Car_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4Vt87rTOaQ6F",
   "metadata": {
    "id": "4Vt87rTOaQ6F"
   },
   "source": [
    "# Car Price Prediction\n",
    "\n",
    "Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6573a28b",
   "metadata": {
    "id": "6573a28b"
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np                                # importing numpy numerical calculation\n",
    "import pandas as pd                                 # impoting pandas dataframe manipulation\n",
    "import warnings                                     # removing the warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "IvGHsDx3bIFZ",
   "metadata": {
    "id": "IvGHsDx3bIFZ"
   },
   "source": [
    "Reading the Csv data file using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5eb19ec4",
   "metadata": {
    "id": "5eb19ec4"
   },
   "outputs": [],
   "source": [
    "# Reading the two different csv files out of which second data set was having some bad line so skiped it\n",
    "car_data_1=pd.read_csv('true_car_listings.csv')\n",
    "car_data=car_data_1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lm91tBtzbXWC",
   "metadata": {
    "id": "lm91tBtzbXWC"
   },
   "source": [
    "Checking the shape of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "587305b3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "587305b3",
    "outputId": "0b4d46da-adc6-4f14-cf76-b3217b1d3748"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(852122, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Vin</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8995</td>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>19VDE2E53EE000083</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10888</td>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>NY</td>\n",
       "      <td>19VDE1F52DE012636</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8995</td>\n",
       "      <td>2013</td>\n",
       "      <td>48851</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>19VDE2E52DE000025</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10999</td>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>CO</td>\n",
       "      <td>19VDE1F71EE003817</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14799</td>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>Lindon</td>\n",
       "      <td>UT</td>\n",
       "      <td>19UDE2F32GA001284</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILXAutomatic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Year  Mileage              City State                Vin   Make  \\\n",
       "0   8995  2014    35725           El Paso    TX  19VDE2E53EE000083  Acura   \n",
       "1  10888  2013    19606  Long Island City    NY  19VDE1F52DE012636  Acura   \n",
       "2   8995  2013    48851           El Paso    TX  19VDE2E52DE000025  Acura   \n",
       "3  10999  2014    39922           Windsor    CO  19VDE1F71EE003817  Acura   \n",
       "4  14799  2016    22142            Lindon    UT  19UDE2F32GA001284  Acura   \n",
       "\n",
       "          Model  \n",
       "0    ILX6-Speed  \n",
       "1    ILX5-Speed  \n",
       "2    ILX6-Speed  \n",
       "3    ILX5-Speed  \n",
       "4  ILXAutomatic  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the total number of rows and columns\n",
    "print(car_data.shape)\n",
    "car_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NbmfRAVibb9o",
   "metadata": {
    "id": "NbmfRAVibb9o"
   },
   "source": [
    "Checking for null values in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "829b6396",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "829b6396",
    "outputId": "c5b2cfaa-c1ca-4bcb-d6ac-a266b4760de0",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price      0.0\n",
       "Year       0.0\n",
       "Mileage    0.0\n",
       "City         0\n",
       "State        0\n",
       "Vin          0\n",
       "Make         0\n",
       "Model        0\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can find that there is no null values in each particular columns \n",
    "car_data[car_data.isnull()].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87_krSmQEA-w",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "87_krSmQEA-w",
    "outputId": "366861e2-3dd7-4f7a-ddf3-851d8a4d1305"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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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>Price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>852122.000000</td>\n",
       "      <td>852122.000000</td>\n",
       "      <td>8.521220e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21464.100210</td>\n",
       "      <td>2013.289145</td>\n",
       "      <td>5.250779e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13596.202241</td>\n",
       "      <td>3.414987</td>\n",
       "      <td>4.198896e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13000.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2.383600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18500.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>4.025600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26995.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>7.218600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>499500.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2.856196e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price           Year       Mileage\n",
       "count  852122.000000  852122.000000  8.521220e+05\n",
       "mean    21464.100210    2013.289145  5.250779e+04\n",
       "std     13596.202241       3.414987  4.198896e+04\n",
       "min      1500.000000    1997.000000  5.000000e+00\n",
       "25%     13000.000000    2012.000000  2.383600e+04\n",
       "50%     18500.000000    2014.000000  4.025600e+04\n",
       "75%     26995.000000    2016.000000  7.218600e+04\n",
       "max    499500.000000    2018.000000  2.856196e+06"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "MF-sjPUXbm10",
   "metadata": {
    "id": "MF-sjPUXbm10"
   },
   "source": [
    " Checking the info of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "pwakSBt4a77v",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pwakSBt4a77v",
    "outputId": "4802d118-6389-475f-c43b-358f49a5e3c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 852122 entries, 0 to 852121\n",
      "Data columns (total 8 columns):\n",
      " #   Column   Non-Null Count   Dtype \n",
      "---  ------   --------------   ----- \n",
      " 0   Price    852122 non-null  int64 \n",
      " 1   Year     852122 non-null  int64 \n",
      " 2   Mileage  852122 non-null  int64 \n",
      " 3   City     852122 non-null  object\n",
      " 4   State    852122 non-null  object\n",
      " 5   Vin      852122 non-null  object\n",
      " 6   Make     852122 non-null  object\n",
      " 7   Model    852122 non-null  object\n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 52.0+ MB\n"
     ]
    }
   ],
   "source": [
    "car_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zK9rFYFzcqtB",
   "metadata": {
    "id": "zK9rFYFzcqtB"
   },
   "source": [
    "# Removing outlier from data frame\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1e258c0",
   "metadata": {
    "colab": {
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     "height": 458
    },
    "id": "f1e258c0",
    "outputId": "31536ab8-6333-472c-f803-8adcaa860286",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(852122, 8)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,figsize=(12,6))\n",
    "# Distribution of price\n",
    "sns.distplot(car_data['Price'],color='r',ax=axes[0])\n",
    "sns.boxplot(y=car_data['Price'],color='r',ax=axes[1])\n",
    "axes[0].set_title('Distribution of Price with outliers')\n",
    "axes[1].set_title('Box plot of Price')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(car_data.shape)\n",
    "# we can find there are a lot of outlier in 'Price' column dataframe we need to remove it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abfa2f43",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "abfa2f43",
    "outputId": "3aba6d58-18c8-45ee-90b4-2910fde85186"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(843596, 8)\n"
     ]
    }
   ],
   "source": [
    "max_threshold=car_data['Price'].quantile(0.99)\n",
    "car_data=car_data[car_data['Price']<max_threshold]\n",
    "\n",
    "# Distribution of price\n",
    "fig, axes = plt.subplots(1,2,figsize=(12,6))\n",
    "sns.distplot(car_data['Price'],color='r',ax=axes[0])\n",
    "sns.boxplot(y=car_data['Price'],color='r',ax=axes[1])\n",
    "axes[0].set_title('Distribution of Price without outliers')\n",
    "axes[1].set_title('Box plot of Price')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(car_data.shape)   \n",
    "# we can find lots of outlier have been removed from 'Price' \n",
    "# -- which is our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8b81188",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "d8b81188",
    "outputId": "0d75eb59-b542-4391-d287-7bec3add0d91"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(843596, 8)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,figsize=(12,6))\n",
    "# Distribution of Year\n",
    "sns.distplot(car_data['Year'],color='r',ax=axes[0])\n",
    "sns.boxplot(y=car_data['Year'],color='r',ax=axes[1])\n",
    "axes[0].set_title('Distribution of Year with outliers')\n",
    "axes[1].set_title('Box plot of Year')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(car_data.shape)\n",
    "# we can find there are a lot of outlier in 'year' column dataframe we need to remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ea2f6aa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "3ea2f6aa",
    "outputId": "a0de1e1b-00fd-4efd-f970-07b37b724151"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(843596, 8)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,figsize=(12,6))\n",
    "# Distribution of Mileage\n",
    "sns.distplot(car_data['Mileage'],color='r',ax=axes[0])\n",
    "sns.boxplot(y=car_data['Mileage'],color='r',ax=axes[1])\n",
    "axes[0].set_title('Distribution of Mileage with outliers')\n",
    "axes[1].set_title('Box plot of Mileage')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(car_data.shape)\n",
    "# we can find there are a lot of outlier in our 'Mileage' column dataframe we need to remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "UKV-3sltr6SR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "UKV-3sltr6SR",
    "outputId": "11bdd2d1-aa06-4b93-96a1-39afc5de65b0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(826724, 8)\n"
     ]
    }
   ],
   "source": [
    "max_threshold=car_data['Mileage'].quantile(0.98)\n",
    "car_data=car_data[car_data['Mileage']<max_threshold]\n",
    "\n",
    "# Distribution of Mileage\n",
    "fig, axes = plt.subplots(1,2,figsize=(12,6))\n",
    "sns.distplot(car_data['Mileage'],color='r',ax=axes[0])\n",
    "sns.boxplot(y=car_data['Mileage'],color='r',ax=axes[1])\n",
    "axes[0].set_title('Distribution of Mileage without outliers')\n",
    "axes[1].set_title('Box plot of Mileage')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(car_data.shape)\n",
    "# After removing 'Mileage' column outliers we can find most of the data have been represented"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "INPzWVy0dYmF",
   "metadata": {
    "id": "INPzWVy0dYmF"
   },
   "source": [
    "# we have removed the outliers we can work on each columns :\n",
    "## 1 : we will try to univariate analysis :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a60GRQho9Dvr",
   "metadata": {
    "id": "a60GRQho9Dvr"
   },
   "outputs": [],
   "source": [
    "# Univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "_UTjIzY57yq7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 613
    },
    "id": "_UTjIzY57yq7",
    "outputId": "594076b8-e0ef-4532-d479-4795fc4a4865"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysis for getting which year old vechile we have more\n",
    "\n",
    "fig, axes = plt.subplots(1,2,figsize=(12,8))\n",
    "sns.countplot(y=car_data['Year'],ax=axes[0],order = car_data['Year'].value_counts().index)\n",
    "sns.countplot(y=car_data['Make'],ax=axes[1],order = car_data['Make'].value_counts().index)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41hoLsHguRD",
   "metadata": {
    "id": "f41hoLsHguRD"
   },
   "source": [
    " We can find that most of the old vehicle that we have are in the years between 2014,2015,2016,2017,2013\n",
    ", \n",
    "we can also find most of the cars are from Ford , chevrolet , nissan , toyota car maker companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2-JgrMb7-RQ4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "2-JgrMb7-RQ4",
    "outputId": "f9a57864-0bf7-4046-bf5c-019c5d247fae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[12,8])\n",
    "sns.countplot(car_data['State'],order = car_data['State'].value_counts().index);\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3yc7XceIdp1N",
   "metadata": {
    "id": "3yc7XceIdp1N"
   },
   "source": [
    "We can find most of the cars are from state TX, CA , FL,IL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "W3cmD_rWwt1L",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W3cmD_rWwt1L",
    "outputId": "64a865cb-b180-4979-c5ff-251314e382eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total 2553 Cities\n",
      "Total 59 State\n",
      "Total 2576 Model\n",
      "Total 53 Make\n"
     ]
    }
   ],
   "source": [
    "Total_City= car_data.pivot_table(columns=['City'], aggfunc='size').sort_values(ascending=False)\n",
    "Total_State= car_data.pivot_table(columns=['State'], aggfunc='size').sort_values(ascending=False)\n",
    "Total_Model= car_data.pivot_table(columns=['Model'], aggfunc='size').sort_values(ascending=False)\n",
    "Total_Make= car_data.pivot_table(columns=['Make'], aggfunc='size').sort_values(ascending=False)\n",
    "\n",
    "print(\"Total\",Total_City.shape[0],'Cities')\n",
    "print(\"Total\",Total_State.shape[0],'State')\n",
    "print(\"Total\",Total_Model.shape[0],'Model')\n",
    "print(\"Total\",Total_Make.shape[0],'Make')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "MrkjfZRJNfGk",
