKM01P54EFn2+dzto3Jbr4G49Z8S3q5tQzTfa/4fPDtb/eN3VyfJq+42Lm37H2vWVyc7ZzJcMpgGaaGlxSFwiHeev8tqh7tmXJpw9Ub+NbHv8Xdv7+bz5/1+YTpmKLThwEsmrmIuQ/OTXjulBNOiU3VtO6ldUweO5mVc1ayeudq7vnsPbFRLp3dndz27G0smrmIBY8viB3jsXmPMbV0KuOLxmdtKqbjnarMbdz6fsM2TEtHC13dXVz0wEWxGNp49Uae3P0kz1z/DGHC/ONv/jEhRlfvXM2imYtoamti5zs7mTllJld/+Gpm3z87Yb9VO1bxw4t/iCfPk3RUhcd4OHPimcf9ubn1888F+uxEssDrdSpne9/Ejx+f7Zwl58YKVnE3jwfmzXMWkI6/RuLjLhjsuYbAebzyyp4GEXB6Kq5eDQcOONs+n7MdHY2SSkPEYAtqejxOZdjChYmNHfF5DYWSHyP+B1sqo3wGM9gIn+gaML3PMdTpMtKR12woKEgeV7m+uK+kT16e8/9v/PVaW5v7Pe/dmm9JL7eWYW6775X0Gz/emSIvPnY3bsz9GOhvqrFbb3U69MjokMEyLIfvmnPLvqP7WLbNmXJpa/VWVs5ZyR3b7qCooIhrPnwNNfU1CSNNFjy+gCXnLmHJuUtiDSbxz4VsiJaOFvzFfr4/5/s8+sdHWbx5MbdecCvjC8fT0NzArHtn0dbZRvXZ1X2O8blHPscL775AQ3MDYev8KIxOJxQv09MJRaeGCpwQoKKkYsRX5Lrx/Ta3NdPa2UpHqIN1l6+j7uo6KkoqWL5tOZdMvwSP8cQaDaEnRqNxt+TcJQAsOXcJV/3qqqT7dYQ6WPzUYjZcvSEWg4GyALWVtTS3NfPKgVfwF/uP+3Nz4+efK/TZiQyzjo7kFca5uEhutIJ11iyYNs15bGhIXKBcJN1SuUYGaxBJRV6eM0fzE084C1Y/8YSzHV+ZOdhixvv3J2/s2L+/Z/9UFuXsr9GkuTnxPTc1Oc81NfV9r4NNJebxwJlnOo1Te/Y4j2eeOfTGklTymos6O5PHVfwIJxnZOjudHtfxMVBTk/sx4NZ8S3q5tQxz032vZEZTU88aL1u3Oo/Llzvpucznc6YaO/NMOP1053HLltwfqSPplcEyTCNehuCfzv0nrqu7Ltbb/8GqBzEYppZOpaKkImEB8RXPr6C8sByvx5t0FAoQW/ciOoJl/oz53PPCPUwumcw7R95h3eXrKPQW4i/2Jz3GKSecQjAUZO/hvVgsHuPhV1f9KlY5Hq34jp+6TEYfDx6MMSx8YmEsLurm1eE1Xm599lZWfGZF0vg6fcLpVJRUxKaj6i8O/cV+9h7ZG5uObNN1m+jo6qAwv5AjwSOUF5az+KnF3PPZezIy5Z2ISE5Kpfd7rhisB73IsQqFnDVWOjud3rqTJ/c0RKRyjXg8yUerxDcihMPQ2tq3l3h8g4W1ic9v3Jh4Xo/HeU200jN6jOh52tuT5zX+x5jX66wL03udmPiGl/b25NOitbf3vJfBRpn4fMnXvYlWEITD8Morxz9Sxa1rxbip7JXM6O4+/gbbbHBrviW93FqGuTXfkl7/9E9w3XU99x8PPpjtHA0uXSOFxd0yWIapy/MQRBtdwKlwvq7uOjzGw9iCsdw1+y4Wb17MhesuZPHmxdw1+y4CJwQYVzgu6SiUPS17+oxgee3Qa3zxo1+kqa2JhU8s5MJ1F/KNp77BpJJJSY/R3NbMoY5DfOIXn2DaqmmcX3s+7V3t1FbWxkblLN2ylPau9uH5gI5R2IZpam2i8f1GmlqbYiN4JD0+CH3QZ0RL1SNVFBcUU312NW+890bS+Hrz/TedOC4L8OpXX+XEsScm3W9SySQe/uPDADS1NVGUX4TFMvfBuXx87cf55LpPsmjmIsJp+NGgWBER18jPT977PX5R8Fzh1gpWyW2hELz8Mpx/Ppx6qvP48ss9P2BSGSHi8znD/KP7RYf9x/dC7OpK3ks8Ok9zZydccUXi81dckdh7+OhRWLo0sZfm0qVOgw44o2OS5TW+IcPjcaY4iz/G6tWJ+4wZA3fdBYsXw4UXOo933dWzZk0qo0wmTHCmv4g/xq23OumpHiMVg40CylVuKnslM/qL3Vyfqsmt+Zb0cmsZ5tZ8S3pFG13AebzuuuzmJ1XRRdVffdV5LC3Ndo5kuGWwDFPDS4o6uzuT9vbv7O6kK9zVZ6qxmvoadh/azRfrv8jay9YmTL+08eqNLNu2jJlTZlJ3dV2skWRS8STefP9NPvfI52LHqn+9np+98DPq5tUlHOOBzz1A2Ib7TEFW/etq3v/gfZb8zpke6u6L7sZjPMNWQT3UivGwDcemVZu2ahqz7p2VMH2aHL+ucFfS2PUYD/5iP8u2LesToxuu3kCht5BVO1bxp4N/4vSfnM78jfN5bN5jCfv96qpf8bMXfsY1H76GmVNmctfsu9h1YBdXPnplnynJum33cb2PsA3TsL9XrOxXrIhIjhozxulVH19hvHFj4qLguSI6qiBe70plkaHatw+qqhJ/gFdVOekAJSXJr5GSkp5jBINwxx2JjRl33JHYKNhfL/HuyH1HKj3Y8vKcqTCqqpzGjKoqZzt+lEltbWJeey8Y6/fD7bcnNojcfntij8nu7uSNRNG89jeypj2uE9XBg8k/1+iC3OlqSI32AI1/z27oAZqfDw88kJjvBx5Q5d9o4vEkj4Fc/z/NrfmW9HJrGeam+17JjM7O5PcfuT5N3qFD8N578Oabzr3fm28624cOZTtnMpzGjEne2SsNZZimGktRnicv6cLheZ48usPdfSq2