My_Internship_Project.ipynb

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      "name": "My_Internship_Project",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AkshadK7/Exploratory-Data-Analysis/blob/master/My_Internship_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wl3MibhqsV4Y",
        "colab_type": "text"
      },
      "source": [
        "#INTRODUCTION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJZu4hdW2VoG",
        "colab_type": "text"
      },
      "source": [
        "**Exploratory Data Analysis** or **(EDA)** is understanding the data sets by summarizing their main characteristics often plotting them visually. This step is very important especially when we arrive at modeling the data in order to apply Machine learning. Plotting in EDA consists of Histograms, Box plot, Scatter plot and many more. It often takes much time to explore the data. Through the process of EDA, we understand the problem better and try to uncover hidden insights."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSCyv9lutrJC",
        "colab_type": "text"
      },
      "source": [
        "###About "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ma4CasS_ttbF",
        "colab_type": "text"
      },
      "source": [
        "**Name:** Akshad Kolhatkar\n",
        "\n",
        "**College:** SRM University\n",
        "\n",
        "**Branch:** CSE BTech \n",
        "\n",
        "**Website:** akshad.netlify.app\n",
        "\n",
        "**Contact:** 9765248740"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IoOtvp68tSWO",
        "colab_type": "text"
      },
      "source": [
        "###Problem Statement"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "STf5V5bCtZ0Q",
        "colab_type": "text"
      },
      "source": [
        " In this project, you as a data scientist are expected to perform Exploratory data analysis on how the different features of a car and its price are related. The data comes from the Kaggle dataset \"Car Features and MSRP\". It describes almost 12,000 car models, sold in the USA between 1990 and 2017, with the market price (new or used)\n",
        "and some features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XI0tYAtlsghv",
        "colab_type": "text"
      },
      "source": [
        "### Objective"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjDTxVEfs3s-",
        "colab_type": "text"
      },
      "source": [
        "The objective of the project is to do data pre-processing and exploratory data analysis of the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlssf6GoY7cb",
        "colab_type": "text"
      },
      "source": [
        "# STEP 1:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvaxetYwdLu_",
        "colab_type": "text"
      },
      "source": [
        "##Importing the Libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3hq4fKg3Hkst",
        "colab_type": "text"
      },
      "source": [
        "Let's first import some Libraries to get started..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2mosSHPYW3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline "
      ],
      "execution_count": 516,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjGmlo3idTQc",
        "colab_type": "text"
      },
      "source": [
        "##Reading and Displaying Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FbDc64JHzzZ",
        "colab_type": "text"
      },
      "source": [
        "We should now include our Dataset in order to process it further and display some stats to perform an EDA Report on it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYA2LWG_a5uO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "1302a90e-a95a-4ed3-a4ce-922e52d5f87f"
      },
      "source": [
        "data = pd.read_csv('data.csv')\n",
        "data.head()"
      ],
      "execution_count": 517,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Fuel Type</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury,Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year  ... city mpg  Popularity   MSRP\n",
              "0  BMW  1 Series M  2011  ...       19        3916  46135\n",
              "1  BMW    1 Series  2011  ...       19        3916  40650\n",
              "2  BMW    1 Series  2011  ...       20        3916  36350\n",
              "3  BMW    1 Series  2011  ...       18        3916  29450\n",
              "4  BMW    1 Series  2011  ...       18        3916  34500\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 517
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6giYUBqCdg_3",
        "colab_type": "text"
      },
      "source": [
        "###**Statistical Summary**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ho1Xx96mdota",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "7fddb569-7f33-4812-e3f6-1829086a31f7"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 518,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11914 entries, 0 to 11913\n",
            "Data columns (total 16 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Make               11914 non-null  object \n",
            " 1   Model              11914 non-null  object \n",
            " 2   Year               11914 non-null  int64  \n",
            " 3   Engine Fuel Type   11911 non-null  object \n",
            " 4   Engine HP          11845 non-null  float64\n",
            " 5   Engine Cylinders   11884 non-null  float64\n",
            " 6   Transmission Type  11914 non-null  object \n",
            " 7   Driven_Wheels      11914 non-null  object \n",
            " 8   Number of Doors    11908 non-null  float64\n",
            " 9   Market Category    8172 non-null   object \n",
            " 10  Vehicle Size       11914 non-null  object \n",
            " 11  Vehicle Style      11914 non-null  object \n",
            " 12  highway MPG        11914 non-null  int64  \n",
            " 13  city mpg           11914 non-null  int64  \n",
            " 14  Popularity         11914 non-null  int64  \n",
            " 15  MSRP               11914 non-null  int64  \n",
            "dtypes: float64(3), int64(5), object(8)\n",
            "memory usage: 1.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSbntyane4u6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b74cb38d-3ad7-47cd-fcba-5bd1b64a53d8"
      },
      "source": [
        "print(\"Shape of the Data =\",data.shape)\n",
        "print(\"Dimensions =\", data.ndim)"
      ],
      "execution_count": 519,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the Data = (11914, 16)\n",
            "Dimensions = 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1GlhOBy3gL6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "81ec7d33-3d76-4340-9a38-6eec551f4470"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 520,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>Year</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11914.000000</td>\n",
              "      <td>11845.00000</td>\n",
              "      <td>11884.000000</td>\n",
              "      <td>11908.000000</td>\n",
              "      <td>11914.000000</td>\n",
              "      <td>11914.000000</td>\n",
              "      <td>11914.000000</td>\n",
              "      <td>1.191400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2010.384338</td>\n",
              "      <td>249.38607</td>\n",
              "      <td>5.628829</td>\n",
              "      <td>3.436093</td>\n",
              "      <td>26.637485</td>\n",
              "      <td>19.733255</td>\n",
              "      <td>1554.911197</td>\n",
              "      <td>4.059474e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.579740</td>\n",
              "      <td>109.19187</td>\n",
              "      <td>1.780559</td>\n",
              "      <td>0.881315</td>\n",
              "      <td>8.863001</td>\n",
              "      <td>8.987798</td>\n",
              "      <td>1441.855347</td>\n",
              "      <td>6.010910e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1990.000000</td>\n",
              "      <td>55.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>170.00000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>2.100000e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2015.000000</td>\n",
              "      <td>227.00000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1385.000000</td>\n",
