EDA_with_Python.ipynb

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      "source": [
        "#Necessary Imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns                       # For dataviz\n",
        "import matplotlib.pyplot as plt             # For dataviz\n",
        "%matplotlib inline\n",
        "sns.set(color_codes=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"dataset.csv\")\n",
        "# To display the top 10 rows\n",
        "df.head(10)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 764
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        "outputId": "7c862265-2c68-4edf-dc5f-f6ee081b0e82"
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      "execution_count": 30,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  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",
              "5  BMW    1 Series  2012  premium unleaded (required)      230.0   \n",
              "6  BMW    1 Series  2012  premium unleaded (required)      300.0   \n",
              "7  BMW    1 Series  2012  premium unleaded (required)      300.0   \n",
              "8  BMW    1 Series  2012  premium unleaded (required)      230.0   \n",
              "9  BMW    1 Series  2013  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",
              "5               6.0            MANUAL  rear wheel drive              2.0   \n",
              "6               6.0            MANUAL  rear wheel drive              2.0   \n",
              "7               6.0            MANUAL  rear wheel drive              2.0   \n",
              "8               6.0            MANUAL  rear wheel drive              2.0   \n",
              "9               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",
              "5                     Luxury,Performance      Compact         Coupe   \n",
              "6                     Luxury,Performance      Compact   Convertible   \n",
              "7                Luxury,High-Performance      Compact         Coupe   \n",
              "8                                 Luxury      Compact   Convertible   \n",
              "9                                 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",
              "3           28        18        3916  29450  \n",
              "4           28        18        3916  34500  \n",
              "5           28        18        3916  31200  \n",
              "6           26        17        3916  44100  \n",
              "7           28        20        3916  39300  \n",
              "8           28        18        3916  36900  \n",
              "9           27        18        3916  37200  "
            ],
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              "      <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",
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              "      <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",
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              "  </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",
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              "    <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",
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              "    <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",
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              "    <tr>\n",
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              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <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>31200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2012</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",
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              "      <td>Luxury,Performance</td>\n",
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              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>3916</td>\n",
              "      <td>44100</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>300.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
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              "      <td>Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
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              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>3916</td>\n",
              "      <td>39300</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2012</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>230.0</td>\n",
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              "      <td>3916</td>\n",
              "      <td>36900</td>\n",
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              "      <th>9</th>\n",
              "      <td>BMW</td>\n",
              "      <td>1 Series</td>\n",
              "      <td>2013</td>\n",
              "      <td>premium unleaded (required)</td>\n",
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              "      <td>Luxury</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>27</td>\n",
              "      <td>18</td>\n",
              "      <td>3916</td>\n",
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              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-33f68696-5ae7-4a8a-9fdb-4d01f28e4b99 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display the tail or the last few rows at the end of the dataset\n",
        "df.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "1vKmq8UEmCYc",
        "outputId": "64b48967-1a90-42f0-8ff6-9f50991c4662"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Make   Model  Year                Engine Fuel Type  Engine HP  \\\n",
              "11904      BMW      Z8  2002     premium unleaded (required)      394.0   \n",
              "11905      BMW      Z8  2003     premium unleaded (required)      394.0   \n",
              "11906    Acura     ZDX  2011     premium unleaded (required)      300.0   \n",
              "11907    Acura     ZDX  2011     premium unleaded (required)      300.0   \n",
              "11908    Acura     ZDX  2011     premium unleaded (required)      300.0   \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",
              "11904               8.0            MANUAL   rear wheel drive              2.0   \n",
              "11905               8.0            MANUAL   rear wheel drive              2.0   \n",
              "11906               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11907               6.0         AUTOMATIC    all wheel drive              4.0   \n",
              "11908               6.0         AUTOMATIC    all wheel drive              4.0   \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  \\\n",
              "11904  Exotic,Luxury,High-Performance      Compact    Convertible   \n",
              "11905  Exotic,Luxury,High-Performance      Compact    Convertible   \n",
