Car_Price_EDA.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "SXlo8waBfCtM"
      },
      "source": [
        "# 1. Importing Libraries and loading the data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "! pip install seaborn --quiet"
      ],
      "metadata": {
        "id": "N-ymMUaqhLe1"
      },
      "execution_count": 27,
      "outputs": []
    },
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      "source": [
        "# Simple python imports\n",
        "import math\n",
        "\n",
        "# Importing data processing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Importing data visualization libraries\n",
        "from prettytable import PrettyTable\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib import style\n",
        "style.use('ggplot')\n",
        "# %config InlineBackend.figure_format = 'retina' # Retina display\n",
        "\n",
        "# Importing jupyter widgets\n",
        "import ipywidgets as widgets\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "\n",
        "# Importing file system libraries\n",
        "import pickle\n",
        "\n",
        "# Importing machine learning libraries\n",
        "# from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, make_scorer\n",
        "# from sklearn.model_selection import GridSearchCV\n",
        "# from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "pd.options.display.max_columns = None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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          "shell.execute_reply": "2022-08-14T19:15:26.611281Z",
          "shell.execute_reply.started": "2022-08-14T19:15:26.523547Z"
        },
        "trusted": true,
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          "height": 206
        },
        "id": "_l-EmTxNfCuG",
        "outputId": "6cfb5dc3-95ed-444c-eab2-4b9045c9328b"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               full_name  selling_price seller_type  \\\n",
              "0      Toyota Land Cruiser 200 V8 Petrol           92.0      Dealer   \n",
              "1  Land Rover Range Rover Sport 3.0 D SE           92.0      Dealer   \n",
              "2          Toyota Land Cruiser Prado VXL           87.5      Dealer   \n",
              "3       BMW 7 Series 730Ld DPE Signature           85.0  Individual   \n",
              "4       BMW 7 Series 730Ld DPE Signature           85.0      Dealer   \n",
              "\n",
              "   km_driven   owner_type fuel_type transmission_type  mileage  engine  \\\n",
              "0      80000  First Owner    Petrol         Automatic     9.00  4461.0   \n",
              "1       9500  First Owner    Diesel         Automatic    12.65  2993.0   \n",
              "2      36000  First Owner    Diesel         Automatic    11.00  2982.0   \n",
              "3      50000  First Owner    Diesel         Automatic    16.77  2993.0   \n",
              "4      19000  First Owner    Diesel         Automatic    17.66  2993.0   \n",
              "\n",
              "   max_power  seats  age  \n",
              "0     285.40    8.0    6  \n",
              "1     296.00    5.0    5  \n",
              "2     170.63    7.0    3  \n",
              "3     261.49    5.0    4  \n",
              "4     355.37    4.0    4  "
            ],
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              "      <th>full_name</th>\n",
              "      <th>selling_price</th>\n",
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              "      <th>0</th>\n",
              "      <td>Toyota Land Cruiser 200 V8 Petrol</td>\n",
              "      <td>92.0</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>80000</td>\n",
              "      <td>First Owner</td>\n",
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              "      <td>4461.0</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Land Rover Range Rover Sport 3.0 D SE</td>\n",
              "      <td>92.0</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>9500</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>12.65</td>\n",
              "      <td>2993.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Toyota Land Cruiser Prado VXL</td>\n",
              "      <td>87.5</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>36000</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>11.00</td>\n",
              "      <td>2982.0</td>\n",
              "      <td>170.63</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BMW 7 Series 730Ld DPE Signature</td>\n",
              "      <td>85.0</td>\n",
              "      <td>Individual</td>\n",
              "      <td>50000</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>16.77</td>\n",
              "      <td>2993.0</td>\n",
              "      <td>261.49</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW 7 Series 730Ld DPE Signature</td>\n",
              "      <td>85.0</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>19000</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>17.66</td>\n",
              "      <td>2993.0</td>\n",
              "      <td>355.37</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "raw_data = pd.read_csv('/content/car_prices_clean_v2.csv')\n",
        "\n",
        "df = raw_data.copy()\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzXU_paefCuI"
      },
      "source": [
        "# 2. Exploratory Data Analysis\n",
        "In this section, we would be exploring the dataset and try to find patterns, which would be helpful in *feature engineering* when we build a model to predict the price of a used car"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMboAYspfCuJ"
      },
      "source": [
        "### 2.0 Helper functions for EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6nADG-bZfCuK"
      },
      "outputs": [],
      "source": [
        "# Function to get a genreal overview of the data with some basic statistics\n",
        "def overview_data(data: pd.DataFrame, col: str) -> None:\n",
        "\t'''\n",
        "\tInput arguments:\n",
        "\t- data : pandas dataframe\n",
        "\t- col : column name\n",
        "\n",
        "\tOutputs:\n",
        "\t- None : Prints the overview of the column:\n",
        "\t\t\t\t- Distinct count\n",
        "\t\t\t\t- Distinct %\n",
        "\t\t\t\t- Missing count\n",
        "\t\t\t\t- Missing %\n",
        "\t\t\t\t- Mean\n",
        "\t\t\t\t- Minimum\n",
        "\t\t\t\t- Maximum\n",
        "\t'''\n",
        "\tt = PrettyTable(['Measure', 'Value'])\n",
        "\tt.add_row(['Distinct Count', data[col].nunique()])\n",
        "\tt.add_row(['Distinct %', data[col].nunique()/data.shape[0]*100])\n",
        "\tt.add_row(['Missing Count', data[col].isna().sum()])\n",
        "\tt.add_row(['Missing %', data[col].isna().sum()/data.shape[0]*100])\n",
        "\tt.add_row(['Mean', data[col].mean()])\n",
        "\tt.add_row(['Minimum', data[col].min()])\n",
        "\tt.add_row(['Maximum', data[col].max()])\n",
        "\n",
        "\t# Display the overview of the dataframe\n",
        "\tprint(f'Overview of column \"{col}\"')\n",
        "\tprint(t)\n",
        "\t\n",
        "\tprint('\\n')\n",
        "\treturn\n",
        "\n",
        "# Function to get the quantile statistics for a numeric column\n",
        "def quantile_statistics_for_column(data: pd.DataFrame, col: str) -> dict:\n",
        "\t'''\n",
        "\tInput arguments:\n",
        "\t- data : pandas dataframe\n",
        "\t- col : column name\n",
        "\n",
        "\tOutputs:\n",
        "\t- quantile_stats: Returns a dictionary with quantile stats for the column which includes:\n",
        "\t\t\t\t\t\t\t- min : Minimum value\n",
        "\t\t\t\t\t\t\t- 5% : 5th percentile value\n",
        "\t\t\t\t\t\t\t- q1 : First quartile value\n",
        "\t\t\t\t\t\t\t- median : Median value\n",
        "\t\t\t\t\t\t\t- q3 : Third quartile value\n",
        "\t\t\t\t\t\t\t- 95% : 95th percentile value\n",
        "\t\t\t\t\t\t\t- max : Maximum value\n",
        "\t\t\t\t\t\t\t- range: Range of values\n",
        "\t\t\t\t\t\t\t- IQR : Interquartile range\n",
        "\t'''\n",
        "\n",
        "\t# Check if the column exists in the dataframe\n",
        "\tif col not in data.columns:\n",
        "\t\tprint(f'Column {col} not found in the dataframe')\n",
        "\t\treturn\n",
        "\t\n",
        "\t# Check if the column is numeric\n",
        "\tif data[col].dtype != 'float64' and data[col].dtype != 'int64':\n",
        "\t\tprint(f'Column {col} is not numeric')\n",
        "\t\treturn\n",
        "\n",
        "\tquantile_stats = {}\n",
        "\tquantile_stats['min'] = data[col].min()\n",
        "\tquantile_stats['5%'] = data[col].quantile(0.05)\n",
        "\tquantile_stats['Q1'] = data[col].quantile(0.25)\n",
        "\tquantile_stats['median'] = data[col].median()\n",
        "\tquantile_stats['Q3'] = data[col].quantile(0.75)\n",
        "\tquantile_stats['95%'] = data[col].quantile(0.95)\n",
        "\tquantile_stats['max'] = data[col].max()\n",
        "\tquantile_stats['range'] = quantile_stats['max'] - quantile_stats['min']\n",
        "\tquantile_stats['IQR'] = quantile_stats['Q3'] - quantile_stats['Q1']\n",
        "\n",
        "\t# Display the quantile stats\n",
        "\tprint(f'Quantile stats for column \"{col}\"')\n",
        "\tt = PrettyTable(['Statistic', 'Value'])\n",
        "\tt.add_row(['Minimum', quantile_stats['min']])\n",
        "\tt.add_row(['5th Percentile', quantile_stats['5%']])\n",
        "\tt.add_row(['First Quartile, Q1', quantile_stats['Q1']])\n",
        "\tt.add_row(['Median', quantile_stats['median']])\n",
        "\tt.add_row(['Third Quartile, Q3', quantile_stats['Q3']])\n",
        "\tt.add_row(['95th Percentile', quantile_stats['95%']])\n",
        "\tt.add_row(['Maximum', quantile_stats['max']])\n",
        "\tt.add_row(['Range', quantile_stats['range']])\n",
        "\tt.add_row(['Interquartile Range', quantile_stats['IQR']])\n",
        "\tprint(t)\n",
        "\n",
        "\tprint('\\n')\n",
        "\treturn quantile_stats\n",
        "\n",
        "# Function to get the descriptive statistics for a numeric column\n",
        "def descriptive_statistics_for_column(data: pd.DataFrame, col: str) -> dict:\n",