   "metadata": {
    "id": "MrkjfZRJNfGk"
   },
   "source": [
    "Creating two derived columns Current year and condition (Lower the value od car condition better the car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "qatXWrlqNG3j",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "qatXWrlqNG3j",
    "outputId": "13e0db09-a27a-4ea2-c6ab-9005bebad7ba"
   },
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Vin</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8995</td>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>19VDE2E53EE000083</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4465.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10888</td>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>NY</td>\n",
       "      <td>19VDE1F52DE012636</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>2178.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8995</td>\n",
       "      <td>2013</td>\n",
       "      <td>48851</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>19VDE2E52DE000025</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>5427.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10999</td>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>CO</td>\n",
       "      <td>19VDE1F71EE003817</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4990.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14799</td>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>Lindon</td>\n",
       "      <td>UT</td>\n",
       "      <td>19UDE2F32GA001284</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILXAutomatic</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Year  Mileage              City State                Vin   Make  \\\n",
       "0   8995  2014    35725           El Paso    TX  19VDE2E53EE000083  Acura   \n",
       "1  10888  2013    19606  Long Island City    NY  19VDE1F52DE012636  Acura   \n",
       "2   8995  2013    48851           El Paso    TX  19VDE2E52DE000025  Acura   \n",
       "3  10999  2014    39922           Windsor    CO  19VDE1F71EE003817  Acura   \n",
       "4  14799  2016    22142            Lindon    UT  19UDE2F32GA001284  Acura   \n",
       "\n",
       "          Model  Year_used    Condition  \n",
       "0    ILX6-Speed          8  4465.625000  \n",
       "1    ILX5-Speed          9  2178.444444  \n",
       "2    ILX6-Speed          9  5427.888889  \n",
       "3    ILX5-Speed          8  4990.250000  \n",
       "4  ILXAutomatic          6  3690.333333  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Current_year=2022\n",
    "\n",
    "car_data['Year_used']=(Current_year-car_data['Year'])\n",
    "\n",
    "car_data['Condition']=(car_data['Mileage']/car_data['Year_used'])\n",
    "\n",
    "car_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oW9w-UTPNw3q",
   "metadata": {
    "id": "oW9w-UTPNw3q"
   },
   "source": [
    "Dropping the Vin column founded to be on no use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "EHdOklYZ3l1G",
   "metadata": {
    "id": "EHdOklYZ3l1G"
   },
   "outputs": [],
   "source": [
    "car_data.drop(columns='Vin',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "RVbQyQLvQGTw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "RVbQyQLvQGTw",
    "outputId": "5726ff2b-058b-4c46-b0e2-322cfd7022cd"
   },
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21008.595685</td>\n",
       "      <td>2013.435411</td>\n",
       "      <td>49866.119674</td>\n",
       "      <td>8.564589</td>\n",
       "      <td>5437.932315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10502.912427</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>35565.529267</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>2788.801696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13495.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>23844.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3426.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18597.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>39884.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5234.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26990.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>69915.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7209.279545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60987.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>165156.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>31374.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price           Year        Mileage      Year_used  \\\n",
       "count  826724.000000  826724.000000  826724.000000  826724.000000   \n",
       "mean    21008.595685    2013.435411   49866.119674       8.564589   \n",
       "std     10502.912427       3.215227   35565.529267       3.215227   \n",
       "min      1500.000000    1997.000000       5.000000       4.000000   \n",
       "25%     13495.000000    2012.000000   23844.000000       6.000000   \n",
       "50%     18597.000000    2014.000000   39884.500000       8.000000   \n",
       "75%     26990.000000    2016.000000   69915.000000      10.000000   \n",
       "max     60987.000000    2018.000000  165156.000000      25.000000   \n",
       "\n",
       "           Condition  \n",
       "count  826724.000000  \n",
       "mean     5437.932315  \n",
       "std      2788.801696  \n",
       "min         0.250000  \n",
       "25%      3426.333333  \n",
       "50%      5234.857143  \n",
       "75%      7209.279545  \n",
       "max     31374.600000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "duJ_Zxlad3Kx",
   "metadata": {
    "id": "duJ_Zxlad3Kx"
   },
   "source": [
    "Checking the unique number of data in cities and state as well as model and make "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "g-uD1nz9hL4b",
   "metadata": {
    "id": "g-uD1nz9hL4b"
   },
   "source": [
    "Most of the cars that we have are from TX , CA , Fl states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9JqwdVSe0C87",
   "metadata": {
    "id": "9JqwdVSe0C87"
   },
   "outputs": [],
   "source": [
    "# Bivariate analysis \n",
    "# we can find that most of relation in continuos variable :\n",
    "# we find that there is a postive corelation between 'Price ' and 'Year'\n",
    "# we find that there is a negative corelation between 'Price ' and 'Mileage'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "h5OkK8l9-Zok",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "id": "h5OkK8l9-Zok",
    "outputId": "a3b27f1b-8b03-49a6-94b7-ec55f9b94910"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6,6])\n",
    "sns.heatmap(car_data.corr(),  annot=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iUUTlTDLhdRh",
   "metadata": {
    "id": "iUUTlTDLhdRh"
   },
   "source": [
    "In this correlation we can find that mostly 'price' and 'year' are positive correlated (+0.48) and 'price' and \n",
    "'mileage' are negative correlated (-0.76)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "YG1jC_CInOYh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "id": "YG1jC_CInOYh",
    "outputId": "aafaec46-5b4d-47e4-88d2-8c677e150c85"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.boxplot(data=car_data, x='Year', y='Price', palette='muted')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "x9zH0ZOQeMI6",
   "metadata": {
    "id": "x9zH0ZOQeMI6"
   },
   "source": [
    "Through this box plot we can find the relation between year and price and we can conclude that we cannnot remove 2018 as outlier as it cointain high price value , we can remove the data below 2003 beacuse price are also too low less than 10 K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "hlN9nvbTbGNU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398
    },
    "id": "hlN9nvbTbGNU",
    "outputId": "54d3ecb0-0f94-4a4a-c3ec-f2b3efd8a94e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/Doulc6zNjJyPPPKIJ/3QQw8F5rX9Dv2dVgs1ooiNkSNHhqbzxT/b50UXXVSQcth2BrXx2muvedLpJnay4b+xDuuk6J9Ay5/OF5trhn+WyNtvvz1021VVVZg6dSpEBFOmTMEOO+Rmnj2b84ct2/ON/9ye7/NNYji/Bx54ILmsd+/euOGGG/JajnLEALzE+duTrVmzJjS/TYeQcuiEGZf2wP4JKzo6OgLzFmO7WsBuRs45c+aEpt1sv0P/jKb+dBx9+umnoel88c/6d/fddxekHFGyGUPalr8DY9j3GJdZM22uGTbHbUJDQwPGjx+fs9pvoPuMpOlmKI3SFVdc4Unnavi/bCRqwQtZhnLCAJw8bDqEsBMmFUpcboziIi43XeVwTohyDOk46ejowBlnnJF2tkqba0Y2x21VVRVuu+22nNV+A9E+2bTtHBuXTr2AaSJUV1cXOvww5Q4DcPKw6RDCTphUKDYdv4hy6aijjvJ0AD7qqKMC8/rnEfCn/b75zW960gcffHCWpey55uZmzJ8/P+3QfzbXjGyeHmR6I2Dju9/9bmi6J+Iy1jnFHwPwEjdlyhRP2t/5xO/oo4/2pP/1X/81MK+/5/TZZ59tVTaibPkD7smTJxeoJNQTcek8aqOhoSEZVPXp0ye0aYS/qVhY07FUr4cFnVHOmtnR0YGnnnoKqorZs2eHlsOmE2E2TaUyvRGwcdppp3nGFw/rOGrLdibMYjwGKDf4TZc4/0QO6cZR/eMf/+hJ33///YF5/+///s+T9k8iQRSVGTNmeNK33HJLYQpCPVKMTYmqqqrwve99DyKCqVOnhjaN8PdrSDe2cltbmyf9wQcfBOZ97rnnPOlM2lNnqrm5OflddHV15Sz4tW36YXMjYKOqqio59GB9fX1Om7f4Z770j1bjF6chESm/GICXOP8U1f60n037NX8wn8vJUIjClENb43JQjAE4kHnHQP/7yWXb/OXLl3vSS5cuzdm2W1pakjcPmzdvDj2323TCtG2SE9WNAGBqwffbb7+c1n4DwJtvvulJv/HGG2nL4Zbr8lB8MQCnrEU1SQJROux/QIUURcfAOLE5t9t0wlyxYkVo2s/mRsBWVN+hbSfMqqqq5GROO++8c8nuU6Uum74KDMDJY+LEiZ70AQccEJg3qkkSiNLhJFBe/o5e/jHViWy425OLSOi53abToX+/TLefRlnJE0XnzmzLkWgL/+mnnxa8PJSdbPoqMAAvcbYdQi6++OLQtFtUkyQQpWM7o16p87ctLtSkLNQzcemQV1VVherqagCmmUjYud3mGmNTWw5EW8kTRefObMuROH63bNlS8PKQvWz7KjAAL3G2j8O++OILTzrdJCRRTJJgy2YGxWLlH66rUDMdxkVzc3PyQt+rVy9etMjDtqY1LuIynntHRweWLVsGAFi2bFloQOGfydGfdrOtEKqqqsJ3vvMdAMDhhx+es0qeqDp3ZuOZZ55JtnNXVTzzzDMFKwtlJ9u+CgzAycN2dss4tIW0mUGxWPmHJ1u5cmWBShIPLS0tyZvJrVu3sgMwecRlmvZi5Q4oVDU0oLCZmMi2QigqUXbutBXlzKqUH9n2VWAATh4cXSKe4nLhigsO3UUUnSg7P9ro6OhIDqX7/PPP56ymOi7vDyiPmVVbW1sxefJkLFq0qNBFiUS2fRUYgJe4vffe25MeN25caH7b0SXi0pGFyot/0pGwJlD+drTpHnsTpRPVeS8uTc3q6+s9s32GBRSVlZWhabdRo0Z50pkMQ+gOlHNVUx2nEbz8M6mGzaxarK6++mp0dnamfaJerLLtq8AAvMT5Z8KcOnVqaH7/ici/vl9cOrKUurh0zooL/yRQL7/8cmBefzvacn96QD13xx134O2338btt9+e0+0ecsghnvS3v/3twLxDhw71pHPZEXnatGmeJijTp08PzPuDH/zAkz722GMD8/pnybz22mtDyxFV++g4jeA1adIkT/qwww4rTEEi0tramnySvnjx4pKsBc92QIryvoqXgVtvvdWTvummm0Lzz5o1y5O+8847A/PGqSNLqYtL56y4uPnmmz3pG2+8sUAloXLT0dGRbLLQ0tKS0/PeY4895kk//PDDgXlXr17tSafrMG/jySef9NSAP/7444F5H3zwQU/6gQceyFk5omofHacRvEp9Vl/bfmXFKpsBKRiAlzjbtsM2+ePUkYXKS7HOoEjF74477kjeAHd1deW8FjwOWlpaPDXPYW2k/UNg+tNutsFYlO2j4zCCF1D6/a5K/f0lZDMgBQNwylqcOrIQEeXDs88+G5ouBTZtpG0m4rENxqJsHx2HEbyA0p/Vt9TfX08wACePqqoqT3r48OGBeTkSBRXK+PHjPem6urrCFITKTqJpRlC6FNi0kfZP1nbZZZcF5k1MuR6UTlUOt0LXVmdiwIABoWm/Up/Vt9TfX08wACePyZMne9JhnTZLvfMIxZf/0fUVV1yRs20PHjzYk/Z3dqPy9q1vfcuTDusoGaVBgwZ50v79tids2kj7b379N8duI0aM8KTTten+8MMPPeliaL5g0yQHAMaOHZusFR4zZgz22GOPqIpWEGPHjk3uq4MGDSq599cTDMDJ4/777/ekm5ubA/OWeucRiq+qqqrkhb6uri6nj5HXrFnjSfs7uxG5Far/gX8ad/9+21OZtpH2d/T3p91ef/11T3revHmh277kkks86QsvvDA0fxxkMwnUpZdeisrKypKsHe7o6MCGDRsAABs3buRgDS7BjbWI0iiXzhXFplevXp7Os6U67vWVV16JK664Iqe130Tp+IfAfPHFFwtUkmgl2kin87e//c2T/utf/5qzMqxfvz40XSrGjh2Lp59+OjRPU1MT2trasHTpUgBAdXU1amtr0djYmI8iZq25udnTTOvee+/FL3/5ywKWKD5YA05ZY+eKeCqXWTO/+OILLFq0KKfDrxGlUw5twKNi2wY8SnGaRM6mLOvWrcO6devyUKrc4GANwRiAk4fNhC/sXEGFdMUVV6CzszO00xdg31bW3y7VP3sfFYb/SU6hnux897vfDU33xKmnnupJn3766Tnbdhz4n1ZdddVVhSkIoptEbvvttw9NZ1uWxsZGzJgxA7W1taitrcWMGTNiX/sNlP5gDU1NTTjzzDNxzDHH4JhjjsGZZ56JpqamjNZlAF7iJk6c6EkfcMABofl32WUXT3rXXXcNzMvOFfG09957e9Ljxo0rUEmi09raiiVLlgAAlixZEjq7mm1bWf+4zjNnzsyylJRL3/jGNzxp/6yRblEGskceeaQn7e+4Xm4mTJjgSfuvOW7+YDTd7J1RzQAc5SRy/hv4kSNHFqwscbDffvt50vvvv3+BShKtbJ5MMAAvcQsWLPCk58+fH5r/o48+8qT/+c9/Bubt6OjAxo0bAbBzRZz4v7O2trYClSQ6/pq0dLXgmWpqavKMsNK7d++czuxH2fv73//uSQe1vW5qasLcuXM9y1555ZWMa6XS8c+6ev311wfmTYylHZT28888HHbzF5cnAh9//HFo2s3/WYV9doBp5xyWzlaUk8i99957nvS7776btiyJiZ22bt1achPa/eY3v/Gk033nxaYnTyYYgJe4RIAclO4J/wgppXbiKFabNm0KTZeCRO13ULqnBgwYgIqKCuy555453S7lT2JCmHQ1kLaWL1/uSS9btiwwbzYjYmQqLn09Ojo6POmVK1cG5n3llVc8af9NVbpt+9PZilO75JaWluRQhVu2bCm5NtL+J5D+dDljAE5Zi9NJjMpLVB3hErUZe+65J8aPH4/bb7+9KNpZ0jaJ73DfffdFXV0dHnnkkaJpLxsnra2tmDx5cmjzrqgdeuihoels1dfXJ2/QevfuXdB2yf5x5As1rnxU/H1w/OlyxgCcslbqnSuKUdBj9lw9fo8L/4WYk0BRvuy0006h6Z4YNmyYJ52ujXSUrr76anR2dnab9MovLk1hbDQ0NCSbfXR1dRXFDJvFyj/k4HnnnVegksRP2QbgiZ6r5GXTZpEzYVIu9evXz5Pu379/YN6zzjorNB22XX+ayMZ//ud/etI/+9nPcrbtqCfXyVRra2tyXofFixcXrBbcP8a4P10KSv09vvDCC550LseJL3ZlG4BTajZtFjkTZvw0NjamHJ+9GB6/+/snJGZPS6Wqqip5w3f44YeHzoQZZT8IKj/+vi933313zrZtO415VK6++mpPOqwW3KYtuu2oJolmIkHpbDU3Nyf/d0VFRUH7L5X6uPJRTtRU7Mo2AE+0E6TscSbMeCqX8dnPOuss7LfffqG130S5Vg7nvajeo22wGVUHvjh1fCyX2T6pu7INwBP80wr70xSMM2HG09ixY5PNLMaMGVOy47MnpsoOq/0myrVyOO9FNWOl7cgtUX3W9fX1yeaVffr0Yf8lKoiyD8BLXVQTGQDlU9NajHbZZRdUVFTwOyHKoaamJvTt29ezrLa2Nmfbj0tzhN13392TDruJ90/W5l/XzXZEDH8/rbPPPjs0f6YaGhqSn21FRUVBO2EWYydWG1HGIMWOnwSAuro61NXVlWTtd6Knd1C6J2xnNaP8GThwIMaPH1+ytd9EhTJw4MBk8NavX79uI5f0RGJymKC0W5SBjX8io9deey0wr7+j6FdffRWY13ZEjCeffNKTfvzxx0PzZ6qqqgpTp06FiGDKlCkFfYoWl/Hco8LR0oIxACcPm5N6nDqyEBFFzT3rXUVFReg48VEPCRplbXl9fX2yJrZXr16hQZPNRDxvv/22J/3GG2+ElsM/gkYuRwhpaGjA+PHjCz4EYVQdTePitNNOS+6bIpLTUYOKHQNw8rC5W21pafGMpcqJeIioHMThCVOUNacNDQ3JALx37945C1KfeeaZ0LSfzRMBW3HpQxKXkW+iUlVVhaOOOgoAcNRRRxX8844TBuDkcdxxx3nSP/rRjwLzlvoMXnESl7ahcfKnP/0JkyZNwhNPPFHoohB109jYiG9+85ueZYccckhRDAlq00TDZpz9qqqq0LRfVJ1BgXjM9AlE+x7j4hvf+AYA4OCDD84of0dHB8444wx8/vnnURar4CINwEVksYgsEJG3RGSes2x7EXlWRNqc39u58l8oIotE5AMROcq1fIKznUUi0iRO9CEi/UTkIWf5ayIyJsr3Uw6ianNHPTNy5EhPetSoUQUqSXzcdNNNAIAbbrihwCWhhExvFIMmQiu1Dlr+Ns7nnntugUpiL9MmGoMHD/akhwwZEph32bJloWm/K664wpO+6qqrQvPbuOKKK9DZ2YnLLrssZ9sE7DtV1tTUeNKjR4/OaXni4PrrrwcA/Pd//3dG+ZubmzF//vySb9aaj7Pd4apap6oTnfQFAJ5T1VoAzzlpiMg+AI4HMA7AZAC/E5HEnjsTwKkAap2fyc7yUwB8qap7ALgZwK/z8H6Kim3N6Z///OfQtNuLL74Ymqbc8berXLFiRYFKEg9/+tOfPGnWgseDbZMBf82qf0SNYldVVZUMUA855JCievyeaRMNmzbgts1mPvnkE096yZIlofkz1dramtzWkiVLcloLbjvwwSuvvOJJv/zyyzkrSxzMnTs3Obb5+vXr8frrr4fm7+jowFNPPQVVxezZs0u6FrwQ1Q3TASSmEmsGcLRr+YOqulFVPwKwCMCBIjIKwBBVfUXN2fz3vnUS23oUwBGSLsIsM7YXRJv2aPX19ckOI71792bv5giVejtBW4na74S414JH3SGv2CQ6M951113JZb169cJvfvObApYqGqNHj0ZlZWVR1X7HxbXXXutJ56oG3F+znsta8CjbrRejSy65xJO+8MILQ/M3NzcnP7Ourq6SrgWPOgBXAC0i8rqInOos21FVlwOA83uEs7wagPv2tt1ZVu387V/uWUdVtwBYDaDb7bqInCoi80RkXtjdOdlpaGhIPjLu1atXwXuTE8WZf5hODttpalkTNazTpk0rqhriTPXp0we1tbUl+d6ikmiiFFXFg78mPVc169Sd7UyfLS0t2Lx5MwBg8+bNJT24Q9QB+CGquj+AKQB+LiJhvfRS1VxryPKwdbwLVO9U1YmqOnH48OHpypx3xVoLFqexVInirLGxEXfffXcyXVFRgXvuuacoOuRFbeTIkaisrOQNPKXFB9ylr5xmKY00AFfVZc7vFQD+BOBAAJ85zUrg/E40Zm0H4O7+WwNgmbO8JsVyzzoi0hvAUABfRPFeykFTU1O3E1ziQAgSl7FUi0lQBzTKvTg1/aiqqkrWetfX1/OG1cEa4niKYiSKTMe8TjRR8s/ke+WVV+akHIcddpgnffjhh+dku9RzcZqlNGqRBeAiUikigxN/A6gH8A6AJwA0ONkaACSG2XgCwPHOyCa7wnS2nOs0U1kjIgc57bt/4lsnsa1jATyvRdjgqrGxEXvvvbdn2de+9rWcbHvAgAGhaT//tMr+E5VfXMZSLUb+mUQ5a2XmTjrpJE+6oaEhIGe87LTTTqisrORkFBR7UYxEYTt753e/+93k3717985ZoHzWWWeFpvOp1KdqHz9+vCddV1cXmr+cnqxH+U3vCOAlEXkbwFwAf1HVpwFcB+BIEWkDcKSThqouBPAwgHcBPA3g56qa6CJ9OoC7YDpm/hPAbGf5LAA7iMgiAL+AM6JKMbrzzjs96d/97nc52a5N+6vGxkbMmjUrefc5dOjQtJ1TymW8zlxK1O74h3i85557ClSi4uMfeeGzzz5Lma+xsbHbiCn/+7//W7CmH8VW21uszeOoZ6IaicL/RDXdE1Zg27B8uR4usNAST0L9TwEGDhxYoBLlXlNTE95//33PsnfffTfteaVcnqxHFoCr6oequp/zM05Vr3WWf66qR6hqrfP7C9c616rq7qq6p6rOdi2fp6r7Oq+dkajlVtUNqnqcqu6hqgeq6odRvZ986N+/P4Dc1X5nKzHN8i233JI2b7mM1xmVRC34D3/4wwKXpLjMmTMnNO3mnuxj6NChRRP8xhXb4Za+qEai6OzsDE2nsv3226Ouri6nzUSam5s9I3gV8vqV6HCYkK6TYrHZtGlTaDqVcnmyXlrPOorcXnvthbq6upzVfmcr02mWy2m8zqiMHj0adXV1bBNuyXaor3HjxqGyshLNzc2h+cirsbGx29CAN998c4FKQ/lS6iNRtLS0JEdU2bJlS0HeX+JJ6PTp0z3Lp02blveyRKWxsRFjxozxLBszZkzBnkDG7Yk9A3DKWnNzc3KSga1bt7IWnPLmyCOPDE37FVvTjzg58MADk3/3798fEyZMKGBpKB/q6+uTTzpEJGcjUdjOEhkV//vJ1ftL1bQiqKNpgrv/St++fUuu2cWll17qSfs71uZT3J7YMwCPQJxGXohSHGoRqDwdd9xxnvSPfvSjApWkPOy2224AgOuuu67AJaF8mDZtWvKpkqp2q6XN1tChQz3pYcOG5WS7tvbbbz9Pev/998/ZtseOHetJp2s64x4Lf+rUqSVXSTB27Fj069cPgKn9LtRgA3F8Ys8AnLL27W9/OzRNFJVHHnnEk37ooYcKVJLyMGTIENTV1bH2uwhlUyH05JNPemrA/R3Gs/XFF95RggsVBPlnzs3VjWViIIOEQYMGZdR5tNTHwt9ll11QUVFR8NrvuM2wyQA8AkHtmzjpBlFuPPvss6HpfCi1J1pECS0tLZ4a8Fw83YzTk+F169aFpntq7NixqKiowIwZMzLKX+pN5DLtVxalOPZrYAAeEf+d3lVXXVWgkkTnhRdeCE1ni4ENpeMfiYMjcxCl1tjYiIkTJ3qWHXDAAaEVQuU0G2EU4hBwklcc92kG4BFxTyAAlOZMWzvuuGNomspX1LVd/uPLn86HKJ9o8SaUcuniiy8OTftFMRthY2NjyoqpQjwZHjFihCfNa1fpi+MMmwzAM5TNBTExgUAp1n4DwLJly0LT2WJTHUrntNNO86QLNbOkf5a3XHbmikJi8g8qL1VVVaisrARgar/TNXWIajbCqGa2tPXf//3fnjQ7F5e+OM6wyQA8QlFMIBAniSEIg9I9UaxTjZMRdT+IDz/0zrm1ePHinGzX1m9/+1tP+tZbb83JdnkTSrk2ZswYVFZWpq39TohqNsJSndmS4i9uM2wyAM9Q0OOzcpYYgjAo3RP+Gs6f/vSnOds25Yd/yLHtttsuZ9v2H4uXXHJJzrZta8CAAQByX/sdxeeXmPwjCmw2E2+2Hf2imo0wDhVT/vOHf6xqKk1xm2GTAbiFcmjXbcM/wUC6CQdsDR8+HABrv4vVn//8Z0/6iSeeyNm2165dG5rOpz333BN1dXU5q/1OiPLz+8UvfuFJn3vuuTnbNlHctbe3h6aJ8oEBuKVSb9dto6KiIjTdU9XV1airq2Ptd0Ty2R44l7XfgBlfNyxdasI+v2xqnn/wgx940t///vett+HX2NiI/v37e5Ylng7EFdvElyeOokRxwADcUhwen8WFvye5P01UV1eHurq6nNbeAsCpp57qSZ9++uk53X5cRPX5AeYGF8ht7fcf//jH0DRRHBx00EGe9MEHH1ygklA5y22bASory5cvD01TvCU6+k2aNKnAJbHnnwnzwQcfzEktbjFqbGzEQQcdhF/+8pfJZbfcckva9YYPH47hw4fn9HOrqqpK/j1gwIDYtLUMUszHQBwUa7v/xNToQWnKv6amJrS1tQEAli5dCsBUEtTW1pZsp3TWgFPW+BivNBRjbfKSJUtC0+XmwAMPTP7du3fvgk4ZP27cOFRWVua09jvqQK8Yj4GodXR04Iwzzkg7Xby/edQuu+wSZbFy4qWXXvKkX3zxxQKVhFJZt25dzmcnjSMG4JS1OEyGQj334x//2JM+8cQT8/r/2Q43N3bbbTcAwI033ljQchTjtNqFPgbiqLm5GfPnz8e9994bmKexsRF33313Mi0iOe+MHAVVDU1T/iVGaJoxYwZqa2tRW1uLGTNmlGztN8AAnHrgtNNOS3a8rKioKNhkKNRzI0eOBFA8NX+HHXaYJ80+GcCQIUNQV1dX0NrvqOTjIlxsx0CUOjo68NRTT0FVMXv27NBa8KqqqmQt+FFHHVUUN15HHnlkaJooHxiAU9aqqqpQX18PAKivry+KEy+lNnLkSNTV1RWk5i9R82Ezq+Shhx7qSTMAL31TpkzxpKdNm5bT7RfyGIib5ubmZK1wV1dXaC04AOy0006orKwsmkqYuMykS+WNAThlJdFsYO7cuejTpw+WLFlStB1y4i4un2vU5bCZVfI3v/mNJ3399ddHUiaKj4suusiTPu+88wpUksKL+lhsaWnB5s2bAQCbN29GS0tLaP5ibHaU6LPEvktUKAzAqUc2btyIvn37ok+fPoF52Ma35/zjK+d6XO24yHRWyThNxEP5M2zYMAC5r/0uRv6ZUmtqanK27fr6ek+AmnjSWSqam5vRq1cvAECvXr3S1vATRYEBOGUl0WygXDpLFFJjY2O3ESXuueeegpRj4MCBnmX+dE9lOqtkuU3EQ8aYMWNQV1dX1rXfgDkW/UHjbbfdlrPtT5s2LdkERVUxffr0nG07DlpaWrBlyxYAwJYtW9LW8BNFgQF4kYlLcwQbiWCdsldVVZWsBd9nn30K9qj36quv9qR/9atfFaQcV155pSd9zTXXFKQcRIVSVVWVrAU//PDDc3pOePLJJz014I8//njOth0H9fX1yae2ffr0KbkafioODMApb0466SRPuqGhoUAlyUzcbnZ23313VFZWFizoBcx404mRbwYOHFiSI24QFYuamhpUVlbirLPOyul2W1paPDXgpVZD3NDQkLzBqKiowMknn1zYAlFZYgBeZBobG5PtZBMqKysLVBo7/p7nP/3pTwtUktwLCtZzGcTHpaPTmDFjABSu9hsALr/8ck/6kksuKVBJiAonqnNCqdcQV1VVYerUqRARTJkypeDnVCpPDMCLkL824umnny5QSewNHz4cQPxrvwFzs3PwwQd7lh1yyCEFKk18xGG8aXbCJIpOOdQQNzQ0YPz48SX53qg4MAAvcsVS+51QXV2Nurq6oqn9Pvfcc0PTCY2NjZg1a5Zn2T333MOOqRFhJ0yi6JRDDXFVVRVuu+22knxvVBwYgBepuro61NXVFVXtdzGqqqrC4MGDAZja77CT9dixY5N/9+/fH3vssUfk5StX7IRJFK1SryFubW3F5MmTsWjRokIXhcoUA3CiNEaPHo3KysrA2m+3sWPHoqKiAjNnzsxDycrX0qVLQ9NE1DOlXkN89dVXo7Ozs9vNPFG+MACnspHthEA2HZ0GDhyI8ePHs/Y7YjfddJMnfcMNNxSoJERUbFpbW7F48WIAwOLFi1kLTgXBAJyIiIjKhn8+A9aCUyH0LnQBiPIl0SHye9/7Hr766qvkcv+UzkREVLoStd9BaaJ8YABOZefmm2/GKaeckkzfcssthSsMZWXYsGFYtWpVMr3ddtsVrjBEJaKpqQltbW0AtvWrqK6uRm1tbUmN6DRmzBhP0J2Y24Aon9gEhcrO2LFjk2PcDh06lO21i1BVVZUnnRhfnohyY926dVi3bl2hixGJSy+91JP2T+xFlA+sAaeyVFtbi0WLFrH2u0j5O021trYWqCREPeeueS4kdy13osP6jBkzClUc6qGOjg5cccUVuPLKK0t2NJtixhpwKmrZTgHP0UqIKG5GjhzpSX/ta18rUElKW7l0wmxubsb8+fNx7733FroolAJrwImIiArIXfM8adKk5N+/+93vClGcklcOnTA7Ojrw1FNPQVUxe/ZsnHzyyawFjxnWgJewbGuHi0lQx6BS6jBE3W2//faeNC8sVCoSteCnn356gUtSuvydLkuxE2ZzczNUFQDQ1dXFWvAYYgBORe/UU0/1pHnhKn1ffPGFJ/35558XqCREuTVy5EjU1dXhxBNPLHRRSpZ/Qrazzz67MAWJUEtLCzZv3gwA2Lx5M1paWgpcIvJjAF7CGhsb0b9/f8+y/v37l1zt8I9//GNPmheu0te7d+/QNBFRkGeffdaTfvrppwtUkujU19ejT58+AMxszvX19QUuEfkxAC9x1157rSd93XXXFagk0eJj2/KyZcuW0DQRUZA5c+aEpktBQ0NDcrjdiooKnHzyyYUtEHXDALzEHXjggcm/+/fvjwkTJhSwNNHhY9vykriwBKWJiIIk2kYHpUtBVVUVpk6dChHBlClT2E8mhiIPwEWkl4i8KSJ/dtLbi8izItLm/N7OlfdCEVkkIh+IyFGu5RNEZIHzWpM4V1sR6SciDznLXxORMVG/n2K02267ASjd2m8qP4ceeqgnfdhhhxWmIERUdI488sjQdKloaGjA+PHjWfsdU/moAT8LwHuu9AUAnlPVWgDPOWmIyD4AjgcwDsBkAL8TkV7OOjMBnAqg1vmZ7Cw/BcCXqroHgJsB/Drat1KchgwZgrq6upKt/abys3btWk96/fr1BSoJERUb/0y6O+64Y4FKEq2qqircdtttrP2OqUgDcBGpAfA9AHe5Fk8H0Oz83QzgaNfyB1V1o6p+BGARgANFZBSAIar6iprnRL/3rZPY1qMAjhA+iyYqefPmzfOkX3311QKVhIiKzf333+9JNzc3B+Qkik7UNeC3ADgPQJdr2Y6quhwAnN8jnOXVAJa48rU7y6qdv/3LPeuo6hYAqwF0u9UTkVNFZJ6IzFu5cmUP3xIRERERUfYiC8BF5F8ArFDV1zNdJcUyDVketo53geqdqjpRVScOHz48w+IQEREREeVelIPnHgLg+yIyFUB/AENE5H4An4nIKFVd7jQvWeHkbwews2v9GgDLnOU1KZa712kXkd4AhgLwztBBRCVHRDwjF1RUcEAnIsoOW66WtqamJrS1tWHp0qUAgOpq04iitra2oPOiRHbVUtULVbVGVcfAdK58XlVPAvAEgAYnWwOAx52/nwBwvDOyya4wnS3nOs1U1ojIQU777p/41kls61jnf5TeeEJE5OE/zLu6ugJyEhF5XX755Z70lVdeWaCSUD6tW7cO69atK3Qxkgoxfdx1AB4WkVMAfALgOABQ1YUi8jCAdwFsAfBzVd3qrHM6gHsBDAAw2/kBgFkA7hORRTA138fn600QERFRcUnUhro99thjWLBgQcnMEp2qxrfQtb2FlHjfZ555JgBgxowZhSxOUl4CcFX9G4C/OX9/DuCIgHzXArg2xfJ5APZNsXwDnACeiIiIKBN9+/bFpk2bMHr06EIXJTJxqu2l7gpRA05ERESUd3GtDc2lcniPpYA9l4io6PTv3z80TUREFGcMwImo6GzcuDE0TUREFGcMwImo6PhHQeHgR0REVEwYgBMRERER5REDcCIiIiKiPGIATkRFpampqdBFICIi6hEG4ERUdHbbbTdP+tvf/naBSkJERGSP44ATUVFJjHE7adIkAECfPn1w7bXd5u8iIiKKLdaAE1FRStSC33DDDQUuCRERkR0G4ERUlIYMGYK6ujpMmDCh0EUhIiKywgCciIiIiCiPGIATEREREeURA3AiIiIiojxiAE5ERERElEcMwImIiIiI8ogBOBERERFRHjEAJyIiIiLKIwbgRERERER5xACciIiIiCiPGIATEREREeURA3AiIiIiojxiAE5ERERElEe9C10AIiIiIvJqampCW1ubZ1kifeaZZyaX1dbWorGxMa9lK4RS+zwYgBMRERHFTFtbGxa+/wGGjto5uWxrrz4AgPbV6wAAq5cvKUjZCqGtrQ3vzH8Lg/tta7yxZVMXAODjD+YDANZs7CpI2bLBAJyIiIgohoaO2hnf/ul/Bb7+4l035rE0hTe4XwUO2Ll/4Ov/WLIhZ/9r7ty5OPfcc3HTTTdhwoQJOdtuAtuAExERERG5XH755ejq6sIll1wSyfYZgBMREREROebOnYu1a9cCANauXYvXX3895/+DTVCIiIiIHKXW2S+VcniPPXH55Zd70pdccglmz56d0/+RUQAuImMBzASwo6ruKyLjAXxfVa/JaWmIiIiICqitrQ0fvPMeqoeOTC7rvdU0GFi75EsAwNLVnxakbLli3uN8jB7aN7ms79bNAID1S94HAHyyelNByhYHidrvoHQuZFoD/v8AnAvgDgBQ1fki8gcADMCJiIiopFQPHYmzDzk58PVbXr43b2WJyuihfXHet6oDX7/+paV5LE28DBo0yBN0Dxo0KOf/I9M24ANVda5v2ZZcF4aIiIiIqJCuvPJKT/qaa3Jf35xpAN4hIrsDUAAQkWMBLM95aYiIiIiICmi33XbzpMeMGZPz/5FpAP5zmOYne4nIUgBnAzg956UhIiIiIiqg5uZmVFSYELmiogL33ntvzv9HRgG4qn6oqt8FMBzAXqr6LVVdnPPSEBEREREVUEtLC7q6zKyaXV1daGlpyfn/yCgAF5FficgwVe1U1TUisp2IsAMmEREREZWU+vp69OnTBwDQp08f1NfX5/x/ZNoEZYqqrkokVPVLAFNzXhoiIiIiogJqaGiAiAAwTVBOPvnknP+PTAPwXiLSL5EQkQEA+oXkJyIiIiIqOlVVVZg6dSpEBFOmTMEOO+yQ8/+R6Tjg9wN4TkTugRkJ5T8ANOe8NEREREREBdbQ0ICPPvooktpvIMMAXFWvF5EFAI4AIACuVtVnIikRERGRJf/U2qmm1QbKd2ptIrJTVVWF2267LbLtZ1oDDlWdDWB2pvlFpD+AF2GaqvQG8KiqXi4i2wN4CMAYAIsB/NBpUw4RuRDAKQC2AmhMBPkiMgHAvQAGAHgKwFmqqk6zmN8DmADgcwA/4ugsRETlp62tDe8vWICdBgwAAPTaZKbR/mrRomSeZevXF6RsRER+oQG4iLykqt8SkTVwJuFJvARAVXVIyOobAXxHVdeKSB8AL4nIbADHAHhOVa8TkQsAXADgfBHZB8DxAMYB2AnAHBEZq6pbAcwEcCqAV2EC8MkwNwOnAPhSVfcQkeMB/BrAj2w/BCIiKn47DRiA08eODXx9ZmtrHktDRBQstBOmqn7L+T1YVYe4fganCb6hxlon2cf5UQDTsa39eDOAo52/pwN4UFU3qupHABYBOFBERgEYoqqvqKrC1Hi710ls61EAR0ii2yoRERERUQylbYIiIhUA5qvqvrYbF5FeAF4HsAeA36rqayKyo6ouBwBVXS4iI5zs1TA13AntzrLNzt/+5Yl1ljjb2iIiqwHsAKAjqEwrVqzo1iYwVVtBthMkKg/+tsMAzwlERJRaptcMwFw3gqQNwFW1S0TeFpHRqvqJTSGd5iN1IjIMwJ9EJCyIT1VzrSHLw9bxbljkVJgmLBg0aBDefud99B00Mvn6ls3mQcB7i1cBADat/TSkmERUSvxth4Hu7YfZdpgyxc6gRKWtra0N77+3ADuNqEwu6yWbAQBfff5hctmyFZ2h28m0E+YoAAtFZC6A5BZV9fuZrKyqq0TkbzBttz8TkVFO7fcoACucbO0AdnatVgNgmbO8JsVy9zrtItIbwFAAX6T4/3cCuBMARowYoX0HjcSIr58SWN4Vb87K5G0RxQYv+j3DtsOUK21tbfjgnfdQPdRU8vTeaip41i75Mpln6WpW8hAVs51GVOL0fxsXmmfmAwtDX880AL8yw3xJIjIcwGYn+B4A4LswnSSfANAA4Drn9+POKk8A+IOI3ATTCbMWwFxV3Soia0TkIACvAfgJgBmudRoAvALgWADPO+3EicoKL/pE8VE9dCTOPuTkwNdvefnevJWlHCQqIJYuXQoAqK6uZmUDxV66UVD6A/gZTBvuBQBmqeqWDLc9CkCz0w68AsDDqvpnEXkFwMMicgqATwAcBwCqulBEHgbwLoAtAH7uNGEBgNOxbRhC93CIswDcJyKLYGq+j8+wbEQlhxd9IipmPe2PsW7dumgLSJRD6WrAm2E6Qf4fgCkA9gFwViYbVtX5AL6eYvnnMBP6pFrnWgDXplg+D0C39uOqugFOAE9ElGDTJIcoTK46XFF62fbHSATjie9jxowZ3fJQPNncdJWadAH4Pqr6NQAQkVkA5kZfJCKinmlra8OC9xegcuRAAMDmCnMR/3DVP5N5Oj+NvraMbfOLX1tbG96Z/xYG99s2au+WTV0AgI8/mJ9ctmZjV97LVorYH6O8tLW1YeGCdzGscnhyWZfpz4ilH64EAKzqXFmIokUuXQC+OfGHM8xfxMUhIsqNypEDMe6UvQJfXzjr/cjL4A/eyj1wK9YhHwf3q8ABO/cPzfOPJRvyVBqi0jKscjiOGHds4OvPLXw0j6XJn3QB+H4i8pXztwAY4KQzmQmTiKjspQveyilwM52F52P00L7JZX23mnqe9UvMDdEnqzcVpGxERPkUGoCraq98FYSo1BVr7R9RLo0e2hfnfas68PXrX1qax9IQERVGpsMQUo7FJRiLSznKQTm3daPiwXMCkR3bTt+ZHl/t7e1YtbYTL951Y+D/XrV8CbCmMvD1QmAn+MwwAC+QuMy+5x8/Gug+hjTHj86dcm3rRsWjra0NC9//AENHbZsXbWuvPgCA9tWm4+rq5UsKUjYqPuUQjLW1tWHBggUYNGgQAGDzZlOz8tFHHyXzrF27Nvl3ulkU082gGHf++MYf2wCcXRhgAF5QcentzfGjicht6Kid8e2f/lfg62E1ckRucRmRKGqDBg3C+PHjA1+fP39bx+t0sygmZlCsqakBVq9LeyzWDB2YRYmjFZf4Js4YgKfRk0dLpXinT0RE8RenITDjMCIRUdwwAE/D32vf32Mf8PbaL4c7fSIiijd/UyJ/MyKATYmICokBeAZseu3zTp/iLspOdnGqdYtKXNq0RtXxqxyUy+x7UTUlissxQFTMGIBTLHDkhfzxdxgCuncacncYst12qde6+Uez8Y9kA2Q3mo3tMRCXjl/FGIxxRKKesX0yTETdMQCnWODIC/ll02HIVqa1bsV80xXFaDbZjEgURcevbMr99jvvo+8gU+4tm02Z31u8Kpln01r7kZSi3j84IlHPcDx3op5hAE5WomxiwJEXyos/cAO6B2/ZBG7FrFhHJOo7aCRGfP2UwNdXvDnLepv+0TOA7v1q2KeGiIoVA3CyYvvYm7KTae0fEK9H+7aiCNyodLBPDRGVKgbgZC3Tx97l0tEpCm1tbXhn/lsY3K8iuWzLpi4AwMcfbGtWsGZjV6TlKJcbAaJyVszNwYhyyX0sLF1qmlFVV1dHsu8zAKfItLW1lfyMX1Ea3K8CB+zcPzTPP5ZsiLQMmXyHAL9HoqhFeTPM5mBE3a1bF20TNwbgRaJYh3eLquMX5U+67xDg90gUtahvhtkcjAie+CkRX82YMSOS/8UAvEhENfQZefFRLBHFFW+GiUoHA/AiwmGzoseRF4iIiChqDMCJfGxGXijGSUiIiIiKTXt7O9Zs7Art+7RmYxfa29sBxL/pLgNwoh5oa2vD+wsWYKcBAwAAvTaZ2vKvFi1K5lm2fn1BykZEROWDFUJe/snN/BObAd0nN8snBuBEPbTTgAE4fezYwNdntrbmsTREVCw4VCvlUlSz0sZFTU0NtnZ+ETo62D+WbEBNTU0yHefJzRiAExERFYB/YjOg++RmnNiMbHA0m+LBAJyIiKhAMp3YjIhKCwNwKkq2bd34mJeIiMhob29H5+qNuP6lpYF5Plm9EZXSnsdSlRcG4CWoHDpi2LZ1++Cd+Rg9tG8y3Xerecy7fokZ0eST1ZuiLjIRERERAAbgJamtrQ3vzH8Lg/uZoHTLpi4AwMcfbHuUuWZjV0HKlks2bd1GD+2L875VHZg3rBaAiIiolNTU1GC9rk17XRzg6tAIbKvgW7rUXDOrq6s5MV2WGICXqMH9KtL2FCYiIiKytW4dJ6TrKQbgRERERJRWoqY70aR1xowZhSxOUWMATkRERCWtHPpGUXFhAE5EREQlrVz6RlHxYABOREREJS/TvlHt7e1Yu3pN6CyJ7as/xSDpzHURqYxUFLoARERERETlhDXgRERERI6amhqs1S9x9iEnB+a55eV7Mahmu/wVikoOA3AiIiIiyrv29nas7vwKzy18NDDPqs6V0PaNqPGNSV7s2ASFiIiIiCiPWANORERFr729HWvXrcPM1tbAPMvWrcNX7e0lV5NGVKxqamogm1biiHHHBuZ5buGjqK4ZnsdS5QdrwImIiIiI8og14EREVPRqamrw1YYNOH3s2MA8M1tbMYS130QUAwzAiago2Mxk197ejs4167Bw1vuB2+tcvg7ta9kcgXLLZgxp7nteNs2IiHLJf30BUl9jcjlTatkF4Js3b8amNcux4s1ZgXk2rVmO9va1eSwVEaXT1taGD96Zj9FD+wIA+m7dDABYv2RbkP3J6k0FKRsRERWvtrY2LFiwAIMGDUou27zZXGM++ugjAMDatbmNCyMLwEVkZwC/BzASQBeAO1X1VhHZHsBDAMYAWAzgh6r6pbPOhQBOAbAVQKOqPuMsnwDgXgADADwF4CxVVRHp5/yPCQA+B/AjVV0c1XsiosIaPbQvzvtWdeDr17+0FIBpjrBp1UaMO2WvwLwLZ72PmmGsgaTc4hjS2WMzIiqkQYMGYfz48YGvz58/P6f/L8oa8C0Afqmqb4jIYACvi8izAE4G8JyqXiciFwC4AMD5IrIPgOMBjAOwE4A5IjJWVbcCmAngVACvwgTgkwHMhgnWv1TVPUTkeAC/BvCjsEL16dMHfQePwoivnxKYZ8Wbs1BTM6wHb52IiEpBe3s71mzsSk5THmTNxi60c4QVIspQZKOgqOpyVX3D+XsNgPcAVAOYDqDZydYM4Gjn7+kAHlTVjar6EYBFAA4UkVEAhqjqK6qqMDXe7nUS23oUwBEiIlG9JyIiIiKinspLG3ARGQPg6wBeA7Cjqi4HTJAuIiOcbNUwNdwJ7c6yzc7f/uWJdZY429oiIqsB7ACgI5p3QhRP7PiVvSg7fmVSe8qa03irqanB1s4vcMDO/UPz/WPJBn6HRJSxyANwERkE4H8AnK2qX4VUUKd6QUOWh63jL8OpME1YPA3sifKpnKfcJQLMMdC5emOyrX4qn6zeiErhDQkR9UzcK6YiDcBFpA9M8P2Aqj7mLP5MREY5td+jAKxwlrcD2Nm1eg2AZc7ymhTL3eu0i0hvAEMBfOEvh6reCeBOABgxYkS3AD1XOPQZFQo7fmUvyo5fmdSexq3mtL29HavWduLFu24MzLNq+RJgTWUeS0VEVFqiHAVFAMwC8J6q3uR66QkADQCuc34/7lr+BxG5CaYTZi2Auaq6VUTWiMhBME1YfgJghm9brwA4FsDzTjvxnLGpsSEKU85T7hIB5hhYr2vTjmQzIEY3JETptLe3Y+3ataGjZKxdu5ZNzfIs7hVTUdaAHwLgxwAWiMhbzrKLYALvh0XkFACfADgOAFR1oYg8DOBdmBFUfu6MgAIAp2PbMISznR/ABPj3icgimJrv4yN8P2lx6DMiKnY1NTXA6nX49k//KzDPi3fdiJqhA/NYKipWfDLs1d7ejrVrOjHzgYWBeZat6MRX64v387DpV1Os7zEXIgvAVfUlpG6jDQBHBKxzLYBrUyyfB2DfFMs3wAngoxJVjU1cZvyyaSNFpSEutTXt7e3YtGYNJ8UiioBtUyKbYzEuT4ZtZseNUk1NDTZv3px2DOmamhq0cxZPcpTdTJgUT2x3SlScbG6k4lLbxQ7R+RPlk+G2tjYsXPAuhlWaZntdZuJCLP1wZTLPqs6VqVYtmJqaGnz1+Sac/m/jAvPMfGAhhuxQvPsdJ1TKDAPwAonLDhr3NlKUeza1NVGXY82WVZwUy2H7NCouTzIonmybEtkci3Fqyz+scnjafjVEuZRJMyJgW1OiIAzAyUpUF322Oy0NNk8yijUoLMba0yjbnRbjjRQ7RBNRoTEAJyKKAdunUWx3SkSUf5k0IwLSNyViAE5WbC/6xdbb26bHftTlSDeDIhD9LIq2j9rK4UlGMdaeFmO707gci3GR1bFIRLHFAJyIiKgAbJr0EVFpYQBeJIqx3Wkx1rrFZSz3TGZQBKKfRTFXj9qIbMXlWIwLHotEpYUBOBERUQHEZUQiIso/BuBFohjbnZYDzvhFREREthiAl6BMOvBF3XmPiIiIqBgUYl4FBuBUlOIy+15cJlQiIqJgrJiiuGEAXoIy6cAXdec9IiIi6pnVy5d4JjZb+/kKAMCgHUYkX68ZumdBylZKCtEfgwE4FSWb2ffa29vRuXojrn9paWDeT1ZvRKWw5oOIqBQVY8VUbW1tt2VtKzYDQHIuhZqhe6bMR/HHAJyIiIgoZhobG7stO/PMMwEAM2bMyHdxKMcYgFPJq6mpwXpdi/O+VR2Y5/qXlmJAjGo+iMjesvXrkyMSdWzcCACo6tfP8/qQgpSMKHq2faM+Wb3J82R4RaepXR9R2QcA8MnqTdhz52jLDJg5TNxznKzdsAoAMKj/sOTr1TAjvPnb8a/b1AUAGNi3Ivl6sWAATkRERc//GP6ztjYAwJA99kguG+Lka3Nei8rS1Z/ilpfvBQCs7PwCADC8cnvP63vuvF2kZSAKk6rZyibnuBiws3ltz51T54u6HG1tqwAA1buZoLsawwPLkTiWd3G9XixNchiAExFR0fM/rg97VJ94LQr+i//ytg4AwCBXwL3nztvl5UagGLmfYgDdn2TwKUYwm75RcWne0tNyFHOTHAbgREREORKXG4FilKrm0v8kY0hAPqJiwwCciEpS56frsHDW+wCADZ+bNoP9d+jveR3DClEyIkolLrWy5NXU1NTtaU0i7b6JrK2tTfkdUmplGYBvWvupp5PClnWfAwB6D9wh+TqvzETxYjOc5OGHH+5Z3rbSXCx2G7b7toXD8tMemIii0d7ejtWdX3k68Pmt6lwJbd8Yq+EFi01bWxsWvL8AlSMHJpdtrtgEAPhw1T8BOBUaRc4/E+b69esBAAMGDEi+nktlF4D3798f++27l2dZW9tKAEDtmGHOkmF8xEVUxNgMgMgOK6YoTOXIgRh3yl6BryeeNhar1J1BTeXMrrvu6smXq0qbsgvAR4wY0e0izEdcRPEX5XCSUXb8cg+b5R8yK/E6USGlDj7iXzFVU1MD2bQSR4w7NjDPcwsfRXXNcOttu0eyAbqPZsORbEqLTfOnXFXalF0ATlSqOPRZdqLs+OVfJ9WQWYl8bApDhRJ12+ti64+R6lj3j2aTGMmG4i3O10UG4EQlgEOfZS/K4INNYajcdbsJDemPERfF3BnU3ZTI34wo8Xqs7nYiZHNdLAQG4Blwzxblnykq8Xo+Zoui4mcz45cN20Av351NgrDdac+4v0f/d5h4PRurly/Bi3fduG07n68AAAzaYUTy9Zqhe2a17VKcfS+Rp5xkel20OTcVM5tjcdmKTsx8YGEy3fGl2beqtuuffH3Itpg5Y92fuPmbEQH5akoUh1lp477vMQBPw7+j+meKArbNFtXW1lZ0j9oof2xm/IqylroQnU0yL0f8253GRVDzFvd3mMhn8z2m/F5WmACrZuhA5/eeOWmSAwTPvpf/fW8VgOxn3wvabiG5b6T8N1GJ17O5kbK5LpYDm2Mxlc++cJq87bCb8zu7zy4uAafNrLTFKt1NVCJP2I0UA/A0bHbopqYmTzrd0GdxmfHLtrNJVLVupc62k0dUtW6F6GzS03JQd7ZPPTKtdcvme8n0sbftvueu0AC6V2pkW6FRDrPvdQsKfTdR5u/sbqTiEujFRU8/j1L7/Ep9/0jZb8h3E2X+Dr/JYACeQz0J1oHCzPhl29nEttYtqkdtpS6sljrftW62d/pR1bpR9jK5YGRb6xbVY++Ux4C/UmNYcdeiRanUgyCiQrG9gT/rrLNSbocBeIHEpfbPthw9vskIuOizY6BXXPaPnt7p57LWrRxENfRZXDqa9mS7udw2EVGhMQCnyNg+bo6y45eNqB57F6NyeFSfDXcHPn/nvcTrtp1pOfQZpZOLdqdEFKypqSlZIZj4feaZZ6K2tjbl9bAnGIAXkSgu+nERZcevHpeDj73JpXuTi1UAtnXeA8I78AVhjS+FyVW7UyLKzMCBA9Nn6gEG4EXC5qJfjM054hJ8xKUcFF9sW0u5lOmQoDw3EUUv17XcYRiAFwlO6EFEVFpshgQlotLCAJyoh+Iw4QARFR/WaueXe2hXTqZEhcYAvESVw4kmDlPulsOEA0RExS5oCN24T6ZEpYsBeAkqhxONzdjDUbaJZ3tgIqL447ma4oYBeAkqhxMN28QTERFRsWIATmXhk9WbcP1LS5PpFZ1miMMRlX2Sr++5c0GKRkRERGWGATiVvFRNbTY5zVIG7Gxe23Pn4m6SQ0RUjkp5fgwqbQzAqeRxpAEiotIT1aRYRPnAAJyIiIiKTjn0d6LSVZE+S3ZE5G4RWSEi77iWbS8iz4pIm/N7O9drF4rIIhH5QESOci2fICILnNeaRESc5f1E5CFn+WsiMiaq90JERERElCuRBeAA7gUw2bfsAgDPqWotgOecNERkHwDHAxjnrPM7EenlrDMTwKkAap2fxDZPAfClqu4B4GYAv47snRARERER5UhkAbiqvgjgC9/i6QCanb+bARztWv6gqm5U1Y8ALAJwoIiMAjBEVV9RVQXwe986iW09CuCIRO04EREREVFc5bsN+I6quhwAVHW5iIxwllcDeNWVr91Zttn52788sc4SZ1tbRGQ1gB0AdERXfCIqJPdwkv6hJBOvczhJIqLcaW9vR+eadVg46/3APJ3L16F9bXvg69RdXDphpqq51pDlYet037jIqTDNWDB69OhsykdEBeYfycA/lCTA4SSJiKg45DsA/0xERjm136MArHCWtwNw11vVAFjmLK9Jsdy9TruI9AYwFN2bvAAAVPVOAHcCwMSJE1MG6UQUbxzxgIgo/2pqarBp1UaMO2WvwDwLZ72PmmE1ga9Td1F2wkzlCQANzt8NAB53LT/eGdlkV5jOlnOd5iprROQgp333T3zrJLZ1LIDnnXbiRERERESxFVkNuIj8EcBhAKpEpB3A5QCuA/CwiJwC4BMAxwGAqi4UkYcBvAtgC4Cfq+pWZ1Onw4yoMgDAbOcHAGYBuE9EFsHUfB8f1XshIiIiIsqVyAJwVT0h4KUjAvJfC+DaFMvnAdg3xfINcAJ4IiIiIqJike8mKEREREREZY0BOBERERFRHjEAJyIiIiLKIwbgRERERER5xACciIiIiCiP4jITJhERERHFUOen3qnoN3y+AQDQf4f+ydcxrBAlS6+pqQltbW1oc2ZPTkziVltb222Ct3xiAE5EREREKdXW1nZb1rbSBLO7DdvdLBiWOl+cDBw4sNBF8GAATkREREQppaolTtQiz5gxI9/FsVbIWu4wbANORERERJRHDMCJiIiIiPKIATgRERERUR4xACciIiIiyiMG4EREREREecQAnIiIiIgojzgMIREREVGMpZpMJmgiGZu8VDgMwImIiIiKgM1kMnGbeIa8GIATERFR0SqHGl+b91JK7zvuerLvMQAnIiKioscaXyqUbPY9BuBERERUtFjjS4XSk32PATgRERERpRVVc5/EdgGUdFMiNwbgRERERJSxKJv7lEtTIgbgRERERJRWVLXRpVrLHYYT8RARERER5REDcCIiIiKiPGITFCIiIioLqToRAijpzn4UTwzAiYiIKDYjUeSjHOXS0Y/iiwE4ERERecQlQM11OVjLTXHBAJyIiIhiE5zGpRxEUWInTCIiIiKiPGINOBGVLHa4olIU1WyElD/l1N6eUmMATkQlLy7tWYlyift1aYjL9xiXcpQLBuBEVLJsa3BYs0hh4rJ/cH8sfnH5DuNSjnLEAJyIyIc1QRSG+wcR9RQDcCIiB2uDKIzN/sH+B0QUhgF4huLy6DFKNu8xm7xA6XbysLnYRvl5xOWiH2U5yuFYtBGXYzEu5cimzFGWo9C15TwWe6Yc3mNU4nI9iisG4JYKfTK1ke3Ob/MebT+PYvr8shHF55HNBcBmu0BmwUdU5chWqe9LtuJyLMalHLZKfcIXHos9E9W5uhyUw/6RDVHVQpchryZOnKjz5s1LGXzU1tZGUitQW1ub/J1JYBNWjmzyLl26FABQXV0dWo646OnnkfidSc1zrj7rKKX6HnO5nwLIaNv5KEehP2sbUe2nttuOUjGWw/azjoNiPQZsFOP3YiuqcyR1F5dzUyZE5HVVnehfzhpwFGdNUCZ547gj2ohLTXyh796j+h5tt5uP/anQn3U24rKfRqkYyxGXMtsq1nJnqpTfX7Ffc4tRMe9PZVsDTkREREQUpaAacE5FT0RERESURwzAiYiIiIjyqOgDcBGZLCIfiMgiEbmg0OUhIiIiIgpT1AG4iPQC8FsAUwDsA+AEEdmnsKUiIiIiIgpW1AE4gAMBLFLVD1V1E4AHAUwvcJmIiIiIiAIVewBeDWCJK93uLCMiIiIiiqViD8AlxbJu4yqKyKkiMk9E5q1cuTIPxSIiIiIiSq3YA/B2ADu70jUAlvkzqeqdqjpRVScOHz48b4UjIiIiIvIr9gD8HwBqRWRXEekL4HgATxS4TEREREREgYp6KnpV3SIiZwB4BkAvAHer6sICF4uIiIiIKFBRB+AAoKpPAXiq0OUgIiIiIspEsTdBISIiIiIqKgzAiYiIiIjySFS7jdpX0kRkJYCPU7xUBaAjw81ElbccyhHltlkOloPliMe2WQ6Wg+WIx7ZZjsKXYxdV7T4En6ryx9yEzCt03nIoRzm8R5aD5SiGcpTDe2Q5WI5iKEc5vEeWo/sPm6AQEREREeURA3AiIiIiojxiAL7NnTHIWw7liHLbLAfLwXLEY9ssB8vBcsRj2yxHPMtRfp0wiYiIiIgKiTXgRERERER5xAC8xIhI0c9uSkRElCu8LlIclV0ALiIHhLz2Y1/6h1lsv7+I7Csi40Skf0i+M2y3HbCdf/UtmtuDbQ0Xke5jVeaYiBwiIr+N+v9kQkT69GDdo0JeO85yW4ekeX0HEfmBiExI8dperr/7+V47yKIM/W3LneF2dxaRc3O93biVQ0TOjiKvk39H2/JEoaflEJGhIa8d4EsPsdjuQPexLCJ7isg5InJMdiX1bPs7rr939b2W0fZFpJeI/FsPyzGxJ+vniojsH/bjy3uS6+9DfK91uwaKyG0icrBleZ4Tkam+Zf62uNbXRRHpLSLTRORc5+dfbAL5OF3nUhGRey3yDheRfVIsH+ePGUSkJmQ706wK2X39p0RkTIZ5M95PU6y7nYgcKCLfTvykyT9CREYnfjJ+P+XWBlxE5gN4GcCFqrrKWbYvgN8B+EJVj3bl/TOA3gD+P1X9MM12ewP4FYD/gJnopwJADYB7AFysqpt9+d9Q1dCdIMP384mqjnal31TVr1usLwAuB3AGAHHKvQXADFW9KkX+PQGcCiAR9L0H4P+p6gdp/k8dgBMB/BDARwAeU9UZrtdnAAjaGTcC+CeAB1R1jYj8Iux/qepNacoiAA53yjNNVXd0vXZZ+Kb1alferQBeBHCSqi71/Y9u36+I9IJ5/9UAnlbVd0TkXwBcBGCA+3tz9r0LnDyjALwBYB6A3QHcqaq3pPpf/v+bbj9zylQP4AQARwH4P1U91pfncABnAtjTWfQegNtU9W8h260CcJyz3WoAf1LV//LlCT2pqeqLrrxh+wdUtbEH5QgNolT1sbDXXdvxHIs9zesEq/8Ks5/urarVrtdOgjl/3+db5z8BdKrqH3zLrwfwoare7lt+DoCRqnp+luVYg9Tfi8AcL54gWkTmAThSVb/0La8HMEtVd3Yt+yfMufPBoLK58r4I4BRVbRORPWACrgcA7ANgrqpemGKdKQAudPIogHcB/FpVn/Lly/j4cm4afg6zrz0B4FmYc+t/AXhLVaf7tv0kwvfr77vyvglgEIA/AnhQVd8N+TzuCdmuquopvvwLAvInvsfxrrx/db0+Aea8JK5tu29YrM5NInIWgOMBjALwEIA/qupbQe/TWedDAEsAPK+qVwb8L9vr4k4A/gpgOYA3nff3dQAjARyuqssC1qtDyHXOyWN1zDjrDAdwPsy+mqzYc3/WvvwHAxgDE78k8v7elyfjGEREHgQwU1Vf8C0/CkCDqp7oWvYBgKNUdbEv73/AHM+7h/yf7wEYB+97vMr1+g8BXAOgGcD1/rjKt63EftofwEQAb8N8xuMBvKaq3wpY76cAzoKJ394CcBCAV1J91iLyfQC/AbATgBUAdgHwnqqOCyqXWzk+ltkfwLkA3hSRqwF8DcBUAL9U1T+7M6rqv4jI0QD+IiJ/ADATQJfr9S9c2W8AMBjArqq6BkiejG90fs6K6P2ILz08LEBNEZyeDeAQAAeo6kcAICK7AZgpIueo6s3JfyTyTQCPAbgDprdv4qT0VxE5RlVf9RRMZCzMyfQEAJ/DnFBFVQ9PUbR5Ie+xN8xB+RiAI2E+54TTnPKkJSLfgDk5/gDA9jAXSn+NaGeKVQcC+CmAHQBc7Vo+H8AfALwqIr9Q1Ufc/y7FdmYB2BkmOGgSkY8BfBMm0P5fX95dVfUd5+9/B/Csqv5ERAbD3EDeEvC//P83VTkSwe+JAL7nlOcQ53+u8+X7HoDbAFwF4Epne/sDuFtEznAHK07ZfuBsdyyAPwHYTVWDakRS1UYrgP1gTn69XMvD9g//e7Mtx6MwJ9q3EpvwlSejABwBn7VNXhEZAOD7MGXfH2ZfPxrmRs/tlwBS3cA8COBvMPul278A2DdF/lth9mNPAJ5pOVTVfSxm4g6Y88WRqrrS+V8nArgWZl90+w6AW0TkFACnq+qikO1up6ptzt8NMIHbmSLSF8DrMIG2+/39J8y54zxs27cmArhORGpU1V2DanN83QfgSwCvwJwzzgXQF8D0gEDyxpD35KGqX3cqQI4H8KiIbMK2YNw/u/Ofu20AGA1zvu+V4rV/sShH8vztBLYpg8BEloC/U6WhqrcCuFVEdoF5n/eIeZKceJ+tKf7HKgBHwJxTnwRwUoo8ttfFX8EEnLd4CizSCOC/YfaxxDKb61w2xwxgbiYfgjlGfub8/5WpMorIfTAVNW8B2Jr4twB+78s6UES+joBzkaq+4Up+zR98O3meEZHf+BafA+BZEZmaOCZF5EKYc8mhQW9QRG6HudYeDuAuAMfC9+RCVR8Wkb8AuAzAPOe9umOym1x/H+5s90EAp6rqAie9L8wNcZCzABwA4FVVPVzME+YrA/JeDROgz3GOz8Nh9oPMqMWsPaX0A3Ni7ALQDmCnNHn3A7AawGKYu9qPYGqT3Hna4DxR8C3vBaAtxfItAL5K8bMGwFcW7+MTX3o5zM55eaqfFOu/CaAqxfLhAN70LZsN4LAUeQ8FMDvF8i4ALwDYw7Xsw6D3ksF7fSpV+TNY71rn+3kO2wLpjzJYbzCAS5zv+9cARvhef8P5PRbAP2Cedgx0v+bL/w6ACufv/gDWwtQ+pvrfb7n+fg7A8ale8/8v//8NKEc7gL8D+DGAwc6ylJ8HTDC3X4rl4wG84Fu23vm+JyWOBZvvG8C3nH3sVZgnE9nuJ1blgAnWH4QJxC5176+W//eTnuSFucgugblROxLm3BH0vcwP2Xa31wAsDMm/sAfluAXmQhl6DvWt82MAC2BqOc8G8D6AMSH5JwP4DCaofCLxE/SeYW5Qj3al306xzXcBbJ9i+Q4wNVhZHV8AFrj+7gUTjA/Odl9O8znuBxMM/hPAyyH5doMJaFoBnA6gb4o8B2VZhm7nl2w/u5BtfB3mOrU14PU3XX+f7Oxb7b48ttfF90PK84EvnZPrHIBhMDXEqV573fnt3s9fCMj7HlLEIinyrQHwPExNv//neV/e1kw/D2fZEQAWwdz03+Ick9ulKc983+9BAFpS5OvrfJfvwwTGgd+jk/+tTJa5XvtHIg+AfmH54cx8CVO7nri2z830Oy+7GnAR2R2muclWAHsDmALgRRG5VlXv8eXtBxOAHQvg39RXQ+6j6nz6voVbRaTbcpgTdUaPxNI8HvS3yVyuKZqOhOijqh3+haq6Urq3j95dUzQ7UNUXpHubO8A8tj4epsbraZggJ6jm7x6keWSqqlNTvRawjtupAD6AeYLxZ1XdEPCdJMqyPYBfAPg3mEdd+6vvkbmvcK3O04FrYJ6s/CQg6yZV7XLW2SAirar6aUDeJSJyJkywvD+Ap52yDQDg/15qRKQJ5rNN/A0nXY3u/gemNvNHALaKyOMI/hxHqurb/oWqOl+6twe+COb7ngngDyLyUMA2PUTkCJjAVwH8SlWfTZEn40f1tuVQ1T8B+JOIVAKYDuA3IrIDzMXQ/8g17PHxAIu8A1Ms3xcmYHsPJgAIOncAQB8RqVRVzxMbp/a/b4r860SkVrfVEify18LcsGRbjkUAjgFwo4gA5sbuZef324n93U1V7xORDTBB1ScADlHVz1Nt3KnxPQ/A/wH4LVy1XT7zReRGAEsB7AGgxVl/WEB+Ue8TzETZPnfeh9tuIvIEzPeW+BtOeldf3uQjcedz+0idJ6IpC2GaRAZSV9MP33oVAEbAnP8rkaI2VET2BnAxTAB7A4CfqeqWgH/1O5jzDETkFVX9Zli5LOzlvEcBsLvr/QrMjUFKzrVnMsxxfARMgBtUC5lsVqWq9zr/4+e+PJ9aXhf9x4TbOl864+scAIjIzjDnu50A/C/M06qrAfwE3Z9cJST2q+XOU8llME8JU3kHpqnM8pD3AACLNPzphVubU6Ptb541BUC35rmq+pyInAxTgfN3AEeo6oY0/yPxma9zmgB9Dt/xJSKTAdwEcxO+v/qe2AZ4T0TuAnA/zPn4JJhzW5B257zxvzA1+V/CfN6prBKRQTBPBh8QkRUwlasZKbsAHMAzMI/8H3XSH4jIwwBuEpGfqqq7o8h8mGBlf1UNOyAB4F0R+Yl2b2d1EsydWk9k/HgQdo/BAWCTxWuBFxKkaLrhC2yOhnk0taOIzIRpi9viym77yNTGSGxr53yL0zZsgIj09l+QROQGmIDiTpjHbmtDtpv8rJ3tXOCcgP8I8wTBby/fBWh318VJfRfbU2CafXwXwI/U6a8A87jLc6MIb1MOf1ONbk03VPUsMZ0AE4/LbgAwREz7uqd87zlVk5yUr6lprnSz04TpBJgT2E4icj7M9+15fOxcSC6Gebp0saq+HPK/Eo/qBcD/g3mSkZJtOVw2OGX5Cmbf69aJWi0eH9vkdfLv5zzuPBHAHBFZCWCwiIxMcaM2C6YZwunqtLUU0znpt85rfpcBmC0i18A0yQBMk4sLYY6xsHKsCCqHqt4G00QJYvoqHALgYJhjfQQAfxvwRGVC4iZkB5jAJVVb4+tgmsH8UlVnB35wxn/CPDoeA6DedWHeB6mbeXwlIvv5by5FZD90P89Nd/3t35Y/vZ+IfJXYHMx55isEt+/tgvk8/gDgSYQHfhCRSTD79NEwgdaDAM5R1dW+fI/AfL83wnwXW2GOcQDdmk8mypoQOHiAs213fwz3DX9i2+7+GHuHbSvFto+EeX//AuA1mPd3qv9G0/f/kk0QnWvNPjDnfE82m3IAGCqp+4YIfPu05XUOME1BXoCJLSbDPPFbCHO9CaqQuUZMX4xfApjhlOEcT8G2VVIMholH5sL0n0qU011JYescAH92rhHu88c34YtPXBUPAqAfzA3UCtcxHtS5+s9O4HsDTJ8nhTnXu10E4FgN6f+Qwr/DPPk5y0m/CFM5k5Kq/sD58wonVhgKpwIshekwx+w5MBV2Q2Gu2xkpx06Y++q2trX+176rqnNc6X0y/aJFpBqmreh6mB1UYdoRDQDwA+3eSe8iVf1Vhts+SH3tq0PyHghgB/8FS0xngaWq+rpv+VakDrIEQH9VdY8ssALmhJgq7w/V1ZkxpHzbwzxROD7o7tsJnC6CaeN6M0znrE2u190X8d1hauES5fAHsv5t94c5YZwA0+ThOfV2IOmCOWltgfek3e3kISJHa/e224lat5+p6nW+5bsElQtm4x+78nYLDlyvna6qgScQW05t0xSYWpx6Va1yvbYK3dsfA+bz+Jaqbpdm21+D0ylJfZ1vnM+6HebxXaqnRykvGGLZocpVjhNgbmb85UjciBwIYA5MW9OUbc6d/TdQqlpV3/qJi/SJqupv8+zPO9Ep13Ewj9QP9r3+M5gAepCzaC2A64L2DTFtH8/FtrbgCwHcoE7byDTlOBHmuE1VDoHpS3MwTAC+D0yN7CvqdIpz5bU5Bv4C4F9T1Zz19BgQkW/BNLW5B97zdQNMp+qXst12FmXZC+Z7ngbTNOYPMI/e/ZUDS2CeGDwI4GFV/Sxkm4ux7ZhK/HZ3lNzNl/9tAIfBdMJ/3vnbXcHwhStvQ9j7UdXmFOUZBqDWSbb6bxhc+f4K8/7/J92x5FqnL0w/rhNhAtr/gen8+KQrz1Mw3/f/hgXzrvz+Sg4PVf33NOtvD3Pc/sh/nRORt1V1P1f6MwCjVXUjekBEDg17Xbs/zatPcXMQtv1+MJ+x+/zxhwxqttNt9y8w33nyu3H+V/8UN5ZNME/D/u6PqXpKzChMVZnGTjn5n2UYgNv0/E3bLCLFOt+B6TAoMG0rnwvYtn9UBwXQAeCv/pO/eHuShz4eFJG/AThZu/dA3gNm9IxMHzml2rbVidd2h5buj0zv91+EnHwZX8Sd/H+BOfk+7j75iukk+4NUF4xsZRJgiRnKbBzMd/6ephhhR0zP/uNSfEZXwrSPdvfwt95PQ8o/QF1Pe2xP6jay3bbNMZxi3d+o6i99y7pgnna9BPM5ej5Ld42eiHyEbTd/KYrsDWycddIGCE6+ETA3nns45blOVb9yAtxvh3weg2DO5WFPqDImIrfAaUbivsilKoeIPAtTG/cWTE3eq6oa9ng3sV6ujwHrphwiMhLA/wfX+RrAb/21kNls27e+zU3Xj2CeYvxaVW/wvbaL/9wWsp2M8zr5F8PUxme8X2e43b4wTxOPhulLIzAjRfwJppJiky//rTDHYdoAy1VbfhRMu+WHYEbvGpMi73Rsa87yV5inlE/5/382bG/KXTc7ic/6r+50qhsP/1MGx2qYNsiP+/KmPbacfEH7dWBFVqY3UinWCzwGbL4bMUNXHuz8ACFN3myPW5vYSUzH8O0Tx6iILIV58iAAzsu0cqAcA/CMa8+k+xjbgKtZhLpGVQgJNqcBWJbiIpIqmN0eZviih9Q7zFyyzOnKLyILVPVrAa957rydZT2q0QtjuUO7H5k+jG29twPLYXGiSRzg34Wp3Qk7wK0/D4sAawhMZ6iJMAGLwHSkeh1mCLWvXHknAHgEpu/BK07wMxOmw+fRvrwZ76dO/h4FFEFydVIK2Lb7e/FcsJwyZ1pb1m34PzFtFcPal2d1g2YTIDj5n4bZF16EeUozWFVPDsg7Daaz0sdO+jKYtqgfAzhLnRGNXPkzvkmzvMjdAbMPr4MJwF+Bqfnu1q/EyR/VMfAWQppy2ASjKcpsve1MzwlO3mqY89MPYNrePwzTdGGtL5/Nd5iTYW5TsSzHVTBPKX+m20YHGwxzk/Gxql7q27bNvtcFUxt6sm4bwevDsJsF2Ta6z/EwzSeeghkx51lfvoyPL9ub8mxudsT0sdoL5niAU5aFMKNqfaiqZzuf6yyYoSHfRsix5WzzLWS4X7tupKbD3EhVIORGyrVORseAkz+j78aV393k7fswgyS4n1Bn/P6c/BnHTiLyDwCT1em7kojLxDxhb1HV0CF2k9spwwA8qBkFgG7t19zrpWsW8TfkoObZ2Qn/rt4xoW0eDy5S1T0Ctt3tNZuTh21Nq+UOvdi17dBHpjYXcd//THuAW34etgHWvTAj6VyVuJA4QUVi5I2f+PKPhznB/RymjSsAnBBWa5NuP3XyvIXMT7wZB+u2JyXLbVvXPKciIkvUNdZ0lGwDBBF5S1XrXOnAIMr57A5S1XVixpK/CWZf/DpMrfFRvvxWN2mu9dwXuekwlQxDU+QbAtM/4WDn93AA76hqgy/fvYjoGJAMm3I4ea1q/zLddhbnhBdgblIfhhkO03Mj6Tu321QIhVbU9IRlOd4BcKB2H950EMzTklRDYybypAuwvg5zLj8WpiPggwAuU9XQJ6Su9cfDdLIfr6q9fK9ZHV9RE5HnYZoHbnHSvWE6Gh8JM6DDPrbHlvN6pvt1xjdStsdAwPsN+25smrzZnBMyjp1E5HVVneBKJ5sUi8g/VDVwwkcPtRwqp9h/YO5gG4J+UuTfG6b37EKYIY56B2x3Qcj/7DYMVpoyvulLL4Y5wXyU4sc/HOLtMMPuiW/5lTA3Aj357P41xc85zmfaniL/opBtBb6WQTnuBXAFnGF/nGUC09Hs9xluYzxChrbKcBuJ4ad2dS0LG+6u23CUQa/BPA3ZHqadegfMxaUqsTzb/dSVPzG26RvOelNTrQNzg/MmTNvhvWBqPZI/vryv+9IXuf7+R0+2bfm9bB/ws0PAfnoPgLsDfmb1oBxfhxm+8p8wE7KcAnOxCsr/NoDtXOX1pP15XX/fDeB8Vzrd0HCZDEsnzjHyM5ixrV+H6Yh0ecA2+zn76vkAHocZNrDbOTHKY8C37o+cdc4NeD3rfS9s27A/JyyG61zu+ul2brf5DmEmBWkK+sl2n86iHGHDZaa8Ztrue846h8B0Bl4OM5TpqQH5doSZVOxl53P+NYC6VMditsdXFD8wo3gNdaWHwhkqEU68YHNsZbFfvwNniF3f8kEwN9pZHwM23w3MefQ1mPkE/h1mYrBMP8N054SMYycExC8wlaQZD0FZjqOgfK4ZPlIWu57kA7ptYJvKDP9fb5gxctvdy9XizhGml/RdABY5NZ2AqR2eh5CRIzKhqv/jKqu7pvU6pB55YY6IXAvgEnX2TmfdK2Fq8rN1iPoezTvbv0pE2lKvAogZNu+H2DbT2iMwB3G2JjjbmiOmveqDCB+xJVXtbZBExzDAjMrwDZhJCcRZ7q6Jtx3xAKr6PpyxU8W0O/09zAnvBl++Olctwh8QXosw1LduokagAib49ZfBZts2Ep9dqs871dODSEbgUdU3YYK888VMw30CgL4iMhumiYF/6M6hMGV3l/uNxObgHbZNnFrEdTBtJ3/nei3lKBaS4bB00r1d9680oF23iNwMUxNV6+T/O8zFsUG3jdzjWSXVdgJkfAw4ZfE35TgHpva8G9t9z2LbVucEy3N7xt8htg0GEAmLcqiIbIfU33uXf4HNvuf5J2YEpZfFTJRzJMx3kDy+xEy8dALMbL6PwTSJCxt1yfr4itj1AN5ynrQLzHX3V2LaVicGjrA5tswKme/XXZpiyD9VXSvdhyi1OgYsv5sPYeKZWphhCjtEZKUGN3nL+JwAu9ipRUSuUdVLfMuvgjMEakZs78SK/QfmsVemeReje+1ETmqe4Uy44/v5DOZRZMaTWoSUfTeYxy7TYGYBzNXnl3FNK8yNxx9hagD/x/lZBHNADupBGcJq1lNNevSfMAH/UpghnA6JYL9KWwMD80jtshT7yKUA7uvB/854P3WtUw1zwnkJphnKjzP5ThBeS/I7ANekWH4NgNt7su18/SCD2uEebr8C5tHsPT3czn84x9IbAJ52Lf86zMg+/vyPOPvDz2Gah3ieDvjy3gET/DwPMyPgNKSYrMvJ2whz89crw3JHdQy84HwWF8B0Yg18f7b7XrbbzuScYPkebb7DyGppLcuxGBk+vbXd9yzLfA/MULQVGea3Or7y8QNTaTQdpjNjtxjB9tiy2a/R/emc+yfwCX8mx4Dtd+OsM8RZ5wqYpyOvA2jO9v351ksbO2FbbLMIPYhtyq4NeFScO9G7YIYye8tZnLx70vDxpIuCZNFR0llvN5jOkoAZGSZlZ0mLcjTDBPVXq2sHFpFLAYxV1R/78t8Dc7DM0RQTg+SSU9t7JMwwi//ue20IzJOC/WH2EcW2Wd5O0Qx7lOegjBm3O3XyZ9pJLHEMHABzwgbSHAOZbjtqKWr0Uo7AEyfOZzcCrs5pTrvZPqr6iS/vYlgMS+esk1G7bssyR3IMBLw/YFub7lTvL9P92nrbvvUDzwk2bL5DEXlVVQ/K9n/lqhw9+B853/eyKEPGx1cc2B5bNvu19HCUnFwdA67t9YO5ziT6CBwEYIW6+pz19LjNsBzu2OZdVf2n1foMwHMr18FmnOTjxJthOWIRyGZLzGys+wDJoSqtDtoc/P/FyPzEaxWsO+tkdFLKZttRyPbGstRlcpHrwbYLfQzEYt+j1KLc90pdoY+tKAU0efs7zMAVqwpXsuwwAKeiVconmriIshYhHzUUPShH3m8s46LULnKpxGXfI69y2Pcoe04b/7/DdDzdmi5/3DEAJyKipFK7yFHx4L5H5YQBOBERERFRHlUUugBEREREROWEATgRERERUR4xACciIiIiyiMG4EREREREecQAnIiIiIgojxiAExGVIRG5WEQWish8EXlLRL4hImeLyMAM1s0oHxERpcZhCImIyoyIfBPATQAOU9WNIlIFoC/MGMwTVbUjzfqLM8lHRESpsQaciKj8jALQoaobAcAJpI8FsBOAv4rIXwFARGaKyDynpvxKZ1ljinz1IvKKiLwhIo+IyKBCvCkiomLBGnAiojLjBMgvARgIYA6Ah1T1BX/Ntohsr6pfiEgvAM8BaFTV+e58Tu35YwCmqGqniJwPoJ+qXlWAt0ZEVBR6F7oARESUX6q6VkQmAJgE4HAAD4nIBSmy/lBEToW5VowCsA+A+b48BznLXxYRwDRleSWqshMRlQIG4EREZUhVtwL4G4C/icgCAA3u10VkVwD/BeAAVf1SRO4F0D/FpgTAs6p6QrQlJiIqHWwDTkRUZkRkTxGpdS2qA/AxgDUABjvLhgDoBLBaRHYEMMWV353vVQCHiMgezrYHisjYCItPRFT0WANORFR+BgGYISLDAGwBsAjAqQBOADBbRJar6uEi8iaAhQA+BPCya/07fflOBvBHEennvH4JgNb8vBUiouLDTphERERERHnEJihERERERHnEAJyIiIiIKI8YgBMRERER5REDcCIiIiKiPGIATkRERESURwzAiYiIiIjyiAE4EREREVEeMQAnIiIiIsqj/x/tTpCv+a9B/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[12,6])\n",
    "sns.boxplot(data=car_data, x='State', y='Price', palette='muted')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vDFdPDPSe57D",
   "metadata": {
    "id": "vDFdPDPSe57D"
   },
   "source": [
    "Through this we can find we can easily get rid of few states only which have very less price else mostly all the state are havign decent price"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "F4DX1FdSeVfO",
   "metadata": {
    "id": "F4DX1FdSeVfO"
   },
   "source": [
    "#Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5p2xQeOuOelQ",
   "metadata": {
    "id": "5p2xQeOuOelQ"
   },
   "source": [
    "Importing the encoder module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "R3Rc5LysSWqX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R3Rc5LysSWqX",
    "outputId": "3f93211d-0be6-4add-b6f8-000b7a0d6f66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: category_encoders in c:\\users\\ankit\\anaconda3\\lib\\site-packages (2.4.0)\n",
      "Requirement already satisfied: patsy>=0.5.1 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from category_encoders) (0.5.2)\n",