              "      <td>2.999500e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>300.00000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>4.223125e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>1001.00000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>5657.000000</td>\n",
              "      <td>2.065902e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Year    Engine HP  ...    Popularity          MSRP\n",
              "count  11914.000000  11845.00000  ...  11914.000000  1.191400e+04\n",
              "mean    2010.384338    249.38607  ...   1554.911197  4.059474e+04\n",
              "std        7.579740    109.19187  ...   1441.855347  6.010910e+04\n",
              "min     1990.000000     55.00000  ...      2.000000  2.000000e+03\n",
              "25%     2007.000000    170.00000  ...    549.000000  2.100000e+04\n",
              "50%     2015.000000    227.00000  ...   1385.000000  2.999500e+04\n",
              "75%     2016.000000    300.00000  ...   2009.000000  4.223125e+04\n",
              "max     2017.000000   1001.00000  ...   5657.000000  2.065902e+06\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 520
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqlE7ULhfI2t",
        "colab_type": "text"
      },
      "source": [
        "###**Finding The Null Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCoppIHCe-v9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "a5701166-46ab-4de7-832a-19f82cb97db2"
      },
      "source": [
        "data.isnull()                          #All the Null Values are represented as True"
      ],
      "execution_count": 521,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine Fuel Type</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>Number of Doors</th>\n",
              "      <th>Market Category</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>False</td>\n",
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              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11909</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11910</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11911</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11913</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11914 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Make  Model   Year  ...  city mpg  Popularity   MSRP\n",
              "0      False  False  False  ...     False       False  False\n",
              "1      False  False  False  ...     False       False  False\n",
              "2      False  False  False  ...     False       False  False\n",
              "3      False  False  False  ...     False       False  False\n",
              "4      False  False  False  ...     False       False  False\n",
              "...      ...    ...    ...  ...       ...         ...    ...\n",
              "11909  False  False  False  ...     False       False  False\n",
              "11910  False  False  False  ...     False       False  False\n",
              "11911  False  False  False  ...     False       False  False\n",
              "11912  False  False  False  ...     False       False  False\n",
              "11913  False  False  False  ...     False       False  False\n",
              "\n",
              "[11914 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 521
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEYKZIZnxCJY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "de8d8565-a8f5-43f7-d937-50ddc747ad28"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 522,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make                    0\n",
              "Model                   0\n",
              "Year                    0\n",
              "Engine Fuel Type        3\n",
              "Engine HP              69\n",
              "Engine Cylinders       30\n",
              "Transmission Type       0\n",
              "Driven_Wheels           0\n",
              "Number of Doors         6\n",
              "Market Category      3742\n",
              "Vehicle Size            0\n",
              "Vehicle Style           0\n",
              "highway MPG             0\n",
              "city mpg                0\n",
              "Popularity              0\n",
              "MSRP                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 522
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-DXPB9gxP3p",
        "colab_type": "text"
      },
      "source": [
        "# STEP 2:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvwnfAb4Jk_6",
        "colab_type": "text"
      },
      "source": [
        "##Eliminating The Irrelevant Column Labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KnCczTJ0KNbs",
        "colab_type": "text"
      },
      "source": [
        "While processing A Dataset, it is very important to clean the data efficiently to reduce errors and provide an accurate Thesis Report."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uCAphU9fgjv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "65476dbc-d48b-4a44-d878-64f3cfc32b74"
      },
      "source": [
        "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": 523,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea9729c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 523
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIa8k_ckxcB7",
        "colab_type": "text"
      },
      "source": [
        "We can clearly see from the Heat Map that the Market Category, Number of Doors and Engine Fuel Type columns contain the maximum number of null(NaN) values which are not of any good relevance. Hence we shall eliminate/drop these from the labelled columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9U3UM_ExJwE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "988af524-562c-4209-fb1d-8c33ee987892"
      },
      "source": [
        "# Dropping the irrelevant columns from the dataframe\n",
        "data = data.drop([\"Market Category\",\"Number of Doors\",\"Engine Fuel Type\"],axis=1)\n",
        "data.shape"
      ],
      "execution_count": 524,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11914, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 524
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvnpBLusxV1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "a26b07df-77af-4ef8-eaa3-1c43d37ee2ab"
      },
      "source": [
        "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": 525,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ead465ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 525
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A626s_8m4zU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "outputId": "8916d270-8bb9-4dbb-ca3a-ada0612a2d0c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 526,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>Engine HP</th>\n",
              "      <th>Engine Cylinders</th>\n",
              "      <th>Transmission Type</th>\n",
              "      <th>Driven_Wheels</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>highway MPG</th>\n",
              "      <th>city mpg</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year  Engine HP  ...  highway MPG city mpg Popularity   MSRP\n",
              "0  BMW  1 Series M  2011      335.0  ...           26       19       3916  46135\n",
              "1  BMW    1 Series  2011      300.0  ...           28       19       3916  40650\n",
              "2  BMW    1 Series  2011      300.0  ...           28       20       3916  36350\n",
              "3  BMW    1 Series  2011      230.0  ...           28       18       3916  29450\n",
              "4  BMW    1 Series  2011      230.0  ...           28       18       3916  34500\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 526
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7vPga4k07ce",
        "colab_type": "text"
      },
      "source": [
        "Hence all the irrelevant columns are now successfully dropped."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdwNbIe83Fbe",
        "colab_type": "text"
      },
      "source": [
        "# STEP 3:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bCstNym3KnA",
        "colab_type": "text"
      },
      "source": [
        "##Renaming the Labelled Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BuIOaqyLfK7",
        "colab_type": "text"
      },
      "source": [
