lab/Lab_2_Exploratory_Data_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/drshahizan/Python_EDA/blob/main/lab/Lab_2_Exploratory_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOvht7vqQGdR"
      },
      "source": [
        "# Exploratory data analysis in Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhA_0CQOTDQy"
      },
      "source": [
        "## Let us understand how to explore the data in Python.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEfC0QszTKX_"
      },
      "source": [
        "\n",
        "![alt text](https://moriohcdn.b-cdn.net/ff3cc511fb.png)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMyUUHXcHdqt"
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      "source": [
        "Image Credits: Morioh"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dB_j6LtTTO5j"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hg00soETQ3z"
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      "source": [
        "**What is Exploratory Data Analysis ?**\n",
        "\n",
        "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 can ask to define the problem statement or definition on our data set which is very important."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfelutoyTS25"
      },
      "source": [
        "**How to perform Exploratory Data Analysis ?**\n",
        "\n",
        "This is one such question that everyone is keen on knowing the answer. Well, the answer is it depends on the data set that you are working. There is no one method or common methods in order to perform EDA, whereas in this tutorial you can understand some common methods and plots that would be used in the EDA process."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3VfNkBBw15s"
      },
      "source": [
        "**What data are we exploring today ?**\n",
        "\n",
        "\n",
        "\n",
        "Since I am a huge fan of cars, I got a very beautiful data-set of cars from Kaggle. The data-set can be downloaded from [here](https://www.kaggle.com/CooperUnion/cardataset). To give a piece of brief information about the data set this data contains more of 10, 000 rows and more than 10 columns which contains features of the car such as Engine Fuel Type, Engine HP, Transmission Type, highway MPG, city MPG and many more. So in this tutorial, we will explore the data and make it ready for modeling."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQDO4JCqTThV"
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      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPSqz1wzTXvz"
      },
      "source": [
        "## 1. Importing the required libraries for EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eLMx1Ebwa92"
      },
      "source": [
        "Below are the libraries that are used in order to perform EDA (Exploratory data analysis) in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGyDovL2QDLa"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns                       #visualisation\n",
        "import matplotlib.pyplot as plt             #visualisation\n",
        "%matplotlib inline     \n",
        "sns.set(color_codes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ye6eXuohTd5Q"
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      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Y3Z2DbKTfJt"
      },
      "source": [
        "## 2. Loading the data into the data frame."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ko5zGJFCySaz"
      },
      "source": [
        "Loading the data into the pandas data frame is certainly one of the most important steps in EDA, as we can see that the value from the data set is comma-separated. So all we have to do is to just read the CSV into a data frame and pandas data frame does the job for us."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgzUzD61IM8h"
      },
      "source": [
        "To get or load the dataset into the notebook, all I did was one trivial step. In Google Colab at the left-hand side of the notebook, you will find a > (greater than symbol). When you click that you will find a tab with three options, you just have to select Files. Then you can easily upload your file with the help of the Upload option. No need to mount to the google drive or use any specific libraries just upload the data set and your job is done. One thing to remember in this step is that uploaded files will get deleted when this runtime is recycled. This is how I got the data set into the notebook."
      ]
    },
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      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/drshahizan/dataset/main/data.csv\")\n",
        "# To display the top 5 rows \n",
        "df.head(5)               "
      ],
      "execution_count": null,
      "outputs": [
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          "data": {
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              "  Make       Model  Year             Engine Fuel Type  Engine HP  \\\n",
              "0  BMW  1 Series M  2011  premium unleaded (required)      335.0   \n",
              "1  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
              "2  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
