1_3_4_Market_Basket_Analysis.ipynb

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        "# <span style=\"color:blue\">1_3_4_Market_Basket_Analysis</span>\n",
        "\n",
        "### <span style=\"color:green\">Project Overview: Market Basket Analysis with Machine Learning</span>\n",
        "\n",
        "#### <span style=\"color:purple\">Introduction</span>\n",
        "Market Basket Analysis (MBA) is a fundamental technique in retail analytics aimed at discovering relationships between products frequently purchased together. By uncovering these associations, businesses can optimize product placement, promotions, and customer segmentation strategies to enhance profitability and customer satisfaction. This project integrates MBA with Machine Learning (ML) approaches to delve deeper into transactional data from a grocery store, extracting actionable insights that drive business decisions.\n",
        "\n",
        "#### **<span style=\"color:brown\">Steps Involved</span>**\n",
        "\n",
        "### **<span style=\"color:orange\">Setup and Initialization</span>**\n",
        "Setting up the project involves importing essential libraries for data manipulation (`pandas`), visualization (`matplotlib`, `seaborn`), association rule mining (`mlxtend`), and network visualization (`networkx`). These libraries form the backbone for conducting comprehensive data analysis and visualization tasks.\n",
        "\n",
        "### **<span style=\"color:red\">Data Loading</span>**\n",
        "The initial step is to load the dataset (`Groceries_dataset.csv`) into a pandas DataFrame (`df`). This dataset contains historical transactional records detailing which items were purchased together during each transaction. Understanding the structure and content of this data is crucial for performing meaningful analyses and deriving valuable insights.\n",
        "\n",
        "### **<span style=\"color:blue\">Initial Exploration</span>**\n",
        "Exploring the dataset begins with examining its structure using methods like `df.head()` to view the first few rows, `df.columns` to inspect column names, and `df.info()` to understand data types and check for missing values. Additionally, checking for special characters ensures data cleanliness and prepares it for further analysis.\n",
        "\n",
        "### **<span style=\"color:orange\">Data Cleaning and Preprocessing</span>**\n",
        "Data cleaning involves handling missing values (`df.isnull().sum()`) and converting data types, such as dates using `pd.to_datetime()`, to ensure consistency and usability in subsequent analyses. Preprocessing steps prepare the dataset for Market Basket Analysis (MBA) by transforming it into a suitable format for pattern mining and association rule generation.\n",
        "\n",
        "### **<span style=\"color:purple\">Exploratory Data Analysis (EDA)</span>**\n",
        "EDA is pivotal for gaining insights into the dataset's characteristics and relationships. Visualizing data distributions using histograms (`sns.histplot()`) and identifying outliers through box plots (`sns.boxplot()`) provides a deeper understanding of transaction patterns and anomalies. These visualizations serve as a foundation for generating hypotheses and informing further analytical steps.\n",
        "\n",
        "### **<span style=\"color:blue\">Market Basket Analysis (MBA)</span>**\n",
        "MBA involves transforming transactional data into a binary format suitable for pattern mining using algorithms like Apriori (`apriori()`) or FP-Growth. These algorithms identify frequent itemsets—combinations of products frequently purchased together—based on predefined thresholds of support. Association rules (`association_rules()`) derived from these itemsets quantify relationships between products, including metrics such as lift and confidence, which are crucial for strategic decision-making in retail.\n",
        "\n",
        "### **<span style=\"color:red\">Integration with Machine Learning Models</span>**\n",
        "Integrating ML models, such as classifiers or clustering algorithms, enhances the predictive capabilities of MBA results. For instance, RandomForestClassifier can predict customer behavior or segment customers based on their purchase histories. This integration facilitates personalized marketing strategies and enhances customer retention efforts by leveraging insights derived from transactional data.\n",
        "\n",
        "### **<span style=\"color:orange\">Optimization and Performance</span>**\n",
        "To optimize computational efficiency, techniques like parallel processing (`joblib.Parallel`) are employed to handle large datasets and complex analytical tasks effectively. By utilizing multiple CPU cores, parallel processing accelerates data preprocessing, model training, and analysis, thereby reducing overall processing time and enhancing scalability.\n",
        "\n",
        "### **<span style=\"color:blue\">Results and Output</span>**\n",
        "The project concludes with visualizing insights using tools like network diagrams (`networkx` and `matplotlib`) to illustrate association rules graphically. These visual representations aid in interpreting complex relationships between products and provide stakeholders with actionable insights for improving product recommendations, promotional strategies, and operational efficiencies. Final outputs, including frequent itemsets and association rules, are saved in CSV format for documentation, further analysis, and integration into business strategies.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "In conclusion, Market Basket Analysis combined with Machine Learning techniques offers retailers a powerful toolkit for understanding customer behavior, optimizing business operations, and driving revenue growth. By leveraging transactional data to uncover hidden patterns and relationships, businesses can make informed decisions that enhance customer experience and operational effectiveness in a competitive market landscape."