        "A Proper Representation of the Data is of key importance while performing an EDA Report for the viewers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pztl0lLG3Igc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Rename multiple columns in one go with a larger dictionary\n",
        "data.rename(\n",
        "    columns={\n",
        "         \"Engine HP\": \"HP\", \n",
        "         \"Engine Cylinders\": \"Cylinders\",\n",
        "         \"Transmission Type\": \"Transmission\", \n",
        "         \"Driven_Wheels\": \"Drive Mode\",\n",
        "         \"highway MPG\": \"MPG-H\",\n",
        "         \"city mpg\": \"MPG-C\", \n",
        "         \"MSRP\": \"Price\"\n",
        "\n",
        "    },\n",
        "    inplace=True\n",
        ")"
      ],
      "execution_count": 527,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2xZsgHo6QhY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "ae23562f-bbc3-4ab3-bf3c-82be9e0e1f1c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 528,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Drive Mode</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year     HP  ...  MPG-H MPG-C Popularity  Price\n",
              "0  BMW  1 Series M  2011  335.0  ...     26    19       3916  46135\n",
              "1  BMW    1 Series  2011  300.0  ...     28    19       3916  40650\n",
              "2  BMW    1 Series  2011  300.0  ...     28    20       3916  36350\n",
              "3  BMW    1 Series  2011  230.0  ...     28    18       3916  29450\n",
              "4  BMW    1 Series  2011  230.0  ...     28    18       3916  34500\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 528
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8L_m6wV23FYD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a09b77dc-6f36-43e1-b390-72a1cbe40873"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 529,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11914, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 529
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7Q4Nbzpak9S",
        "colab_type": "text"
      },
      "source": [
        "# STEP 4:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqIpbzaYasNh",
        "colab_type": "text"
      },
      "source": [
        "## Cleaning The Data : Checking Duplicates, Null values and Missing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ppr6FO_7OHFG",
        "colab_type": "text"
      },
      "source": [
        "Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set,\n",
        "table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or\n",
        "deleting the dirty or coarse data.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_uomo1843cc",
        "colab_type": "text"
      },
      "source": [
        "###**Checking Duplicate Values**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKnk3fq_5Uu4",
        "colab_type": "text"
      },
      "source": [
        "This is often an important check to perform because a huge data set as in this case contains more than 10, 000 rows often have some duplicate data, so here we remove all the duplicate value from the data-set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5LlaW16ao9A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "566e29ae-ad60-4033-e303-c1a3ccdea6e3"
      },
      "source": [
        "data.duplicated().sum()\n",
        "data.duplicated(subset=None, keep=False)"
      ],
      "execution_count": 530,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "11909    False\n",
              "11910    False\n",
              "11911    False\n",
              "11912    False\n",
              "11913    False\n",
              "Length: 11914, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 530
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybEYnWh_RVbH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1d179b19-ab4c-4705-9d00-abfdc56f0e1b"
      },
      "source": [
        "#data.duplicated(subset=None, keep='first')\n",
        "data.count()"
      ],
      "execution_count": 531,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             11914\n",
              "Model            11914\n",
              "Year             11914\n",
              "HP               11845\n",
              "Cylinders        11884\n",
              "Transmission     11914\n",
              "Drive Mode       11914\n",
              "Vehicle Size     11914\n",
              "Vehicle Style    11914\n",
              "MPG-H            11914\n",
              "MPG-C            11914\n",
              "Popularity       11914\n",
              "Price            11914\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 531
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ8El-3czZxo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9a275015-6e8a-4557-83fc-aa65093786b0"
      },
      "source": [
        "data = data.drop_duplicates(subset=None, keep='first')\n",
        "data.duplicated().sum()"
      ],
      "execution_count": 532,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 532
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kYtWDED4lsc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a43cd062-7710-47d8-a22a-ffbc15a07228"
      },
      "source": [
        "data.count()"
      ],
      "execution_count": 533,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             11122\n",
              "Model            11122\n",
              "Year             11122\n",
              "HP               11053\n",
              "Cylinders        11092\n",
              "Transmission     11122\n",
              "Drive Mode       11122\n",
              "Vehicle Size     11122\n",
              "Vehicle Style    11122\n",
              "MPG-H            11122\n",
              "MPG-C            11122\n",
              "Popularity       11122\n",
              "Price            11122\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 533
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qYI5nBV31lp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b47999b4-6892-4e82-ee54-5613dac8d362"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 534,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11122, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 534
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7O_PH7H2Ca5",
        "colab_type": "text"
      },
      "source": [
        "###**Eliminating The Null Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RdgtoD82GUb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9fc5b321-4057-457f-cc81-cc24a071e1c9"
      },
      "source": [
        "print(data.isnull().sum())\n",
        "data.isnull()"
      ],
      "execution_count": 535,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Make              0\n",
            "Model             0\n",
            "Year              0\n",
            "HP               69\n",
            "Cylinders        30\n",
            "Transmission      0\n",
            "Drive Mode        0\n",
            "Vehicle Size      0\n",
            "Vehicle Style     0\n",
            "MPG-H             0\n",
            "MPG-C             0\n",
            "Popularity        0\n",
            "Price             0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11122 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Make  Model   Year     HP  ...  MPG-H  MPG-C  Popularity  Price\n",
              "0      False  False  False  False  ...  False  False       False  False\n",
              "1      False  False  False  False  ...  False  False       False  False\n",
              "2      False  False  False  False  ...  False  False       False  False\n",
              "3      False  False  False  False  ...  False  False       False  False\n",
              "4      False  False  False  False  ...  False  False       False  False\n",
              "...      ...    ...    ...    ...  ...    ...    ...         ...    ...\n",
              "11909  False  False  False  False  ...  False  False       False  False\n",
              "11910  False  False  False  False  ...  False  False       False  False\n",
              "11911  False  False  False  False  ...  False  False       False  False\n",
              "11912  False  False  False  False  ...  False  False       False  False\n",
              "11913  False  False  False  False  ...  False  False       False  False\n",