        "\t'''\n",
        "\tInput arguments:\n",
        "\t- data : pandas dataframe\n",
        "\t- col : column name\n",
        "\n",
        "\tOutputs:\n",
        "\t- descriptive_stats : Return a dictionary with descriptive stats for the column which includes:\n",
        "\t\t\t\t\t\t\t\t- mean: Mean\n",
        "\t\t\t\t\t\t\t\t- sum: Sum\n",
        "\t\t\t\t\t\t\t\t- var: Variance\n",
        "\t\t\t\t\t\t\t\t- std : Standard deviation\n",
        "\t\t\t\t\t\t\t\t- CV : Coefficient of variation\n",
        "\t\t\t\t\t\t\t\t- kurtosis: Kurtosis\n",
        "\t\t\t\t\t\t\t\t- MAD: Mean Absolute Deviation\n",
        "\t\t\t\t\t\t\t\t- skewness: Skewness\n",
        "\t\t\t\t\t\t\t\t- mono: true if monotonous\n",
        "\n",
        "\t'''\n",
        "\n",
        "\t# Check if the column exists in the dataframe\n",
        "\tif col not in data.columns:\n",
        "\t\tprint(f'Column {col} not found in the dataframe')\n",
        "\t\treturn\n",
        "\t\n",
        "\t# Check if the column is numeric\n",
        "\tif data[col].dtype != 'float64' and data[col].dtype != 'int64':\n",
        "\t\tprint(f'Column {col} is not numeric')\n",
        "\t\treturn\n",
        "\t\n",
        "\tdescriptive_stats = {}\n",
        "\tdescriptive_stats['mean'] = data[col].mean()\n",
        "\tdescriptive_stats['sum'] = data[col].sum()\n",
        "\tdescriptive_stats['var'] = data[col].var()\n",
        "\tdescriptive_stats['std'] = data[col].std()\n",
        "\tdescriptive_stats['CV'] = descriptive_stats['std'] / descriptive_stats['mean']\n",
        "\tdescriptive_stats['kurtosis'] = data[col].kurtosis()\n",
        "\tdescriptive_stats['MAD'] = data[col].mad()\n",
        "\tdescriptive_stats['skewness'] = data[col].skew()\n",
        "\tdescriptive_stats['mono'] = data[col].is_monotonic_increasing or data[col].is_monotonic_decreasing\n",
        "\n",
        "\t# Display the descriptive stats\n",
        "\tprint(f'Descriptive stats for column \"{col}\"')\n",
        "\tt = PrettyTable(['Statistic', 'Value'])\n",
        "\tt.add_row(['Mean', descriptive_stats['mean']])\n",
        "\tt.add_row(['Sum', descriptive_stats['sum']])\n",
        "\tt.add_row(['Variance', descriptive_stats['var']])\n",
        "\tt.add_row(['Standard deviation', descriptive_stats['std']])\n",
        "\tt.add_row(['Coefficient of variation', descriptive_stats['CV']])\n",
        "\tt.add_row(['Kurtosis', descriptive_stats['kurtosis']])\n",
        "\tt.add_row(['Mean absolute deviation', descriptive_stats['MAD']])\n",
        "\tt.add_row(['Skewness', descriptive_stats['skewness']])\n",
        "\tt.add_row(['Monotonous', descriptive_stats['mono']])\n",
        "\tprint(t)\n",
        "\n",
        "\tprint('\\n')\n",
        "\treturn descriptive_stats\n",
        "\n",
        "# Function to get the max and min values for a numeric column\n",
        "def extremums_for_column(data: pd.DataFrame, col: str, n: int = 5) -> pd.DataFrame:\n",
        "\t'''\n",
        "\tInput arguments:\n",
        "\t- data : pandas dataframe\n",
        "\t- col : column name\n",
        "\n",
        "\tOutputs:\n",
        "\t- extremums: Return a dataframe with extremums for the column which includes:\n",
        "\t\t\t\t\t- max_n: \"n\" largest values in the column\n",
        "\t\t\t\t\t- min_n: \"n\" smallest values in the column\n",
        "\n",
        "\t'''\n",
        "\n",
        "\t# Check if the column exists in the dataframe\n",
        "\tif col not in data.columns:\n",
        "\t\tprint(f'Column \"{col}\" not found in the dataframe')\n",
        "\t\treturn\n",
        "\t\n",
        "\t# Check if the column is numeric\n",
        "\tif data[col].dtype != 'float64' and data[col].dtype != 'int64':\n",
        "\t\tprint(f'Column \"{col}\" is not numeric')\n",
        "\t\treturn\n",
        "\t\n",
        "\textremums = {}\n",
        "\textremums['max_n'] = data[col].value_counts().sort_index(ascending=False).head(5)\n",
        "\textremums['min_n'] = data[col].value_counts().sort_index(ascending=True).head(5)\n",
        "\n",
        "\t# Display the extremums\n",
        "\tprint(f'Extremums for column \"{col}\"')\n",
        "\n",
        "\tprint(f'Maximum {n} values')\n",
        "\tt = PrettyTable(['Value', 'Count', 'Freq'])\n",
        "\tfor (index, value) in extremums['max_n'].iteritems():\n",
        "\t\tt.add_row([index, value, value / len(data[col]) * 100])\n",
        "\tprint(t)\n",
        "\n",
        "\tprint(f'Minimum {n} values')\n",
        "\tt = PrettyTable(['Value', 'Count', 'Freq'])\n",
        "\tfor (index, value) in extremums['min_n'].iteritems():\n",
        "\t\tt.add_row([index, value, value / len(data[col]) * 100])\n",
        "\tprint(t)\n",
        "\n",
        "\tprint('\\n')\n",
        "\treturn extremums\n",
        "\n",
        "# Function to check the most frquently occuring values (preferably for a categorical column)\n",
        "def common_values(data: pd.DataFrame, col: str, n: int = 5) -> pd.DataFrame:\n",
        "\t'''\n",
        "\tInput arguments:\n",
        "\t- data : pandas dataframe\n",
        "\t- col : column name\n",
        "\n",
        "\tOutputs:\n",
        "\t- common_values: Return a dataframe with common values for the column which includes:\n",
        "\t\t\t\t\t\t- value: Common value in the column\n",
        "\t\t\t\t\t\t- count: Count of the value in the column\n",
        "\t\t\t\t\t\t- freq: Frequency of the value in the column\n",
        "\t'''\n",
        "\n",
        "\tn_values = data[col].value_counts().nlargest(n)\n",
        "\tcommon_values = pd.DataFrame()\n",
        "\tcommon_values['value'] = n_values.index\n",
        "\tcommon_values['count'] = n_values.values\n",
        "\tcommon_values['freq'] = 100 * common_values['count'] / data[col].count()\n",
        "\n",
        "\t# Display the common values\n",
        "\tprint(f'Common values for column \"{col}\"')\n",
        "\tt = PrettyTable(['Value', 'Count', 'Frequency'])\n",
        "\tfor (v, c, f) in zip(common_values['value'], common_values['count'], common_values['freq']):\n",
        "\t\tt.add_row([v, c, f])\n",
        "\tprint(t)\n",
        "\n",
        "\tprint('\\n')\n",
        "\treturn common_values\n",
        "\n",
        "def categorical_overview(data: pd.DataFrame, col: str) -> pd.DataFrame:\n",
        "\t'''\n",
        "\tInput arguments:\n",
        "\t- data : pandas dataframe\n",
        "\t- col : column name\n",
        "\n",
        "\tOutputs:\n",
        "\t- None: Prints the overview of the categorical column:\n",
        "\t\t\t- Distinct: Number of distinct values in the column\n",
        "\t\t\t- Distinct (%) : Percentage of distinct values in the column\n",
        "\t\t\t- Missing: Number of missing values in the column\n",
        "\t\t\t- Missing (%) : Percentage of missing values in the column\n",
        "\t'''\n",
        "\n",
        "\tt = PrettyTable(['Measure', 'Value'])\n",
        "\tt.add_row(['Distinct Count', data[col].nunique()])\n",
        "\tt.add_row(['Distinct %', data[col].nunique()/data.shape[0]*100])\n",
        "\tt.add_row(['Missing Count', data[col].isna().sum()])\n",
        "\tt.add_row(['Missing %', data[col].isna().sum()/data.shape[0]*100])\n",
        "\n",
        "\t# Display the unique values\n",
        "\tprint(f'Overview of column \"{col}\"')\n",
        "\tprint(t)\n",
        "\n",
        "\tprint('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "xlsGl0KzfCuP"
      },
      "outputs": [],
      "source": [
        "def analyze_data(df: pd.DataFrame, col: str, **kwargs) -> dict():\n",
        "\tn = kwargs.get('n', 5)\n",
        "\t\n",
        "\t# Check if column exists in the dataframe\n",
        "\tif col not in df.columns:\n",
        "\t\tprint(f'Column \"{col}\" not found in the dataframe')\n",
        "\t\treturn\n",
        "\t\t\n",
        "\t# Check if the column is numeric\n",
        "\tif df[col].dtype == 'float64' or df[col].dtype == 'int64':\n",
        "\t\tprint(f'\\tAnalyzing: Column \"{col}\" is numeric\\n')\t\t\n",
        "\t\toverview_data(df, col)\n",
        "\t\tquantile_stats = quantile_statistics_for_column(df, col)\n",
        "\t\tdescriptive_stats = descriptive_statistics_for_column(df, col)\n",
        "\t\textremums = extremums_for_column(df, col, n)\n",
        "\t\tcommon_val = common_values(df, col, n)\n",
        "\t\treturn { 'quantile_stats': quantile_stats, \n",
        "\t\t\t\t\t'descriptive_stats': descriptive_stats, \n",
        "\t\t\t\t\t'extremums': extremums,\n",
        "\t\t\t\t\t'common_values': common_val\n",
        "\t\t\t\t\t}\n",
        "\telse:\n",
        "\t\tprint(f'Column \"{col}\" is categorical')\n",
        "\t\tcategorical_overview(df, col)\n",
        "\t\tcommon_val = common_values(df, col, n)\n",
        "\t\treturn { 'common_values': common_val }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWwbxwKvfCuQ"
      },
      "source": [
        "## 2.1 Variable Types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jF3NHkuOfCuR",
        "outputId": "946d48a7-c109-485a-f3c6-14456effca23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10321 entries, 0 to 10320\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   full_name          10321 non-null  object \n",
            " 1   selling_price      10321 non-null  float64\n",
            " 2   seller_type        10321 non-null  object \n",
            " 3   km_driven          10321 non-null  int64  \n",
            " 4   owner_type         10321 non-null  object \n",
            " 5   fuel_type          10321 non-null  object \n",
            " 6   transmission_type  10321 non-null  object \n",
            " 7   mileage            10321 non-null  float64\n",
            " 8   engine             10321 non-null  float64\n",
            " 9   max_power          10321 non-null  float64\n",
            " 10  seats              10321 non-null  float64\n",
            " 11  age                10321 non-null  int64  \n",
            "dtypes: float64(5), int64(2), object(5)\n",
            "memory usage: 967.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcgkdcmQfCuS"
      },
      "source": [
        "The categorical variables are:\n",
        "- `full_name`\n",