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      "metadata": {
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      "cell_type": "code",
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      "metadata": {
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      "source": [
        "### <span style=\"color:red\">Setting Up Environment and Handling Warnings</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Setting up the environment and handling warnings is crucial for ensuring smooth execution of data analysis and algorithm implementation in Market Basket Analysis (MBA). This step involves importing necessary libraries, managing warnings, and preparing the environment for subsequent data processing and analysis tasks.\n",
        "\n",
        "#### **<span style=\"color:green\">Library Import and Setup</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Importing Essential Libraries</span>**\n",
        "- **Python Libraries**: Importing `pandas`, `matplotlib`, `seaborn`, `mlxtend`, and `networkx` for data manipulation, visualization, MBA algorithms, and network graph visualization.\n",
        "- **IO Module**: Importing `io` module from `IPython.utils` for capturing output during code execution.\n",
        "\n",
        "### **<span style=\"color:purple\">Handling Deprecation Warnings</span>**\n",
        "- **Warnings Management**: Ignoring `DeprecationWarning` related to asynchronous execution (`should_run_async`) using `warnings.simplefilter()` and `io.capture_output()` to manage and suppress warnings effectively.\n",
        "\n",
        "### **<span style=\"color:orange\">Environment Preparation</span>**\n",
        "- **Environment Setup**: Configuring the environment to handle large datasets, manage computational resources efficiently, and ensure compatibility with Python libraries and tools used in MBA.\n",
        "\n",
        "### **<span style=\"color:blue\">Conclusion</span>**\n",
        "Setting up the environment and handling warnings lays a solid foundation for executing MBA tasks effectively. It ensures a stable environment for importing, processing, and analyzing data without disruptions from unnecessary warnings or deprecated features."
      ],
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      "cell_type": "code",
      "source": [
        "import warnings  # Importing warnings module to handle warnings"
      ],
      "metadata": {
        "id": "DtME8SzK3mcN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.utils import io  # Importing io module from IPython.utils for capturing output"
      ],
      "metadata": {
        "id": "4iUDGC_78ZZa"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "with io.capture_output() as captured:  # Capturing output using io.capture_output()\n",
        "    warnings.simplefilter(\"ignore\", category=DeprecationWarning)  # Ignoring DeprecationWarning for `should_run_async`\n",
        "\n"
      ],
      "metadata": {
        "id": "MNeSlihE8fhw"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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      "metadata": {
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    {
      "cell_type": "code",
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    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:orange\">Setup and Initialization</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Setting up and initializing the project involves importing essential libraries and tools necessary for data manipulation, visualization, and conducting Market Basket Analysis (MBA) with Machine Learning (ML) integration.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Components</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Libraries Import</span>**\n",
        "Importing the required libraries is crucial for various stages of the project:\n",
        "\n",
        "- **pandas (`import pandas as pd`)**: Pandas is fundamental for data manipulation tasks, such as loading datasets (`pd.read_csv()`), handling missing values (`df.isnull()`), and transforming data (`pd.to_datetime()`).\n",
        "\n",
        "- **matplotlib.pyplot (`import matplotlib.pyplot as plt`)**: Matplotlib is essential for creating visualizations, including histograms (`plt.hist()`), scatter plots (`plt.scatter()`), and bar plots (`plt.bar()`). It provides extensive customization options for visualizing data distributions and patterns.\n",
        "\n",
        "- **seaborn (`import seaborn as sns`)**: Seaborn complements Matplotlib by offering high-level interfaces for statistical data visualization. It simplifies the creation of visually appealing and informative plots, such as kernel density estimates (`sns.kdeplot()`) and box plots (`sns.boxplot()`).\n",
        "\n",
        "- **PdfPages (`from matplotlib.backends.backend_pdf import PdfPages`)**: PdfPages allows saving multiple plots into a single PDF file (`PdfPages.savefig()`) for efficient document management and sharing of visual insights.\n",
        "\n",
        "- **mlxtend (`from mlxtend.frequent_patterns import apriori, association_rules, fpgrowth`)**: MLxtend provides implementations of popular MBA algorithms like Apriori (`apriori()`) and FP-Growth (`fpgrowth()`), enabling the extraction of frequent itemsets and association rules from transactional data.\n",
        "\n",
        "- **networkx (`import networkx as nx`)**: Networkx is used for creating and visualizing network graphs (`nx.draw()`) to represent association rules and their relationships, offering a clear and intuitive representation of item associations.\n",
        "\n",
        "### **<span style=\"color:purple\">Environment Setup</span>**\n",
        "Ensuring the environment is properly configured involves:\n",
        "- Verifying that all necessary libraries (`pandas`, `matplotlib`, `seaborn`, `mlxtend`, `networkx`) are installed and up-to-date using package managers (`pip`, `conda`).\n",
        "- Setting up the working directory (`os.chdir()`) and defining file paths (`file_path`) to facilitate seamless data access and manipulation.\n",
        "\n",