              "\n",
              "[11122 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 535
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mih3jF1S5EbT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e471f7db-bf8a-497d-8197-40dd58509056"
      },
      "source": [
        "data=data.dropna()\n",
        "print(data.isnull().sum())"
      ],
      "execution_count": 536,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Make             0\n",
            "Model            0\n",
            "Year             0\n",
            "HP               0\n",
            "Cylinders        0\n",
            "Transmission     0\n",
            "Drive Mode       0\n",
            "Vehicle Size     0\n",
            "Vehicle Style    0\n",
            "MPG-H            0\n",
            "MPG-C            0\n",
            "Popularity       0\n",
            "Price            0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OF5UNLd06sFY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "0b66607c-265b-49e7-f1d8-7ffea5dcf990"
      },
      "source": [
        "data.count()"
      ],
      "execution_count": 537,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make             11024\n",
              "Model            11024\n",
              "Year             11024\n",
              "HP               11024\n",
              "Cylinders        11024\n",
              "Transmission     11024\n",
              "Drive Mode       11024\n",
              "Vehicle Size     11024\n",
              "Vehicle Style    11024\n",
              "MPG-H            11024\n",
              "MPG-C            11024\n",
              "Popularity       11024\n",
              "Price            11024\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 537
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mR7WoNl76vJZ",
        "colab_type": "text"
      },
      "source": [
        "Hence we can see that all the null values have been successfully removed!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDga5zjqPv_p",
        "colab_type": "text"
      },
      "source": [
        "**Therefore we can conclude now that the data is now completely cleaned for further Data Analysis.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fR7r33zFNmM",
        "colab_type": "text"
      },
      "source": [
        "# STEP 5:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xQPYyhLFRii",
        "colab_type": "text"
      },
      "source": [
        "## Plotting Graphs and Outlier Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkDeWQyZSYxu",
        "colab_type": "text"
      },
      "source": [
        "In Statistics, **Outliers** are the extreme values that deviate from other observations on data indicating a variability in a measurement, experimental errors or a novelty. \n",
        "\n",
        "Such datapoints could could cause anomalies while performing Data Analysis and hence should be eliminated from the data. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z22cHbxwFc3C",
        "colab_type": "text"
      },
      "source": [
        "###**Plotting Outlier Graphs using Boxplot**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gn4wVl3i2vUm",
        "colab_type": "text"
      },
      "source": [
        "In descriptive statistics, a box plot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also\n",
        "have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the term box and whisker plot. Outliers may be plotted as individual points"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwcDvR4gFiBl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "03b1af3f-67fd-4335-bfe6-acad8eb809ab"
      },
      "source": [
        "fig, ax= plt.subplots(2,3)\n",
        "\n",
        "ax[0,0].boxplot(x=data['HP'])\n",
        "ax[0,0].set_title(\"HP\")\n",
        "\n",
        "ax[0,1].boxplot(x=data['Cylinders'])\n",
        "ax[0,1].set_title(\"Cylinders\")\n",
        "\n",
        "ax[0,2].boxplot(x=data['MPG-H'])\n",
        "ax[0,2].set_title(\"MPG-H\")\n",
        "\n",
        "ax[1,0].boxplot(x=data['MPG-H'])\n",
        "ax[1,0].set_title(\"MPG-H\")\n",
        "\n",
        "ax[1,1].boxplot(x=data['MPG-C'])\n",
        "ax[1,1].set_title(\"MPG-C\")\n",
        "\n",
        "ax[1,2].boxplot(x=data['Price'])\n",
        "ax[1,2].set_title(\"Price\")\n",
        "\n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": 538,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EwKhnJoIz-B",
        "colab_type": "text"
      },
      "source": [
        "###**Plotting Outlier Graphs using Scatterplot**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhYAZOYm2_Lx",
        "colab_type": "text"
      },
      "source": [
        "A scatterplot is a type of data display that shows the relationship between two numerical variables. Each member of the dataset gets plotted as a point whose x-y coordinates relates to its values for the two variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyAj9D5VIrr1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1b5579e9-abc0-4ff0-cd1d-c03f583cd378"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "ax.scatter(data['HP'], data['Price'])\n",
        "ax.set_xlabel('Price', fontsize=20)\n",
        "ax.set_ylabel('HP', fontsize=20)\n",
        "plt.show()"
      ],
      "execution_count": 539,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-tQ0DeUJiSe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d8e54d30-f32b-4516-8e8f-7bf8e783c6d4"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 540,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Drive Mode</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year     HP  ...  MPG-H MPG-C Popularity  Price\n",
              "0  BMW  1 Series M  2011  335.0  ...     26    19       3916  46135\n",
              "1  BMW    1 Series  2011  300.0  ...     28    19       3916  40650\n",
              "2  BMW    1 Series  2011  300.0  ...     28    20       3916  36350\n",
              "3  BMW    1 Series  2011  230.0  ...     28    18       3916  29450\n",
              "4  BMW    1 Series  2011  230.0  ...     28    18       3916  34500\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 540
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUY_gvZVJQnF",
        "colab_type": "text"
      },
      "source": [
        "###**Removing the Outliers using Interquartile Range Function**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85Jeb9Wk3YHM",
        "colab_type": "text"
      },
      "source": [
        "The interquartile range (IQR) is a measure of variability, based on dividing a data set into quartiles. Quartiles divide a rank-ordered data set into four equal parts. The values that divide each part are called the first, second, and third quartiles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EKSDSCqJXVx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "56759c9e-6ccb-4170-d7f9-ec8b4ab5e1bd"
      },
      "source": [
        "Q1 = data.quantile(0.25)\n",
        "Q3 = data.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": 541,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Year              9.00\n",
            "HP              131.00\n",
            "Cylinders         2.00\n",
            "MPG-H             8.00\n",
            "MPG-C             6.00\n",
            "Popularity     1460.00\n",
            "Price         21426.25\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK3LIkGXJpE7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8e71d7fc-d9de-40bc-fbd0-fe099e8da979"
      },
      "source": [
        "data = data[~((data < (Q1 - 1.5 * IQR)) |(data > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "data.shape"
      ],
      "execution_count": 542,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8546, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 542
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjmV28zWJ5iP",
        "colab_type": "text"
      },
      "source": [
        "Hence we conclude that we have eliminated all the outliers from the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PeHIX0zoKLBg",
        "colab_type": "text"
      },
      "source": [
        "# STEP 6:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ErT6aqPsKO4U",
        "colab_type": "text"
      },
      "source": [