        "- `seller_type`\n",
        "- `owner_type`\n",
        "- `fuel_type`\n",
        "- `transmission_type`\n",
        "- `seats`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfk6Tsv1fCuT",
        "outputId": "ee24e250-9293-43bf-8835-8ee9e3bd0285"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maruti Swift Dzire VDI                      141\n",
            "Mahindra XUV500 W8 2WD                       63\n",
            "Maruti Vitara Brezza ZDi Plus                57\n",
            "Maruti Vitara Brezza ZDi                     51\n",
            "Maruti Baleno Delta 1.2                      50\n",
            "                                           ... \n",
            "Hyundai i20 1.4 Asta AT (O) with Sunroof      1\n",
            "Tata Safari Storme VX 4WD                     1\n",
            "Toyota Innova Crysta 2.5 VX BSIV              1\n",
            "Mahindra Scorpio S4 Plus 4WD                  1\n",
            "Nissan Micra Fashion Edition XL CVT           1\n",
            "Name: full_name, Length: 2126, dtype: int64\n",
            "---------------------------------------- \n",
            "\n",
            "Diesel      6581\n",
            "Petrol      3667\n",
            "CNG           64\n",
            "Electric       9\n",
            "Name: fuel_type, dtype: int64\n",
            "---------------------------------------- \n",
            "\n",
            "Dealer              6787\n",
            "Individual          3430\n",
            "Trustmark Dealer     104\n",
            "Name: seller_type, dtype: int64\n",
            "---------------------------------------- \n",
            "\n",
            "Manual       7004\n",
            "Automatic    3317\n",
            "Name: transmission_type, dtype: int64\n",
            "---------------------------------------- \n",
            "\n",
            "First Owner     10320\n",
            "Second Owner        1\n",
            "Name: owner_type, dtype: int64\n",
            "---------------------------------------- \n",
            "\n",
            "5.0     7848\n",
            "7.0     1956\n",
            "8.0      281\n",
            "4.0       91\n",
            "6.0       76\n",
            "9.0       46\n",
            "2.0       16\n",
            "10.0       7\n",
            "Name: seats, dtype: int64\n",
            "---------------------------------------- \n",
            "\n"
          ]
        }
      ],
      "source": [
        "categorical_columns = ['full_name', 'fuel_type', 'seller_type', 'transmission_type', 'owner_type', 'seats']\n",
        "for column in categorical_columns:\n",
        "\tprint(f'{df[column].value_counts()}')\n",
        "\tprint('--'*20, '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acWsqnDkfCuU"
      },
      "source": [
        "## 2.2 Desciptive Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5jQDBEvfCuU"
      },
      "source": [
        "### 2.2.1 Numeric Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3EbEnlsfCuV"
      },
      "source": [
        "#### Selling price: `selling_price`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZO7q-xc6fCuV"
      },
      "source": [
        "Summary: \n",
        "- The mean selling price is around INR 11.07 Lakhs\n",
        "- The median price is lower than the mean at around INR 7.65 Lakhs\n",
        "- The maximum selling price is INR 92 Lakhs\n",
        "- The Coefficient of variation is about 87.1%\n",
        "- As such, the kurtosis is ~15"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hmes8e5zfCuW",
        "outputId": "3f117ad3-5b6e-4cd5-b931-df4b90f7253a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tAnalyzing: Column \"selling_price\" is numeric\n",
            "\n",
            "Overview of column \"selling_price\"\n",
            "+----------------+--------------------+\n",
            "|    Measure     |       Value        |\n",
            "+----------------+--------------------+\n",
            "| Distinct Count |        808         |\n",
            "|   Distinct %   | 7.828698769499079  |\n",
            "| Missing Count  |         0          |\n",
            "|   Missing %    |        0.0         |\n",
            "|      Mean      | 11.070784807673675 |\n",
            "|    Minimum     |        5.0         |\n",
            "|    Maximum     |        92.0        |\n",
            "+----------------+--------------------+\n",
            "\n",
            "\n",
            "Quantile stats for column \"selling_price\"\n",
            "+---------------------+-------+\n",
            "|      Statistic      | Value |\n",
            "+---------------------+-------+\n",
            "|       Minimum       |  5.0  |\n",
            "|    5th Percentile   |  5.25 |\n",
            "|  First Quartile, Q1 |  6.1  |\n",
            "|        Median       |  7.65 |\n",
            "|  Third Quartile, Q3 | 11.35 |\n",
            "|   95th Percentile   |  30.0 |\n",
            "|       Maximum       |  92.0 |\n",
            "|        Range        |  87.0 |\n",
            "| Interquartile Range |  5.25 |\n",
            "+---------------------+-------+\n",
            "\n",
            "\n",
            "Descriptive stats for column \"selling_price\"\n",
            "+--------------------------+--------------------+\n",
            "|        Statistic         |       Value        |\n",
            "+--------------------------+--------------------+\n",
            "|           Mean           | 11.070784807673675 |\n",
            "|           Sum            |     114261.57      |\n",
            "|         Variance         | 93.12171099254435  |\n",
            "|    Standard deviation    | 9.649959118698087  |\n",
            "| Coefficient of variation | 0.8716598946092107 |\n",
            "|         Kurtosis         | 15.64598356846234  |\n",
            "| Mean absolute deviation  | 5.894012032758245  |\n",
            "|         Skewness         | 3.513319830975561  |\n",
            "|        Monotonous        |        True        |\n",
            "+--------------------------+--------------------+\n",
            "\n",
            "\n",
            "Extremums for column \"selling_price\"\n",
            "Maximum 5 values\n",
            "+-------+-------+----------------------+\n",
            "| Value | Count |         Freq         |\n",
            "+-------+-------+----------------------+\n",
            "|  92.0 |   2   | 0.019377967251235348 |\n",
            "|  87.5 |   1   | 0.009688983625617674 |\n",
            "|  85.0 |   4   | 0.038755934502470696 |\n",
            "|  84.0 |   1   | 0.009688983625617674 |\n",
            "|  82.5 |   1   | 0.009688983625617674 |\n",
            "+-------+-------+----------------------+\n",
            "Minimum 5 values\n",
            "+-------+-------+----------------------+\n",
            "| Value | Count |         Freq         |\n",
            "+-------+-------+----------------------+\n",
            "|  5.0  |  209  |  2.0249975777540934  |\n",
            "|  5.01 |   2   | 0.019377967251235348 |\n",
            "|  5.02 |   1   | 0.009688983625617674 |\n",
            "|  5.04 |   2   | 0.019377967251235348 |\n",
            "|  5.05 |   6   | 0.05813390175370604  |\n",
            "+-------+-------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"selling_price\"\n",
            "+-------+-------+--------------------+\n",
            "| Value | Count |     Frequency      |\n",
            "+-------+-------+--------------------+\n",
            "|  5.5  |  405  | 3.9240383683751574 |\n",
            "|  6.5  |  376  | 3.643057843232245  |\n",
            "|  6.0  |  300  | 2.9066950876853017 |\n",
            "|  7.5  |  272  | 2.635403546168007  |\n",
            "|  7.0  |  225  | 2.1800213157639763 |\n",
            "+-------+-------+--------------------+\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quantile_stats': {'min': 5.0,\n",
              "  '5%': 5.25,\n",
              "  'Q1': 6.1,\n",
              "  'median': 7.65,\n",
              "  'Q3': 11.35,\n",
              "  '95%': 30.0,\n",
              "  'max': 92.0,\n",
              "  'range': 87.0,\n",
              "  'IQR': 5.25},\n",
              " 'descriptive_stats': {'mean': 11.070784807673675,\n",
              "  'sum': 114261.57,\n",
              "  'var': 93.12171099254435,\n",
              "  'std': 9.649959118698087,\n",
              "  'CV': 0.8716598946092107,\n",
              "  'kurtosis': 15.64598356846234,\n",
              "  'MAD': 5.894012032758245,\n",
              "  'skewness': 3.513319830975561,\n",
              "  'mono': True},\n",
              " 'extremums': {'max_n': 92.0    2\n",
              "  87.5    1\n",
              "  85.0    4\n",
              "  84.0    1\n",
              "  82.5    1\n",
              "  Name: selling_price, dtype: int64, 'min_n': 5.00    209\n",
              "  5.01      2\n",
              "  5.02      1\n",
              "  5.04      2\n",
              "  5.05      6\n",
              "  Name: selling_price, dtype: int64},\n",
              " 'common_values':    value  count      freq\n",
              " 0    5.5    405  3.924038\n",
              " 1    6.5    376  3.643058\n",
              " 2    6.0    300  2.906695\n",
              " 3    7.5    272  2.635404\n",
              " 4    7.0    225  2.180021}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "analyze_data(df, 'selling_price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "8j22kO5MfCuW",
        "outputId": "cb43647a-b1f4-4f89-f126-f99e99258a27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Histogram for the column 'selling_price'\n",
        "fig, hist = plt.subplots(1, 2, figsize=(15, 6))\n",
        "# Set the title for the histogram\n",
        "\n",
        "xlimleft = 4\n",
        "xlimright = 15\n",
        "bins = round( 50 * max(df['selling_price']) / xlim)\n",
        "\n",
        "\n",
        "hist_norm = sns.histplot(df['selling_price'], bins=100, color='salmon', edgecolor='black', linewidth=0.3, ax=hist[0])\n",
        "hist_norm.set_title('Histogram for the column \"selling_price\"')\n",
        "hist_norm.set_xlabel('Selling price in INR')\n",
        "hist_norm.set_ylabel('Frequency')\n",
        "\n",