        "### **<span style=\"color:red\">Tool Initialization</span>**\n",
        "Initializing tools such as PdfPages (`PdfPages()`) ensures that plots generated during analysis can be saved systematically to PDF format (`PdfPages.savefig()`) for later reference and presentation purposes.\n",
        "\n",
        "### **<span style=\"color:blue\">Dependencies Installation</span>**\n",
        "Installing dependencies via package managers (`pip install <package>` or `conda install <package>`) ensures that all required libraries (`pandas`, `matplotlib`, `seaborn`, `mlxtend`, `networkx`) are available and compatible with the project's Python environment. This step ensures smooth execution of data analysis and visualization tasks without dependency-related issues.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "The careful importation and setup of libraries are foundational to the project's success, enabling robust data analysis, insightful visualization, and the application of advanced MBA and ML techniques to derive actionable insights from transactional data in retail settings."
      ],
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      "cell_type": "code",
      "source": [
        "import pandas as pd  # Importing pandas library for data manipulation"
      ],
      "metadata": {
        "id": "uxl5HGc-36Wa"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Importing matplotlib library for plotting"
      ],
      "metadata": {
        "id": "QBDsNf8q4Iiq"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Importing seaborn library for enhanced plotting"
      ],
      "metadata": {
        "id": "b2IQgiV-4Ilp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.backends.backend_pdf import PdfPages  # Importing PdfPages for saving plots to PDF"
      ],
      "metadata": {
        "id": "4wumv3_p4Iou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.frequent_patterns import apriori, association_rules, fpgrowth  # Importing MLxtend functions for Market Basket Analysis\n"
      ],
      "metadata": {
        "id": "JJjN_2yO4Ith"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx  # Importing networkx for visualization of association rules"
      ],
      "metadata": {
        "id": "4K1kyzpa36Zr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mQpaC4aI36cl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c1dZygONVsSa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Data Loading</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Loading the dataset (`Groceries_dataset.csv`) into a pandas DataFrame (`df`) marks the initial phase of data preparation for Market Basket Analysis (MBA) and subsequent Machine Learning (ML) integration. This process involves accessing the dataset file, reading its contents, and structuring it into a format suitable for exploratory data analysis (EDA) and advanced analytics.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Steps</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">File Path Definition</span>**\n",
        "Define the file path (`file_path`) where the dataset (`Groceries_dataset.csv`) resides. This step ensures direct access to the dataset file during the loading process, facilitating seamless data retrieval using pandas' `pd.read_csv()` function.\n",
        "\n",
        "### **<span style=\"color:purple\">Data Loading Process</span>**\n",
        "Execute the data loading process by employing pandas (`pd.read_csv(file_path)`) to import the dataset into a DataFrame (`df`). This function reads the CSV file, parses its contents, and stores them as rows and columns within the DataFrame structure. It is essential to validate successful loading by inspecting the DataFrame's initial rows (`df.head()`), examining column names (`df.columns`), and reviewing data types (`df.info()`) to ensure data integrity and alignment with analytical objectives.\n",
        "\n",
        "### **<span style=\"color:orange\">Initial Exploration</span>**\n",
        "Conduct an exploratory overview of the loaded dataset to gain preliminary insights into its characteristics and contents. This phase includes:\n",
        "- **Missing Values Check (`df.isnull().sum()`)**: Identify and quantify any missing data points across columns to assess data completeness and determine appropriate strategies for handling missing values.\n",
        "- **Special Characters Examination**: Scrutinize string columns for the presence of special characters that may impact data quality or require preprocessing steps such as encoding or normalization.\n",
        "- **Statistical Summary (`df.describe()`)**: Generate descriptive statistics to summarize numerical attributes of the dataset, revealing distributional patterns, central tendencies, and variability within the data.\n",
        "\n",
        "### **<span style=\"color:blue\">Data Validation</span>**\n",
        "Validate the dataset's integrity and coherence with predefined expectations and business requirements. This involves:\n",
        "- **Data Consistency Check**: Ensure consistency in data structure, including verifying column names, data types, and categorical encoding where applicable.\n",
        "- **Quality Assurance**: Address anomalies such as outliers, discrepancies in data formats, or inconsistencies that may hinder accurate analysis or model performance.\n",
        "- **Preprocessing Preparation**: Prepare the dataset for subsequent preprocessing steps by addressing data validation outcomes, refining data cleansing strategies, and establishing a robust foundation for analytical modeling and interpretation.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "Effective data loading and initial exploration serve as critical preparatory stages in the analytical pipeline, enabling comprehensive data understanding, proactive data quality management, and informed decision-making through advanced MBA and ML techniques. By leveraging these foundational processes, organizations can derive actionable insights, optimize operational efficiencies, and enhance customer-centric strategies in dynamic retail environments."