              "3  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
              "4  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
              "\n",
              "   Engine Cylinders Transmission Type     Driven_Wheels  Number of Doors  \\\n",
              "0               6.0            MANUAL  rear wheel drive              2.0   \n",
              "1               6.0            MANUAL  rear wheel drive              2.0   \n",
              "2               6.0            MANUAL  rear wheel drive              2.0   \n",
              "3               6.0            MANUAL  rear wheel drive              2.0   \n",
              "4               6.0            MANUAL  rear wheel drive              2.0   \n",
              "\n",
              "                         Market Category Vehicle Size Vehicle Style  \\\n",
              "0  Factory Tuner,Luxury,High-Performance      Compact         Coupe   \n",
              "1                     Luxury,Performance      Compact   Convertible   \n",
              "2                Luxury,High-Performance      Compact         Coupe   \n",
              "3                     Luxury,Performance      Compact         Coupe   \n",
              "4                                 Luxury      Compact   Convertible   \n",
              "\n",
              "   highway MPG  city mpg  Popularity   MSRP  \n",
              "0           26        19        3916  46135  \n",
              "1           28        19        3916  40650  \n",
              "2           28        20        3916  36350  \n",
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      "source": [
        "df.tail(5)                        # To display the botton 5 rows"
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Make   Model  Year                Engine Fuel Type  Engine HP  \\\n",
              "11909    Acura     ZDX  2012     premium unleaded (required)      300.0   \n",
              "11910    Acura     ZDX  2012     premium unleaded (required)      300.0   \n",
              "11911    Acura     ZDX  2012     premium unleaded (required)      300.0   \n",
              "11912    Acura     ZDX  2013  premium unleaded (recommended)      300.0   \n",
              "11913  Lincoln  Zephyr  2006                regular unleaded      221.0   \n",
              "\n",
              "       Engine Cylinders Transmission Type      Driven_Wheels  Number of Doors  \\\n",
              "11909               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11910               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11911               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11912               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11913               6.0         AUTOMATIC  front wheel drive              4.0   \n",
              "\n",
              "                  Market Category Vehicle Size  Vehicle Style  highway MPG  \\\n",
              "11909  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
              "11910  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
              "11911  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
              "11912  Crossover,Hatchback,Luxury      Midsize  4dr Hatchback           23   \n",
              "11913                      Luxury      Midsize          Sedan           26   \n",
              "\n",
              "       city mpg  Popularity   MSRP  \n",
              "11909        16         204  46120  \n",
              "11910        16         204  56670  \n",
              "11911        16         204  50620  \n",
              "11912        16         204  50920  \n",
              "11913        17          61  28995  "
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              "      <th>11909</th>\n",
              "      <td>Acura</td>\n",
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              "      <td>all wheel drive</td>\n",
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              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>46120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11910</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>56670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11911</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>50620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11912</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2013</td>\n",
              "      <td>premium unleaded (recommended)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>all wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Crossover,Hatchback,Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>4dr Hatchback</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>204</td>\n",
              "      <td>50920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11913</th>\n",
              "      <td>Lincoln</td>\n",
              "      <td>Zephyr</td>\n",
              "      <td>2006</td>\n",
              "      <td>regular unleaded</td>\n",
              "      <td>221.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AUTOMATIC</td>\n",