        "hist_norm_xlim = sns.histplot(df['selling_price'], bins=bins, color='salmon', edgecolor='black', linewidth=0.6 ,ax=hist[1])\n",
        "hist_norm_xlim.set_xlim(xlimleft, xlimright)\n",
        "hist_norm_xlim.set_title('Selling price between 4 and 15 lakhs')\n",
        "hist_norm_xlim.set_xlabel('Selling price in INR')\n",
        "hist_norm_xlim.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wL9Ib1qZfCuX"
      },
      "source": [
        "There is a **strong positive skewness** in the selling price. To better analyze the data we would be plotting the log transformation along with the square root transformation below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "V_Xyr2tkfCuY",
        "outputId": "f33b38d6-aed1-44f4-b192-d2329a1e326c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the histogram of the log and root transformation of the column 'selling_price'\n",
        "fig, hist = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "hist_log = sns.histplot(np.log(df['selling_price']), bins=60, color='salmon', edgecolor='black', linewidth=0.7, ax=hist[0])\n",
        "hist_log.set_title('Histogram: Log (\"selling_price\")')\n",
        "hist_log.set_xlabel('Log - Selling price in INR')\n",
        "hist_log.set_ylabel('Frequency')\n",
        "\n",
        "hist_root = sns.histplot(np.sqrt(df['selling_price']), bins=60, color='salmon', edgecolor='black', linewidth=0.7, ax=hist[1])\n",
        "hist_root.set_title('Histogram: Sqrt (\"selling_price_inr\")')\n",
        "hist_root.set_xlabel('Sqrt - Selling price in INR')\n",
        "hist_root.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "PLBBozaTfCuZ",
        "outputId": "fa6eaac1-af31-4260-935a-173f168746c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Box plot and violin plot for the log transformation of column 'selling_price_inr'\n",
        "fig, ran = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "box = sns.boxplot(data=np.log(df['selling_price']), ax=ran[0])\n",
        "box.set_title('Box plot: \"selling_price_inr\"')\n",
        "box.set_xlabel('Selling price in INR')\n",
        "box.set_ylabel('Frequency')\n",
        "\n",
        "violin = sns.violinplot(data=np.log(df['selling_price']), ax=ran[1])\n",
        "violin.set_title('Violin plot: \"selling_price_inr\"')\n",
        "violin.set_xlabel('Selling price in INR')\n",
        "violin.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WhnsGEUfCuZ"
      },
      "source": [
        "#### Distance driven: `km_driven`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWQi55BafCuZ"
      },
      "source": [
        "Summary: \n",
        "- The mean distance driven is around 53,280 km or 33,100 miles\n",
        "- The median is around 47,900 km or 29,760 miles\n",
        "- The CV is very large, 106%\n",
        "- The kurtosis of 1868 suggests that outlier values are present and influence the data.\n",
        "- Skewness is 30, i.e. the data is highly skewed (positive skewness)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eD8K58w8fCua",
        "outputId": "4a9109b4-e8d6-4d7a-9ff1-3874673bb61c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tAnalyzing: Column \"km_driven\" is numeric\n",
            "\n",
            "Overview of column \"km_driven\"\n",
            "+----------------+--------------------+\n",
            "|    Measure     |       Value        |\n",
            "+----------------+--------------------+\n",
            "| Distinct Count |        2480        |\n",
            "|   Distinct %   | 24.028679391531828 |\n",
            "| Missing Count  |         0          |\n",
            "|   Missing %    |        0.0         |\n",
            "|      Mean      | 53280.97878112586  |\n",
            "|    Minimum     |        581         |\n",
            "|    Maximum     |      3800000       |\n",
            "+----------------+--------------------+\n",
            "\n",
            "\n",
            "Quantile stats for column \"km_driven\"\n",
            "+---------------------+----------+\n",
            "|      Statistic      |  Value   |\n",
            "+---------------------+----------+\n",
            "|       Minimum       |   581    |\n",
            "|    5th Percentile   |  9000.0  |\n",
            "|  First Quartile, Q1 | 26000.0  |\n",
            "|        Median       | 47900.0  |\n",
            "|  Third Quartile, Q3 | 70000.0  |\n",
            "|   95th Percentile   | 120000.0 |\n",
            "|       Maximum       | 3800000  |\n",
            "|        Range        | 3799419  |\n",
            "| Interquartile Range | 44000.0  |\n",
            "+---------------------+----------+\n",
            "\n",
            "\n",
            "Descriptive stats for column \"km_driven\"\n",
            "+--------------------------+--------------------+\n",
            "|        Statistic         |       Value        |\n",
            "+--------------------------+--------------------+\n",
            "|           Mean           | 53280.97878112586  |\n",
            "|           Sum            |     549912982      |\n",
            "|         Variance         | 3236020623.2480955 |\n",
            "|    Standard deviation    | 56886.03188172027  |\n",
            "| Coefficient of variation | 1.0676611650736314 |\n",
            "|         Kurtosis         | 1868.9165350140556 |\n",
            "| Mean absolute deviation  | 27826.921436180182 |\n",
            "|         Skewness         | 30.857781854942605 |\n",
            "|        Monotonous        |       False        |\n",
            "+--------------------------+--------------------+\n",
            "\n",
            "\n",
            "Extremums for column \"km_driven\"\n",
            "Maximum 5 values\n",
            "+---------+-------+----------------------+\n",
            "|  Value  | Count |         Freq         |\n",
            "+---------+-------+----------------------+\n",
            "| 3800000 |   1   | 0.009688983625617674 |\n",
            "| 1325000 |   1   | 0.009688983625617674 |\n",
            "|  950000 |   1   | 0.009688983625617674 |\n",
            "|  850000 |   1   | 0.009688983625617674 |\n",
            "|  825000 |   1   | 0.009688983625617674 |\n",
            "+---------+-------+----------------------+\n",
            "Minimum 5 values\n",
            "+-------+-------+----------------------+\n",
            "| Value | Count |         Freq         |\n",
            "+-------+-------+----------------------+\n",
            "|  581  |   1   | 0.009688983625617674 |\n",
            "|  1000 |   9   | 0.08720085263055906  |\n",
            "|  1001 |   3   | 0.02906695087685302  |\n",
            "|  1005 |   1   | 0.009688983625617674 |\n",
            "|  1041 |   1   | 0.009688983625617674 |\n",
            "+-------+-------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"km_driven\"\n",
            "+-------+-------+--------------------+\n",
            "| Value | Count |     Frequency      |\n",
            "+-------+-------+--------------------+\n",
            "| 50000 |  362  | 3.5074120724735973 |\n",
            "| 40000 |  319  | 3.0907857765720377 |\n",
            "| 70000 |  276  | 2.6741594806704776 |\n",
            "| 60000 |  260  | 2.5191357426605947 |\n",
            "| 30000 |  239  | 2.3156670865226237 |\n",
            "+-------+-------+--------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_km_driven = analyze_data(df, 'km_driven', n=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "VSQrHpxWfCua",
        "outputId": "0f326e72-f5e8-4744-f7b8-95890d00903c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the histogram of the column 'km_driven'\n",
        "fig, hist = plt.subplots(1, 2, figsize=(15, 6))\n",
        "xlim = 3e5\n",
        "bins = round( 50 * max(df['km_driven']) / xlim)\n",
        "\n",
        "hist_norm = sns.histplot(df['km_driven'], bins=50, color='salmon', edgecolor='black', linewidth=0.3, ax=hist[0])\n",
        "hist_norm.set_title('Histogram: km_driven')\n",
        "hist_norm.set_xlabel('Km driven')\n",
        "hist_norm.set_ylabel('Frequency')\n",
        "\n",
        "hist_norm_xlim = sns.histplot(df['km_driven'], bins=bins, color='salmon', edgecolor='black', linewidth=0.6 ,ax=hist[1])\n",
        "hist_norm_xlim.set_xlim([0, xlim])\n",
        "hist_norm_xlim.set_title('Histogram: km_driven $x \\in $(0 - 3,00,000)')\n",
        "hist_norm_xlim.set_xlabel('Km driven')\n",
        "hist_norm_xlim.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yRxR_SnfCub"
      },
      "source": [
        "Since it is highly skewed, we would be plotting the log and sqrt transformation of the distance driven."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "ZTs4OqwAfCub",
        "outputId": "3d22c78d-abe9-4dc9-b2af-c0e0311c26a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the histogram of the log and root transformation of the column 'km_driven'\n",
        "fig, hist = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "hist_log = sns.histplot(np.log(df['km_driven']), bins=50, color='salmon', edgecolor='black', linewidth=0.7, ax=hist[0])\n",
        "hist_log.set_title('Histogram: Log (\"km_driven\")')\n",
        "hist_log.set_xlabel('Log - Km driven')\n",
        "hist_log.set_ylabel('Frequency')\n",
        "\n",
        "hist_root = sns.histplot(np.sqrt(df['km_driven']), bins=50, color='salmon', edgecolor='black', linewidth=0.7, ax=hist[1])\n",
        "hist_root.set_title('Histogram: Box-Cox (\"km_driven\")')\n",
        "hist_root.set_xlabel('Sqrt - Km driven')\n",
        "hist_root.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "juEkKcxufCuc",
        "outputId": "d1abffd9-9a08-41e3-90f6-975aff354e8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Box plot: Sqrt (\"km_driven\")')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Let's use a box plot to visualize the distribution of the column 'km_driven'\n",
        "fig, box = plt.subplots(1, 3, figsize=(24, 6))\n",
        "for box_plot in box:\n",
        "\tbox_plot.set_xlabel('Km driven')\n",
        "\tbox_plot.set_ylabel('Frequency')\n",
        "\n",
        "box_original = sns.boxplot(data=df['km_driven'], ax=box[0], color='red')\n",
        "box_original.set_title('Box plot: \"km_driven\"')\n",