      "Requirement already satisfied: numpy>=1.14.0 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from category_encoders) (1.20.3)\n",
      "Requirement already satisfied: pandas>=0.21.1 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from category_encoders) (1.3.4)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from category_encoders) (1.7.1)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from category_encoders) (1.0.2)\n",
      "Requirement already satisfied: statsmodels>=0.9.0 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from category_encoders) (0.12.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from pandas>=0.21.1->category_encoders) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from pandas>=0.21.1->category_encoders) (2021.3)\n",
      "Requirement already satisfied: six in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->category_encoders) (2.2.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install category_encoders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Bwn7YrxbOjdy",
   "metadata": {
    "id": "Bwn7YrxbOjdy"
   },
   "source": [
    "Using binary encording for categorical vaiable as our data is nominal and it suit best "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "CvWR-3kGIp8t",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "CvWR-3kGIp8t",
    "outputId": "3673b39c-1ce4-4fa0-8229-313e97f7b990"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "      <th>City_0</th>\n",
       "      <th>City_1</th>\n",
       "      <th>City_2</th>\n",
       "      <th>City_3</th>\n",
       "      <th>City_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Model_2</th>\n",
       "      <th>Model_3</th>\n",
       "      <th>Model_4</th>\n",
       "      <th>Model_5</th>\n",
       "      <th>Model_6</th>\n",
       "      <th>Model_7</th>\n",
       "      <th>Model_8</th>\n",
       "      <th>Model_9</th>\n",
       "      <th>Model_10</th>\n",
       "      <th>Model_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8995</td>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>8</td>\n",
       "      <td>4465.625000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10888</td>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>9</td>\n",
       "      <td>2178.444444</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>8995</td>\n",
       "      <td>2013</td>\n",
       "      <td>48851</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10999</td>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>8</td>\n",
       "      <td>4990.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14799</td>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Year  Mileage  Year_used    Condition  City_0  City_1  City_2  \\\n",
       "0   8995  2014    35725          8  4465.625000       0       0       0   \n",
       "1  10888  2013    19606          9  2178.444444       0       0       0   \n",
       "2   8995  2013    48851          9  5427.888889       0       0       0   \n",
       "3  10999  2014    39922          8  4990.250000       0       0       0   \n",
       "4  14799  2016    22142          6  3690.333333       0       0       0   \n",
       "\n",
       "   City_3  City_4  ...  Model_2  Model_3  Model_4  Model_5  Model_6  Model_7  \\\n",
       "0       0       0  ...        0        0        0        0        0        0   \n",
       "1       0       0  ...        0        0        0        0        0        0   \n",
       "2       0       0  ...        0        0        0        0        0        0   \n",
       "3       0       0  ...        0        0        0        0        0        0   \n",
       "4       0       0  ...        0        0        0        0        0        0   \n",
       "\n",
       "   Model_8  Model_9  Model_10  Model_11  \n",
       "0        0        0         0         1  \n",
       "1        0        0         1         0  \n",
       "2        0        0         0         1  \n",
       "3        0        0         1         0  \n",
       "4        0        0         1         1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding\n",
    "import category_encoders as ce\n",
    "\n",
    "colus=car_data.drop(['Price','Year','Mileage','Year_used','Condition'], axis=1)\n",
    "\n",
    "nums=car_data.drop(['City','State','Make','Model'], axis=1)\n",
    "\n",
    "ce_binary = ce.BinaryEncoder(cols = ['City','State','Make','Model'])\n",
    "\n",
    "# fit and transform and prest, you've got encoded data\n",
    "\n",
    "dat=ce_binary.fit_transform(colus,nums)\n",
    "\n",
    "\n",
    "result = pd.concat([nums, dat], axis=1)\n",
    "result.head()\n",
    "\n",
    "#car_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "Utmo4Lo3c-ki",
   "metadata": {
    "id": "Utmo4Lo3c-ki"
   },
   "outputs": [],
   "source": [
    "car_data_enc=result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "zTgp_6aSGY9z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "zTgp_6aSGY9z",
    "outputId": "e4cb92fb-5665-417c-9129-ea07b8d06b2e"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "      <th>City_0</th>\n",
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       "      <th>City_3</th>\n",
       "      <th>City_4</th>\n",
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       "      <th>Model_3</th>\n",
       "      <th>Model_4</th>\n",
       "      <th>Model_5</th>\n",
       "      <th>Model_6</th>\n",
       "      <th>Model_7</th>\n",
       "      <th>Model_8</th>\n",
       "      <th>Model_9</th>\n",
       "      <th>Model_10</th>\n",
       "      <th>Model_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
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       "      <td>826724.000000</td>\n",
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       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "      <td>826724.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21008.595685</td>\n",
       "      <td>2013.435411</td>\n",
       "      <td>49866.119674</td>\n",
       "      <td>8.564589</td>\n",
       "      <td>5437.932315</td>\n",
       "      <td>0.040442</td>\n",
       "      <td>0.202392</td>\n",
       "      <td>0.296159</td>\n",
       "      <td>0.404656</td>\n",
       "      <td>0.448436</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466758</td>\n",
       "      <td>0.591144</td>\n",
       "      <td>0.457941</td>\n",
       "      <td>0.416815</td>\n",
       "      <td>0.522460</td>\n",
       "      <td>0.476856</td>\n",
       "      <td>0.511226</td>\n",
       "      <td>0.468657</td>\n",
       "      <td>0.487809</td>\n",
       "      <td>0.533538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10502.912427</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>35565.529267</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>2788.801696</td>\n",
       "      <td>0.196993</td>\n",
       "      <td>0.401783</td>\n",
       "      <td>0.456562</td>\n",
       "      <td>0.490826</td>\n",
       "      <td>0.497334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498894</td>\n",
       "      <td>0.491623</td>\n",
       "      <td>0.498228</td>\n",
       "      <td>0.493032</td>\n",
       "      <td>0.499496</td>\n",
       "      <td>0.499464</td>\n",
       "      <td>0.499874</td>\n",
       "      <td>0.499017</td>\n",
       "      <td>0.499852</td>\n",
       "      <td>0.498874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13495.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>23844.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3426.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18597.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>39884.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5234.857143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26990.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>69915.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7209.279545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60987.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>165156.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>31374.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price           Year        Mileage      Year_used  \\\n",
       "count  826724.000000  826724.000000  826724.000000  826724.000000   \n",
       "mean    21008.595685    2013.435411   49866.119674       8.564589   \n",
       "std     10502.912427       3.215227   35565.529267       3.215227   \n",
       "min      1500.000000    1997.000000       5.000000       4.000000   \n",
       "25%     13495.000000    2012.000000   23844.000000       6.000000   \n",
       "50%     18597.000000    2014.000000   39884.500000       8.000000   \n",
       "75%     26990.000000    2016.000000   69915.000000      10.000000   \n",
       "max     60987.000000    2018.000000  165156.000000      25.000000   \n",
       "\n",
       "           Condition         City_0         City_1         City_2  \\\n",
       "count  826724.000000  826724.000000  826724.000000  826724.000000   \n",
       "mean     5437.932315       0.040442       0.202392       0.296159   \n",
       "std      2788.801696       0.196993       0.401783       0.456562   \n",
       "min         0.250000       0.000000       0.000000       0.000000   \n",
       "25%      3426.333333       0.000000       0.000000       0.000000   \n",
       "50%      5234.857143       0.000000       0.000000       0.000000   \n",
       "75%      7209.279545       0.000000       0.000000       1.000000   \n",
       "max     31374.600000       1.000000       1.000000       1.000000   \n",
       "\n",
       "              City_3         City_4  ...        Model_2        Model_3  \\\n",
       "count  826724.000000  826724.000000  ...  826724.000000  826724.000000   \n",
       "mean        0.404656       0.448436  ...       0.466758       0.591144   \n",
       "std         0.490826       0.497334  ...       0.498894       0.491623   \n",
       "min         0.000000       0.000000  ...       0.000000       0.000000   \n",
       "25%         0.000000       0.000000  ...       0.000000       0.000000   \n",
       "50%         0.000000       0.000000  ...       0.000000       1.000000   \n",
       "75%         1.000000       1.000000  ...       1.000000       1.000000   \n",
       "max         1.000000       1.000000  ...       1.000000       1.000000   \n",
       "\n",
       "             Model_4        Model_5        Model_6        Model_7  \\\n",
       "count  826724.000000  826724.000000  826724.000000  826724.000000   \n",
       "mean        0.457941       0.416815       0.522460       0.476856   \n",
       "std         0.498228       0.493032       0.499496       0.499464   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       1.000000       0.000000   \n",
       "75%         1.000000       1.000000       1.000000       1.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "             Model_8        Model_9       Model_10       Model_11  \n",
       "count  826724.000000  826724.000000  826724.000000  826724.000000  \n",
       "mean        0.511226       0.468657       0.487809       0.533538  \n",
       "std         0.499874       0.499017       0.499852       0.498874  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000       0.000000  \n",
       "50%         1.000000       0.000000       0.000000       1.000000  \n",
       "75%         1.000000       1.000000       1.000000       1.000000  \n",
       "max         1.000000       1.000000       1.000000       1.000000  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_data_enc.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flUbMIhxiJRe",
   "metadata": {
    "id": "flUbMIhxiJRe"
   },
   "source": [
    "Using this reg plot we got information regarding how price and year is correlated with positive slope and negative intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43OwpBRmfrAt",
   "metadata": {
    "id": "43OwpBRmfrAt"
   },
   "source": [
    "\n",
    "# Spliting data file into Train and test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "g_cCep1CvrrJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "g_cCep1CvrrJ",
    "outputId": "8840680c-014b-4219-a8b0-fd65c73fa5ef"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Mileage</th>\n",
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       "      <th>City_4</th>\n",
       "      <th>City_5</th>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
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       "      <td>9</td>\n",
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       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
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       "      <td>22142</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage  Year_used    Condition  City_0  City_1  City_2  City_3  \\\n",
       "0  2014    35725          8  4465.625000       0       0       0       0   \n",
       "1  2013    19606          9  2178.444444       0       0       0       0   \n",
       "2  2013    48851          9  5427.888889       0       0       0       0   \n",
       "3  2014    39922          8  4990.250000       0       0       0       0   \n",
       "4  2016    22142          6  3690.333333       0       0       0       0   \n",
       "\n",
       "   City_4  City_5  ...  Model_2  Model_3  Model_4  Model_5  Model_6  Model_7  \\\n",
       "0       0       0  ...        0        0        0        0        0        0   \n",
       "1       0       0  ...        0        0        0        0        0        0   \n",
       "2       0       0  ...        0        0        0        0        0        0   \n",
       "3       0       0  ...        0        0        0        0        0        0   \n",
       "4       0       0  ...        0        0        0        0        0        0   \n",
       "\n",
       "   Model_8  Model_9  Model_10  Model_11  \n",
       "0        0        0         0         1  \n",
       "1        0        0         1         0  \n",
       "2        0        0         0         1  \n",
       "3        0        0         1         0  \n",