o2sprPi0M+XYczc8R2e4E6/xUlRQREVxBYtmLoo1nESnfzpj4hlUlFQkHO/7O77PN//+m2y7YRuhcIg8Tx7Lty3n+o9cn7RCfWrpVO781J0Jx35s3mNMLZ3K+KLxGVvbIdqIUvlQZey89fPrmeGfkfScoXCId4+8S2uwlZVzVrLi+RXsfGcnlQ9VsuNLOzQtVZp4Pd6ksRu2YSYWT6SptYn1DevZdsM2Pgh9wBvvvcHXNn2NiuIKVnxmBcYY6q6uY91L65hQNIEHqx5kUskk8kweYRum5YMWTi47me9f9H0+X/d51l2+LmlcptJAEgqH2Hd0H53dnRTkFTB57GS8HqeYam5rpvLhyoQGncqHFSsikqOCQSgrg2efdSpVo4s15uIokry85FMkaUFfGchA04hBaj/Ay8udnoUejzOdTu8fN52dfecGb2rqGc0CPT3Uek9HFq2kio6s6f1875E1yaYai5++y+9PzGv0X1T82irxn0nvadEGmkooOrKmd17jr8XBGlaiveZ7H2OoI1U8Hmd6sh07nGP7fM5nkOuVwKEQjBuX+F11d2u6m9Gko8OZ7iZ+Sr9/+qfcn/LGrfmW9HJrGeam+17JjFTuYXJRKOQsqh4/Je0DD+T+NSfpdfQoPPVUYhn24IMwf/5xdzrK8Tvn3OHz+Fh/xfqE3v7rr1hPgaeA/Lz8hCmYoj3/L3rgIi5cdyGffeizHGg/wNuH3+YbT32Dtw+/zapLVvUZrVL1SBW7Duzirtl3MXPKzNjxvj3r27R1tbH3yF5e3v8yP9n5E/753H+moqSCJ659ImHfQFmAScWT+hz79q23c7D9IG+0vJGxKZqa25pjjS7R81Y+VElzW9+pDULhEC/vf5lP/OITnFd7Hos3L+bOT93JzCkzaTzcSDCk/6DTZWzB2D4jVR6sepAnXnsCr/Gy6dpNfPmcL2OxzPnlHC5dfykAi2YuYs4v5/DXP/5r1r20ju/P+T6hcIjJYyfT1NrEXw7/hdcOvcY3P/5N2rvamVo6lcbDjbR0tCSdksznHfgHfzQmzq89n1NXn8r5tefz8v6XCYWd//Dau9qTNujk+lR6IjJKeb1w5Aj86U/wzjvO45EjiZW9ucLjcdaEePZZ2L3beXzlldyvYJXsGWwaMXBivbLSWch+61bnsbKy5xro6HBGhbz6qtOY8uqrznb8uiljxsAPfpA4iuQHP0hsRBisd3BhoXPu+Ofr6pz0qHDYqTBas8bJ65o1zna0QSQcdkaUXHKJs/DqJZc42/ENL9G1VeI/k1deSdxnsOm7ioqSj6wpKkr9GOkcqeLxOGvSBALOoxvKBK/X6aka/129915ulr2SGV5v3/XJKipyPwbcmm9JL7eWYW6675XMKCiA9esT7z/Wr8/96RK7uuALX0gcSfyFLyR28pGRr7gY5s5NLMPmznXSj5NKwRSVF5XzfvB91ly6huL8Ytq62hg3ZhwN+xv48KQPJyxqf+sFt8amHps5ZSZLzl3CB6EPqCip4Lvnf5fl25azeu5qVs5ZSXlhOS0dLbHRHsX5xVT/upo1l67h0vWXUnlaJfPPms+n7/+005BxWiW3XHALc345JzaqpLaylqVbllJRXMHdc+4mZEMJI0hmTpnJopmLuOTBS1IaiXKsgqFg0orxZI0o+47u67PuyILHF7ByzkoWb148aCW9pG6sbyxjvGP6xO7lf3M5X3/y66z4zAq8Hi/5Jp/ffuG3dIe78RgPS55eEovhRTMXxWIwPuaaWpuorazlr8b9Fd22m0BZgBXPr2DtZWsTRlzVz6/HXzzwD/5kMVH1SBXba7ZzUtlJ5Jl+Rp2ZHO9BISKjV7LeU7lozBg491z45CcTFx/X9BDSn/6mEdu+HU46yUkrLXXWHonuF23wiM6bbS1897s9U2T5fM62tT3nsRb+7d8Se4D/27/Bj3+cmJ8JExJ7B8crLXUaWdascX48tbU52/Hzd0dH25xySs8xrO05lrXOYvbx77e6Gp57rucY/a2tsmNHT2XqYAu4jh8PJ56YmNcTT3TSowY7hltHqqRLVxfcfXdizNx9N/zoR9nOmQyXsWPhX/6lZ22n6P9pY8dmO2cDc2u+Jb3cXIa55b5XMqOoyBmtFX8PM25cYueRXJTBRdXFRaxNXobF/y45Rmp4SVFzW3Os4SIqUBbgmepnaO9qx1/s57ma52hpb2Gsb2yswrr3lF+1lbXc/ImbOdh+kMWbF8fS1162ltU7V9PS0ULj4Ub+esJfs3vRbrweLxf+4sLYeavPru6zfkZNfQ3P1TzHofZDzL5/dsIxb37mZpacu6TPCJhMTOfl8/oSKsZnTpnJrRfcSrftpqm1CX+xP9bQ09+aOf5if0qV9JK6ptampLG7vWY7i2YuoqOrgzHeMbze8nqswTAaP01tTUnjp6a+hpVzVlL1aBU19TVsvWErXo831uBy8zM3s+bSNXxo3IcozC9kaunU2HcftmGa25oJhoL4vL5YXAy0jhJAUX4RtZW1CXmsraylKD/H/yMXkdGpv95T8RW1ueLw4eSLj2/bBieckN28SW5KZRqxo0eTN84895zzQzwvr+8PnNraxCkprIVFi/pOgxf/Iyg/31kDpXdl5cSJzvPNzU6P4d5TXzz3HJx8srPt9cL+/X2nGhs3znl+sHVkILW1VQabjszjgenTndE2/TWapNKwEh2pMhoZkzxmjMl2zmS4tLYm/z/tueeciuxc5dZ8S3q5tQxz032vZMaRI/3fb+Xy74nBpqyV0SGDZdgo6fp0/PqrFLbW4vP6MMZgMIz1jcVjPATKAv1WWE8smsjnHvlcn9Ee35/zff799X8nUBbAWuuMMHi/MeG85YXlSfMRCoeSHnP5J5fjL/YPOBIlbMM0tTbR+H7jcU1DFm00CZQFYtOtLXxiIR/60YechdCbexZCL8grSDod1UmlJ6V9JM5o11/shm2YBY8voMBbwJvvvxlr0Ig+v+DxBSw5d0m/MVdeWB77uzvcTaG3kNU7nZFcKz69gmAoyJKnl1CQV5DQ6NLQ3MCse2cxbdW0hLjoLyYK8pyhqeOLxnNi6YmsuXQNW6u3subSNZxYeiLji8YjIpJzurqSV8Lm4rB19fSSoSooSD7dVfx0EoPFVWdn8oXm4xtvwuGeyqfoPgsWJI5q6a+ysrXV2U7lWgwGYdUqp4fx1q3O46pVPY0m0XnLe7/f+EaiwaYAi76fwaYjS2V6LzdOATZcUokZGdnc9P9vPLfmW9LLrWWY4lfcGgN5ecmnec31tWkkvTIYv7pLT1GeJy9ppXCeJ48x3jHsPbI3tjbF4qcWs+HqDf02eHTb7qTp73W8xxVnXMFj8x7j25u/nXS9jP7Wz/B6vEmPOe2EafiL/f2uuTFQRfhQeYyHGf4Z7PjSDh658pE+Ffnx671MHjuZunl1CeuO1M2r48TSE9Xokmb9xW7Yhmk83MiR4BGK84v7HYHUX8y1dLTE/s7Py2dc4Thu/9TtLN68mAvXXcjizYu5/VO3J4xeGmgdoP5iYvLYyYATX9PLp/PRyR9l2gnT+OjkjzK9fLriRURyUyoVtbki2tMrnnp6yUAmT4ZnnoFdu5y1WXbtcrYnT+7ZZ7BroL9RJPGVS6mMNBlsn1Suxbw8p4dx/Foyixb17GOM0+M4/kd57x7Iqayt0t90ZM1910OUY9Rfg198zMjI5vUmv+Zzfa0Jt+Zb0sutZZib7nslM9waAx0dsHRpYuebpUsT1xyUkS+D8asayxT58nzUVtYmVArXVtbiy/PR3tXOtRuvjVUm179ezx3b7mBK6ZSkFdbB7mDS9L1H9vJX4/7KGYHS1kTd1XWcXHZyQmX0upfWseHqDX3y0d7ZTuVplX2OubtlNyUFJX0WV49O5zVQRXh/Bhoh4zGe2PRlA42y8Xq8zPDP4LkbnmPPoj08d8NzzPDPwOvRjWW69Re70ZFZ3eFu2rraksbkuMJxfWIw+voVz68gUBZg49UbGZM3hoPtB2MNb298/Q223bCNE0tOTDjmQOsAeT1ezpp0FttrtrNn0R6212znrElnJcRENL4CJwSoKKlQo4uI5K6CguS9p3JxgckxY2DDhsS8btigNV6kf9YmX/w3fgowjyd5Y0V0dEZ/o2biG/xSGUUyWMOhz5f8Wuw9EmWgHsbWwurViT/KV6/u+36jU4C99ZbzOGNG4miUVKYjk+PTX+V1rlf8SPqUlDjTDcZf8xs3Oum5zK35lvRyaxnmpvteyYxU7rdykdfbd3rWigo1eo82GSzDFEkpCoVDlPpKExYoL/WVEgqH6A73HcFS/3o9P5jzA+rm1cUWDI+um3H3f97dZ/HxR698lI5QB9ZaSgpKWH3Jaq761VVOQ8hplWz+/Gbe++A99h3dh7U2lo/o+heHOg7xw0t+GDt3oCzAhqs3cDR4FGstH6n4CDu+tCNhXQ2A9q72ARtIeouOkIk21kQbcWb4ZwDE1u6IVur3XlfE5/XFjrPr4K6kx1Flenr1F7se42HtZWuZXDKZgryCPuun1M2rY3/rflbtWMV3zvsOK+es5KxJZ3Gw/SATiiaw7vJ1dNtuav9Qy1dnfhWD0+tzf9t+Kh+qpKKkglsvuJXp5dMpKSjB6/FijBkwLrweLyeVnZSVz0lEJK28Xpg0KXGByUmTcvMm/uhR+OlPYdMm54d9d7ezkOt3v5vYW18kat++/tciiK6bAs40Ws8+68RUXh785jdw2mnOcx4PrF8P117bM4/9+vV91yqpre279kr8PgUFTuVk7zVeoj+UQiHnGoy/FouLE6fSG2zUTH4+3HRT33z0HhU22Noq0Yak3vOI53qlhJsUFiaPh8LCbOdMhktbGyxfnrg4+fLlzuLk43N4imK35lvSy61lmJvueyUzQiEoLU2MgdLS3J+6uLQU/uVf+l5zpaXZzpkMpwyWYarhTlHYhlm+bXmsQSIYCrJ823LCNswY75ikowXebX2Xwx8c5nfX/449i/awcs5Kbn7mZu576T5ufuZmVs5ZyWtfe43aylqC3UGqf13NGWvOYM4v59Da2RobOVL/ej1zfjmHo8GjAJT6SgmGgtz/v/fjMR5q6ms4r/Y8LvzFhdxywS28/Y23efK6JwmFQ3SEOjgSPAKQMFIAoKG5gVcPvtrvNGTx7z06wuWdI+9w2zO39Rkhc6j9UMKUZYs2LeozSiI6ygYGnnJK0qu/2LVYVu9cjcWy6MlFAPzu+t/x2tdeY82la1j4xEJq6mu45YJbKMovYmLxRLweL+MKx/Haodeo/nU1cx+cy8XTLybP5FGUX8Sh9kO8c+QdHr7yYdZdvo6fv/hzTvvxaXx87cf5Y/MfeffIu0lH3+SZHO/BIyIyVNEe7Kec4lTEnnJKYnouyc+HLVucRb9PP9153LJFU41J/7q6nLiuq3NGgNTVOdvx8yAXFsK558InP+ksGP/JTzrb0cqjcNj5MbNmjXOMNWuc7fipxtrbk0//0N7es08wCM8/7zTw7N7tPD7/fM+1Zi1873s928Ggsx0/WmWw6QU6O5PnI349mlSkMh2ZHJ/o9xwfV/HpMvJ1dUFTU2JaU1PurzPg1nxLerm1DHPTfa9kRjjsNBbH328tX57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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(df)\n",