        "\n",
        "box_log = sns.boxplot(data=np.log(df['km_driven']), ax=box[1], color='#326EAD')\n",
        "box_log.set_title('Box plot: Log (\"km_driven\")')\n",
        "\n",
        "box_root = sns.boxplot(data=np.sqrt(df['km_driven']), ax=box[2], color='#ff7f00')\n",
        "box_root.set_title('Box plot: Sqrt (\"km_driven\")')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTHyMk_8fCuc"
      },
      "source": [
        "As is apparent from the above plots, there are outliers present in the data which need to be treated in the **Feature Engineering** section."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vip75GZTfCuc"
      },
      "source": [
        "#### Engine Size: `engine`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JU8_F_MsfCud"
      },
      "source": [
        "Summary: \n",
        "- There are only 127 distinct values \n",
        "- The mean engine size is 1685 cc\n",
        "- The median is 1498 cc\n",
        "- The CV is acceptable, 32%\n",
        "- The kurtosis of 2.96\n",
        "- Skewness 1.40, meaning the data is not very skewed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bof6yQwtfCud",
        "outputId": "53c64a76-38e7-49f2-9178-da133f172e7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tAnalyzing: Column \"engine\" is numeric\n",
            "\n",
            "Overview of column \"engine\"\n",
            "+----------------+--------------------+\n",
            "|    Measure     |       Value        |\n",
            "+----------------+--------------------+\n",
            "| Distinct Count |        127         |\n",
            "|   Distinct %   | 1.2305009204534443 |\n",
            "| Missing Count  |         0          |\n",
            "|   Missing %    |        0.0         |\n",
            "|      Mean      | 1685.5550818719116 |\n",
            "|    Minimum     |        0.0         |\n",
            "|    Maximum     |       5998.0       |\n",
            "+----------------+--------------------+\n",
            "\n",
            "\n",
            "Quantile stats for column \"engine\"\n",
            "+---------------------+--------+\n",
            "|      Statistic      | Value  |\n",
            "+---------------------+--------+\n",
            "|       Minimum       |  0.0   |\n",
            "|    5th Percentile   | 1197.0 |\n",
            "|  First Quartile, Q1 | 1248.0 |\n",
            "|        Median       | 1498.0 |\n",
            "|  Third Quartile, Q3 | 1995.0 |\n",
            "|   95th Percentile   | 2953.0 |\n",
            "|       Maximum       | 5998.0 |\n",
            "|        Range        | 5998.0 |\n",
            "| Interquartile Range | 747.0  |\n",
            "+---------------------+--------+\n",
            "\n",
            "\n",
            "Descriptive stats for column \"engine\"\n",
            "+--------------------------+---------------------+\n",
            "|        Statistic         |        Value        |\n",
            "+--------------------------+---------------------+\n",
            "|           Mean           |  1685.5550818719116 |\n",
            "|           Sum            |      17396614.0     |\n",
            "|         Variance         |  298690.21462557715 |\n",
            "|    Standard deviation    |  546.5255846029326  |\n",
            "| Coefficient of variation | 0.32424071481305883 |\n",
            "|         Kurtosis         |  2.9618460941925506 |\n",
            "| Mean absolute deviation  |  444.6598685818592  |\n",
            "|         Skewness         |  1.4027378714671863 |\n",
            "|        Monotonous        |        False        |\n",
            "+--------------------------+---------------------+\n",
            "\n",
            "\n",
            "Extremums for column \"engine\"\n",
            "Maximum 5 values\n",
            "+--------+-------+----------------------+\n",
            "| Value  | Count |         Freq         |\n",
            "+--------+-------+----------------------+\n",
            "| 5998.0 |   2   | 0.019377967251235348 |\n",
            "| 5461.0 |   4   | 0.038755934502470696 |\n",
            "| 4999.0 |   1   | 0.009688983625617674 |\n",
            "| 4951.0 |   4   | 0.038755934502470696 |\n",
            "| 4806.0 |   3   | 0.02906695087685302  |\n",
            "+--------+-------+----------------------+\n",
            "Minimum 5 values\n",
            "+-------+-------+----------------------+\n",
            "| Value | Count |         Freq         |\n",
            "+-------+-------+----------------------+\n",
            "|  0.0  |   2   | 0.019377967251235348 |\n",
            "|  72.0 |   3   | 0.02906695087685302  |\n",
            "| 909.0 |   4   | 0.038755934502470696 |\n",
            "| 998.0 |   85  |  0.8235636081775022  |\n",
            "| 999.0 |   46  | 0.44569324677841293  |\n",
            "+-------+-------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"engine\"\n",
            "+--------+-------+--------------------+\n",
            "| Value  | Count |     Frequency      |\n",
            "+--------+-------+--------------------+\n",
            "| 1197.0 |  1366 | 13.235151632593741 |\n",
            "| 1248.0 |  1280 | 12.40189904079062  |\n",
            "| 1498.0 |  900  | 8.720085263055905  |\n",
            "| 2179.0 |  577  | 5.590543551981397  |\n",
            "| 1497.0 |  501  | 4.854180796434454  |\n",
            "+--------+-------+--------------------+\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quantile_stats': {'min': 0.0,\n",
              "  '5%': 1197.0,\n",
              "  'Q1': 1248.0,\n",
              "  'median': 1498.0,\n",
              "  'Q3': 1995.0,\n",
              "  '95%': 2953.0,\n",
              "  'max': 5998.0,\n",
              "  'range': 5998.0,\n",
              "  'IQR': 747.0},\n",
              " 'descriptive_stats': {'mean': 1685.5550818719116,\n",
              "  'sum': 17396614.0,\n",
              "  'var': 298690.21462557715,\n",
              "  'std': 546.5255846029326,\n",
              "  'CV': 0.32424071481305883,\n",
              "  'kurtosis': 2.9618460941925506,\n",
              "  'MAD': 444.6598685818592,\n",
              "  'skewness': 1.4027378714671863,\n",
              "  'mono': False},\n",
              " 'extremums': {'max_n': 5998.0    2\n",
              "  5461.0    4\n",
              "  4999.0    1\n",
              "  4951.0    4\n",
              "  4806.0    3\n",
              "  Name: engine, dtype: int64, 'min_n': 0.0       2\n",
              "  72.0      3\n",
              "  909.0     4\n",
              "  998.0    85\n",
              "  999.0    46\n",
              "  Name: engine, dtype: int64},\n",
              " 'common_values':     value  count       freq\n",
              " 0  1197.0   1366  13.235152\n",
              " 1  1248.0   1280  12.401899\n",
              " 2  1498.0    900   8.720085\n",
              " 3  2179.0    577   5.590544\n",
              " 4  1497.0    501   4.854181}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "analyze_data(df, 'engine', n=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "6sy5mXdpfCud",
        "outputId": "3f6ade04-0dbd-4932-8f2f-4da91df9c8eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the histogram of the column 'engine_cc'\n",
        "fig, hist = plt.subplots(1, 1, figsize=(10, 6))\n",
        "\n",
        "hist_or = sns.histplot(df['engine'], bins=50, color='salmon', edgecolor='black', linewidth=0.8, ax=hist)\n",
        "hist_or.set_title('Histogram: engine(in cc)')\n",
        "hist_or.set_xlabel('Engine cc')\n",
        "hist_or.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "dyALv_rmfCud",
        "outputId": "a7f0c149-6fe8-497e-bb6f-85f16c143fe6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Box plot and violin plot for the column 'engine_cc'\n",
        "fig, ran = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "box = sns.boxplot(data=df['engine'], ax=ran[0])\n",
        "box.set_title('Box plot: \"engine(in cc)\"')\n",
        "box.set_xlabel('Engine cc')\n",
        "box.set_ylabel('Frequency')\n",
        "\n",
        "violin = sns.violinplot(data=df['engine'], ax=ran[1])\n",
        "violin.set_title('Violin plot: \"engine(in cc)\"')\n",
        "violin.set_xlabel('Engine cc')\n",
        "violin.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5kklV-GfCue"
      },
      "source": [
        "- Due to their being so few distinct values, we can categorize the engine size into ordinal groups later in the **Feature Engineering** section to see its impact on the model prediction.\n",
        "- The presence of outliers is clearly visible in the above plots."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60o8uvmcfCue"
      },
      "source": [
        "#### Horsepower: `max_power`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fhk0v93FfCuf"
      },
      "source": [
        "Summary: \n",
        "- There are 387 distinct values (which is large but certainly not continuous considering the dataset size)\n",
        "- The mean power is 118 bhp\n",
        "- The median is 102 bhp \n",
        "- The CV is high, 40%\n",
        "- The kurtosis is 7.3 indicating the presence of fatter tails\n",
        "- Skewness 2.06 indicates postively skewed data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hnj597kNfCuf",
        "outputId": "e2b62aba-2585-4e2f-8d4d-0d03ff2b2f74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tAnalyzing: Column \"max_power\" is numeric\n",
            "\n",
            "Overview of column \"max_power\"\n",
            "+----------------+--------------------+\n",
            "|    Measure     |       Value        |\n",
            "+----------------+--------------------+\n",
            "| Distinct Count |        387         |\n",
            "|   Distinct %   | 3.7496366631140394 |\n",
            "| Missing Count  |         0          |\n",
            "|   Missing %    |        0.0         |\n",
            "|      Mean      | 118.53692423214804 |\n",
            "|    Minimum     |       41.57        |\n",
            "|    Maximum     |       600.0        |\n",
            "+----------------+--------------------+\n",
            "\n",
            "\n",