              "      <td>front wheel drive</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Luxury</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>Sedan</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>61</td>\n",
              "      <td>28995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0270f929-1c6b-4c1e-8f90-4c0ca086bc62')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0270f929-1c6b-4c1e-8f90-4c0ca086bc62 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0270f929-1c6b-4c1e-8f90-4c0ca086bc62');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjQnr4SPzaL5"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAmC369yTpMF"
      },
      "source": [
        "## 3. Checking the types of data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ESKxikIzA1d"
      },
      "source": [
        "Here we check for the datatypes because sometimes the MSRP or the price of the car would be stored as a string, if in that case, we have to convert that string to the integer data only then we can plot the data via a graph. Here, in this case, the data is already in integer format so nothing to worry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPbKQ0noRptD",
        "outputId": "8b8df319-6844-4fb0-c531-36d85e9ad0cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make                  object\n",
              "Model                 object\n",
              "Year                   int64\n",
              "Engine Fuel Type      object\n",
              "Engine HP            float64\n",
              "Engine Cylinders     float64\n",
              "Transmission Type     object\n",
              "Driven_Wheels         object\n",
              "Number of Doors      float64\n",
              "Market Category       object\n",
              "Vehicle Size          object\n",
              "Vehicle Style         object\n",
              "highway MPG            int64\n",
              "city mpg               int64\n",
              "Popularity             int64\n",
              "MSRP                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beRJyQAezdX8"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoHuBhXxT5E9"
      },
      "source": [
        "## 4. Dropping irrelevant columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3cy877Mze4H"
      },
      "source": [
        "This step is certainly needed in every EDA because sometimes there would be many columns that we never use in such cases dropping is the only solution. In this case, the columns such as Engine Fuel Type, Market Category, Vehicle style, Popularity, Number of doors, Vehicle Size doesn't make any sense to me so I just dropped for this instance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvSkK8swTr9H",
        "outputId": "87a5a834-cf67-4c46-e7c6-80e046be029f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "df = df.drop(['Engine Fuel Type', 'Market Category', 'Vehicle Style', 'Popularity', 'Number of Doors', 'Vehicle Size'], axis=1)\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Make       Model  Year  Engine HP  Engine Cylinders Transmission Type  \\\n",
              "0  BMW  1 Series M  2011      335.0               6.0            MANUAL   \n",
              "1  BMW    1 Series  2011      300.0               6.0            MANUAL   \n",
              "2  BMW    1 Series  2011      300.0               6.0            MANUAL   \n",
              "3  BMW    1 Series  2011      230.0               6.0            MANUAL   \n",
              "4  BMW    1 Series  2011      230.0               6.0            MANUAL   \n",
              "\n",
              "      Driven_Wheels  highway MPG  city mpg   MSRP  \n",
              "0  rear wheel drive           26        19  46135  \n",
              "1  rear wheel drive           28        19  40650  \n",
              "2  rear wheel drive           28        20  36350  \n",
              "3  rear wheel drive           28        18  29450  \n",
              "4  rear wheel drive           28        18  34500  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3484c2e1-1991-49cd-bf03-dd271fbf0be5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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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>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>highway MPG</th>\n",
              "      <th>city mpg</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>26</td>\n",
              "      <td>19</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>28</td>\n",
              "      <td>19</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>28</td>\n",
              "      <td>20</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>28</td>\n",
              "      <td>18</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>28</td>\n",
              "      <td>18</td>\n",
              "      <td>34500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3484c2e1-1991-49cd-bf03-dd271fbf0be5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3484c2e1-1991-49cd-bf03-dd271fbf0be5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3484c2e1-1991-49cd-bf03-dd271fbf0be5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20OeQBpWz89v"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caAownWdUZso"
      },
      "source": [