    "g.map_upper(sns.scatterplot,color='red')\n",
    "g.map_lower(sns.scatterplot, color='green')\n",
    "g.map_diag(plt.hist, color = 'grey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Independent and Dependent Features*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selling price being the Dependent feature\n",
    "X=df.iloc[:,1:]  #independent\n",
    "y=df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting  features :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ExtraTreesRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.35968656 0.03692738 0.0008824  0.07832981 0.23265924 0.0127665\n",
      " 0.12778484 0.15096328]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "feat = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat.plot(kind='barh')\n",
    "plt.title(\"Features as their Importance\", size =18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above , It can be concluded that Present Price is the most important feature..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Train Test Split*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "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=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using  *Random Forest Regressor*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using RandomizdSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "\n",
    "max_features = ['auto', 'sqrt']\n",
    "\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "## Selecting Parameters \n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying randomizedsearchcv\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   4.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   5.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   6.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   4.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   7.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   2.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   1.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   1.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   1.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   1.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   5.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking for best Parameters \n",
    "\n",
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.027732954421797"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.2078 ,  0.54949,  5.06834,  9.22646, 16.39781,  5.02807,\n",
       "        3.4098 ,  0.7659 ,  4.6066 ,  4.43975,  3.10378,  0.87514,\n",
       "        5.023  ,  7.85216,  7.87254, 10.48578,  7.24259,  4.0881 ,\n",
       "        0.4986 ,  1.46545,  4.05005,  4.66699,  5.60635,  9.4721 ,\n",