            "Quantile stats for column \"max_power\"\n",
            "+---------------------+--------+\n",
            "|      Statistic      | Value  |\n",
            "+---------------------+--------+\n",
            "|       Minimum       | 41.57  |\n",
            "|    5th Percentile   |  74.0  |\n",
            "|  First Quartile, Q1 |  86.7  |\n",
            "|        Median       | 102.0  |\n",
            "|  Third Quartile, Q3 | 140.0  |\n",
            "|   95th Percentile   | 201.15 |\n",
            "|       Maximum       | 600.0  |\n",
            "|        Range        | 558.43 |\n",
            "| Interquartile Range |  53.3  |\n",
            "+---------------------+--------+\n",
            "\n",
            "\n",
            "Descriptive stats for column \"max_power\"\n",
            "+--------------------------+--------------------+\n",
            "|        Statistic         |       Value        |\n",
            "+--------------------------+--------------------+\n",
            "|           Mean           | 118.53692423214804 |\n",
            "|           Sum            |    1223419.595     |\n",
            "|         Variance         | 2279.8506433657703 |\n",
            "|    Standard deviation    | 47.74778155439026  |\n",
            "| Coefficient of variation | 0.4028093512944444 |\n",
            "|         Kurtosis         | 7.398626837037089  |\n",
            "| Mean absolute deviation  | 35.604371268841255 |\n",
            "|         Skewness         | 2.061479869882212  |\n",
            "|        Monotonous        |       False        |\n",
            "+--------------------------+--------------------+\n",
            "\n",
            "\n",
            "Extremums for column \"max_power\"\n",
            "Maximum 5 values\n",
            "+-------+-------+----------------------+\n",
            "| Value | Count |         Freq         |\n",
            "+-------+-------+----------------------+\n",
            "| 600.0 |   1   | 0.009688983625617674 |\n",
            "| 575.0 |   1   | 0.009688983625617674 |\n",
            "| 552.0 |   1   | 0.009688983625617674 |\n",
            "| 507.0 |   1   | 0.009688983625617674 |\n",
            "| 500.0 |   1   | 0.009688983625617674 |\n",
            "+-------+-------+----------------------+\n",
            "Minimum 5 values\n",
            "+-------+-------+----------------------+\n",
            "| Value | Count |         Freq         |\n",
            "+-------+-------+----------------------+\n",
            "| 41.57 |   3   | 0.02906695087685302  |\n",
            "|  45.0 |   4   | 0.038755934502470696 |\n",
            "|  58.0 |   4   | 0.038755934502470696 |\n",
            "| 58.16 |   2   | 0.019377967251235348 |\n",
            "| 58.33 |   2   | 0.019377967251235348 |\n",
            "+-------+-------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"max_power\"\n",
            "+-------+-------+--------------------+\n",
            "| Value | Count |     Frequency      |\n",
            "+-------+-------+--------------------+\n",
            "|  88.5 |  654  | 6.336595291153958  |\n",
            "|  74.0 |  423  | 4.098440073636276  |\n",
            "|  81.8 |  402  | 3.8949714174983043 |\n",
            "|  98.6 |  331  | 3.2070535800794495 |\n",
            "| 140.0 |  315  | 3.052029842069567  |\n",
            "+-------+-------+--------------------+\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'quantile_stats': {'min': 41.57,\n",
              "  '5%': 74.0,\n",
              "  'Q1': 86.7,\n",
              "  'median': 102.0,\n",
              "  'Q3': 140.0,\n",
              "  '95%': 201.15,\n",
              "  'max': 600.0,\n",
              "  'range': 558.43,\n",
              "  'IQR': 53.3},\n",
              " 'descriptive_stats': {'mean': 118.53692423214804,\n",
              "  'sum': 1223419.595,\n",
              "  'var': 2279.8506433657703,\n",
              "  'std': 47.74778155439026,\n",
              "  'CV': 0.4028093512944444,\n",
              "  'kurtosis': 7.398626837037089,\n",
              "  'MAD': 35.604371268841255,\n",
              "  'skewness': 2.061479869882212,\n",
              "  'mono': False},\n",
              " 'extremums': {'max_n': 600.0    1\n",
              "  575.0    1\n",
              "  552.0    1\n",
              "  507.0    1\n",
              "  500.0    1\n",
              "  Name: max_power, dtype: int64, 'min_n': 41.57    3\n",
              "  45.00    4\n",
              "  58.00    4\n",
              "  58.16    2\n",
              "  58.33    2\n",
              "  Name: max_power, dtype: int64},\n",
              " 'common_values':    value  count      freq\n",
              " 0   88.5    654  6.336595\n",
              " 1   74.0    423  4.098440\n",
              " 2   81.8    402  3.894971\n",
              " 3   98.6    331  3.207054\n",
              " 4  140.0    315  3.052030}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "analyze_data(df, 'max_power', n=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "cpcUqH9efCuf",
        "outputId": "23e301f2-011d-4067-d928-98c3a4ef27d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the histogram of the column 'max_power_bhp'\n",
        "fig, hist = plt.subplots(1, 1, figsize=(10, 6))\n",
        "\n",
        "hist_or = sns.histplot(df['max_power'], bins=50, color='salmon', edgecolor='black', linewidth=0.8, ax=hist)\n",
        "hist_or.set_title('Histogram: max_power(in bhp)')\n",
        "hist_or.set_xlabel('Max power in bhp')\n",
        "hist_or.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "ycOijBmNfCug",
        "outputId": "a70622f0-3a2b-4b67-98fb-920b3079905b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the box plot and violin plot for the column 'max_power_bhp'\n",
        "fig, ran = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "box = sns.boxplot(data=df['max_power'], ax=ran[0])\n",
        "box.set_title('Box plot: \"max_power(in bhp)\"')\n",
        "box.set_xlabel('Max power in bhp')\n",
        "box.set_ylabel('Frequency')\n",
        "\n",
        "violin = sns.violinplot(data=df['max_power'], ax=ran[1])\n",
        "violin.set_title('Violin plot: \"max_power(in bhp)\"')\n",
        "violin.set_xlabel('Max power in bhp')\n",
        "violin.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZW5tRg3YfCug"
      },
      "source": [
        "We see some similar patterns in the above plots as we saw in the `engine_cc` plots. The presence of outliers seem to be affecting the mean and spread of the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5C_oWtnfCug"
      },
      "source": [
        "### 2.2.2 Categorical Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Y_Dn7sqfCum"
      },
      "source": [
        "#### Name: `name`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYQYHR9VfCum"
      },
      "source": [
        "Summary: \n",
        "- There are 2126 distinct values (15%) <span style=\"color: #FF5555;\">(very high cardinality)</span>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3X7_A6bOfCum",
        "outputId": "4465f1db-0105-4ba6-df62-27e2ce20e44c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column \"full_name\" is categorical\n",
            "Overview of column \"full_name\"\n",
            "+----------------+--------------------+\n",
            "|    Measure     |       Value        |\n",
            "+----------------+--------------------+\n",
            "| Distinct Count |        2126        |\n",
            "|   Distinct %   | 20.598779188063173 |\n",
            "| Missing Count  |         0          |\n",
            "|   Missing %    |        0.0         |\n",
            "+----------------+--------------------+\n",
            "\n",
            "\n",
            "Common values for column \"full_name\"\n",
            "+-------------------------------+-------+---------------------+\n",
            "|             Value             | Count |      Frequency      |\n",
            "+-------------------------------+-------+---------------------+\n",
            "|     Maruti Swift Dzire VDI    |  141  |  1.3661466912120919 |\n",
            "|     Mahindra XUV500 W8 2WD    |   63  |  0.6104059684139134 |\n",
            "| Maruti Vitara Brezza ZDi Plus |   57  |  0.5522720666602073 |\n",
            "|    Maruti Vitara Brezza ZDi   |   51  |  0.4941381649065013 |\n",
            "|    Maruti Baleno Delta 1.2    |   50  | 0.48444918128088366 |\n",
            "+-------------------------------+-------+---------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_name = analyze_data(df, 'full_name')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZuvIEzxfCun"
      },
      "source": [
        "#### Fuel: `fuel_type`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98ZjWefYfCun"
      },
      "source": [
        "Summary: \n",
        "- 4 distinct values\n",
        "- *Diesel* and *Pertrol* make up for 99.2% of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rwq_3Px1fCun",
        "outputId": "fd46d816-3932-47db-80f6-b3f368ab66f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column \"fuel_type\" is categorical\n",
            "Overview of column \"fuel_type\"\n",
            "+----------------+----------------------+\n",
            "|    Measure     |        Value         |\n",
            "+----------------+----------------------+\n",
            "| Distinct Count |          4           |\n",
            "|   Distinct %   | 0.038755934502470696 |\n",
            "| Missing Count  |          0           |\n",
            "|   Missing %    |         0.0          |\n",
            "+----------------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"fuel_type\"\n",
            "+----------+-------+---------------------+\n",
            "|  Value   | Count |      Frequency      |\n",
            "+----------+-------+---------------------+\n",
            "|  Diesel  |  6581 |  63.76320124018991  |\n",
            "|  Petrol  |  3667 |    35.52950295514   |\n",
            "|   CNG    |   64  |  0.620094952039531  |\n",
            "| Electric |   9   | 0.08720085263055906 |\n",
            "+----------+-------+---------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_fuel = analyze_data(df, 'fuel_type')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "B6Qs0D4AfCun",