              "11906      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11907      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11908      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11909      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11910      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11911      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11912      Crossover,Hatchback,Luxury      Midsize  4dr Hatchback   \n",
              "11913                          Luxury      Midsize          Sedan   \n",
              "\n",
              "       highway MPG  city mpg  Popularity    MSRP  \n",
              "11904           19        12        3916  130000  \n",
              "11905           19        12        3916  131500  \n",
              "11906           23        16         204   46020  \n",
              "11907           23        16         204   56570  \n",
              "11908           23        16         204   50520  \n",
              "11909           23        16         204   46120  \n",
              "11910           23        16         204   56670  \n",
              "11911           23        16         204   50620  \n",
              "11912           23        16         204   50920  \n",
              "11913           26        17          61   28995  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d51a7d9-93fa-4767-b2b7-bd7cde8d2cf0\" class=\"colab-df-container\">\n",
              "    <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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11904</th>\n",
              "      <td>BMW</td>\n",
              "      <td>Z8</td>\n",
              "      <td>2002</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>394.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Exotic,Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "      <td>3916</td>\n",
              "      <td>130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11905</th>\n",
              "      <td>BMW</td>\n",
              "      <td>Z8</td>\n",
              "      <td>2003</td>\n",
              "      <td>premium unleaded (required)</td>\n",
              "      <td>394.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>MANUAL</td>\n",
              "      <td>rear wheel drive</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Exotic,Luxury,High-Performance</td>\n",
              "      <td>Compact</td>\n",
              "      <td>Convertible</td>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "      <td>3916</td>\n",
              "      <td>131500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11906</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2011</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>46020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11907</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2011</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>56570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11908</th>\n",
              "      <td>Acura</td>\n",
              "      <td>ZDX</td>\n",
              "      <td>2011</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>50520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11909</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>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",
              "    <div class=\"colab-df-buttons\">\n",
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              "                                                    [key], {});\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking all columns data types\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZAkg1eNmMsy",
        "outputId": "a08fc854-ea7f-4eff-8671-87d03ba1b753"
      },
      "execution_count": 32,
      "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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping irrelevant columns\n",
        "df = df.drop(['Engine Fuel Type', 'Market Category', 'Vehicle Style', 'Popularity', 'Number of Doors', 'Vehicle Size'], axis=1)\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "l4NbhlCLmVnJ",
        "outputId": "e9883b01-7eb7-4938-811f-e41cbf40bee4"
      },
      "execution_count": 33,
      "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  "
            ],
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          "metadata": {},
          "execution_count": 33
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    },
    {
      "cell_type": "code",
      "source": [
        "# Renaming columns\n",
        "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GYbWPtw1mmiE",
        "outputId": "d401739e-d143-4da3-a218-63e2087dc6fe"
      },
      "execution_count": 34,
      "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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              "      <td>BMW</td>\n",
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              "      <td>2011</td>\n",
              "      <td>300.0</td>\n",
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              "      <td>36350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW</td>\n",
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              "      <td>2011</td>\n",
              "      <td>230.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MANUAL</td>\n",
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              "      <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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-022f38bb-bd54-499e-8758-b95103f729e1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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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              "      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",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-022f38bb-bd54-499e-8758-b95103f729e1 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-022f38bb-bd54-499e-8758-b95103f729e1');\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",
              "\n",
              "\n",
              "<div id=\"df-563948ab-4133-4f64-85ae-9fca1e08754b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-563948ab-4133-4f64-85ae-9fca1e08754b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-563948ab-4133-4f64-85ae-9fca1e08754b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the dims of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5hw1HRtm8hY",
        "outputId": "55f5cc71-9996-4401-f373-21de0dfc5bb7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11914, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the dataset rows\n",
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4qHZxejnEh9",
        "outputId": "fc296b04-21e2-45b8-d724-8921c210081b"
      },
      "execution_count": 36,
      "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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows_df = df[df.duplicated()]\n",
        "print(\"number of duplicate rows: \", duplicate_rows_df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9Qf2mcQpOrY",