        "### Plotting the Most Represented Car Brands and finding the average price among the top car brands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KItOJ_RG0oOR",
        "colab_type": "text"
      },
      "source": [
        "A Better Visualisation of the Data contributes to a better Decision Making in an Exploratory Data Analysis  :) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Pff7wKdKsvW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "3d38ddda-cfc2-4306-ab54-b3ce822f721a"
      },
      "source": [
        "# Percentage of car per brand\n",
        "counts = data['Make'].value_counts()*100/sum(data['Make'].value_counts())\n",
        "\n",
        "# Top 10 car brands\n",
        "popular_labels = counts.index[:10]\n",
        "    \n",
        "# Plot\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.barh(popular_labels, width=counts[:10])\n",
        "plt.title('Top 10 Car Brands', fontsize = 20)\n",
        "plt.show()"
      ],
      "execution_count": 543,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJPZClIyLFkF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "678ea2f2-b496-42e0-d968-6bd493b2460c"
      },
      "source": [
        "prices = data[['Make','Price']].loc[(data['Make'] == 'Chevrolet')|\n",
        "               (data['Make'] == 'Ford')|\n",
        "               (data['Make'] == 'Volkswagen')|\n",
        "               (data['Make'] == 'Toyota')|\n",
        "               (data['Make'] == 'Dodge')|\n",
        "               (data['Make'] == 'Nissan')|\n",
        "               (data['Make'] == 'GMC')|\n",
        "               (data['Make'] == 'Honda')|\n",
        "               (data['Make'] == 'Mazda')].groupby('Make').mean()\n",
        "print(prices)"
      ],
      "execution_count": 544,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   Price\n",
            "Make                    \n",
            "Chevrolet   28142.855891\n",
            "Dodge       26076.722353\n",
            "GMC         33261.400853\n",
            "Honda       27381.364611\n",
            "Mazda       20495.820106\n",
            "Nissan      28352.543860\n",
            "Toyota      27769.421793\n",
            "Volkswagen  29032.817476\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZcCRw4u_nui",
        "colab_type": "text"
      },
      "source": [
        "Hence we can conclude by the plots that **Chevrolet** is the most represented Car Brand amongst all others. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1spgnF7faXSN",
        "colab_type": "text"
      },
      "source": [
        "# STEP 7:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHkowmABaZoM",
        "colab_type": "text"
      },
      "source": [
        "##Plotting the correlation matrix and Documenting the Insights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRhNjopIahnO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "1a3154a0-9f6c-4672-d145-95758dd5072a"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 545,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.325104</td>\n",
              "      <td>-0.106597</td>\n",
              "      <td>0.373319</td>\n",
              "      <td>0.335236</td>\n",
              "      <td>0.200710</td>\n",
              "      <td>0.591439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP</th>\n",
              "      <td>0.325104</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.734094</td>\n",
              "      <td>-0.453451</td>\n",
              "      <td>-0.554768</td>\n",
              "      <td>0.095161</td>\n",
              "      <td>0.745852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>-0.106597</td>\n",
              "      <td>0.734094</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.691395</td>\n",
              "      <td>-0.746361</td>\n",
              "      <td>0.013373</td>\n",
              "      <td>0.389049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG-H</th>\n",
              "      <td>0.373319</td>\n",
              "      <td>-0.453451</td>\n",
              "      <td>-0.691395</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.936978</td>\n",
              "      <td>0.093002</td>\n",
              "      <td>-0.121262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG-C</th>\n",
              "      <td>0.335236</td>\n",
              "      <td>-0.554768</td>\n",
              "      <td>-0.746361</td>\n",
              "      <td>0.936978</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.081110</td>\n",
              "      <td>-0.197330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Popularity</th>\n",
              "      <td>0.200710</td>\n",
              "      <td>0.095161</td>\n",
              "      <td>0.013373</td>\n",
              "      <td>0.093002</td>\n",
              "      <td>0.081110</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.113604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0.591439</td>\n",
              "      <td>0.745852</td>\n",
              "      <td>0.389049</td>\n",
              "      <td>-0.121262</td>\n",
              "      <td>-0.197330</td>\n",
              "      <td>0.113604</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Year        HP  Cylinders  ...     MPG-C  Popularity     Price\n",
              "Year        1.000000  0.325104  -0.106597  ...  0.335236    0.200710  0.591439\n",
              "HP          0.325104  1.000000   0.734094  ... -0.554768    0.095161  0.745852\n",
              "Cylinders  -0.106597  0.734094   1.000000  ... -0.746361    0.013373  0.389049\n",
              "MPG-H       0.373319 -0.453451  -0.691395  ...  0.936978    0.093002 -0.121262\n",
              "MPG-C       0.335236 -0.554768  -0.746361  ...  1.000000    0.081110 -0.197330\n",
              "Popularity  0.200710  0.095161   0.013373  ...  0.081110    1.000000  0.113604\n",
              "Price       0.591439  0.745852   0.389049  ... -0.197330    0.113604  1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 545
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFi9bM_1Ksah",
        "colab_type": "text"
      },
      "source": [
        "###**Heat Maps and Correlation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSHZRw-OL8WR",
        "colab_type": "text"
      },
      "source": [
        "A heatmap is a graphical representation of data in which data values are represented as colors. That is, it uses color in order to\n",
        "communicate a value to the reader.\n",
        "\n",
        "A correlation matrix is a table showing correlation coefficients between variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLyZ0LkkJURc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8f72eb8e-8ce5-44da-e57e-cd63729a98e9"
      },
      "source": [
        "data_corr=data.corr()\n",
        "f,ax=plt.subplots(figsize=(10,5))\n",
        "sns.heatmap(data_corr,cmap='viridis',annot=True)\n",
        "plt.title(\"Correlation between features\",weight='bold',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 546,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7jlcASIK9a5",
        "colab_type": "text"
      },
      "source": [
        "###**Scatter Plots**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwYz2WmFMRmM",
        "colab_type": "text"
      },
      "source": [
        "A Scatter Plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUgL-R5YarKs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5cfc3cdf-3455-4b83-ac33-4dce0b4d8636"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "ax.scatter(data['HP'], data['Price'])\n",
        "ax.set_xlabel('HP', fontsize=15)\n",
        "ax.set_ylabel('Price', fontsize=15)\n",
        "plt.title(\"HP vs Price\",weight='bold',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 547,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qn8Urle1CNNB",
        "colab_type": "text"
      },
      "source": [
        "###**Documenting The Insights**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGTWRoN8KROE",
        "colab_type": "text"
      },
      "source": [
        "**Which are most correlational factors amongst all?**\n",
        "\n",
        "*   Cylinders & HP\n",
        "*   Highway MPG & City MPG\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dHMnRfiKTl8",
        "colab_type": "text"
      },
      "source": [
        "**Which are most Anti-correlational factors amongst all?**\n",