       "4        0        0         1         1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=car_data_enc.drop(['Price'],1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "312162cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.80952381 0.21628691 0.19047619 ... 0.         0.         1.        ]\n",
      " [0.76190476 0.11868532 0.23809524 ... 0.         1.         0.        ]\n",
      " [0.76190476 0.29576569 0.23809524 ... 0.         0.         1.        ]\n",
      " ...\n",
      " [0.95238095 0.01494995 0.04761905 ... 1.         1.         0.        ]\n",
      " [0.9047619  0.17520935 0.0952381  ... 1.         1.         0.        ]\n",
      " [0.95238095 0.01816519 0.04761905 ... 0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "#scaler.fit(X)\n",
    "X_check= scaler.fit_transform(X)\n",
    "print(X_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "HPyLwoUl0yfH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HPyLwoUl0yfH",
    "outputId": "982b6040-9117-47d9-bd6c-8f1ba6eb6a9b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     8995\n",
       "1    10888\n",
       "2     8995\n",
       "3    10999\n",
       "4    14799\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=car_data_enc['Price']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "SMyzE2hX1EMT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SMyzE2hX1EMT",
    "outputId": "5dad1282-0227-4176-a97d-74a3eeec0e97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(744051, 40)\n",
      "(82673, 40)\n",
      "(744051,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X_check,y, train_size=0.9, test_size=0.1,random_state=95)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hVYB2zQ4Ov78",
   "metadata": {
    "id": "hVYB2zQ4Ov78"
   },
   "source": [
    "# Normalise the value using min max scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2ojfLsiP13_b",
   "metadata": {
    "id": "2ojfLsiP13_b"
   },
   "outputs": [],
   "source": [
    "\n",
    "#from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#scaler = MinMaxScaler()\n",
    "#scaler.fit(X_train)\n",
    "#X_train_std = scaler.transform(X_train)\n",
    "\n",
    "#scaler.fit(X_test)\n",
    "#X_test_std = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2046c2fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\ankit\\anaconda3\\lib\\site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from xgboost) (1.20.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from xgboost) (1.7.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kJkWzG85O8Kv",
   "metadata": {
    "id": "kJkWzG85O8Kv"
   },
   "source": [
    "#Xtreme gradient boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "eIRQ_eUqMG92",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eIRQ_eUqMG92",
    "outputId": "9be2feca-3eb8-42b3-f53b-08f40f5c2869"
   },
   "outputs": [],
   "source": [
    "# importing the XG boost model\n",
    "import xgboost as xgb\n",
    "\n",
    "# Making a data matrix that Xg model undesrstands\n",
    "data_dmatrix = xgb.DMatrix(data=X_check,label=y)\n",
    "xgbr_params = {'n_estimators': 450,\n",
    "          'colsample_bytree': 0.3,\n",
    "          'learning_rate': 0.1,\n",
    "          'max_depth': 10,\n",
    "          'alpha': 10,\n",
    "          'objective': 'reg:squarederror',\n",
    "              'eta':0.01}\n",
    "#Changing the xg model parameters for modelling\n",
    "xg_reg = xgb.XGBRegressor(**xgbr_params)\n",
    "\n",
    "\n",
    "# trainning the model \n",
    "xg_reg.fit(X_train,y_train)\n",
    "\n",
    "# predicting the y test and y train\n",
    "y_test_preds = xg_reg.predict(X_test)\n",
    "y_train_preds = xg_reg.predict(X_train)\n",
    "\n",
    "train_acur=xg_reg.score(X_train, y_train)\n",
    "train_acur=train_acur*100\n",
    "\n",
    "\n",
    "test_acur=xg_reg.score(X_test, y_test)\n",
    "test_acur=test_acur*100  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "K6YbzYTSan0m",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K6YbzYTSan0m",
    "outputId": "57ea512b-5b6d-4db4-d5e6-33b0392bee4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGModel Accuracy Train: 92.4122 %\n",
      "XGModel Accuracy Test: 90.0160 %\n",
      "Train - RMSE: 2893.065154\n",
      "Test - RMSE: 3319.364138\n",
      "NRMSE Train: 0.048634\n",
      "NRMSE Test: 0.055800\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# printing the model accuracy \n",
    "\n",
    "print(\"XGModel Accuracy Train: %.4f\" % train_acur,'%')\n",
    "print(\"XGModel Accuracy Test: %.4f\" % test_acur,'%')\n",
    "rmse_1 = np.sqrt(mean_squared_error(y_train, y_train_preds))\n",
    "print(\"Train - RMSE: %f\" % (rmse_1))\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_test_preds))\n",
    "print(\"Test - RMSE: %f\" % (rmse))\n",
    "\n",
    "NRMSE_1=rmse_1/(car_data_enc['Price'].max()-car_data_enc['Price'].min())\n",
    "print(\"NRMSE Train: %f\" % (NRMSE_1))\n",
    "\n",
    "# normalize rmse error   rsme/(price max- price min)  \n",
    "NRMSE=rmse/(car_data_enc['Price'].max()-car_data_enc['Price'].min())\n",
    "print(\"NRMSE Test: %f\" % (NRMSE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "585678d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16822.723, 14080.392, 15699.16 , 16028.604, 21673.299],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_reg.predict(X_check[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ca615c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_xgb=car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "78b063b0",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
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       "  </thead>\n",
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       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
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       "      <th>1</th>\n",
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       "      <td>48851</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>9</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>CO</td>\n",
       "      <td>Acura</td>\n",
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       "      <td>8</td>\n",
       "      <td>4990.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>Lindon</td>\n",
       "      <td>UT</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILXAutomatic</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage              City State   Make         Model  Year_used  \\\n",
       "0  2014    35725           El Paso    TX  Acura    ILX6-Speed          8   \n",
       "1  2013    19606  Long Island City    NY  Acura    ILX5-Speed          9   \n",
       "2  2013    48851           El Paso    TX  Acura    ILX6-Speed          9   \n",
       "3  2014    39922           Windsor    CO  Acura    ILX5-Speed          8   \n",
       "4  2016    22142            Lindon    UT  Acura  ILXAutomatic          6   \n",
       "\n",
       "     Condition  \n",
       "0  4465.625000  \n",
       "1  2178.444444  \n",
       "2  5427.888889  \n",
       "3  4990.250000  \n",
       "4  3690.333333  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xg_n=new_xgb.drop(['Price'],1)\n",
    "\n",
    "Xg_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ff08f5e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "yg_n=new_xgb['Price']\n",
    "yg_n=yg_n.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0129c46a",
   "metadata": {},
   "source": [
    "## Creating a pipeline line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0e303d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "import category_encoders as ce\n",
    "\n",
    "col_tra = make_column_transformer((MinMaxScaler(), ['Year', 'Mileage','Year_used', 'Condition']),\n",
    "    (ce.BinaryEncoder(),['City','State','Make','Model']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "39225e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(transformers=[('minmaxscaler', MinMaxScaler(),\n",
       "                                 ['Year', 'Mileage', 'Year_used', 'Condition']),\n",
       "                                ('binaryencoder', BinaryEncoder(),\n",
       "                                 ['City', 'State', 'Make', 'Model'])])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_tra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "178b6b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.80952381, 0.21628691, 0.19047619, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.76190476, 0.11868532, 0.23809524, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.76190476, 0.29576569, 0.23809524, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.95238095, 0.01494995, 0.04761905, ..., 1.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.9047619 , 0.17520935, 0.0952381 , ..., 1.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.95238095, 0.01816519, 0.04761905, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_tra.fit_transform(Xg_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f9daf97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6a758826",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_xgb=make_pipeline(col_tra,xg_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b6cd26af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year  Mileage     City State   Make       Model  Year_used  Condition\n",
      "0  2014    35725  El Paso    TX  Acura  ILX6-Speed          8   4465.625\n"
     ]
    }
   ],
   "source": [
    "print(Xg_n[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d57300df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16822.723, 14080.392, 15699.16 , 16028.604, 21673.299],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_xgb.predict(Xg_n[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1afebff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('model_xg.pkl', 'wb') as files:\n",
    "    pickle.dump(pipe_xgb, files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1cae60a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_xg.pkl' , 'rb') as f:\n",
    "    lr_che = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ef122cf4",
   "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>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4465.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>NY</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>2178.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage              City State   Make       Model  Year_used  \\\n",
       "0  2014    35725           El Paso    TX  Acura  ILX6-Speed          8   \n",
       "1  2013    19606  Long Island City    NY  Acura  ILX5-Speed          9   \n",
       "\n",
       "     Condition  \n",
       "0  4465.625000  \n",
       "1  2178.444444  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xg_n[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "4ccb7a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(Xg_n[:1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "06a4fc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_1=Xg_n[:5].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4e8bc3c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2014 35725 'El Paso' ' TX' 'Acura' 'ILX6-Speed' 8 4465.625]\n",
      " [2013 19606 'Long Island City' ' NY' 'Acura' 'ILX5-Speed' 9\n",
      "  2178.4444444444443]\n",
      " [2013 48851 'El Paso' ' TX' 'Acura' 'ILX6-Speed' 9 5427.888888888889]\n",
      " [2014 39922 'Windsor' ' CO' 'Acura' 'ILX5-Speed' 8 4990.25]\n",
      " [2016 22142 'Lindon' ' UT' 'Acura' 'ILXAutomatic' 6 3690.3333333333335]]\n"
     ]
    }
   ],
   "source": [
    "print(t_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "55bb509f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(t_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "1de157f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_2=pd.DataFrame(t_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "b7ac8b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_2.rename(columns = {0:'Year', \n",
    "                       1:'Mileage',\n",
    "                      2:'City', \n",
    "                       3:'State',\n",
    "                      4:'Make', \n",
    "                       5:'Model',\n",
    "                      6:'Year_used', \n",
    "                       7:'Condition',\n",
    "                                 }, \n",
    "            inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "eaa53750",
   "metadata": {
    "scrolled": true
   },
   "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>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4465.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>NY</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>2178.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>48851</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>5427.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>CO</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4990.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>Lindon</td>\n",
       "      <td>UT</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILXAutomatic</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Mileage              City State   Make         Model Year_used  \\\n",
       "0  2014   35725           El Paso    TX  Acura    ILX6-Speed         8   \n",
       "1  2013   19606  Long Island City    NY  Acura    ILX5-Speed         9   \n",
       "2  2013   48851           El Paso    TX  Acura    ILX6-Speed         9   \n",
       "3  2014   39922           Windsor    CO  Acura    ILX5-Speed         8   \n",
       "4  2016   22142            Lindon    UT  Acura  ILXAutomatic         6   \n",
       "\n",
       "     Condition  \n",
       "0     4465.625  \n",
       "1  2178.444444  \n",
       "2  5427.888889  \n",
       "3      4990.25  \n",
       "4  3690.333333  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "2fd9584c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16822.723, 14080.392, 15699.16 , 16028.604, 21673.299],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_che.predict(t_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fV9_gHo_PAzp",
   "metadata": {
    "id": "fV9_gHo_PAzp"
   },
   "source": [
    "##Gradient boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "YzTVjP5GZZ7F",
   "metadata": {
    "id": "YzTVjP5GZZ7F"