        "outputId": "549c9550-c496-487d-ac44-6a699739fcd2"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of duplicate rows:  (0, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping duplicates\n",
        "df = df.drop_duplicates()\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Y5aWc0Z9nRMb",
        "outputId": "ff6a8897-ed3e-455c-9505-63d5030b07c2"
      },
      "execution_count": 37,
      "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  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "      <td>MANUAL</td>\n",
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              "      <td>230.0</td>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fb391678-59e2-4487-a3e5-e3db55783a2e 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-fb391678-59e2-4487-a3e5-e3db55783a2e');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1547a56b-9c1b-4b65-b501-9b6f9ce9f397')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1547a56b-9c1b-4b65-b501-9b6f9ce9f397 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Counting the rows after the drop\n",
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJVkwTolnVXo",
        "outputId": "a993d892-0585-4743-bd03-de025fea67c7"
      },
      "execution_count": 38,
      "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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many row has null values\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vholxCWnYcw",
        "outputId": "9fa0b6cf-5630-476b-f3e4-05018fc4c194"
      },
      "execution_count": 39,
      "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": "code",
      "source": [
        " # Dropping the missing values\n",
        "df = df.dropna()\n",
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQs78EoCnqa-",
        "outputId": "8038acf3-5028-458d-f090-e74a925eb000"
      },
      "execution_count": 40,
      "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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# After dropping the values\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9nejGFZnwlZ",
        "outputId": "6b0e5202-0d49-4506-deb6-e91aeb0b83ac"
      },
      "execution_count": 41,
      "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": "code",
      "source": [
        "# Detecting outliers using boxplot\n",
        "sns.boxplot(x=df['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "wRDCQA28n2Bz",
        "outputId": "448a76cf-aef4-4b7c-9f46-5b9084033eda"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df['HP'])"
      ],
      "metadata": {
        "id": "al99kZWfn9Me",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "a8cb585e-de0d-49ed-a9a2-33619122f825"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='HP'>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df['Cylinders'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "wEV8Xc8PpYWn",
        "outputId": "9e62a678-f681-49e8-e15d-bc7480568fdb"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Cylinders'>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the first and the third Quantils\n",
        "Quant1 = df.quantile(0.25)\n",
        "Quant3 = df.quantile(0.75)\n",
        "IQR = Quant3 - Quant1\n",
        "print(IQR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4JJrhcwpbYi",
        "outputId": "f2bcca13-7ca1-4f79-cc25-966e9886f54f"
      },
      "execution_count": 46,
      "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-353c17d9e4f1>:1: FutureWarning: The default value of numeric_only in DataFrame.quantile is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  Quant1 = df.quantile(0.25)\n",
            "<ipython-input-46-353c17d9e4f1>:2: FutureWarning: The default value of numeric_only in DataFrame.quantile is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  Quant3 = df.quantile(0.75)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~((df < (Quant1 - 1.5 * IQR)) |(df > (Quant3 + 1.5 * IQR))).any(axis=1)]\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elS6RJZfpfLW",
        "outputId": "b8d56a7d-4a17-42ea-ae47-8ad6ff6de4e6"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-4835912b3851>: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 < (Quant1 - 1.5 * IQR)) |(df > (Quant3 + 1.5 * IQR))).any(axis=1)]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9191, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**As observed earlier, approximately 1600 rows exhibited outlier values. However, it's not feasible to entirely eliminate all outliers, as even after applying the aforementioned technique, there might be 1–2 outliers remaining. This is acceptable, considering the initial count of over 100 outliers, as having some outliers removed is better than none.**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "C94lt52fp8kG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting a Histogram\n",
        "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');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "qj3oey2PqD5V",
        "outputId": "c8f15f07-0874-4c9d-984a-1b0214ec8ec7"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the heatmap\n",
        "plt.figure(figsize=(10,5))\n",
        "map= df.corr()\n",
        "sns.heatmap(map,cmap=\"BrBG\",annot=True)\n",
        "map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "YpCZAya6qJbW",
        "outputId": "b3a17824-90eb-4a96-e9a0-f00ac0a6df62"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-d5a55955562d>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  map= df.corr()\n"
          ]
        },
        {
          "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",
              "  <div id=\"df-2ae1dd2a-ca7d-4fd0-a64f-c758b792ff27\" class=\"colab-df-container\">\n",
              "    <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>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.326726</td>\n",
              "      <td>-0.133920</td>\n",
              "      <td>0.378479</td>\n",
              "      <td>0.338145</td>\n",
              "      <td>0.592983</td>\n",
              "    </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",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ae1dd2a-ca7d-4fd0-a64f-c758b792ff27')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2ae1dd2a-ca7d-4fd0-a64f-c758b792ff27 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-2ae1dd2a-ca7d-4fd0-a64f-c758b792ff27');\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",
              "\n",
              "\n",
              "<div id=\"df-1371555b-da09-4f72-9e45-cfbcb37a19ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1371555b-da09-4f72-9e45-cfbcb37a19ad')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1371555b-da09-4f72-9e45-cfbcb37a19ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the scatter plot for HP = f(Price)\n",
        "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "H_jxz4EfqTxH",
        "outputId": "18f92ea6-142b-42fe-8464-875273bf91c1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}