      ],
      "metadata": {
        "id": "gv_eXDoEVss6"
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    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "kwMetLKj36fj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/Groceries_dataset.csv\"  # Setting file path for the dataset"
      ],
      "metadata": {
        "id": "r8darLqE36x4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(file_path)  # Reading dataset from file path"
      ],
      "metadata": {
        "id": "wf5ItlHB3mfU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XDaM0r8U3mhy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset head"
      ],
      "metadata": {
        "id": "_5NYn6iH3mkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset Head:\")  # Printing message\n",
        "print(df.head())  # Displaying the first few rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fhif5kAa3mqK",
        "outputId": "e3b07fed-b80f-415e-acf4-8db07569ce09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Head:\n",
            "   Member_number        Date   itemDescription\n",
            "0           1808  21-07-2015    tropical fruit\n",
            "1           2552  05-01-2015        whole milk\n",
            "2           2300  19-09-2015         pip fruit\n",
            "3           1187  12-12-2015  other vegetables\n",
            "4           3037  01-02-2015        whole milk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AtsMx3x23mvq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rS3y2vWY3m2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset information"
      ],
      "metadata": {
        "id": "BFZ1F9eK3m8R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Info:\")  # Printing message\n",
        "print(df.info())  # Displaying information about the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nl7te6i-3nBb",
        "outputId": "13c6f0cd-5558-4239-e558-fb26997180d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38765 entries, 0 to 38764\n",
            "Data columns (total 3 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Member_number    38765 non-null  int64 \n",
            " 1   Date             38765 non-null  object\n",
            " 2   itemDescription  38765 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 908.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Sn53Jigf3nEK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O0_f880T42Pi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset columns"
      ],
      "metadata": {
        "id": "fC7DCqiC3nGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Columns:\")  # Printing message\n",
        "print(df.columns)  # Displaying column names of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2PGbvFP3ipS",
        "outputId": "4f933689-25e8-4675-8904-42856062f768"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Columns:\n",
            "Index(['Member_number', 'Date', 'itemDescription'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ISE-QdCO3isn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZpiBM5Qn3ivb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset description"
      ],
      "metadata": {
        "id": "ZEjzlqh33i35"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Description:\")  # Printing message\n",
        "print(df.describe(include='all'))  # Displaying descriptive statistics of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVE0BSOt3i7M",
        "outputId": "a24beef5-6a18-44ff-f249-842f75c277aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Description:\n",
            "        Member_number        Date itemDescription\n",
            "count    38765.000000       38765           38765\n",
            "unique            NaN         728             167\n",
            "top               NaN  21-01-2015      whole milk\n",
            "freq              NaN          96            2502\n",
            "mean      3003.641868         NaN             NaN\n",
            "std       1153.611031         NaN             NaN\n",
            "min       1000.000000         NaN             NaN\n",
            "25%       2002.000000         NaN             NaN\n",
            "50%       3005.000000         NaN             NaN\n",
            "75%       4007.000000         NaN             NaN\n",
            "max       5000.000000         NaN             NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jaeSV0Wp3i-j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0U44OH8k3jAb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for special characters in string columns"
      ],
      "metadata": {
        "id": "GYNJoOCc3jBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_special_characters(df):\n",
        "    \"\"\"\n",
        "    Checks for special characters in string columns of a DataFrame.\n",
        "\n",
        "    Args:\n",
        "    df (pd.DataFrame): Input DataFrame.\n",
        "\n",
        "    Returns:\n",
        "    set: Set of special characters found in string columns.\n",
        "    \"\"\"\n",
        "    special_characters = set()  # Initializing an empty set for special characters\n",
        "    for column in df.select_dtypes(include=[object]).columns:\n",
        "        for value in df[column].dropna().unique():\n",
        "            special_characters.update([char for char in str(value) if not char.isalnum() and not char.isspace()])\n",