   },
   "outputs": [],
   "source": [
    "# applying gradient boosting\n",
    "gbr_params = {'n_estimators': 150,\n",
    "          'max_depth': 10,\n",
    "          'min_samples_split': 5,\n",
    "          'learning_rate': 0.1,\n",
    "          'loss': 'ls'}\n",
    "\n",
    "gbr = GradientBoostingRegressor(**gbr_params)  \n",
    "gbr.fit(X_train, y_train) \n",
    "\n",
    "y_train_pred=gbr.predict(X_train)\n",
    "y_test_pred=gbr.predict(X_test)\n",
    "\n",
    "train_acur=gbr.score(X_train, y_train)\n",
    "train_acur=train_acur*100\n",
    "test_acur=gbr.score(X_test, y_test)\n",
    "test_acur=test_acur*100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0H59wo-5jIPj",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0H59wo-5jIPj",
    "outputId": "482cbad5-dd43-480e-b451-ac350290fbfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBModel Accuracy Train: 92.1250 %\n",
      "GBModel Accuracy Test: 89.4890 %\n",
      "(RMSE) on train set: 2947.3031\n",
      "(RMSE) on test set: 3405.8486\n",
      "NRMSE Train: 0.049545\n",
      "NRMSE Train: 0.057254\n"
     ]
    }
   ],
   "source": [
    "# printing the model accuracy \n",
    "\n",
    "print(\"GBModel Accuracy Train: %.4f\" % train_acur,'%')\n",
    "\n",
    "print(\"GBModel Accuracy Test: %.4f\" % test_acur,'%')\n",
    "\n",
    "# Create the mean squared error\n",
    "\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train,y_train_pred ))\n",
    "print(\"(RMSE) on train set: {:.4f}\".format(rmse_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test,y_test_pred ))\n",
    "print(\"(RMSE) on test set: {:.4f}\".format(rmse_test))\n",
    "\n",
    "\n",
    "NRMSE_train=rmse_train/(car_data_enc['Price'].max()-car_data_enc['Price'].min())\n",
    "print(\"NRMSE Train: %f\" % (NRMSE_train))\n",
    "NRMSE_test=rmse_test/(car_data_enc['Price'].max()-car_data_enc['Price'].min())\n",
    "print(\"NRMSE Train: %f\" % (NRMSE_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "xztg83cI9ROQ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "id": "xztg83cI9ROQ",
    "outputId": "7ee365a8-fa29-4523-ae9a-4518d74228c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_score = np.zeros((gbr_params[\"n_estimators\"],), dtype=np.float64)\n",
    "for i, y_pred in enumerate(gbr.staged_predict(X_test_std)):\n",
    "    test_score[i] = gbr.loss_(y_test, y_pred)\n",
    "\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.title(\"Deviance\")\n",
    "plt.plot(\n",
    "    np.arange(gbr_params[\"n_estimators\"]) + 1,\n",
    "    gbr.train_score_,\n",
    "    \"b-\",\n",
    "    label=\"Training Set Deviance\",\n",
    ")\n",
    "plt.plot(\n",
    "    np.arange(gbr_params[\"n_estimators\"]) + 1, test_score, \"r-\", label=\"Test Set Deviance\"\n",
    ")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.xlabel(\"Boosting Iterations\")\n",
    "plt.ylabel(\"Deviance\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Y36gNAZMAtzA",
   "metadata": {
    "id": "Y36gNAZMAtzA"
   },
   "source": [
    "Artificial Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e3f732b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras-tuner in c:\\users\\ankit\\anaconda3\\lib\\site-packages (1.1.1)\n",
      "Collecting keras-tuner"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002A34E9DA730>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /packages/c7/d3/506771d6114853c270103b3348f992086517c380dbefad60c8a42ae165c3/keras_tuner-1.1.2-py3-none-any.whl\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Downloading keras_tuner-1.1.2-py3-none-any.whl (133 kB)\n",
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      "Installing collected packages: keras-tuner\n",
      "  Attempting uninstall: keras-tuner\n",
      "    Found existing installation: keras-tuner 1.1.1\n",
      "    Uninstalling keras-tuner-1.1.1:\n",
      "      Successfully uninstalled keras-tuner-1.1.1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in c:\\users\\ankit\\anaconda3\\lib\\site-packages (2.8.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install keras-tuner --upgrade\n",
    "!pip install tensorflow\n",
    "!pip install keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "AFmLwO7QA0Xc",
   "metadata": {
    "id": "AFmLwO7QA0Xc"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from kerastuner.tuners import RandomSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "StfFp8b2BKsG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "StfFp8b2BKsG",
    "outputId": "3f0ad238-00c5-45b7-f677-70acba6cd845"
   },
   "outputs": [
    {
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       "      <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>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>8</td>\n",
       "      <td>4990.250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage  Year_used    Condition  City_0  City_1  City_2  City_3  \\\n",
       "0  2014    35725          8  4465.625000       0       0       0       0   \n",
       "1  2013    19606          9  2178.444444       0       0       0       0   \n",
       "2  2013    48851          9  5427.888889       0       0       0       0   \n",
       "3  2014    39922          8  4990.250000       0       0       0       0   \n",
       "4  2016    22142          6  3690.333333       0       0       0       0   \n",
       "\n",
       "   City_4  City_5  ...  Model_2  Model_3  Model_4  Model_5  Model_6  Model_7  \\\n",
       "0       0       0  ...        0        0        0        0        0        0   \n",
       "1       0       0  ...        0        0        0        0        0        0   \n",
       "2       0       0  ...        0        0        0        0        0        0   \n",
       "3       0       0  ...        0        0        0        0        0        0   \n",
       "4       0       0  ...        0        0        0        0        0        0   \n",
       "\n",
       "   Model_8  Model_9  Model_10  Model_11  \n",
       "0        0        0         0         1  \n",
       "1        0        0         1         0  \n",
       "2        0        0         0         1  \n",
       "3        0        0         1         0  \n",
       "4        0        0         1         1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1=X\n",
    "X1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "bNAbe3UcCM9b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bNAbe3UcCM9b",
    "outputId": "54e60b10-dadc-4dc7-e580-aa6b4c0a8838"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(826724, 1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1=y\n",
    "y1=y1.values.reshape(-1,1)\n",
    "y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "BuIVJ-A8tEkO",
   "metadata": {
    "id": "BuIVJ-A8tEkO"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X1)\n",
    "X_new = scaler.transform(X1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "yAU9Y6p2hCok",
   "metadata": {
    "id": "yAU9Y6p2hCok"
   },
   "outputs": [],
   "source": [
    "X_train_ch,X_test_ch,y_train_ch,y_test_ch=train_test_split(X_new,y1, train_size=0.9, test_size=0.1,random_state=95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "lYctGLuTybbp",
   "metadata": {
    "id": "lYctGLuTybbp"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cuvfvlugyc5k",
   "metadata": {
    "id": "cuvfvlugyc5k"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "7441/7441 [==============================] - 102s 14ms/step - loss: 26907552.0000 - mae: 3384.8813 - mse: 26907552.0000\n",
      "Epoch 2/8\n",
      "7441/7441 [==============================] - 103s 14ms/step - loss: 12139405.0000 - mae: 2375.3665 - mse: 12139405.0000\n",
      "Epoch 3/8\n",
      "7441/7441 [==============================] - 98s 13ms/step - loss: 11198176.0000 - mae: 2265.5327 - mse: 11198176.0000\n",
      "Epoch 4/8\n",
      "7441/7441 [==============================] - 101s 14ms/step - loss: 10633930.0000 - mae: 2200.8503 - mse: 10633930.0000\n",
      "Epoch 5/8\n",
      "7441/7441 [==============================] - 103s 14ms/step - loss: 10251712.0000 - mae: 2156.1189 - mse: 10251712.0000\n",
      "Epoch 6/8\n",
      "7441/7441 [==============================] - 102s 14ms/step - loss: 9972851.0000 - mae: 2119.4368 - mse: 9972851.0000\n",
      "Epoch 7/8\n",
      "7441/7441 [==============================] - 101s 14ms/step - loss: 9744438.0000 - mae: 2093.5015 - mse: 9744438.0000\n",
      "Epoch 8/8\n",
      "7441/7441 [==============================] - 101s 14ms/step - loss: 9517438.0000 - mae: 2064.7607 - mse: 9517438.0000\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "#input\n",
    "model.add(Dense(units=2048, input_dim=40, kernel_initializer='normal', activation='relu'))\n",
    "#1\n",
    "model.add(Dense(units=1024, kernel_initializer='normal', activation='relu'))\n",
    "#2\n",
    "model.add(Dense(units=512, kernel_initializer='normal', activation='relu'))\n",
    "#3\n",
    "model.add(Dense(units=256, kernel_initializer='normal', activation='relu'))\n",
    "#4\n",
    "model.add(Dense(units=128, kernel_initializer='normal', activation='relu'))\n",
    "#5\n",
    "model.add(Dense(units=64, kernel_initializer='normal', activation='relu'))\n",
    "#6\n",
    "model.add(Dense(units=32, kernel_initializer='normal', activation='relu'))\n",
    "#7\n",
    "model.add(Dense(units=16, kernel_initializer='normal', activation='relu'))\n",
    "#8\n",
    "model.add(Dense(units=8, kernel_initializer='normal', activation='relu'))\n",
    "#9\n",
    "model.add(Dense(1, kernel_initializer='normal'))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics = ['mae','mse'])\n",
    "\n",
    "history=model.fit(X_train_ch, y_train_ch ,batch_size = 100, epochs = 8)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "57715660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2584/2584 [==============================] - 9s 4ms/step - loss: 9925505.0000 - mae: 2081.7781 - mse: 9925505.0000\n"
     ]
    }
   ],
   "source": [
    "test_loss = model.evaluate(X_test_ch,y_test_ch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dbc10cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "144f233f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "\n",
    "loss = history.history['loss']\n",
    "acc = history.history['mse']\n",
    "\n",
    "\n",
    "pyplot.plot(history.history['mse'])\n",
    "\n",
    "\n",
    "pyplot.show()\n",
    "epochs = range(1, len(loss)+1)\n",
    "plt.plot(epochs, loss, 'ro', label='Training loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "y_pred = model.predict(X_test_ch)\n",
    "\n",
    "plt.plot(y_test_ch, color = 'red', label = 'Real data')\n",
    "plt.plot(y_pred, color = 'blue', label = 'Predicted data')\n",
    "plt.title('Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "044eae2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final score (RMSE): 3150.476811875757\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Predict\n",
    "pred = model.predict(X_test_ch)\n",
    "\n",
    "# Measure MSE error.  \n",
    "score = np.sqrt(metrics.mean_squared_error(pred,y_test_ch))\n",
    "print(\"Final score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "55132fb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 score on the Train set is:\t0.917\n",
      "The R2 score on the Test set is:\t0.910\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "y_train_pred = model.predict(X_train_ch)\n",
    "y_test_pred = model.predict(X_test_ch)\n",
    "\n",
    "# Calculates and prints r2 score of training and testing data\n",
    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train_ch, y_train_pred)))\n",
    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test_ch, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5a9db46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "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": [
    "sns.regplot(y_test_ch, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1b4416bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5.1000e+01, 4.4700e+02, 8.5310e+03, 6.6068e+04, 6.5500e+03,\n",
       "        7.5900e+02, 2.2000e+02, 3.9000e+01, 3.0000e+00, 5.0000e+00]),\n",
       " array([-20660.73046875, -14520.70546875,  -8380.68046875,  -2240.65546875,\n",
       "          3899.36953125,  10039.39453125,  16179.41953125,  22319.44453125,\n",
       "         28459.46953125,  34599.49453125,  40739.51953125]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 81,
     "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": [
    "plt.hist(y_test_ch - pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1e521971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values are:  [[20911.615]\n",
      " [12839.671]\n",
      " [15560.511]\n",
      " [38919.17 ]\n",
      " [16933.816]\n",
      " [35651.816]\n",
      " [16150.44 ]\n",
      " [29816.332]\n",
      " [36916.496]\n",
      " [17206.223]]\n",
      "Real values are:  [[24950]\n",
      " [12990]\n",
      " [12995]\n",
      " [41356]\n",
      " [17988]\n",
      " [52999]\n",
      " [16797]\n",
      " [15999]\n",
      " [36000]\n",
      " [18995]]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test_ch[:10])\n",
    "print(\"Predicted values are: \", predictions)\n",
    "print(\"Real values are: \", y_test_ch[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f95b9054",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chart_regression(pred, y1, sort=True):\n",
    "    t = pd.DataFrame({'pred': pred, 'y': y1.flatten()})\n",
    "    if sort:\n",
    "        t.sort_values(by=['y'], inplace=True)\n",
    "    plt.plot(t['y'].tolist(), label='expected')\n",
    "    plt.plot(t['pred'].tolist(), label='prediction')\n",
    "    plt.ylabel('output')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "03997463",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "chart_regression(pred.flatten(),y_test_ch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b4b6acb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20911.615],\n",
       "       [12839.671],\n",
       "       [15560.511],\n",
       "       [38919.17 ],\n",
       "       [16933.816],\n",
       "       [35651.816],\n",
       "       [16150.44 ],\n",
       "       [29816.332],\n",