       "        0.24326,  0.77204,  0.49631,  0.63347,  0.48192,  4.80672,\n",
       "        5.28758,  5.8459 ,  0.49625,  8.75296,  3.48977,  1.17409,\n",
       "        5.59505,  7.48038,  0.26841,  8.83   ,  9.28903, 19.39585,\n",
       "        4.88952,  4.1342 ,  5.39885, 11.76638,  0.32648,  0.90355,\n",
       "        4.99525,  8.38553,  7.35672,  3.54699,  4.6885 , 20.78656,\n",
       "        1.09899,  1.0473 ,  0.51168,  2.76872,  3.7167 ,  1.31938,\n",
       "        4.01506,  9.08874,  3.17752, 20.91536,  4.17105,  5.53065,\n",
       "       10.41169,  4.82864,  0.57613,  2.98495,  3.40021,  3.03257,\n",
       "        0.6527 ,  5.45025,  0.83098,  2.99084,  0.51003,  9.14812,\n",
       "        1.12109,  2.71755,  0.50828, 10.09928,  7.4285 ,  5.51465,\n",
       "        5.07315,  0.94612,  6.11736,  6.04519,  0.458  ,  5.00452,\n",
       "        0.61492])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b16c148160>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rkpKSGOhD0NYjO5GfOgYJBtOAXm82mFAwakLgzYGIokfxTN1ut8NsNgemBUGAKIrQ6XpeqtPp8Prrr2PNmjUoKytT3KEgaGCxDCw8IkkQtDFZV6S1O7vQcPoAls68Cab4c2O9CDpt0DQAaDToM89o1MNiNuHaCbPwq82vw6l1ICspfVBqjzXD9RgKFdunf+FqH8VQN5vNcDgcgWlJkgKBftayZcuwePFi3Hvvvdi6dSuuuuqqi27P75fR0RF71zRbLKaYrCvSqvd/CRkyZoyaiOOd555B6hclOF3eoHVlGX3mud0+dIhOzBgxEQDw4e5tWDT5+sgXHoOG6zEUKrZP/5TaJz09MaTtKHa/FBUVoaamBgBgs9lgtVoDyw4cOIAHH3wQsixDr9fDYDBAq+UFNUOJ7WQjkuLMyEsdWH/6WaOTM5CVmI66E1+HqTIiGgjFM/WSkhLU1taitLQUsiyjoqIC69evR05ODubPn49JkyZhyZIl0Gg0KC4uxuzZswejbgqTHSf3oGCUtc/DMAZiRsYk1Bz6F/ySBIFv7kRRoRjqWq0Wq1evDpqXn58f+P7BBx/Egw8+GP7KKOJO2ltx0n4GP5hSEpbtFWZY8Y+9m3Gg/QgmjMgNyzaJ6NLwdGoY23GyEQBQmBHaqIxKZozq6Ve39W6XiAYfQ30Y23GyEclxZuRaMsOyvbSEFGQnjQy8WRDR4GOoD1OyLMN2shEFGeHpTz+rMGMSdrbshV/yh22bRBQ6hvowddLeilOOtrB1vZw1Y5QVTp8L+9qOhHW7RBQahvowdbbfe8Yoq8Kal2ZGBvvViaKJoT5M7TjZCIsxEbmWrLBud4TJgjFJGexXJ4oShvowJMsydpxsRMEoa0TG6pmRMRH1p9ivThQNDPVh6ET3aZx2toe9P/2sGRkT4fS5sffM4Yhsn4gujqE+DO1o6e1P7+3/DrfCQL/6nohsn4gujqE+DO042QSLMRE5yRkR2X5KfBJykjOxo6UpItsnootjqA9DO1uaItafflbBKCvqeb060aBjqA8zZ69PLwjzpYznm5FhhUv0YG8b+9WJBhNDfZjZebKnS6QgIzyhrtHK6BS7gr688GJ675vGrpa9YdkPEYXm0h4dT0PezpYmJMYlYGyYrk93i158caghaF7xuEKkmSzIThyJnSebcOvUG8KyLyJSxjP1YWZnSxOmj5wQ1vFeLqYgw4pdp/bCL0kR3xcR9WCoDyOtjnYc7z4d8f70swpGWWH3OnGw4+ig7I+IGOrDytlLDMPVn67k7JvH2X58Ioo8hvowsqulCSZ9PPJTxgzK/kaZRyDDPAI7eb060aBR/KBUkiSsWrUKjY2NMBgMKC8vR27uuUeVvfbaa3jvvfcAANdccw0fbRfDdrY0YdrI8YP6/NDpo6zYdnQXZFmO6HXxRNRD8be7uroaXq8XVVVVWLFiBSorKwPLjhw5gnfffRdvvvkmqqqq8Nlnn2HPHt4aHovaXV043HkSMwap6+WsglFWdHnsaO44Pqj7JRquFEO9rq4OxcXFAIDCwkLU19cHlmVkZODll1+GIAjQarUQRRFxcXGRq5YG7Oz14tNHTRjU/Z4dr51DBhANDsXuF7vdDrPZHJgWBAGiKEKn00Gv1yM1NRWyLOMXv/gFpkyZgry8vH63JwgaWCymy688zARBG5N1hUtD+37E6+Mwe9xk6IS+/+0euwumeENgWtBpg6YBQKNBn3kXWs9o1MNi7mnL5ORcjDSnYE/bftxpWRiuHycmqf0Yulxsn/6Fq30UQ91sNsPhcASmJUmCTnfuZR6PB0888QQSEhLw9NNPK+7Q75fR0eEcYLmRY7GYYrKucNl6aBemj5wAe7cXgLfPcrfog9N1br5flIKmAUCW0WfehdZzu33oEM+1ZcHIifjyyG60tdsH5fr4aFH7MXS52D79U2qf9PTEkLaj+BtWVFSEmpoaAIDNZoPVeq5PVpZl/OhHP8LEiROxevVqCIIQ0k5pcJ12tOFI10kUZU6Oyv6LMiejw92Ng+3HorJ/ouFE8Uy9pKQEtbW1KC0thSzLqKiowPr165GTkwNJkvDFF1/A6/Vi8+bNAICf/OQnmDlzZsQLp9B9daLnw+uizClR2f/MzEm9dTQgP3VwLqckGq4UQ12r1WL16tVB8/Lz8wPf79q1K/xVUVhtP9EAizERY1PC+zzSUKUnpGJMUga2n2jALRwHhiii1NvBSQB6usi+OtGAmRmTotqfPTNzEna27IXPL0atBqLhgKGucoc7T+CMqxNFWdHpTz+rKHMy3KIHe1oPRLUOIrVjqKvc9hM9w+LOjNKHpGfNyJgIrUaD7Sd4cxpRJDHUVW77iQZkJaYjw5wW1ToS4xIwITU38CZDRJHBUFcxv+THjpONUT9LP6soazL2nD4Ip88d7VKIVIuhrmKNrYfg9Lmjdn36+WZmToZf9nPURqIIYqir2PYTDdBAg8KMidEuBQAwbeR4GAQ9vmIXDFHEMNRVbOvRnZgwIgfJxtBuL440g6DHtJHj8a9ju6NdCpFqMdRVqtXZgT2tBzE3J7bu7r1qdAGaO0/geNepaJdCpEoMdZXaemQHAGDumMIoVxLs6jEzAABbeusjovBiqKtU7REbshNHItcSnaEBLiYzMR15KdnYcsQW7VKIVImhrkIOrwtfnWjAnDGFMfkIuTljClF/ai863fZol0KkOgx1FfryeD1EyY85ObHV9XLW1WNmQJJlfHGMg8ERhRtDXYVqD9tgMSZiSnq+8spRYB2RixHxFnbBEEUAQ11lfH4R247uwtVjZkDQxuZ/r1ajxdVjZuDLY7vh9fuiXQ6RqsTmbz0N2M6WJjh9LsyJsatezjdnzAy4RQ9sHOCLKKwY6irz2eGvYNQZYmZogIspzJyEeF0cu2CIwoyhriIe0YuPD36Bq0fPQJzOEO1y+mUQ9LgyexpqD9sgSnxwBlG4MNRV5LPD22H3OrHAWhztUkLynXFXod3dhS85bABR2CiGuiRJWLlyJZYsWYKysjI0Nzf3WaetrQ033HADPB5PRIqk0LzXtBlZiemYESMDeCmZPXoaLMZEfLD3s2iXQqQaiqFeXV0Nr9eLqqoqrFixApWVlUHLN2/ejOXLl6O1tTViRZKyo50nsbOlCTdN+HZUn0V6KXRaHUryr8bWo7vQ7uqKdjlEqqD4219XV4fi4p4/5wsLC1FfXx+8Aa0W69evh8ViiUyFFJJ/7PsMWo0WN+bPiXYpl+Sm8XPhl/3YdGBrtEshUgWd0gp2ux1mszkwLQgCRFGETtfz0rlz517SDgVBA4vFdIllRp4gaGOyrlCIfhH/3P85ivNmIj8reKwXu8cJh88VNE+nFSBK/qB5Wg1gij/34aqg0wZNA4DmvHUutp7RqIfF3H9bnq0rIy0Fk9LH4v19m7HkihIkGhP6/2Fj2FA+hgYD26d/4WofxVA3m81wOByBaUmSAoE+EH6/jI4O54BfHykWiykm6wrF5ubtaHN1oSRvTp+foVPswuYDwZcNzh47GV8caugzz+nyBqb9ohQ0DQCyjD7zLrSe2+1Dh9h/W36zruzEUdh0YBu2HdqNWRnT+n1dLBvKx9BgYPv0T6l90tNDey6CYvdLUVERampqAAA2mw1WqzXEEmmwvNv4CdJMFlyZNTXapQyIdcRY6LQCPtr/RbRLIRryFEO9pKQEBoMBpaWleO655/D4449j/fr12LRp02DURwpsJxvx1YkG/GBKCQStEO1yBiROZ8CE1FxsPrQd3R6H8guI6KIU+1G0Wi1Wr14dNC8/v+9AUR999FH4qqKQyLKMV7f/BWkmC/7XxGujXc5lKcqajIbWA3i7oRp3Ft4c7XKIhqyhce0bXdC2ozvx9en9WDbj+zF/B6mS9IRUfGvMdPz56008Wye6DAz1IUqSJbz61TvISkzHTeOH1mWMF7N4+g1w+lz489fV0S6FaMhiqA9Rnx76Fw60H8WdhTdDpx341UixZGxKFopzi/B2wyZ08WydaEAY6kOQ3evEy3VvY6wlG9flXRntcsKqrOD7cPpceJtn60QDoo5TvCHOCy9cojtoXrzOCAP69pPLsoz/9/nrOO1sxwvf/Qm6/faQXjeYNFoZneK52/4NOh28YvBIjH74z38ZAGBc6mjMy70CbzdUY4G1GCMTUiNaK5HaMNRjgEt097lBqHhcIQwX+PDzw/1b8PGhL7F85iLkpmSG/LrB5Ba9QTc3Xexmp4u5p+j/4Itj9Xih9jVUlvx4yIxlQxQL+NsyhBztPIk1297AjIyJWDLtu9EuJ2KykkbigVm3YvuJBvyt8dNol0M0pDDUh4gujwOrP/0t9IIOj3/77ph9/mi4LLTOw5XZ0/C7f23E0c6T0S6HaMhQdzKoRKfbjkc//CWOdJ7Ek8X3Ii0hJdolRZxGo8GKOXfCoNOj8rNX+YBqohAx1GNcp7sbj374n2juOI5nrv+/mJU9NMd3GYg0kwUPX12GPa0H8ewnv4XPz8feESlhqMeww50nsOJ/XsCRrhY8e/2DmJ09dEcwHKh5uVfgoW8txedHd6Bi8+/hly581QwR9WCoxyBZllG9byt+9PdytLm68PP5Dw2rM/Tz3TzpOjwwazE2N29H5Wev8oydqB+8pDHGdHkcqDn0L+xrO4yZmZPx2LeXI83Ep0rdMrUEPknEK9vfxrGuFjwx716MThoV7bKIYg5DfYAu5YahUHhEL748Vo+vTvRcz3174QLcWXBz4BrtC+3vYjfwDFXn37R0fnveNv27GJ00Cr/c8gc88Ldn8eDs23Dj+DnwaXxBbXOhm51i4aascDr/eIjGzxcLNZwv3L+XQxFDfYAu5Yah/rQ62vFWw//g/cbNcIteTErLw5ycQiycHPwA6Qvtr78beIai829aulB7FucWYVLaWFR+9ipe2PIa/t70KW4tKEGX0wmNRgPgwjc7xcJNWeF0/vEQjZ8vFmo4X7h+L4cyhnoUiJKI7ccb8OH+z7G5eTskWUJeSjauGl2AkeYR0S4v5qUnpOIXJT/BB/s+w+s738OzH/0OWYkjUZg5CeNSRke7PKKoYqgPEofXhR0nG7Ht2C5sbt6OLo8dZoMJ/3vy9bh+wpVoOnUk2iUOKYJWi4XWeSjJvxp/aazGmzs+wPtNNTDqDNjbfhBJ+kRkJqZziAEadhjqESDLMk4727Gn9SAaWw9h96l9aDh9EH7ZD6MuDlePmYHr82bjiqwpMAh6dIpdDPUBMgh63GSdi3ghHkc6T2L3qf34aN+X8EkijDoD8iyjkWvJxNSMPCQnJUW7XKKIY6gPkM8votPdDbvXiS6PA10eO3a07MGJrlYc6TwJh88FANBpBYxLGY1bp96AWVlTMGVkPgyCPsrVq49Wo0WuJQu5lixMy87DX3Z+ggNtR3Gw4ygaWg/gg321GJOUgWmjxmNK+jhMSc/HmOQMnsmT6iiGuiRJWLVqFRobG2EwGFBeXo7c3NzA8rfeegtvvvkmdDod/u3f/g3XXXddRAuOJFmWYfc60ebqQru7C23Ojt7vO3HG2Yl2VxfOuDrR5upEl8fe5/Up8UnITc7E/HHfQq4lCxNHjMW41NEM8UFm0hsxYUQuJozIhSRLaHW0Q68X0NByCJ81b8c/9n4GADDq4pCfMhrjU3MwNiULY5IzMSZpFFLjkwMfug42WZbh8Llg9zrh8Lrg9Lng8LnhEb3w+n3w+UX4ZT9cfjf2tR6FRgNoNQKgkZBsSIJRZ+j9iuv96vneIOihF3QXfROTZRk+SYRH9MLj98EteuAWPfCIXrhFb+98L3x+EaLkhyj54ZJcvTVoIGi0gFaCRZ+EeL0RJr0RJn08EvRGJBhMMIb5g0pZluHxe2HvbSOn1w2Hz4V2bwcaWw/BL/khyRIAwCO5YdYlQKfVQS/oYNTF9dSnMyLBEI+kuAQYdXFR+z8PN8VQr66uhtfrRVVVFWw2GyorK7Fu3ToAwOnTp7Fhwwb8+c9/hsfjwdKlSzF37lwYDIPzSbMsy5BkGZIs9R5oPQecTxLh9fvgFXsOTlfvAerw9vyyfPPsusvjQKe7G11eO9qcXfBJfW9s0Wt1sMQnYUR8MrIS0zFt5HgkGuPR6uiE2WBCUpwZiXEJuG78FUg9uFF6AAAKM0lEQVTW8U/8WKLVaDHSPALF4wpRNj0JkizhWNcpfH16P/aeOYz9bUfwzwOfw+k7dxmcUWfAyIRUpCekIt2UgmRjIlKMiUiKMwcCy6gzQC/oodfqoNMK0ADolOPR1eWCX/JDlP0Q/X54/N5AMDp97p4A8rnR7XWg2+NAt8cZOA67PHZ0e52BMLoU/9y/JaT1BI0ArUbTOyCcBpIswS9J8MuXf3nsP/d/3u9+zXHxMOnikWCI723Dnjcdg6CHTitA0ArQ9NYkyRL8shR4M3GL3nNvcl4X7F5XyDVXH9iquI6gEZAYZ0JynBlJcWYkxSUg8eyXwYQEgwkJ+t669cFvlobe40Av6CBoBOi0ArQaLbQaTVTeKBRDva6uDsXFxQCAwsJC1NfXB5bt3LkTM2fOhMFggMFgQE5ODvbs2YOCgoKwF2o72YinNq2BTxIhyTIAufffgTHpe96hk+ISkBKfjEkZeTBpjUgxJiM1Pgmp8clI6f3XbDD1+c/pFLv6XDpFsU+r0WJMcgbGJGfgxvFzAfQ877XV2YEjnSdxuPMETnS34pSjDaccZ3Co/Tg6Pd0Qwzw8QbwuLhAYSXFm5KVkB8IkKS4BZkMCzIZ4mAzxMOmMiOsNP73Q8ybSJXZjW/NuyLIMv+zHzNETEYc4eERv70mMN+hs2+v39ZzpS2JvaMqQZRmCVgutRgtBI8Cg08Og1SNOp4dRF4f43rP9uN6zf4NgCOxfpxXQLdqxtbkeMnpOrGZkTYABcT0nUr6eM2eHzw1H74mUDz602bvg8Lng8nnQ4e6CW/TC6/f2vrH0hPnZQBQ0QmDfcYIBaSYLTPoMJBhMMBviYe4N2p43iXiY9HEQNSJ2Ht/X80ar0UIDYHbOFCQICfD5Rfj8Itx+D5xeN5w+N+xeJ7q9TnR/4ySvy2PHse7TsJ85hG6PEx6/d8D/z1qNBoAGWo0GcYIBz5c8jEnpeWE7ji5EI8v9J+OTTz6JG264Addccw0A4Nprr0V1dTV0Oh3++te/oqmpCT/96U8BAI8++igWLVqEOXPU8SBkIqKhRvFTIrPZDIfj3EOAJUmCTqe74DKHw4HExMQIlElERKFQDPWioiLU1NQAAGw2G6xWa2BZQUEB6urq4PF40N3djf379wctJyKiwaXY/XL26pempibIsoyKigrU1NQgJycH8+fPx1tvvYWqqirIsoz7778fN95442DVTkRE51EMdSIiGjp45wURkYow1ImIVITDBPSSZRnz5s3D2LFjAfRck79ixYroFhUDlO4oJmDRokWBq75Gjx6N5557LsoVxYYdO3bghRdewIYNG9Dc3Iyf/exn0Gg0mDBhAp5++mlotcP7nPKb7bN792488MADgfy57bbbsGDBggFtl6He6/Dhw5g6dSpeeumlaJcSU/q7o5gAj8cDANiwYUOUK4ktv//97/Huu+8iPj4eAPDcc8/hxz/+Mb71rW9h5cqV2LRpE0pKSqJcZfSc3z5ff/017rrrLixfvvyytz283yq/Yffu3WhpaUFZWRnuvfdeHDhwINolxYT+7igmYM+ePXC5XFi+fDnuuOMO2Gy8yxgAcnJysGbNmsD07t27MXv2bADAvHnzsGVLaMMaqNX57VNfX49PPvkEt99+O5544gnY7X3HlgrVsAz1P/3pT/je974X9JWWlob77rsPGzZswP333x+4S3a4s9vtMJvNgWlBECCKfPDzWUajEXfffTdeeeUVPPPMM3jkkUfYPgBuvPHGwE2KQE/35tmhNhISEtDd3R2t0mLC+e1TUFCARx99FH/84x8xZswYrF27dsDbHpbdL7feeituvfXWoHkulwuCIAAAZs2ahZaWlqADcbjq745iAvLy8pCbmwuNRoO8vDxYLBacPn0amZmZ0S4tpnyz/9zhcCCJY9sHKSkpCbRJSUkJnn322QFva1ieqV/If/3Xf+EPf/gDgJ4/qbOysoZ9oAP931FMwMaNG1FZWQkAaGlpgd1uR3p6epSrij1TpkzBtm3bAAA1NTWYNWtWlCuKLXfffTd27twJAPj8888xderUAW+Lp1y97rvvPvz0pz/Fp59+CkEQeAVDr5KSEtTW1qK0tDRwRzGdc8stt+Dxxx/HbbfdBo1Gg4qKCv4lcwGPPfYYnnrqKfzyl7/EuHHjeOf5eVatWoVnn30Wer0eaWlpl3WmzjtKiYhUhN0vREQqwlAnIlIRhjoRkYow1ImIVIShTkSkIgx1imm/+93v8MMf/hDLly/H3XfffdFhCrZt24aHH34YADB3bs8DpX/+85/j+PHjYaljzZo1uPHGG1FWVoaysjKUlpYGrrs+v96z1xsTRQMvqKWYtW/fPnz00Ud44403oNFo0NDQgMceewzvvvtuSK9/8sknw1rPD3/4Q9x2220AgP379+ORRx7BX/7yl6B17rvvvrDuk+hSMdQpZqWmpuL48ePYuHEj5s2bh8mTJ2Pjxo1obGxEeXk5AMBisVz0hqiysjKsWrUK77//Po4ePYozZ87g+PHjePzxx1FcXIyPP/4YL774IsxmM5KTkzFx4kQ89NBDIdXW0dEBk8kEALjuuuswbtw4jBs3Dt3d3ViwYAFmz56Nxx9/HMePH4fP58NTTz2FadOm4emnn0ZzczMkSQqMWkgUTgx1ilmpqalYt24dXn/9daxduxZGoxEPP/wwXnnlFVRUVGD8+PH405/+hJdffhlz5szpd1sGgwEvv/wyamtr8eqrr2LOnDkoLy9HVVUV0tLSQho7/7XXXsP7778PrVaLpKSkwF1/J06cwNtvv42UlBT87Gc/AwC8+eabyM7Oxq9+9Ss0NTVhy5YtaGhoQEpKCioqKtDe3o5ly5bhvffeu/yGIvoGhjrFrObmZpjN5sCQDbt27cJ9990Ht9uNZ555BgDg8/mQl5enuK3JkycDADIyMuD1etHW1gaz2Yy0tDQAPYO4tba29ruNb3a/fFNKSgpSUlKC5h04cADz5s0DAFitVlitVqxatQp1dXWBPndRFNHe3t7ntUSXg6FOMauxsRFvvPEGXnrpJcTFxSEvLw+JiYkYNWoUnn/+eWRlZaGurg6nT59W3Nb5g7ONGDECDocDbW1tSE1NxY4dO5CdnT2gOi/0BJ/8/Hzs2rUL3/nOd3DkyBH8+te/xowZM5CRkYEHHngAbrcb69atQ3Jy8oD2SXQxDHWKWTfccAP279+PW2+9FSaTCbIs49FHH0VGRgYee+wx+P1+AD1XuZw6deqStq3VavHUU0/h3nvvRWJiIiRJCutj+kpLS/HEE09g2bJl8Pv9eOKJJzBx4kT8+7//O5YtWwa73Y6lS5cO+0e6UfhxQC8atn7729/irrvugsFgwCOPPIJvf/vbWLRoUbTLIrosPFOnYSshIQGLFy+G0WhEdnY2FixYgLKysj7r5eXlYfXq1VGokOjS8UydiEhF2KFHRKQiDHUiIhVhqBMRqQhDnYhIRRjqREQqwlAnIlKR/w+ibdNFgVVHFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predict, color='seagreen')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a Normal Distribution curve !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b16c00be10>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y_test,predict, color ='brown')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking for errors !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.8871753846153858\n",
      "Mean Squared Error: 3.9989340606087933\n",
      "Root Mean Squared Error: 1.9997334973962888\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}