        "    return special_characters"
      ],
      "metadata": {
        "id": "4zrZMkF_2dPZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ykwwdk0B5Ina"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSpecial Characters in String Columns:\")  # Printing message\n",
        "special_characters = check_special_characters(df)  # Checking for special characters in string columns\n",
        "print(special_characters)  # Displaying special characters found"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHsMVCka5IqV",
        "outputId": "f0c8f541-f01d-4271-bee2-38a6cd5ee8df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Special Characters in String Columns:\n",
            "{'-', '.', '/', '(', ')'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5CUsMLcr5Iva"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "0gRvOv8I5Iyy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing Values:\")  # Printing message\n",
        "print(df.isnull().sum())  # Checking for missing values in the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFYl9Bbh5I17",
        "outputId": "b23e0f5e-023c-41a2-e6a2-156e952bd3a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing Values:\n",
            "Member_number      0\n",
            "Date               0\n",
            "itemDescription    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yKCA0N-m5I4y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DFgxpUFb1ATI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Exploratory Data Analysis (EDA)</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Exploratory Data Analysis (EDA) is a critical phase in data exploration and understanding, aimed at uncovering patterns, trends, and relationships within the dataset (`df`). This process involves a combination of statistical techniques and visualizations to gain insights into the dataset's structure and characteristics before applying more advanced analytical methods.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Components</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Data Visualization</span>**\n",
        "Visualizations play a pivotal role in EDA, enabling intuitive exploration of data distributions and relationships:\n",
        "- **Histograms (`plt.hist()`, `sns.histplot()`)**: Visualize the distribution of numerical variables, revealing patterns such as skewness, central tendency, and dispersion.\n",
        "- **Scatter Plots (`plt.scatter()`)**: Explore relationships between two numerical variables to identify correlations or clusters within the data.\n",
        "- **Box Plots (`sns.boxplot()`)**: Detect outliers and understand the distributional spread and variability of numerical features.\n",
        "- **Heatmaps (`sns.heatmap()`)**: Display correlation matrices to uncover relationships between variables, highlighting strong or weak correlations.\n",
        "\n",
        "### **<span style=\"color:purple\">Statistical Summaries</span>**\n",
        "Descriptive statistics provide numerical summaries of data attributes, offering insights into central tendencies and variability:\n",
        "- **Summary Statistics (`df.describe()`)**: Compute mean, median, quartiles, and standard deviations for numerical columns, revealing data dispersion and identifying potential outliers.\n",
        "- **Value Counts (`df[column].value_counts()`)**: Count occurrences of unique values in categorical columns, facilitating the understanding of categorical distributions and dominant categories.\n",
        "\n",
        "### **<span style=\"color:orange\">Pattern Identification</span>**\n",
        "Identify and explore patterns within the dataset that may influence business strategies and decision-making:\n",
        "- **Seasonal Trends**: Analyze temporal patterns in transactional data to identify seasonal fluctuations or trends that may impact sales or customer behavior.\n",
        "- **Association Rules**: Apply MBA algorithms (`apriori`, `fpgrowth`) to uncover frequent itemsets and association rules, identifying co-purchased items and market basket patterns.\n",
        "- **Segmentation Analysis**: Use clustering techniques (e.g., K-Means) to segment customers based on transactional behavior or demographic characteristics, enabling targeted marketing strategies.\n",
        "\n",
        "### **<span style=\"color:blue\">Insight Extraction</span>**\n",
        "Extract actionable insights from EDA findings to drive strategic decisions and business outcomes:\n",
        "- **Customer Behavior**: Understand customer preferences and purchasing habits through analysis of transactional data and association rules.\n",
        "- **Operational Efficiency**: Identify inefficiencies or bottlenecks in supply chain management or inventory stocking based on transactional patterns and sales trends.\n",
        "- **Market Trends**: Explore market trends and consumer preferences through visualizations and statistical summaries, guiding product development and marketing campaigns.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "Exploratory Data Analysis (EDA) serves as a foundational step in leveraging data for business intelligence and decision-making. By combining visual exploration with statistical rigor, organizations can uncover hidden insights, optimize operations, and enhance customer satisfaction in competitive retail environments."