        "outputId": "0ed13f4e-5f16-4c87-f260-e86041ce478d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the bar plot for the column 'fuel_type'\n",
        "fig, bar = plt.subplots(1, 1, figsize=(10, 6))\n",
        "\n",
        "bar = sns.countplot(x=df['fuel_type'], edgecolor='#000', linewidth= 1)\n",
        "bar.set_title('Bar Plot: fuel_type')\n",
        "bar.set_xlabel('Fuel Type')\n",
        "bar.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDyn5wmOfCun"
      },
      "source": [
        "#### Seller Type: `seller_type`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUa3IDsFfCuo"
      },
      "source": [
        "Summary: \n",
        "- There are only 3 distinct values\n",
        "- ~66% \"Dealers\", ~33% \"Individual\" and ~1% \"Trustmark Dealer\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksPnAIfOfCuo",
        "outputId": "3a915ddf-4d4f-4b7d-d2c3-97607be7c965"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column \"seller_type\" is categorical\n",
            "Overview of column \"seller_type\"\n",
            "+----------------+---------------------+\n",
            "|    Measure     |        Value        |\n",
            "+----------------+---------------------+\n",
            "| Distinct Count |          3          |\n",
            "|   Distinct %   | 0.02906695087685302 |\n",
            "| Missing Count  |          0          |\n",
            "|   Missing %    |         0.0         |\n",
            "+----------------+---------------------+\n",
            "\n",
            "\n",
            "Common values for column \"seller_type\"\n",
            "+------------------+-------+-------------------+\n",
            "|      Value       | Count |     Frequency     |\n",
            "+------------------+-------+-------------------+\n",
            "|      Dealer      |  6787 | 65.75913186706714 |\n",
            "|    Individual    |  3430 | 33.23321383586862 |\n",
            "| Trustmark Dealer |  104  | 1.007654297064238 |\n",
            "+------------------+-------+-------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_seller_type = analyze_data(df, 'seller_type')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "03FHfF3lfCuo",
        "outputId": "eb6bdb54-9ca6-464e-a9c9-3a996b0c86c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the bar plot for the column 'seller_type'\n",
        "fig, bar = plt.subplots(1, 1, figsize=(6, 6))\n",
        "\n",
        "bar = sns.countplot(x=df['seller_type'], edgecolor='#000', linewidth= 1)\n",
        "bar.set_title('Bar Plot: seller_type')\n",
        "bar.set_xlabel('Seller')\n",
        "bar.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Csqi0NqnfCuo"
      },
      "source": [
        "#### Transmission: `transmission_type`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_27zlRzfCup"
      },
      "source": [
        "Summary: \n",
        "- 2 distinct values\n",
        "- *Manual* transmission cars make up for ~67.8% of the dataset rest are *Automatic*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNmo4NdzfCup",
        "outputId": "785e5f49-5ce4-4875-cdfb-1fd3ac6f31e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column \"transmission_type\" is categorical\n",
            "Overview of column \"transmission_type\"\n",
            "+----------------+----------------------+\n",
            "|    Measure     |        Value         |\n",
            "+----------------+----------------------+\n",
            "| Distinct Count |          2           |\n",
            "|   Distinct %   | 0.019377967251235348 |\n",
            "| Missing Count  |          0           |\n",
            "|   Missing %    |         0.0          |\n",
            "+----------------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"transmission_type\"\n",
            "+-----------+-------+--------------------+\n",
            "|   Value   | Count |     Frequency      |\n",
            "+-----------+-------+--------------------+\n",
            "|   Manual  |  7004 | 67.86164131382618  |\n",
            "| Automatic |  3317 | 32.138358686173824 |\n",
            "+-----------+-------+--------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_transmission = analyze_data(df, 'transmission_type')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "obGvLZ41fCup",
        "outputId": "969a16e5-0f2a-448b-bb66-4fad39101845"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the bar plot for the column 'fuel'\n",
        "fig, bar = plt.subplots(1, 1, figsize=(6, 6))\n",
        "\n",
        "bar = sns.countplot(x=df['transmission_type'], edgecolor='#000', linewidth= 1)\n",
        "bar.set_title('Bar Plot: transmission_type')\n",
        "bar.set_xlabel('Transmission type')\n",
        "bar.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEiw1MisfCup"
      },
      "source": [
        "#### Owner: `owner_type`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfqtiDkcfCuq"
      },
      "source": [
        "Summary: \n",
        "- 2 distinct values\n",
        "- The data is dominated by *First Owner* type (~99.99%)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0FXfBwffCuq",
        "outputId": "9048d1f6-1629-4612-fc23-be51c875354f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column \"owner_type\" is categorical\n",
            "Overview of column \"owner_type\"\n",
            "+----------------+----------------------+\n",
            "|    Measure     |        Value         |\n",
            "+----------------+----------------------+\n",
            "| Distinct Count |          2           |\n",
            "|   Distinct %   | 0.019377967251235348 |\n",
            "| Missing Count  |          0           |\n",
            "|   Missing %    |         0.0          |\n",
            "+----------------+----------------------+\n",
            "\n",
            "\n",
            "Common values for column \"owner_type\"\n",
            "+--------------+-------+----------------------+\n",
            "|    Value     | Count |      Frequency       |\n",
            "+--------------+-------+----------------------+\n",
            "| First Owner  | 10320 |  99.99031101637438   |\n",
            "| Second Owner |   1   | 0.009688983625617672 |\n",
            "+--------------+-------+----------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_owner = analyze_data(df, 'owner_type')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "SE3OVKIXfCuq",
        "outputId": "de7efad4-8722-47f3-b387-0bb72468d671"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the bar plot for the column 'fuel'\n",
        "fig, bar = plt.subplots(1, 1, figsize=(8, 6))\n",
        "\n",
        "bar = sns.countplot(x=df['owner_type'], edgecolor='#000', linewidth= 1)\n",
        "bar.set_title('Bar Plot: owner_type')\n",
        "bar.set_xlabel('Owner type')\n",
        "bar.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJDjtjA1fCuq"
      },
      "source": [
        "#### Seats: `seats`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2jjJLwlfCuq"
      },
      "source": [
        "Summary: \n",
        "This one's tricky. While seats has integer datatype and my helper function recognises it as numeric, I think we should treat it as a categorical variable.\n",
        "- 8 distinct values\n",
        "- Most cars have *5* seats (about 75%)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BiW99AOPfCur",
        "outputId": "3182f99a-7613-452c-b9b7-d0a46607dfa0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tAnalyzing: Column \"seats\" is numeric\n",
            "\n",
            "Overview of column \"seats\"\n",
            "+----------------+---------------------+\n",
            "|    Measure     |        Value        |\n",
            "+----------------+---------------------+\n",
            "| Distinct Count |          8          |\n",
            "|   Distinct %   | 0.07751186900494139 |\n",
            "| Missing Count  |          0          |\n",
            "|   Missing %    |         0.0         |\n",
            "|      Mean      |  5.475825985854084  |\n",
            "|    Minimum     |         2.0         |\n",
            "|    Maximum     |         10.0        |\n",
            "+----------------+---------------------+\n",
            "\n",
            "\n",
            "Quantile stats for column \"seats\"\n",
            "+---------------------+-------+\n",
            "|      Statistic      | Value |\n",
            "+---------------------+-------+\n",
            "|       Minimum       |  2.0  |\n",
            "|    5th Percentile   |  5.0  |\n",
            "|  First Quartile, Q1 |  5.0  |\n",
            "|        Median       |  5.0  |\n",
            "|  Third Quartile, Q3 |  5.0  |\n",
            "|   95th Percentile   |  7.0  |\n",
            "|       Maximum       |  10.0 |\n",
            "|        Range        |  8.0  |\n",
            "| Interquartile Range |  0.0  |\n",
            "+---------------------+-------+\n",
            "\n",
            "\n",
            "Descriptive stats for column \"seats\"\n",
            "+--------------------------+---------------------+\n",
            "|        Statistic         |        Value        |\n",
            "+--------------------------+---------------------+\n",
            "|           Mean           |  5.475825985854084  |\n",
            "|           Sum            |       56516.0       |\n",
            "|         Variance         |  0.895176219328546  |\n",
            "|    Standard deviation    |  0.9461375266463887 |\n",
            "| Coefficient of variation | 0.17278444002614088 |\n",
            "|         Kurtosis         |  1.2540173455849115 |\n",
            "| Mean absolute deviation  |  0.7604293610055687 |\n",
            "|         Skewness         |  1.4083810186110608 |\n",
            "|        Monotonous        |        False        |\n",
            "+--------------------------+---------------------+\n",
            "\n",
            "\n",
            "Extremums for column \"seats\"\n",
            "Maximum 5 values\n",
            "+-------+-------+---------------------+\n",
            "| Value | Count |         Freq        |\n",
