exploratory_data_analysis.ipynb

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      "authorship_tag": "ABX9TyO9qZXTbmcZ0va0/lNg9KJB",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ericyoc/exploratory_data_analysis_demo/blob/main/exploratory_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ORz1HTM8TFQt"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from textwrap import wrap"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A. Combine, clean and pre-process the data\n",
        "def preprocess_data(file_path):\n",
        "    encodings = ['utf-8', 'latin1', 'iso-8859-1', 'cp1252']\n",
        "    for encoding in encodings:\n",
        "        try:\n",
        "            df = pd.read_csv(file_path, encoding=encoding)\n",
        "            df.dropna(inplace=True)\n",
        "            return df\n",
        "        except UnicodeDecodeError:\n",
        "            continue\n",
        "    raise ValueError(\"Unable to decode the CSV file.\")"
      ],
      "metadata": {
        "id": "gvAR1vmyT0W0"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A.2. inspect_dataset(dataset)\n",
        "def inspect_dataset(df):\n",
        "    # Print the first five rows of the dataset\n",
        "    print(\"First five rows of the dataset:\")\n",
        "    print(df.head())\n",
        "    print()\n",
        "\n",
        "    # Print descriptive statistics of the dataset\n",
        "    print(\"Descriptive statistics:\")\n",
        "    print(df.describe())\n",
        "    print()\n",
        "\n",
        "    # Print information about the dataset\n",
        "    print(\"Dataset information:\")\n",
        "    print(df.info())\n",
        "    print()\n",
        "\n",
        "    # Print the number of missing values in each column\n",
        "    print(\"Missing values in each column:\")\n",
        "    print(df.isnull().sum())\n",
        "    print()\n",
        "\n",
        "    # Print the number of unique values in each column\n",
        "    print(\"Unique values in each column:\")\n",
        "    print(df.nunique())\n",
        "    print()\n",
        "\n",
        "    # Print the data types of each column\n",
        "    print(\"Data types of each column:\")\n",
        "    print(df.dtypes)\n",
        "    print()"
      ],
      "metadata": {
        "id": "FMVqwfFXVPq4"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# B. Perform a concise Exploratory Data Analysis (EDA)\n",
        "def perform_eda(df):\n",
        "    print(\"Dataset shape:\", df.shape)\n",
        "    print(\"Dataset info:\")\n",
        "    print(df.info())\n",
        "    print(\"Descriptive statistics:\")\n",
        "    print(df.describe())\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.histplot(data=df, x='Age', kde=True)\n",
        "    plt.title('Figure 1 - Age Distribution')\n",
        "    plt.xlabel('Age')\n",
        "    plt.ylabel('Count')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    explanation1 = \"The Age Distribution plot (Figure 1) shows the distribution of customer ages in the dataset. It helps identify the most common age ranges and any potential outliers or unusual patterns in the age distribution. Understanding the age distribution is important for targeted marketing strategies and customer segmentation.\"\n",
        "    wrapped_explanation1 = \"\\n\".join(wrap(explanation1, width=80))\n",
        "    print(\"Explanation:\", wrapped_explanation1)\n",
        "    print()\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.scatterplot(data=df, x='Age', y='Spending Score (1-100)')\n",
        "    plt.title('Figure 2 - Age vs Spending Score')\n",
        "    plt.xlabel('Age')\n",
        "    plt.ylabel('Spending Score')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    explanation2 = \"The Age vs Spending Score plot (Figure 2) visualizes the relationship between customer age and their spending score. It helps identify any potential patterns or trends in spending behavior across different age groups. If a clear relationship is observed, it suggests that age may be a significant factor influencing spending habits, which can be valuable for targeted marketing and customer segmentation.\"\n",
        "    wrapped_explanation2 = \"\\n\".join(wrap(explanation2, width=80))\n",
        "    print(\"Explanation:\", wrapped_explanation2)\n",
        "    print()"
      ],
      "metadata": {
        "id": "6kZdvUZcT26G"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# C. Based on the EDA, formulate and test 2 hypotheses\n",
        "def test_hypotheses(df):\n",
        "    try:\n",
        "        # Hypothesis 1: There is a significant relationship between Age and Spending Score\n",
        "        print(\"Hypothesis 1: There is a significant relationship between Age and Spending Score\")\n",