      ],
      "metadata": {
        "id": "AFuktSyrWWyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save EDA results to a PDF"
      ],
      "metadata": {
        "id": "6SDPrnmfxlpv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eda_pdf_path = \"/content/eda_analysis.pdf\""
      ],
      "metadata": {
        "id": "v5pFnJ9ixlsu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NB_m1iRewtbK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EDA - Visualization 1: Distribution of Member_number"
      ],
      "metadata": {
        "id": "vfgopdqr5hsD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting plot size\n",
        "sns.histplot(df['Member_number'], bins=50, kde=True)  # Creating histogram plot\n",
        "plt.title('Distribution of Member_number')  # Setting title for the plot\n",
        "plt.xlabel('Member_number')  # Setting label for x-axis\n",
        "plt.ylabel('Frequency')  # Setting label for y-axis\n",
        "plt.savefig(\"/content/distribution_of_member_number.pdf\")  # Saving distribution plot as PDF\n",
        "plt.show()  # Make sure plt.show() is included to display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Zv125g2T5hvT",
        "outputId": "e0c4064c-c64f-4ae0-cd97-ef067ff6ece0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y6SAZDsK57Wz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UEQWcp-o57kS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EDA - Visualization 2: Frequency of Top 20 itemDescription"
      ],
      "metadata": {
        "id": "Db9XGL6O57nb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Setting plot size\n",
        "item_counts = df['itemDescription'].value_counts().head(20)  # Counting top 20 frequent items\n",
        "item_counts.plot(kind='bar')  # Creating bar plot\n",
        "plt.title('Top 20 Most Frequent itemDescription')  # Setting title for the plot\n",
        "plt.xlabel('itemDescription')  # Setting label for x-axis\n",
        "plt.ylabel('Frequency')  # Setting label for y-axis\n",
        "plt.savefig(\"/content/frequency_of_top_20_item_description.pdf\")  # Saving top 20 item description frequencies as PDF\n",
        "plt.show()  # Make sure plt.show() is included to display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "id": "YUd0k4cc5hx6",
        "outputId": "c9d14be7-0278-4b28-dd26-b9ba547897da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MFAN3-vI6H50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zIqwFhkE6H9m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier detection using box plot"
      ],
      "metadata": {
        "id": "T6Cfgbvz6IDN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting plot size\n",
        "sns.boxplot(x=df['Member_number'])  # Creating box plot\n",
        "plt.title('Box Plot of Member_number')  # Setting title for the plot\n",
        "plt.savefig(\"/content/box_plot_member_number.pdf\")  # Saving box plot of Member_number as PDF\n",
        "plt.show()  # Make sure plt.show() is included to display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "uN6M5Edn6IG7",
        "outputId": "3750ceaa-f8a1-4ec7-b04c-9640afc67b1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Wndzbcb6WpX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yQ_U2nv16Ws_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Preprocessing the data for Market Basket Analysis</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Preparing the dataset (`df`) for Market Basket Analysis (MBA) involves a series of steps to transform raw transactional data into a structured format suitable for mining frequent itemsets and association rules. This phase not only ensures data cleanliness and consistency but also enhances the effectiveness of MBA algorithms in revealing meaningful patterns and relationships.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Steps</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Data Cleaning</span>**\n",
        "Cleanse the dataset to address data quality issues such as missing values, outliers, and inconsistent formatting:\n",
        "- **Missing Values Handling**: Impute missing values or drop incomplete records (`df.dropna()`), ensuring robust data completeness for accurate analysis.\n",
        "- **Outlier Detection**: Identify and address outliers using statistical methods (e.g., Z-score analysis or interquartile range), maintaining data integrity and reducing skewness in item frequency distributions.\n",
        "- **Normalization and Standardization**: Normalize numerical features or standardize data scales to mitigate biases and ensure fair comparison across different item categories.\n",
        "\n",
        "### **<span style=\"color:purple\">Transaction Encoding</span>**\n",
        "Encode transactional data into a suitable format for MBA algorithms, typically using a sparse matrix representation or transaction-item matrix:\n",
        "- **One-Hot Encoding**: Convert categorical data (e.g., item descriptions) into binary vectors indicating item presence in each transaction (`pd.get_dummies()`), facilitating efficient computation of itemset support and association rules.\n",
        "- **Transaction Consolidation**: Aggregate transaction data to consolidate multiple purchases by the same member or transaction ID, simplifying pattern discovery and analysis.\n",
        "\n",
        "### **<span style=\"color:orange\">Feature Engineering</span>**\n",
        "Enrich transactional data with additional features to enhance MBA insights and predictive modeling capabilities:\n",
        "- **Association Rule Metrics**: Calculate additional metrics such as lift, confidence, and support for generated association rules (`association_rules() from mlxtend`), providing deeper insights into item co-occurrences and transactional dependencies.\n",
        "- **Customer Segmentation**: Segment customers based on transactional behavior or demographic attributes using clustering algorithms (e.g., K-Means), enabling personalized marketing strategies and targeted promotions.\n",
        "\n",
        "### **<span style=\"color:blue\">Data Transformation</span>**\n",
        "Transform transactional data to meet algorithmic requirements and analytical objectives:\n",
        "- **Dimensionality Reduction**: Apply techniques like Principal Component Analysis (PCA) to reduce feature dimensionality and improve computational efficiency in large-scale MBA applications.\n",
        "- **Temporal Analysis**: Incorporate temporal features (e.g., transaction timestamps) to analyze seasonal trends or time-dependent patterns in purchasing behavior, optimizing inventory management and promotional planning.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "Comprehensive data preprocessing is essential for extracting actionable insights and maximizing the utility of Market Basket Analysis (MBA) in retail and e-commerce environments. By implementing rigorous data cleaning, encoding, and feature engineering techniques, organizations can uncover valuable patterns, optimize operational strategies, and enhance customer satisfaction through targeted marketing initiatives."