        "## 5. Renaming the columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UABLiEcyz-2G"
      },
      "source": [
        "In this instance, most of the column names are very confusing to read, so I just tweaked their column names. This is a good approach it improves the readability of the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wHW41whURub",
        "outputId": "ef9b1e79-74f8-4719-c6bd-f3ec98109f6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = df.rename(columns={\"Engine HP\": \"HP\", \"Engine Cylinders\": \"Cylinders\", \"Transmission Type\": \"Transmission\", \"Driven_Wheels\": \"Drive Mode\",\"highway MPG\": \"MPG-H\", \"city mpg\": \"MPG-C\", \"MSRP\": \"Price\" })\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Make       Model  Year     HP  Cylinders Transmission        Drive Mode  \\\n",
              "0  BMW  1 Series M  2011  335.0        6.0       MANUAL  rear wheel drive   \n",
              "1  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "2  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "3  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "4  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "\n",
              "   MPG-H  MPG-C  Price  \n",
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tcGiOmV0afN"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiW7x_O4WIDX"
      },
      "source": [
        "## 6. Dropping the duplicate rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LpR5NW70hXm"
      },
      "source": [
        "This is often a handy thing to do because a huge data set as in this case contains more than 10, 000 rows often have some duplicate data which might be disturbing, so here I remove all the duplicate value from the data-set. For example prior to removing I had 11914 rows of data but after removing the duplicates 10925 data meaning that I had 989 of duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nBN_BCDWSmv",
        "outputId": "b07ed02c-81f9-4617-a20c-8f4447a934bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11914, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yB8t6o0wH7If",
        "outputId": "b7875d58-9a56-4924-8153-076e4c84a300",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"number of duplicate rows: \", duplicate_rows_df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of duplicate rows:  (989, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chhNvMFCIzqI"
      },
      "source": [
        "Now let us remove the duplicate data because it's ok to remove them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuobmetTV820",
        "outputId": "ccd8bd1b-272d-4e14-bfd3-f2e96e668e14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.count()      # Used to count the number of rows"
      ],
      "execution_count": null,
      "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",
              "MPG-H           11914\n",
              "MPG-C           11914\n",
              "Price           11914\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MJKjbzHI40K"
      },
      "source": [
        "So seen above there are 11914 rows and we are removing 989 rows of duplicate data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiOsEF6WVTSj",
        "outputId": "2f91a284-00a7-42ce-800e-ebafb16eca6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = df.drop_duplicates()\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Make       Model  Year     HP  Cylinders Transmission        Drive Mode  \\\n",
              "0  BMW  1 Series M  2011  335.0        6.0       MANUAL  rear wheel drive   \n",
              "1  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "2  BMW    1 Series  2011  300.0        6.0       MANUAL  rear wheel drive   \n",
              "3  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "4  BMW    1 Series  2011  230.0        6.0       MANUAL  rear wheel drive   \n",
              "\n",
              "   MPG-H  MPG-C  Price  \n",
              "0     26     19  46135  \n",
              "1     28     19  40650  \n",
              "2     28     20  36350  \n",
              "3     28     18  29450  \n",
              "4     28     18  34500  "
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make            10925\n",
              "Model           10925\n",
              "Year            10925\n",
              "HP              10856\n",
              "Cylinders       10895\n",
              "Transmission    10925\n",
              "Drive Mode      10925\n",
              "MPG-H           10925\n",
              "MPG-C           10925\n",
              "Price           10925\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCLUdZOQ1PDP"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkXUQtyQW3Dy"
      },
      "source": [
        "## 7. Dropping the missing or null values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5pKvJi41YCp"
      },
      "source": [
        "This is mostly similar to the previous step but in here all the missing values are detected and are dropped later. Now, this is not a good approach to do so, because many people just replace the missing values with the mean or the average of that column, but in this case, I just dropped that missing values. This is because there is nearly 100 missing value compared to 10, 000 values this is a small number and this is negligible so I just dropped those values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tzdlg-1OWjMz",