        "        corr_coef = df['Age'].corr(df['Spending Score (1-100)'])\n",
        "        print(\"Correlation Coefficient (Age vs Spending Score):\", corr_coef)\n",
        "\n",
        "        # Interpreting the correlation coefficient\n",
        "        if abs(corr_coef) >= 0.5:\n",
        "            conclusion1 = \"There is a strong relationship between Age and Spending Score.\"\n",
        "        elif 0.3 <= abs(corr_coef) < 0.5:\n",
        "            conclusion1 = \"There is a moderate relationship between Age and Spending Score.\"\n",
        "        else:\n",
        "            conclusion1 = \"There is a weak or no relationship between Age and Spending Score.\"\n",
        "\n",
        "        print(\"Conclusion 1:\", conclusion1)\n",
        "        print()\n",
        "\n",
        "    except KeyError:\n",
        "        print(\"Error: 'Age' or 'Spending Score (1-100)' column not found in the dataset.\")\n",
        "\n",
        "    try:\n",
        "        # Hypothesis 2: Gender affects the Annual Income of customers\n",
        "        print(\"Hypothesis 2: Gender affects the Annual Income of customers\")\n",
        "        male_income = df[df['Gender'] == 'Male']['Annual Income (k$)']\n",
        "        female_income = df[df['Gender'] == 'Female']['Annual Income (k$)']\n",
        "        income_diff = male_income.mean() - female_income.mean()\n",
        "        print(\"Difference in Mean Annual Income (Male - Female):\", income_diff)\n",
        "\n",
        "        # Interpreting the income difference\n",
        "        if abs(income_diff) >= 10:\n",
        "            conclusion2 = \"There is a significant difference in Annual Income between Male and Female customers.\"\n",
        "        else:\n",
        "            conclusion2 = \"There is no significant difference in Annual Income between Male and Female customers.\"\n",
        "\n",
        "        print(\"Conclusion 2:\", conclusion2)\n",
        "        print()\n",
        "\n",
        "    except KeyError:\n",
        "        print(\"Error: 'Gender' or 'Annual Income (k$)' column not found in the dataset.\")"
      ],
      "metadata": {
        "id": "pnxUNM-tT5jW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# D. Identify a set of independent variables and build a linear regression model\n",
        "def build_linear_regression(df):\n",
        "    X = df[['Age', 'Annual Income (k$)']]\n",
        "    y = df['Spending Score (1-100)']\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    model = LinearRegression()\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    y_pred = model.predict(X_test)\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "    print(\"Linear Regression Model:\")\n",
        "    print(\"R-squared:\", r2)\n",
        "    print(\"Mean Squared Error:\", mse)"
      ],
      "metadata": {
        "id": "aLmDrz2nT8ew"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# E. Use a machine learning model to predict\n",
        "def predict_spending_score(df):\n",
        "    X = df[['Age', 'Annual Income (k$)']]\n",
        "    y = df['Spending Score (1-100)']\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    model = LinearRegression()\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    new_data = pd.DataFrame({'Age': [30], 'Annual Income (k$)': [50]})\n",
        "    predicted_score = model.predict(new_data)\n",
        "    print(\"Predicted Spending Score for Age 30 and Annual Income 50k:\", predicted_score[0])"
      ],
      "metadata": {
        "id": "-wGlaeWWT-rw"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Main function\n",
        "def main():\n",
        "    file_path = 'ml_data/Mall_Customers.csv'\n",
        "    df = preprocess_data(file_path)\n",
        "    inspect_dataset(df)\n",
        "    perform_eda(df)\n",
        "    test_hypotheses(df)\n",
        "    build_linear_regression(df)\n",
        "    predict_spending_score(df)"
      ],
      "metadata": {
        "id": "y-HlnD4DUCIz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5wRjilhcUEay",
        "outputId": "74878ca0-97b4-445a-f046-af585b4f5396"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First five rows of the dataset:\n",
            "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
            "0           1    Male   19                  15                      39\n",
            "1           2    Male   21                  15                      81\n",
            "2           3  Female   20                  16                       6\n",
            "3           4  Female   23                  16                      77\n",
            "4           5  Female   31                  17                      40\n",
            "\n",
            "Descriptive statistics:\n",