        "*   Cylinders & Highway MPG\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMyZ_Y0VayQO",
        "colab_type": "text"
      },
      "source": [
        "Hence all the values are successfully plotted in the correlation matrix and are statistically represented by the Scatterplot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gGJ0I30bIul",
        "colab_type": "text"
      },
      "source": [
        "# STEP 8:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ErLKc6X8H0yn",
        "colab_type": "text"
      },
      "source": [
        "##Performing an Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNn5CZf1dj61",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "6621b912-3ccf-4b82-b55f-cb60dbe2d6b0"
      },
      "source": [
        "#creating new column function for 'Price Range' for easy visualization\n",
        "def getrange(Price):\n",
        " if (Price >= 0 and Price < 25000):\n",
        "      return '0 - 25000'\n",
        " if (Price >= 25000 and Price < 50000):\n",
        "      return '25000 - 50000'\n",
        " if (Price >= 50000 and Price < 75000):\n",
        "      return '50000 - 75000'\n",
        " if (Price >= 75000 and Price < 100000):\n",
        "      return '75000 - 100000'\n",
        "\n",
        "data['Price Range'] = data.apply(lambda x:getrange(x['Price']),axis = 1)\n",
        "data['Price Range'].value_counts()"
      ],
      "execution_count": 548,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000 - 50000     4648\n",
              "0 - 25000         2993\n",
              "50000 - 75000      904\n",
              "75000 - 100000       1\n",
              "Name: Price Range, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 548
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDkN94ADbeY7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "4e0a552b-c12a-41ae-f0f0-3dcd0c8662c6"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.title(\"Price vs Year\",fontweight = 30,fontsize = 20)\n",
        "sns.barplot(data['Year'],data['Price'],color='red')"
      ],
      "execution_count": 549,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea8bbb908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 549
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bY524mUpRI24",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "40aa2c3c-988c-4408-bcb4-b3e326705c45"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (15,9)\n",
        "x = pd.crosstab(data['Price Range'],data['Vehicle Size'])\n",
        "x.div(x.sum(1).astype(float),axis = 0).plot(kind = 'bar',stacked = False)\n",
        "plt.title('Price vs Size',fontweight = 30,fontsize = 20)\n",
        "plt.show()\n"
      ],
      "execution_count": 550,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXsN6MmVYqTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "a3ae3c1c-e48e-4627-ef8f-ba7283d524c9"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (15,9)\n",
        "x = pd.crosstab(data['Price Range'],data['Cylinders'])\n",
        "color = plt.cm.copper(np.linspace(0,1,9))\n",
        "x.div(x.sum(1).astype(float),axis = 0).plot(kind = 'bar',stacked = True ,color=color)\n",
        "plt.title(\"Price vs Cylinders\",fontweight = 30,fontsize = 20)\n",
        "plt.show()"
      ],
      "execution_count": 551,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "derWTcPOR-DS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "305994ff-7225-47f1-a225-9c3103f5a725"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (15,10)\n",
        "x = pd.crosstab(data['Price Range'],data['Vehicle Style'])\n",
        "x.div(x.sum(1).astype(float),axis = 0).plot(kind = 'bar',stacked = True)\n",
        "plt.title(\"Price vs Vehicle Style\",fontweight = 30,fontsize = 20)\n",
        "plt.show()"
      ],
      "execution_count": 552,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MADcfARdFjz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "2c246a0c-305d-492c-dd35-64cb4bb0c439"
      },
      "source": [
        "plt.title(\"Price vs Popularity\",fontweight = 30,fontsize = 20)\n",
        "sns.barplot(data['Popularity'],data['Price'])"
      ],
      "execution_count": 553,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea8af4860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 553
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMQE5Mk_HYQ4",
        "colab_type": "text"
      },
      "source": [
        "##Documenting the Findings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3AuyazQHlc_",
        "colab_type": "text"
      },
      "source": [
        "1.   The Car Price is increased gradually after the year 2000.\n",
        "2.   The Price is always maximum for MPG-H 13 and lowest for MPG-H 15.\n",
        "3.   The Number of Cylinders are directly proportional to the Price.\n",
        "4.   The Price is always maximum for MPG-C 10 and lowest for MPG-C 31.\n",
        "5.   Automatic type of transmission is preferred transmission over all  price ranges and hence, is independent of price.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwW2PMkLHuSr",
        "colab_type": "text"
      },
      "source": [
        "# STEP 9:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFpxPtq4HzKk",
        "colab_type": "text"
      },
      "source": [
        "## Splitting The Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjHAFAAt4dXA",
        "colab_type": "text"
      },
      "source": [
        "It is standard in Machine Learning to split data into training and test sets. The reason for this is very straightforward: if you try and evaluate your system on data you have trained it on, you are doing something unrealistic. The whole point of a machine learning system is to be able to work with unseen data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryl7S02TE98k",
        "colab_type": "text"
      },
      "source": [
        "###**Label Encoding**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sK-VrB8D5NBW",
        "colab_type": "text"
      },
      "source": [
        "Label Encoding refers to converting the labels into numeric form so as to convert it into the machine-readable form. Machine learning algorithms can then decide in a better way on how those labels must be operated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEjMy0SoGjGL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "21eb8450-d8a9-4009-84d1-b6d9730de17f"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 554,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Drive Mode</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Price</th>\n",
              "      <th>Price Range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series M</td>\n",
              "      <td>2011</td>\n",
              "      <td>335.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>46135</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>3916</td>\n",
              "      <td>40650</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>36350</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Coupe</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>29450</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
              "      <td>34500</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Make       Model  Year     HP  ...  MPG-C Popularity  Price    Price Range\n",
              "0  BMW  1 Series M  2011  335.0  ...     19       3916  46135  25000 - 50000\n",
              "1  BMW    1 Series  2011  300.0  ...     19       3916  40650  25000 - 50000\n",
              "2  BMW    1 Series  2011  300.0  ...     20       3916  36350  25000 - 50000\n",
              "3  BMW    1 Series  2011  230.0  ...     18       3916  29450  25000 - 50000\n",
              "4  BMW    1 Series  2011  230.0  ...     18       3916  34500  25000 - 50000\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 554
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwuZuFrDIBYu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "# creating an encoder\n",
        "le = LabelEncoder()\n",
        "# label encoding for Engine Fuel Tpe\n",
        "#data['Engine Fuel Type'] = le.fit_transform(data['Engine Fuel Type'])\n",
        "# label encoding for Transmission\n",
        "data['Transmission'] = le.fit_transform(data['Transmission'])\n",
        "# label encoding for Drive Mode\n",
        "data['Drive Mode'] = le.fit_transform(data['Drive Mode'])\n",