       "       [36916.496],\n",
       "       [17206.223]], dtype=float32)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test_ch[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6eee40a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('myModel2.h5')\n",
    "#import tensorflow as tf\n",
    "#new_model = tf.keras.models.load_model('myModel2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "cb87504b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.85714286, 0.19958704, 0.14285714, 0.150101  , 0.        ,\n",
       "        1.        , 0.        , 0.        , 1.        , 1.        ,\n",
       "        1.        , 0.        , 1.        , 1.        , 0.        ,\n",
       "        1.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "        0.        , 1.        , 1.        , 1.        , 0.        ,\n",
       "        0.        , 1.        , 1.        , 1.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 1.        , 0.        ,\n",
       "        0.        , 1.        , 0.        , 1.        , 0.        ]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_ch[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "59136357",
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_model.predict(X_test_ch[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "98a68457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.85714286 0.19958704 0.14285714 0.150101   0.         1.\n",
      "  0.         0.         1.         1.         1.         0.\n",
      "  1.         1.         0.         1.         0.         0.\n",
      "  1.         0.         0.         1.         1.         1.\n",
      "  0.         0.         1.         1.         1.         0.\n",
      "  0.         0.         1.         1.         0.         0.\n",
      "  1.         0.         1.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_ch[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b50e0832",
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_model.predict(te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "42a05e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=car_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9c4525b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "19df201c",
   "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>Year</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8995</td>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4465.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10888</td>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>NY</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>2178.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8995</td>\n",
       "      <td>2013</td>\n",
       "      <td>48851</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>5427.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10999</td>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>CO</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4990.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14799</td>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>Lindon</td>\n",
       "      <td>UT</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILXAutomatic</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Year  Mileage              City State   Make         Model  \\\n",
       "0   8995  2014    35725           El Paso    TX  Acura    ILX6-Speed   \n",
       "1  10888  2013    19606  Long Island City    NY  Acura    ILX5-Speed   \n",
       "2   8995  2013    48851           El Paso    TX  Acura    ILX6-Speed   \n",
       "3  10999  2014    39922           Windsor    CO  Acura    ILX5-Speed   \n",
       "4  14799  2016    22142            Lindon    UT  Acura  ILXAutomatic   \n",
       "\n",
       "   Year_used    Condition  \n",
       "0          8  4465.625000  \n",
       "1          9  2178.444444  \n",
       "2          9  5427.888889  \n",
       "3          8  4990.250000  \n",
       "4          6  3690.333333  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0e7bc1fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Mileage</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year_used</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>35725</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4465.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>19606</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>NY</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>2178.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>48851</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX6-Speed</td>\n",
       "      <td>9</td>\n",
       "      <td>5427.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>39922</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>CO</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILX5-Speed</td>\n",
       "      <td>8</td>\n",
       "      <td>4990.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>22142</td>\n",
       "      <td>Lindon</td>\n",
       "      <td>UT</td>\n",
       "      <td>Acura</td>\n",
       "      <td>ILXAutomatic</td>\n",
       "      <td>6</td>\n",
       "      <td>3690.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Mileage              City State   Make         Model  Year_used  \\\n",
       "0  2014    35725           El Paso    TX  Acura    ILX6-Speed          8   \n",
       "1  2013    19606  Long Island City    NY  Acura    ILX5-Speed          9   \n",
       "2  2013    48851           El Paso    TX  Acura    ILX6-Speed          9   \n",
       "3  2014    39922           Windsor    CO  Acura    ILX5-Speed          8   \n",
       "4  2016    22142            Lindon    UT  Acura  ILXAutomatic          6   \n",
       "\n",
       "     Condition  \n",
       "0  4465.625000  \n",
       "1  2178.444444  \n",
       "2  5427.888889  \n",
       "3  4990.250000  \n",
       "4  3690.333333  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_n=new_data.drop(['Price'],1)\n",
    "\n",
    "X_n.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e901e43b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "edab1589",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_new=new_data['Price']\n",
    "y_new=y_new.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "67e32b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "import category_encoders as ce\n",
    "col_tra = make_column_transformer((MinMaxScaler(), ['Year', 'Mileage', 'Year_used', 'Condition']),\n",
    "    (ce.BinaryEncoder(),['City','State','Make','Model']))\n",
    "\n",
    "#col_tra = ColumnTransformer([(MinMaxScaler(),['Year', 'Mileage', 'Year_used', 'Condition']),\n",
    " #   (ce.BinaryEncoder(),['City','State','Make','Model'])  ])\n",
    "\n",
    "#transformer = ColumnTransformer(transformers=[\n",
    " #   ('tnf1',MinMaxScaler(),['Year', 'Mileage', 'Year_used', 'Condition']),\n",
    "  #  ('tnf2',ce.BinaryEncoder(),['City','State','Make','Model'])])   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f05aeb11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(transformers=[('minmaxscaler', MinMaxScaler(),\n",
       "                                 ['Year', 'Mileage', 'Year_used', 'Condition']),\n",
       "                                ('binaryencoder', BinaryEncoder(),\n",
       "                                 ['City', 'State', 'Make', 'Model'])])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_tra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "030e6924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.80952381, 0.21628691, 0.19047619, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.76190476, 0.11868532, 0.23809524, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.76190476, 0.29576569, 0.23809524, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.95238095, 0.01494995, 0.04761905, ..., 1.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.9047619 , 0.17520935, 0.0952381 , ..., 1.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.95238095, 0.01816519, 0.04761905, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_tra.fit_transform(X_n)\n",
    "#col_tran = transformer.fit_transform(X_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "874dd5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ColumnTransformer(transformers=[('minmaxscaler', MinMaxScaler(),\n",
      "                                 ['Year', 'Mileage', 'Year_used', 'Condition']),\n",
      "                                ('binaryencoder', BinaryEncoder(),\n",
      "                                 ['City', 'State', 'Make', 'Model'])])\n"
     ]
    }
   ],
   "source": [
    "print(col_tra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "25e47c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b15b967f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe=make_pipeline(col_tra,model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f99f30f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_10 (Dense)            (None, 2048)              83968     \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1024)              2098176   \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 512)               524800    \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 16)                528       \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 8)                 136       \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,882,177\n",
      "Trainable params: 2,882,177\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1e0b38e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>30</th>\n",
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       "      <th>39</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.809524</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.761905</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.761905</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.241700</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.159047</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.134041</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.117615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3    4    5    6    7    8    9   ...  \\\n",
       "0  0.809524  0.216287  0.190476  0.142326  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "1  0.761905  0.118685  0.238095  0.069426  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2  0.761905  0.295766  0.238095  0.172996  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "3  0.809524  0.241700  0.190476  0.159047  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "4  0.904762  0.134041  0.095238  0.117615  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "    30   31   32   33   34   35   36   37   38   39  \n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new=pd.DataFrame(X_new)\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "67f06b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15630.241],\n",
       "       [15218.564],\n",
       "       [13967.446],\n",
       "       [13307.025],\n",
       "       [15822.579],\n",
       "       [ 9102.681],\n",
       "       [17093.305],\n",
       "       [14016.887],\n",
       "       [12745.581],\n",
       "       [13091.179]], dtype=float32)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_new[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9e6acde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(826724, 8)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_n.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b37f7578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15630.241],\n",
       "       [15218.564],\n",
       "       [13967.446],\n",
       "       [13307.025],\n",
       "       [15822.579],\n",
       "       [ 9102.681],\n",
       "       [17093.305],\n",
       "       [14016.887],\n",
       "       [12745.581],\n",
       "       [13091.179]], dtype=float32)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict(X_n[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5ff58d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pipreqs\n",
      "  Downloading pipreqs-0.4.11-py2.py3-none-any.whl (32 kB)\n",
      "Collecting yarg\n",
      "  Downloading yarg-0.1.9-py2.py3-none-any.whl (19 kB)\n",
      "Collecting docopt\n",
      "  Downloading docopt-0.6.2.tar.gz (25 kB)\n",
      "Requirement already satisfied: requests in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from yarg->pipreqs) (2.26.0)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from requests->yarg->pipreqs) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from requests->yarg->pipreqs) (3.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from requests->yarg->pipreqs) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\ankit\\anaconda3\\lib\\site-packages (from requests->yarg->pipreqs) (1.26.7)\n",
      "Building wheels for collected packages: docopt\n",
      "  Building wheel for docopt (setup.py): started\n",
      "  Building wheel for docopt (setup.py): finished with status 'done'\n",
      "  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13724 sha256=7e02604f49d06588e97f061a45ac7468b006aedfde4a9cbed4647a9de073555c\n",
      "  Stored in directory: c:\\users\\ankit\\appdata\\local\\pip\\cache\\wheels\\70\\4a\\46\\1309fc853b8d395e60bafaf1b6df7845bdd82c95fd59dd8d2b\n",
      "Successfully built docopt\n",
      "Installing collected packages: yarg, docopt, pipreqs\n",
      "Successfully installed docopt-0.6.2 pipreqs-0.4.11 yarg-0.1.9\n"
     ]
    }
   ],
   "source": [
    "!pip install pipreqs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "50119418",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pipreqs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9060\\2576325295.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpipreqs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pipreqs' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "a6957eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0\n"
     ]
    }
   ],
   "source": [
    "print(pickle.format_version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5683ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3495e8ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Car_predict.ipynb",
   "provenance": []
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}