      ],
      "metadata": {
        "id": "dK6IJ87HXH_E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing the data for Market Basket Analysis"
      ],
      "metadata": {
        "id": "93WV-Wan6W2_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])  # Converting 'Date' column to datetime format"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGrDzRwx6W6Y",
        "outputId": "aae8fa3b-87a5-4a06-d98d-29a5b47b8d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-58-a13abe358116>:1: UserWarning: Parsing dates in %d-%m-%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
            "  df['Date'] = pd.to_datetime(df['Date'])  # Converting 'Date' column to datetime format\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "basket = df.groupby(['Member_number', 'itemDescription'])['itemDescription'].count().unstack().reset_index().fillna(0).set_index('Member_number')\n"
      ],
      "metadata": {
        "id": "8lCNdE0p6IKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "basket = basket.applymap(lambda x: 1 if x > 0 else 0)  # Converting counts to binary values (1 or 0)"
      ],
      "metadata": {
        "id": "socDuXJi5h2d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vAg5g-Fy6kdq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P5reb99UXYV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Apply the Apriori algorithm with advanced parameters</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Applying the Apriori algorithm is a fundamental step in Market Basket Analysis (MBA), aimed at identifying frequent itemsets from transactional data. Fine-tuning the algorithm with advanced parameters enhances its ability to extract meaningful associations between items, thereby facilitating actionable insights for business decision-making.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Considerations</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Parameter Tuning</span>**\n",
        "Optimize the performance of the Apriori algorithm by adjusting key parameters that influence itemset discovery and rule generation:\n",
        "- **Minimum Support (`min_support`)**: Define the minimum threshold for itemset frequency to be considered frequent. Higher support thresholds yield fewer but more significant itemsets (`apriori(basket, min_support=0.01, use_colnames=True)`).\n",
        "- **Maximum Itemset Size (`max_len`)**: Limit the size of generated itemsets to focus on combinations of a specific length, reducing computational complexity and enhancing interpretability.\n",
        "\n",
        "### **<span style=\"color:purple\">Memory Efficiency</span>**\n",
        "Manage memory usage and computational resources when processing large-scale transactional datasets:\n",
        "- **Sparse Matrix Representation**: Utilize sparse matrix formats or optimized data structures to store transaction-item relationships efficiently, minimizing memory overhead and accelerating algorithm execution (`sparse.csr_matrix`).\n",
        "- **Parallel Processing**: Leverage parallel computing frameworks (e.g., multiprocessing or distributed computing) to distribute workload and expedite itemset generation across multiple CPU cores or clusters.\n",
        "\n",
        "### **<span style=\"color:orange\">Algorithmic Enhancements</span>**\n",
        "Explore advanced techniques and optimizations to improve Apriori algorithm performance and scalability:\n",
        "- **Pruning Strategies**: Implement pruning strategies (e.g., hash-based or transaction reduction) to eliminate infrequent itemsets early in the algorithm, focusing computational efforts on potentially significant associations (`mlxtend.frequent_patterns.apriori`).\n",
        "- **Dynamic Threshold Adjustment**: Dynamically adjust support thresholds or significance levels based on data characteristics or business requirements, ensuring adaptive and responsive MBA insights.\n",
        "\n",
        "### **<span style=\"color:blue\">Result Interpretation</span>**\n",
        "Interpret and evaluate Apriori algorithm results to derive actionable insights and strategic recommendations:\n",
        "- **Association Rules**: Extract association rules (antecedents → consequents) based on predefined metrics such as lift, confidence, and support, identifying meaningful item co-purchases or market basket patterns (`mlxtend.frequent_patterns.association_rules`).\n",
        "- **Visualization**: Visualize frequent itemsets and association rules using graphical representations (e.g., network graphs or heatmaps), facilitating intuitive understanding and decision support in retail merchandising or customer segmentation strategies.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By applying the Apriori algorithm with advanced parameters and considerations, organizations can uncover valuable associations and patterns within transactional data, driving targeted marketing initiatives, optimizing product assortments, and enhancing customer satisfaction in retail and e-commerce domains."