        "outputId": "40187cc5-f3d5-4d92-8927-1709be323d53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(df.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Make             0\n",
            "Model            0\n",
            "Year             0\n",
            "HP              69\n",
            "Cylinders       30\n",
            "Transmission     0\n",
            "Drive Mode       0\n",
            "MPG-H            0\n",
            "MPG-C            0\n",
            "Price            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWJqTVxTJQnO"
      },
      "source": [
        "This is the reason in the above step while counting both Cylinders and Horsepower (HP) had 10856 and 10895 over 10925 rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbbV0xHPWoad",
        "outputId": "af181a25-f2f7-466c-da23-f31e85a804c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df = df.dropna()    # Dropping the missing values.\n",
        "df.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Make            10827\n",
              "Model           10827\n",
              "Year            10827\n",
              "HP              10827\n",
              "Cylinders       10827\n",
              "Transmission    10827\n",
              "Drive Mode      10827\n",
              "MPG-H           10827\n",
              "MPG-C           10827\n",
              "Price           10827\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t7L9l2mJSoX"
      },
      "source": [
        "Now we have removed all the rows which contain the Null or N/A values (Cylinders and Horsepower (HP))."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-DmX1O4Wtox",
        "outputId": "59c5160e-d849-4397-bc88-c8911117b772",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(df.isnull().sum())   # After dropping the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Make            0\n",
            "Model           0\n",
            "Year            0\n",
            "HP              0\n",
            "Cylinders       0\n",
            "Transmission    0\n",
            "Drive Mode      0\n",
            "MPG-H           0\n",
            "MPG-C           0\n",
            "Price           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bk8RAHqQJVJK"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Py3sQc_ZxyU"
      },
      "source": [
        "## 8. Detecting Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g6CJPofZzHN"
      },
      "source": [
        "An outlier is a point or set of points that are different from other points. Sometimes they can be very high or very low. It's often a good idea to detect and remove the outliers. Because outliers are one of the primary reasons for resulting in a less accurate model. Hence it's a good idea to remove them. The outlier detection and removing that I am going to perform is called IQR score technique. Often outliers can be seen with visualizations using a box plot. Shown below are the box plot of MSRP, Cylinders, Horsepower and EngineSize. Herein all the plots, you can find some points are outside the box they are none other than outliers. The technique of finding and removing outlier that I am performing in this assignment is taken help of a tutorial from[ towards data science](https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtxX_y6zZ2ri",
        "outputId": "8dcc8baf-8660-4c8b-d754-c46e908396d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.boxplot(x=df['Price'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b6823f070>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEMCAYAAABnWmXlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQT0lEQVR4nO3da2xUdf7H8c9MO0jrLp0CpU6xgGS1kgARrRoobQWBkkhpVAhIxERMEZQ0rhFbFvgbhCCjxBihgN34ZA3rBV1w5aYhQRAChAeIFwgUpRfbUqC0sMq1M+f/gDAKBTuUzreXeb8eOTNnzvnNb35593hoZ1yO4zgCAJhwt/UAACCaEF0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwFBscxvU1/+mYPDmf5W3R4+/qK7u1xYNKpowT+FhnprHHIUn0vPkdruUmHj7DR9vNrrBoNOi6F55LprHPIWHeWoecxSetpwnLi8AgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaa/bqeW/Xvf/9LlZXlOn26QZKUkOBVampfTZnyTKQPDQDtTsSjW1lZrkOlR0K3jx0/GelDAkC7FfHoSlJMV6/FYQCg3eOaLgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhmIjsdOdO7erW7c4DRr0YFjbSlJGRlYkhgIA7UpEortjxzZ5PDFhRXfHjm2SiC6A6MDlBQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDsW09gEOHDkqSpk2b0sYjuVpa2oDQ2LzeRCUkJOjixQs6duyYHMeRx+NRr153KBgMqKamWi6XS263W8FgUD179lJ9fZ0aGxslKbSt5Oj48Vo1NjYqIcGrhoZ6eTwe+Xy99fe/vxq6b9WqZcrNfVwrVryjoqL/U2pq37DHfeX5M2cWKCHBG4mpQSurqCiT37/wpt/ra/cxa9YiFRbOb/E+WqozrrlIvibOdG/gSnCly29AeXmZampq5DiOJOnSpUuqqqpUTU21JMlxHAUCATmOoxMnakPB/eO2VVW/6NKlS3IcRw0N9aHHKirK9N///keS9MUXa1VaekgrV76rc+fO6b33lt/UuK88/8r+0P6VlBS36L2+dh9nz569pX20VGdcc5F8TW0a3fz8Z9ry8O3K9u1fq6KiXDt2bJPjODp79jdJUnV1lSory8PaR0NDfej5O3Zs1+nTDZEcMlpBRUWZqqurJN3ce93a+2ipzrjmIv2aIhLd06cb9PPPP8vvX6iKinIFG8+HHgs2nldFRbn8/oUKBBr/ZC/RJRBoVEnJcgWDTpPHwj17+eKLtaHnB4PBTnXm0VmVlBRfdbslZ6qtsY+W6oxrLtKvicsL7Uh1ddV1fxBdOYtpzq5dO0PPDwQatWvXzlYdH1rfte9tuO91a++jpTrjmov0a4pIdBMSvOrfv78KC+erT5++csd2/f2AsV3Vp09fFRbOj8ShO7SUlN6KiWn6b5spKb3Dev7QoRmh58fExGro0IxWHR9a37XvbbjvdWvvo6U645qL9Gtq0zPd6wUmWsXExGr69Flyu11NHnv++Vlh7SM39/HQ891ut8aPf6JVx4jWN336i1fdDve9bu19tFRnXHORfk1tGt1//vNfbXn4diUr6xH16dNXw4dny+VyKT7+dkmXz1rC/RUgrzcx9Pzhw7M6za/vdGZ9+vQLnZnezHvd2vtoqc645iL9mrimewNpaQNC/+31Jqpv337y+XxyuS7/BPR4POrdO1U+X4okyeVyKSYmRi6XS0lJyYqN/f0s/sq2vXvfKY/HI5fLJa83MfRYnz79Qj9Nc3Mf1913p2nmzALFxcXd9FnLled3hjOOaDF9+osteq+v3Ud8fLzpWe4VnXHNRfI1uZwrv3h6A3V1v173X9T/jN+/UB5PjF5++R/y+xfqSOXJqx7/W2rP0DVdv3+hJEXtNd6kpL/qxIn/tfUw2j3mqXnMUXgiPU9ut0s9evzlxo9H7MgAgCaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIZiI7HT4cOz1a1bXNjbAkC0iEh0MzKylJT0V5048b+wtgWAaMHlBQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADAUa3GQwPmGa+7paXFYAGh3Ih7d1NS+kqTTpy+HNyHBG7oPAKJNxKM7ZcozkT4EAHQYXNMFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAw1+3U9brerxTu/ledGE+YpPMxT85ij8ERynprbt8txHCdiRwcAXIXLCwBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYOiWonv06FFNmjRJOTk5mjRpksrKyppsEwgEtGDBAo0aNUqjR4/WmjVrbuWQHVI487Rs2TINHTpUeXl5ysvL04IFC+wH2ob8fr9GjhyptLQ0HT58+LrbsJbCm6doX0v19fXKz89XTk6OcnNzNWvWLJ06darJdufOndNLL72k0aNHa+zYsdq6davNAJ1bMHXqVGfdunWO4zjOunXrnKlTpzbZZu3atc60adOcQCDg1NXVOZmZmU5lZeWtHLbDCWee3n33XWfJkiXWQ2s39u7d61RXVzsjRoxwDh06dN1tWEvhzVO0r6X6+npn9+7dodtLlixx5syZ02S7ZcuWOXPnznUcx3GOHj3qDBs2zPn1118jPr4Wn+nW1dXpwIEDGjdunCRp3LhxOnDgQJOfKBs3btTEiRPldrvVvXt3jRo1Sps3b761nxQdSLjzFO3S09Pl8/n+dJtoX0tSePMU7bxerx5++OHQ7fvuu0/V1dVNttu0aZMmTZokSerXr58GDhyo7du3R3x8LY5uTU2NkpOTFRMTI0mKiYlRr169VFNT02S7lJSU0G2fz6djx4619LAdTrjzJEkbNmxQbm6upk2bpn379lkPtd2L9rV0M1hLlwWDQX344YcaOXJkk8eqq6vVu3fv0G2r9dTsRzvCxuTJkzVjxgx5PB7t3LlTL7zwgjZu3KjExMS2Hho6GNbS7xYuXKj4+Hg9/fTTbT2UkBaf6fp8PtXW1ioQCEi6/I8cx48fb/K/Pj6f76pT+5qaGt1xxx0tPWyHE+48JSUlyePxSJIyMjLk8/lUWlpqPt72LNrXUrhYS5f5/X6Vl5frnXfekdvdNHUpKSmqqqoK3bZaTy2Obo8ePTRgwACtX79ekrR+/XoNGDBA3bt3v2q7sWPHas2aNQoGgzp16pS2bNminJycWxt1BxLuPNXW1ob+++DBg6qqqtJdd91lOtb2LtrXUrhYS9Lbb7+tH374QcXFxerSpct1txk7dqw+/vhjSVJZWZm+//57ZWZmRnxst/Qh5j/99JOKiop05swZdevWTX6/X/3791d+fr4KCgo0aNAgBQIBvf7669q5c6ckKT8/P3TxOlqEM0+FhYX68ccf5Xa75fF4VFBQoOzs7LYeuplFixbpq6++0smTJ5WYmCiv16sNGzawlq4RzjxF+1oqLS3VuHHj1K9fP3Xt2lWSdOedd6q4uFh5eXkqKSlRcnKyzp49q6KiIh08eFBut1uzZ8/WqFGjIj4+vjkCAAzxF2kAYIjoAoAhogsAhoguABgiugCiXjgfJHSthoYGvfzyy8rJydFjjz2m5cuXh/U8oosOZciQIaqsrGzrYaCTefTRR7V69eqr/iy4OUVFRRo8eLC+/PJLbdiwIexfX+TPgNHmRo4cqZMnTyomJkZxcXHKysrS/PnzdfvttzfZNpo/RwCRk56eft379+/fr6VLl+q3336TJBUUFOiRRx5RWVmZDh8+rJUrV4a2TUpKCutYRBftwqpVqzRs2DDV1tbqueee08qVK/XKK6+EHm9sbFRsLMsVds6cOaPXXntNJSUl6tWrl44fP64JEyZo/fr1OnLkiJKTkzV37lwdPHhQPXv21Kuvvqq777672f1yeQHtSnJysjIzM1VaWqq0tDStXr1aY8aM0ZgxYyRJaWlpKi8vlySdP39eS5Ys0YgRI/TAAw/oqaee0vnz5yVJ3377rSZPnqz09HSNHz9ee/bsabPXhI5p3759+uWXX5Sfn6+8vDzl5+fL5XKpvLxcwWBQ+/fv1xNPPKG1a9dq4sSJmjlzZlj75dQB7UpNTY22b9+u0aNH6+uvv9aWLVv0ySefhP6c84/8fr+OHDmijz76SD179tT+/fvldrtVW1ur559/Xm+++aYyMzO1a9cuFRQUaNOmTU0+8wK4EcdxQj/4r8fn84UuS4wZM0azZ8/WqVOnml1jnOmiXXjxxReVnp6uKVOm6MEHH9SMGTMkSdOnT5fX620S3WAwqM8++0xz584NfV7x/fffry5duujzzz9XVlaWsrOz5Xa7lZGRoYEDB2rbtm1t8dLQQQ0ZMkTl5eXavXt36L7vvvtOjuNo4MCBio+PD3162969e5WQkBDWx2dypot2obi4WMOGDWty/42+JaG+vl4XLlxQampqk8eqq6u1efPmq77zqrGx8apvEwD+6I8fJPTss8+GPkhoxYoVeuutt7R48WJdunRJqampWrVqldxutxYvXqw5c+bo4sWLiouL0/Lly+VyuZo9FtFFu3ajRZyYmKjbbrtNlZWVuvfee696zOfzKS8vT4sWLbIYIjqBefPmad68eU3uHzx4sD744IPrPmfQoEH69NNPb/pYXF5Ah+R2u/Xkk0/qjTfeCH1I/L59+3Tx4kWNHz9eW7du1TfffKNAIKALFy5oz549fLUP2gWiiw6rsLBQ99xzjyZMmKCHHnpIS5cuVTAYlM/n04oVK/Tee+9p6NChys7O1vvvv69gMNjWQwb4PF0AsMSZLgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGPp/Ir1qNLbS7t0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HtvQYVHZ8u5",
        "outputId": "25f3f1de-107c-4501-bc5a-a7078c42ed8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.boxplot(x=df['HP'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b6817da60>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEMCAYAAABnWmXlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAO4ElEQVR4nO3dbWyUZb7H8d90ivZBbHkQKM/hHCSwBNGycgSFQ0FgA62um0AgFnMAqzxKgidUznr6AshhYiS6trTgmpP4YnU1W1m2JoY9QpYAC0JATV2C3SJllhaB8uDSAmlnrvOi6YRS2kI7/U/b+/t5N3PNXHNddybf3r07mfqcc04AABNxsV4AAHgJ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADMW39YArV2oUDnvro7z9+j2k6urrsV5GzHh9/xLHgP23f/9xcT716ZPc4nib0Q2HneeiK8mTe76d1/cvcQzYf+fsn8sLAGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoChNv9dT0/yu999qGCwos3H9erlV11d6J7nvXbtqiQpJSW13Wu707BhI7R48ZKozQega/BUdIPBCp0q+7v8CdGLoySFbjZE9+JP9VGdD0DP46noSpI/IVVJI2ZGdc7aii8lKWrzNs4HoOfhmi4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIY6JboHD+7XwYP7O2Nq9FC8Z+AV8Z0x6YEDf5EkTZ06rTOmRw/EewZeweUFADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAEPxsV4AcKfc3PW6cKHK7PWefXaufvGLTBUVvafMzF/q7bf/JzI2atRonT5dpuHDR+rs2TNNnjdkyFCdO/cPPfHEJCUkJOvQob80GU9MTNaGDf+lLVvyVFdXpyVLlurw4UNasWKtUlJSW1xPSckfVVz8e40fP0Glpd9qwYJFmjs3U0eOHNKOHflasWKtfv7zf9N3332rbdsCWr/+DY0bNz7y/LNnzygQ2KTc3P/WsGEjonOQPObEiRPKy8trdmyjgTNddDmWwZWkP//5C/3pT5+prOyUCgt/02Ts9OkySWoWXEk6d+4fkqTjx481C64k3bhRo507C1RXVydJ+vDD/1VZ2Snt3l3c6nqKi38vSSot/VaS9MknH0mSfvvbIknSzp3bJUmFhe/JOaft299t8vydOwt048YN7diR3+rroGWBQOCuxzYaiC66lNzc9TF53X37/k/OOdXW1kR13srKc7fdcnLO6cCB/bp27epdH19S8se73r9jx3sKheolSaFQvf7wh08ia62trdHf/lYqqeGHQ+NrVlaeUzBYEaWdeMd3332rmprmxzZafM4519oDqquvKxxu9SHNbNy4XteuXdPw4V3rV5uzZyt0M+TXQ/8yL6rz1lZ8KUlKGjEzKvNdL/9cCf5QzI5fr15+1dWFTF/z7NkKpaSk6Px527PcWPD74zVt2r8rO3tps7GlSxe3a86kpGTl57+vX//6P5uEfvDgIdq8+a37nu+RR3rr4sV/tmst3d3q1S83+eHbeGzvVVycT/36PdTyeIdWB+C+hUL1+utfD0Z1zsZIND2zbn4bbbvzt51o//bTKX9IS0lJVUpKqjZseLMzpm+3QGCT/h68FOtltCkuPkHDh/WP2fGLxVlOILBJkjxzpvvUU1OjOmdSUrKkhjPbO890cX+SkpKbnelGE2e66FIGDEiL9RI6XVxcnLKyXrjr2AsvLLzr/ZMnP9Xk9rx5zze5vXLla5KknJxVTe5/5ZXV7V2mZ61YsabJ7cZjGy1EF13K1q1vx+R1Z8yYJZ/PF/WzmqZnmj75fD49/fS0Fj8yNn/+c3e9/5VX1sjvb/jF1O+P169+tSCy1qSk5MjHmoYPHxl5zcGDh/CRsXb42c8mKDm5+bGNFqKLLsf6bPfZZ+cqM/OXGj16jFasWNtkbNSo0ZIaYnanIUOGSpKeeGKSpkyZ3mw8MTFZOTmr1KtXL0nSkiX/odGjx7R4ltuo8Wx3/PgJkqQFCxZJkpYvf1WSlJOzUlLDGZnP52t2JpaTs0qJiYmc5XbAhg0b7npso6FTPr3QeH2uq17TjdanDBpF+9MLtRVf6l89ek23q7xnvPzXe4n9d2T/fHoBALoQogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoCh+M6Y9Omnp3fGtOjBeM/AKzolulOnTuuMadGD8Z6BV3B5AQAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAzFx3oB1kI3r6q24suozykpavM2zNc/KnMB6Fo8Fd1hw0bc0+N69fKrri50z/Neu9ZwGFNSUtu1rub63/NaAXQvnoru4sVL7ulxjzzSWxcv/rOTVwPAi7imCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABgiugBgiOgCgCGiCwCGiC4AGCK6AGCI6AKAIaILAIaILgAYavPf9cTF+SzW0eV4dd+NvL5/iWPA/tu3/7ae53POuXbNDAC4b1xeAABDRBcADBFdADBEdAHAENEFAENEFwAMEV0AMER0AcAQ0QUAQ56K7pUrV/Tyyy9rzpw5yszM1OrVq3X58mVJ0tdff62srCzNmTNHS5cuVXV1deR5rY11V/n5+RozZoy+//57Sd7a/61bt5SXl6fZs2crMzNTb775piTphx9+0MKFCzVnzhwtXLhQZ86ciTyntbHuZt++fXr++ef13HPPKSsrS3v27JHUs/cfCASUkZHR5D0vtX/PHToezkOuXLniDh8+HLm9detW98Ybb7hQKORmzZrljh496pxzrqCgwOXm5jrnXKtj3VVpaalbtmyZmzFjhjt16pTn9r9p0ya3ZcsWFw6HnXPOXbx40TnnXHZ2ttu1a5dzzrldu3a57OzsyHNaG+tOwuGwmzRpkjt16pRzzrmTJ0+6iRMnulAo1KP3f/ToUVdZWRl5zzdq7547cjw8Fd07ffHFF+6ll15y33zzjZs3b17k/urqajdx4kTnnGt1rDu6deuWW7BggQsGg5E3oJf2f/36dZeenu6uX7/e5P5Lly659PR0V19f75xzrr6+3qWnp7vq6upWx7qbcDjsnnzySXfs2DHnnHNfffWVmz17tmf2f3t027vnjh6PNr9lrKcKh8P66KOPlJGRoaqqKg0ePDgy1rdvX4XDYV29erXVsdTU1FgsvUPeffddZWVlaejQoZH7vLT/YDCo1NRU5efn68iRI0pOTtZrr72mhIQEDRw4UH6/X5Lk9/s1YMAAVVVVyTnX4ljfvn1juZ375vP59M4772jlypVKSkpSTU2Ndu7cqaqqKk/s/3bt3XNHj4enrunebtOmTUpKStKLL74Y66WYOXHihEpLS7V48eJYLyVmQqGQgsGgxo0bp+LiYr3++utas2aNamtrY700E/X19dqxY4e2b9+uffv2qbCwUOvWrfPM/rsCT57pBgIBVVRUqKioSHFxcUpLS1NlZWVk/PLly4qLi1NqamqrY93N0aNHVV5erpkzZ0qSzp8/r2XLlik7O9sT+5ektLQ0xcfHa/78+ZKkxx57TH369FFCQoJ+/PFHhUIh+f1+hUIhXbhwQWlpaXLOtTjW3Zw8eVIXLlxQenq6JCk9PV2JiYl68MEHPbH/26WlpbVrzx09Hp470922bZtKS0tVUFCgBx54QJI0fvx43bx5U8eOHZMkffzxx5o7d26bY91NTk6ODhw4oL1792rv3r0aNGiQPvjgAy1fvtwT+5caLo9MnjxZBw8elNTwV+jq6mqNHDlSY8eOVUlJiSSppKREY8eOVd++fdWvX78Wx7qbQYMG6fz58zp9+rQkqby8XNXV1RoxYoQn9n+71vbV3rF74akvMS8rK9P8+fM1cuRIJSQkSJKGDh2qgoICHT9+XHl5ebp165aGDBmit956S/3795ekVse6s4yMDBUVFenRRx/11P6DwaA2btyoq1evKj4+XuvWrdP06dNVXl6u3Nxc/fTTT3r44YcVCAQ0atQoSWp1rLvZvXu33n//ffl8Df/hYO3atZo1a1aP3v/mzZu1Z88eXbp0SX369FFqaqo+//zzdu+5I8fDU9EFgFjz3OUFAIglogsAhoguABgiugBgiOgCgCGiCwCGiC66tIyMDB06dKjJfcXFxVq0aFFkfMKECXr88cc1ZcoU5ebmqqamJhZLBe4J0UW3V1RUpBMnTuizzz5TaWmpCgsLY70koEVEFz3GwIED9cwzz6isrCzWSwFaRHTRY1RVVWn//v0aO3ZsrJcCtMiT3zKG7mXVqlWR7y6VpLq6Oo0bN67ZeO/evTV9+nS9+uqrsVgmcE+ILrq8goICTZkyJXK7uLhYn376aYvjQFfG5QUAMER0AcAQ0QUAQ3yfLgAY4kwXAAwRXQAwRHQBwBDRBQBDRBcADBFdADBEdAHAENEFAENEFwAM/T/iifm2RSOqBQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xz3MwIjbaBUr",
        "outputId": "b380adad-b012-4b66-bc40-263391817979",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.boxplot(x=df['Cylinders'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b67ca3ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFG9hck7aHUx",
        "outputId": "36a0cb6c-c10d-40f6-d22f-e0f30f799bb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "Q1 = df.quantile(0.25)\n",
        "Q3 = df.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Year             9.0\n",
            "HP             130.0\n",
            "Cylinders        2.0\n",
            "MPG-H            8.0\n",
            "MPG-C            6.0\n",
            "Price        21327.5\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCv110_cJiDz"
      },
      "source": [
        "Don't worry about the above values because it's not important to know each and every one of them because it's just important to know how to use this technique in order to remove the outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igh_mRXeaJrI",
        "outputId": "7bbde392-ad44-453d-b662-2f7d60b75ce8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-f4e1682787c4>:1: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version.  Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n",
            "  df = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9191, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Nh93DCGJkqT"
      },
      "source": [
        "As seen above there were around 1600 rows were outliers. But you cannot completely remove the outliers because even after you use the above technique there maybe 1–2 outlier unremoved but that ok because there were more than 100 outliers. Something is better than nothing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z87kHbgvaQbb"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqFPRda8eEp_"
      },
      "source": [
        "## 9. Plot different features against one another (scatter), against frequency (histogram)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-W6Q9-hJosZ"
      },
      "source": [