        "# label encoding for Vehicle Size\n",
        "data['Vehicle Size'] = le.fit_transform(data['Vehicle Size'])\n",
        "# label encoding for Vehicle Style\n",
        "data['Vehicle Style'] = le.fit_transform(data['Vehicle Style'])\n",
        "# label encoding for Make\n",
        "data['Make'] = le.fit_transform(data['Make'])\n",
        "# label encoding for Model\n",
        "data['Model'] = le.fit_transform(data['Model'])\n",
        "# label encoding for Price Range\n",
        "data['Price'] = le.fit_transform(data['Price'])"
      ],
      "execution_count": 555,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjEtdBrfGnHR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "5bd1da34-a668-4290-bc95-b4f37fa4558e"
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": 556,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Year</th>\n",
              "      <th>HP</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Drive Mode</th>\n",
              "      <th>Vehicle Size</th>\n",
              "      <th>Vehicle Style</th>\n",
              "      <th>MPG-H</th>\n",
              "      <th>MPG-C</th>\n",
              "      <th>Popularity</th>\n",
              "      <th>Price</th>\n",
              "      <th>Price Range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11909</th>\n",
              "      <td>0</td>\n",
              "      <td>636</td>\n",
              "      <td>2012</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>3944</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11910</th>\n",
              "      <td>0</td>\n",
              "      <td>636</td>\n",
              "      <td>2012</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>4415</td>\n",
              "      <td>50000 - 75000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11911</th>\n",
              "      <td>0</td>\n",
              "      <td>636</td>\n",
              "      <td>2012</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>4187</td>\n",
              "      <td>50000 - 75000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>0</td>\n",
              "      <td>636</td>\n",
              "      <td>2013</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>4201</td>\n",
              "      <td>50000 - 75000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11913</th>\n",
              "      <td>19</td>\n",
              "      <td>637</td>\n",
              "      <td>2006</td>\n",
              "      <td>221.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>61</td>\n",
              "      <td>2196</td>\n",
              "      <td>25000 - 50000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Make  Model  Year     HP  ...  MPG-C  Popularity  Price    Price Range\n",
              "11909     0    636  2012  300.0  ...     16         204   3944  25000 - 50000\n",
              "11910     0    636  2012  300.0  ...     16         204   4415  50000 - 75000\n",
              "11911     0    636  2012  300.0  ...     16         204   4187  50000 - 75000\n",
              "11912     0    636  2013  300.0  ...     16         204   4201  50000 - 75000\n",
              "11913    19    637  2006  221.0  ...     17          61   2196  25000 - 50000\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 556
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CakMumazcB0K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# splitting the dependent and independent variables\n",
        "#x = data.iloc[:,:12]\n",
        "#y = data.iloc[:,12]\n",
        "#x=data.iloc[:,:-1] \n",
        "#y=data.iloc[:,-1] \n",
        "#y = data['Price'].values\n",
        "#print(x.shape)\n",
        "#print(y.shape)\n",
        "#x.head()"
      ],
      "execution_count": 557,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAgvMbA9o2UX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dependent and independent variables\n",
        "x = data[['Popularity', 'Year', 'HP', 'Cylinders', 'MPG-H', 'MPG-C']].values\n",
        "y = data['Price'].values"
      ],
      "execution_count": 558,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAvtTRt_bnf7",
        "colab_type": "text"
      },
      "source": [
        "###**Splitting the Dataset into Train and Test Labels**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpwJ_ypl547p",
        "colab_type": "text"
      },
      "source": [
        "In Machine Learning, we basically try to create a model to predict the test data. So, we use the training data to fit the model and testing data to test the model. The test set is generally what is used to evaluate the models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qqzpBQtofib",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Scaling \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "sc_y = StandardScaler()\n",
        "x = sc_x.fit_transform(x)\n",
        "y = sc_y.fit_transform(y.reshape(-1,1))"
      ],
      "execution_count": 559,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnPBLPLfbw2W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "6d8619b5-1ead-4feb-97b1-2899340e6b36"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 0)\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 560,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6836, 6)\n",
            "(6836, 1)\n",
            "(1710, 6)\n",
            "(1710, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uO5z4Jl3j32S",
        "colab_type": "text"
      },
      "source": [
        "# STEP 10:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBYF_ASCj7cW",
        "colab_type": "text"
      },
      "source": [
        "## Implementing Various Machine Learning Algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz7YKKFtkFlt",
        "colab_type": "text"
      },
      "source": [
        "The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEsGEMKPlDog",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#IMPLEMENTING STANDARD SCALER Normalization\n",
        "#from sklearn.preprocessing import MinMaxScaler\n",
        "# creating a scaler\n",
        "#mm = MinMaxScaler()\n",
        "# feeding the independent variable into the scaler\n",
        "#x_train = mm.fit_transform(x_train)\n",
        "#x_test = mm.transform(x_test)"
      ],
      "execution_count": 561,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGzDKYkBhYvJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#IMPLEMENTING STANDARD SCALER Normalization\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "#scaler = StandardScaler()\n",
        "#scaler.fit(x_train)\n",
        "\n",
        "#x_train = scaler.transform(x_train)\n",
        "#x_test = scaler.transform(x_test)"
      ],
      "execution_count": 562,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRpFPzvBsJeM",
        "colab_type": "text"
      },
      "source": [
        "###**Linear Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdEGr7toFjgl",
        "colab_type": "text"
      },
      "source": [
        "In statistics, Linear Regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called **Simple Linear Regression**. It attempts to model the relationship between two variables by fitting a linear equation to observed data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dRRxGwojNAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4327c260-d6e6-476b-dce9-7fd9e73c67e6"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(x_train, y_train)"
      ],
      "execution_count": 563,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 563
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5HYmo-FjOXo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "28fd0cc9-a64f-474a-b1b8-88de9d4e8dc9"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = regressor.predict(x_test)\n",
        "plt.scatter(y_test,y_pred)"
      ],
      "execution_count": 564,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8ea855bb00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 564
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hox5RxzYjg9g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "b1062b51-668b-44e7-c678-4eb8a412c891"
      },
      "source": [
        "sns.distplot((y_test-y_pred),bins=50)"
      ],
      "execution_count": 565,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea855beb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 565