            "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
            "count  200.000000  200.000000          200.000000              200.000000\n",
            "mean   100.500000   38.850000           60.560000               50.200000\n",
            "std     57.879185   13.969007           26.264721               25.823522\n",
            "min      1.000000   18.000000           15.000000                1.000000\n",
            "25%     50.750000   28.750000           41.500000               34.750000\n",
            "50%    100.500000   36.000000           61.500000               50.000000\n",
            "75%    150.250000   49.000000           78.000000               73.000000\n",
            "max    200.000000   70.000000          137.000000               99.000000\n",
            "\n",
            "Dataset information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Genre                   200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n",
            "None\n",
            "\n",
            "Missing values in each column:\n",
            "CustomerID                0\n",
            "Genre                     0\n",
            "Age                       0\n",
            "Annual Income (k$)        0\n",
            "Spending Score (1-100)    0\n",
            "dtype: int64\n",
            "\n",
            "Unique values in each column:\n",
            "CustomerID                200\n",
            "Genre                       2\n",
            "Age                        51\n",
            "Annual Income (k$)         64\n",
            "Spending Score (1-100)     84\n",
            "dtype: int64\n",
            "\n",
            "Data types of each column:\n",
            "CustomerID                 int64\n",
            "Genre                     object\n",
            "Age                        int64\n",
            "Annual Income (k$)         int64\n",
            "Spending Score (1-100)     int64\n",
            "dtype: object\n",
            "\n",
            "Dataset shape: (200, 5)\n",
            "Dataset info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Genre                   200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n",
            "None\n",
            "Descriptive statistics:\n",
            "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
            "count  200.000000  200.000000          200.000000              200.000000\n",
            "mean   100.500000   38.850000           60.560000               50.200000\n",
            "std     57.879185   13.969007           26.264721               25.823522\n",
            "min      1.000000   18.000000           15.000000                1.000000\n",
            "25%     50.750000   28.750000           41.500000               34.750000\n",
            "50%    100.500000   36.000000           61.500000               50.000000\n",
            "75%    150.250000   49.000000           78.000000               73.000000\n",
            "max    200.000000   70.000000          137.000000               99.000000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explanation: The Age Distribution plot (Figure 1) shows the distribution of customer ages in\n",
            "the dataset. It helps identify the most common age ranges and any potential\n",
            "outliers or unusual patterns in the age distribution. Understanding the age\n",
            "distribution is important for targeted marketing strategies and customer\n",
            "segmentation.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explanation: The Age vs Spending Score plot (Figure 2) visualizes the relationship between\n",
            "customer age and their spending score. It helps identify any potential patterns\n",
            "or trends in spending behavior across different age groups. If a clear\n",
            "relationship is observed, it suggests that age may be a significant factor\n",
            "influencing spending habits, which can be valuable for targeted marketing and\n",
            "customer segmentation.\n",
            "\n",
            "Hypothesis 1: There is a significant relationship between Age and Spending Score\n",
            "Correlation Coefficient (Age vs Spending Score): -0.32722684603909014\n",
            "Conclusion 1: There is a moderate relationship between Age and Spending Score.\n",
            "\n",
            "Hypothesis 2: Gender affects the Annual Income of customers\n",
            "Error: 'Gender' or 'Annual Income (k$)' column not found in the dataset.\n",
            "Linear Regression Model:\n",
            "R-squared: 0.019631778132180422\n",
            "Mean Squared Error: 483.5568217540833\n",
            "Predicted Spending Score for Age 30 and Annual Income 50k: 56.471739448237564\n"
          ]
        }
      ]
    }
  ]
}