        "### Histogram\n",
        "\n",
        "Histogram refers to the frequency of occurrence of variables in an interval. In this case, there are mainly 10 different types of car manufacturing companies, but it is often important to know who has the most number of cars. To do this histogram is one of the trivial solutions which lets us know the total number of car manufactured by a different company."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAnd4DSyeHDb",
        "outputId": "c48bff30-f46d-4bcd-b2eb-adf56b9ca1ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        }
      },
      "source": [
        "df.Make.value_counts().nlargest(40).plot(kind='bar', figsize=(10,5))\n",
        "plt.title(\"Number of cars by make\")\n",
        "plt.ylabel('Number of cars')\n",
        "plt.xlabel('Make');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c37WtYYWJuAQ"
      },
      "source": [
        "### Heat Maps\n",
        "\n",
        "Heat Maps is a type of plot which is necessary when we need to find the dependent variables. One of the best way to find the relationship between the features can be done using heat maps. In the below heat map we know that the price feature depends mainly on the Engine Size, Horsepower, and Cylinders."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhHfLVTj6nmy",
        "outputId": "4c54713f-07f7-490f-d2fc-eabd0e9b53dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "c= df.corr()\n",
        "sns.heatmap(c,cmap=\"BrBG\",annot=True)\n",
        "c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Year        HP  Cylinders     MPG-H     MPG-C     Price\n",
              "Year       1.000000  0.326726  -0.133920  0.378479  0.338145  0.592983\n",
              "HP         0.326726  1.000000   0.715237 -0.443807 -0.544551  0.739042\n",
              "Cylinders -0.133920  0.715237   1.000000 -0.703856 -0.755540  0.354013\n",
              "MPG-H      0.378479 -0.443807  -0.703856  1.000000  0.939141 -0.106320\n",
              "MPG-C      0.338145 -0.544551  -0.755540  0.939141  1.000000 -0.180515\n",
              "Price      0.592983  0.739042   0.354013 -0.106320 -0.180515  1.000000"
            ],
            "text/html": [
              "\n",
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              "      <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>Price</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.326726</td>\n",
              "      <td>-0.133920</td>\n",
              "      <td>0.378479</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP</th>\n",
              "      <td>0.326726</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.715237</td>\n",
              "      <td>-0.443807</td>\n",
              "      <td>-0.544551</td>\n",
              "      <td>0.739042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>-0.133920</td>\n",
              "      <td>0.715237</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.703856</td>\n",
              "      <td>-0.755540</td>\n",
              "      <td>0.354013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG-H</th>\n",
              "      <td>0.378479</td>\n",
              "      <td>-0.443807</td>\n",
              "      <td>-0.703856</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.939141</td>\n",
              "      <td>-0.106320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MPG-C</th>\n",
              "      <td>0.338145</td>\n",
              "      <td>-0.544551</td>\n",
              "      <td>-0.755540</td>\n",
              "      <td>0.939141</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.180515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0.592983</td>\n",
              "      <td>0.739042</td>\n",
              "      <td>0.354013</td>\n",
              "      <td>-0.106320</td>\n",
              "      <td>-0.180515</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-55efa518-83c1-463e-8359-bcb00447e877 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-55efa518-83c1-463e-8359-bcb00447e877');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ROThOLQfRZw"
      },
      "source": [
        "### Scatterplot\n",
        "\n",
        "We generally use scatter plots to find the correlation between two variables. Here the scatter plots are plotted between Horsepower and Price and we can see the plot below. With the plot given below, we can easily draw a trend line. These features provide a good scattering of points."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aOfHNFefSrX",
        "outputId": "e022956c-b9bb-4206-d775-64fa9e88e47e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "ax.scatter(df['HP'], df['Price'])\n",
        "ax.set_xlabel('HP')\n",
        "ax.set_ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQXy8o_gKFS5"
      },
      "source": [
        "**Hence the above are some of the steps involved in Exploratory data analysis, these are some general steps that you must follow in order to perform EDA. There are many more yet to come but for now, this is more than enough idea as to how to perform a good EDA given any data sets. Stay tuned for more updates.**\n",
        "\n",
        "## Thank you."
      ]
    }
  ]
}