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G88tBtEijhuk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "17eefb01-6944-4e63-f283-e563a0bd968c"
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        "print('R2 Score:', metrics.r2_score(y_test, y_pred))"
      ],
      "execution_count": 567,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.4176259735877096\n",
            "Root Mean Squared Error: 0.5341308273109304\n",
            "R2 Score: 0.7075895169982236\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypbkloLssq3W",
        "colab_type": "text"
      },
      "source": [
        "###**Polynomial Linear Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUom-5-vGQNP",
        "colab_type": "text"
      },
      "source": [
        "Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rq6u_9b3s00T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b0e5553-948a-41f9-867d-86fb5d9620cd"
      },
      "source": [
        "# Fitting Polynomial Regression to the dataset\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 4)\n",
        "x_poly = poly_reg.fit_transform(x_train)\n",
        "poly_reg.fit(x_poly, y_train)\n",
        "lin_reg_2 = LinearRegression()\n",
        "lin_reg_2.fit(x_poly, y_train)"
      ],
      "execution_count": 568,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 568
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqhvNmhHtGHK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "d8e86cea-7554-42bd-a462-79563fbe6ca2"
      },
      "source": [
        "# Predicting a new result with Polynomial Regression\n",
        "y_pred=lin_reg_2.predict(poly_reg.fit_transform(x_test))\n",
        "plt.scatter(y_test,y_pred)"
      ],
      "execution_count": 569,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8ea83a2ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 569
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkVUm1NJtG3h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "ebdd2821-615d-404d-ffb5-3d7380463b14"
      },
      "source": [
        "sns.distplot((y_test-y_pred),bins=50)"
      ],
      "execution_count": 570,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea83dbda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 570
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnfLI2W4tKoT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "ace103d2-7aad-4b91-f995-550663af3b6a"
      },
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        "print('R2 Score:', metrics.r2_score(y_test, y_pred))"
      ],
      "execution_count": 571,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.3094835337096744\n",
            "Root Mean Squared Error: 0.40924064067283955\n",
            "R2 Score: 0.8283455123016693\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBkVL9gxvIcn",
        "colab_type": "text"
      },
      "source": [
        "###**SVM**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ll5RsFtHGhyS",
        "colab_type": "text"
      },
      "source": [
        "A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASI4sISIvLk3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "0603dd25-9d49-4934-a5a7-16f227f9b9a3"
      },
      "source": [
        "# Fitting SVR to the dataset\n",
        "from sklearn.svm import SVR\n",
        "regressor = SVR(kernel = 'rbf')\n",
        "regressor.fit(x_train, y_train)"
      ],
      "execution_count": 572,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 572
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nb6JeLDmvaq4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "35c4b95e-4534-4299-eb5d-1ed257ce1165"
      },
      "source": [
        "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
        "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ],
      "execution_count": 573,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 573
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jetvxpNvdj0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "cd796603-ee4d-4362-e639-6ac0ee0b4440"
      },
      "source": [
        "# Predicting a new result\n",
        "y_pred = regressor.predict(x_test)\n",
        "plt.scatter(y_test,y_pred)"
      ],
      "execution_count": 574,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8ea826c0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 574
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hycKJDxBviyS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "5b40c3c4-f564-4a92-8445-45ac7cb067ee"
      },
      "source": [
        "sns.distplot((y_test-y_pred),bins=50)"
      ],
      "execution_count": 575,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea82a0828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 575
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWaoTK0MvmFI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "296cd5ad-21af-43a0-dc01-4717487b5a3d"
      },
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        "print('R2 Score:', metrics.r2_score(y_test, y_pred))"
      ],
      "execution_count": 576,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.2956929873413738\n",
            "Root Mean Squared Error: 0.4027913464024967\n",
            "R2 Score: 0.8337131473599289\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOVo6YiSyD-t",
        "colab_type": "text"
      },
      "source": [
        "###**Random Forests**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZ1aCnP_G5R_",
        "colab_type": "text"
      },
      "source": [
        "Random forests or random decision forests are an ensemble learning method for regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdIEtWyXyKc4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "3fbb8b95-610d-4a88-d80f-24795e1ae70e"
      },
      "source": [
        "# Fitting Random Forest Regression to the dataset\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)\n",
        "regressor.fit(x_train, y_train)"
      ],
      "execution_count": 577,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=300, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 577
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSXmrbjhyOF0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "fe459070-057c-44cd-c9f2-b88d52e41a29"
      },
      "source": [
        "y_pred = regressor.predict(x_test)\n",
        "plt.scatter(y_test,y_pred)"
      ],
      "execution_count": 578,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8ea815cfd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 578
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vO-g-G_yV1K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "a25173e2-ebc5-4af7-f143-bd6ba2244b06"
      },
      "source": [
        "sns.distplot((y_test-y_pred),bins=50)"
      ],
      "execution_count": 579,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea86730b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 579
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12v2QTGAyZwd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "1eb6c402-7ccd-443f-c582-63e26717427f"
      },
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        "print('R2 Score:', metrics.r2_score(y_test, y_pred))"
      ],
      "execution_count": 580,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.1861213813159454\n",
            "Root Mean Squared Error: 0.24845897162945524\n",
            "R2 Score: 0.9367286622420182\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IKcCmq3G3NA",
        "colab_type": "text"
      },
      "source": [
        "Hence, we conclude that **Random Forests** has proven to be the best fit for the Test data with the Maximum R2 Score minimizing all the Errors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nCPwJaLh_FG",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "796PoY8WiDw2",
        "colab_type": "text"
      },
      "source": [
        "We have succesfully Completed our EDA Report and Implemented various Machine Learning Models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FlfC9NaiO30",
        "colab_type": "text"
      },
      "source": [
        "## My Thoughts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YcTVyWticdM",
        "colab_type": "text"
      },
      "source": [
        "It was a splendid journey developing this project and I would always be keen to find myself working out for many more opportunities in the Forthcoming Future.\n",
        "\n",
        "Thank You !"
      ]
    }
  ]
}