            "+-------+-------+---------------------+\n",
            "|  10.0 |   7   | 0.06782288537932371 |\n",
            "|  9.0  |   46  | 0.44569324677841293 |\n",
            "|  8.0  |  281  |  2.722604398798566  |\n",
            "|  7.0  |  1956 |  18.951651971708166 |\n",
            "|  6.0  |   76  |  0.7363627555469431 |\n",
            "+-------+-------+---------------------+\n",
            "Minimum 5 values\n",
            "+-------+-------+---------------------+\n",
            "| Value | Count |         Freq        |\n",
            "+-------+-------+---------------------+\n",
            "|  2.0  |   16  | 0.15502373800988278 |\n",
            "|  4.0  |   91  |  0.8816975099312082 |\n",
            "|  5.0  |  7848 |  76.03914349384749  |\n",
            "|  6.0  |   76  |  0.7363627555469431 |\n",
            "|  7.0  |  1956 |  18.951651971708166 |\n",
            "+-------+-------+---------------------+\n",
            "\n",
            "\n",
            "Common values for column \"seats\"\n",
            "+-------+-------+--------------------+\n",
            "| Value | Count |     Frequency      |\n",
            "+-------+-------+--------------------+\n",
            "|  5.0  |  7848 | 76.03914349384749  |\n",
            "|  7.0  |  1956 | 18.951651971708166 |\n",
            "|  8.0  |  281  | 2.722604398798566  |\n",
            "|  4.0  |   91  | 0.8816975099312082 |\n",
            "|  6.0  |   76  | 0.7363627555469431 |\n",
            "+-------+-------+--------------------+\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "data_seats = analyze_data(df, 'seats')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "ktOPD3q1fCur",
        "outputId": "25f5cf37-f148-4fb6-c71e-a74b4e7a0f82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the bar plot for the column 'fuel'\n",
        "fig, bar = plt.subplots(1, 1, figsize=(8, 6))\n",
        "\n",
        "bar = sns.countplot(x=df['seats'], edgecolor='#000', linewidth= 1)\n",
        "bar.set_title('Bar Plot: seats')\n",
        "bar.set_xlabel('Number of Seats')\n",
        "bar.set_ylabel('Frequency')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pp0_5F0ffCur"
      },
      "source": [
        "## 2.3 Correlation Analysis (bivariate analysis)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qYdgiG4fCur"
      },
      "source": [
        "### 2.3.1 Qualitative Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLtwAQkjfCur"
      },
      "source": [
        "#### Categorical and Numerical"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zK_9Mh6fCur"
      },
      "source": [
        "Descriptive statistics or histogram of Y​ per unique values of Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "AmxeBOKmfCus"
      },
      "outputs": [],
      "source": [
        "# # %matplotlib inline\n",
        "# # Due to high cardinality, drop the column 'name'\n",
        "# col = df.columns.values\n",
        "# col = np.delete(col, np.where(col == 'name'))\n",
        "\n",
        "# feature_1 = widgets.ToggleButtons(\n",
        "#     options=col,\n",
        "#     description='Feature 1:',\n",
        "#     disabled=False,\n",
        "#     button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
        "# )\n",
        "\n",
        "# feature_2 = widgets.ToggleButtons(\n",
        "#     options=col,\n",
        "#     description='Feature 2:',\n",
        "#     disabled=False,\n",
        "#     button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
        "# )\n",
        "\n",
        "\n",
        "# def update_plot(feature_1, feature_2):\n",
        "# \t# Check if both the features are numerical\n",
        "# \tfeature_1_is_numerical = df[feature_1].dtype in ['int64', 'float64']\n",
        "# \tfeature_2_is_numerical = df[feature_2].dtype in ['int64', 'float64']\n",
        "\n",
        "# \tif feature_1_is_numerical and feature_2_is_numerical:\n",
        "# \t\t# Plot the scatter plot for the two features\n",
        "# \t\tfig, scatter = plt.subplots(1, 1, figsize=(10, 6))\n",
        "# \t\tscatter = sns.scatterplot(x=df[feature_1], y=df[feature_2], data=df, ax=scatter)\n",
        "# \t\tscatter.set_title('Scatter Plot: ' + feature_1 + ' vs ' + feature_2)\n",
        "# \t\tscatter.set_xlabel(feature_1)\n",
        "# \t\tscatter.set_ylabel(feature_2)\n",
        "# \t\tplt.show()\n",
        "\t\n",
        "# \telif feature_1_is_numerical and not feature_2_is_numerical:\n",
        "# \t\t# Plot the histogram of the feature_1 per unique value of feature_2\n",
        "# \t\tfig, hist = plt.subplots(1, 1, figsize=(10, 6))\n",
        "# \t\thist = sns.histplot(x=df[feature_1], hue=df[feature_2], bins=50, data=df, ax=hist)\n",
        "# \t\thist.set_title('Histogram: ' + feature_1 + ' vs ' + feature_2)\n",
        "# \t\thist.set_xlabel(feature_1)\n",
        "# \t\thist.set_ylabel('Frequency')\n",
        "# \t\tplt.show()\n",
        "\t\n",
        "# \telif not feature_1_is_numerical and feature_2_is_numerical:\n",
        "# \t\t# Plot the histogram of the feature_2 per unique value of feature_1\n",
        "# \t\tfig, hist = plt.subplots(1, 1, figsize=(10, 6))\n",
        "# \t\thist = sns.histplot(x=df[feature_2], hue=df[feature_1],  bins=50, data=df, ax=hist)\n",
        "# \t\thist.set_title('Histogram: ' + feature_1 + ' vs ' + feature_2)\n",
        "# \t\thist.set_xlabel(feature_2)\n",
        "# \t\thist.set_ylabel('Frequency')\n",
        "# \t\tplt.show()\n",
        "\t\n",
        "# \telse:\n",
        "# \t\t# Get the contingency table for the two features\n",
        "# \t\tcont_table = pd.crosstab(df[feature_1], df[feature_2])\n",
        "# \t\t# Plot the contigency table\n",
        "# \t\tfig, cont = plt.subplots(1, 1, figsize=(10, 6))\n",
        "# \t\tcont = sns.heatmap(cont_table, annot=True, fmt='d', ax=cont)\n",
        "# \t\tcont.set_title('Contingency Table: ' + feature_1 + ' vs ' + feature_2)\n",
        "# \t\tcont.set_xlabel(feature_2)\n",
        "# \t\tcont.set_ylabel(feature_1)\n",
        "# \t\tplt.show()\n",
        "\n",
        "# interact(update_plot, feature_1=feature_1, feature_2=feature_2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Contingency table with unique counts of features 'fuel' per unique value of feature 'seller_type'\n",
        "cont_table = pd.crosstab(df['fuel_type'], df['seller_type'])\n",
        "cont_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fJkg-9wxrFEv",
        "outputId": "dc02e670-15c1-4b42-de85-5f882a6f5021"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "seller_type  Dealer  Individual  Trustmark Dealer\n",
              "fuel_type                                        \n",
              "CNG              50          10                 4\n",
              "Diesel         4298        2272                11\n",
              "Electric          6           3                 0\n",
              "Petrol         2433        1145                89"
            ],
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              "    <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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>seller_type</th>\n",
              "      <th>Dealer</th>\n",
              "      <th>Individual</th>\n",
              "      <th>Trustmark Dealer</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CNG</th>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Diesel</th>\n",
              "      <td>4298</td>\n",
              "      <td>2272</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electric</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Petrol</th>\n",
              "      <td>2433</td>\n",
              "      <td>1145</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          document.querySelector('#df-73edc7ad-3fcf-45e6-b583-5405fdad3e36 button.colab-df-convert');\n",
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              "            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",
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            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-Zci3gyfCuu"
      },
      "source": [
        "#### Both Numerical"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVN8T9TlfCuv"
      },
      "source": [
        "Sampled scatter plot: works best with a mixture of ordinal categorical and interval data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "feature_1 = df['engine']\n",
        "feature_2 = df['max_power']\n",
        "fig, scatter = plt.subplots(1, 1, figsize=(10, 6))\n",
        "scatter = sns.scatterplot(x = feature_1, y=feature_2, data=df, ax=scatter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "EAUcv53MNIxo",
        "outputId": "b63221a6-f766-4450-f03c-415eedbf026a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fh08kk1SfCuv"
      },
      "source": [
        "#### Both Categorical"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCCi-fv8fCuv"
      },
      "source": [
        "Contingency tables with unique counts of​ X ​per unique value of Y​ "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_1 = df['fuel_type']\n",
        "feature_2 = df['transmission_type']\n",
        "cont_table = pd.crosstab(feature_1, feature_2)\n",
        "\n",
        "fig, cont = plt.subplots(1, 1, figsize=(10, 6))\n",
        "cont = sns.heatmap(cont_table, annot=True, fmt='d')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "pe3I1bK3OrTG",
        "outputId": "5946d72d-7a57-4c24-8fe7-840aff546340"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eKnH9Y2fCux"
      },
      "source": [
        "# 3. Data Cleaning\n",
        "Before performing any analysis, we need to clean the data. Here a number of preprocessing steps are performed to clean the data.\n",
        "\n",
        "*Refer to the notebook \"[Car_price_Data_cleaning.ipynb](https://github.com/Ayush863/Car_Prices-A_Prediction_Service/blob/main/Car_price_Data_cleaning.ipynb)\" for this step*"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.10.4 ('Python_Work')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.4"
    },
    "vscode": {
      "interpreter": {
        "hash": "b0913d48e5c2b2011c5d793b1f488f6eb19a712506b8e75c3a31346f35154db8"
      }
    },
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}