      ],
      "metadata": {
        "id": "JhqDIdS0XYjn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the Apriori algorithm with advanced parameters"
      ],
      "metadata": {
        "id": "8pDqmJ1T6khn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets = apriori(basket, min_support=0.01, use_colnames=True)  # Applying Apriori algorithm"
      ],
      "metadata": {
        "id": "YCBvtGzK6kjx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cd6kJ7QU6klP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate the association rules with additional metrics and filtering"
      ],
      "metadata": {
        "id": "bjVnUqNV6koc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)  # Generating association rules"
      ],
      "metadata": {
        "id": "v4mNJUDO6krY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rules = rules[(rules['lift'] >= 1) & (rules['confidence'] >= 0.2)]  # Filtering association rules"
      ],
      "metadata": {
        "id": "TQocOK5I6kuv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g2aLeDvH6yf1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ca2oPG3tXlIs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Feature Engineering (Example: adding total transactions per member)</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Feature engineering involves creating new features or transforming existing ones to enhance the predictive power of machine learning models or derive meaningful insights from data. In the context of Market Basket Analysis (MBA), feature engineering enriches transactional data with additional attributes that facilitate deeper understanding of customer behavior and purchasing patterns.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Concepts</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Total Transactions</span>**\n",
        "Calculate the total number of transactions per member (`basket['Total_transactions'] = basket.sum(axis=1)`), providing a holistic view of member engagement and purchase frequency:\n",
        "- **Behavioral Insights**: Identify loyal or frequent shoppers based on transaction volume, enabling targeted marketing campaigns or loyalty program incentives.\n",
        "- **Segmentation Criteria**: Segment customers into groups (e.g., high-frequency buyers, occasional shoppers) to tailor product recommendations and promotional strategies accordingly.\n",
        "\n",
        "### **<span style=\"color:purple\">Historical Patterns</span>**\n",
        "Capture historical transactional patterns and trends to predict future buying behavior or recommend personalized product offerings:\n",
        "- **Temporal Analysis**: Analyze seasonal fluctuations or temporal trends in transaction counts to optimize inventory management and promotional planning.\n",
        "- **Sequential Pattern Mining**: Explore sequential patterns or purchase sequences (e.g., frequent item sequences) to understand sequential dependencies in customer purchasing decisions.\n",
        "\n",
        "### **<span style=\"color:orange\">Customer Profiling</span>**\n",
        "Create customer profiles based on transactional attributes and behavioral metrics derived from feature engineering:\n",
        "- **RFM Analysis**: Calculate Recency, Frequency, and Monetary (RFM) metrics to segment customers by their transactional recency, purchase frequency, and spending habits, guiding targeted marketing strategies and customer retention efforts.\n",
        "- **Predictive Modeling**: Use engineered features as inputs for machine learning models (e.g., clustering algorithms or predictive analytics) to forecast customer lifetime value or predict future purchase likelihood.\n",
        "\n",
        "### **<span style=\"color:blue\">Strategic Insights</span>**\n",
        "Derive actionable insights and strategic recommendations from enriched transactional data to drive business growth and operational efficiency:\n",
        "- **Cross-Selling Opportunities**: Identify cross-sell or upsell opportunities based on frequent item associations or complementary product recommendations.\n",
        "- **Market Basket Optimization**: Optimize product assortments and placement strategies in retail environments to enhance basket size and profitability through strategic product bundling or category management.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "Feature engineering in Market Basket Analysis (MBA) transforms transactional data into actionable insights, empowering organizations to make data-driven decisions, enhance customer satisfaction, and capitalize on market opportunities effectively."
      ],
      "metadata": {
        "id": "BOSD01-_Xxae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering (Example: adding total transactions per member)"
      ],
      "metadata": {
        "id": "I58zUBGo6ylY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "basket['Total_transactions'] = basket.sum(axis=1)  # Calculating total transactions per member"
      ],
      "metadata": {
        "id": "ooNABABy6ym9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "69HU9hle6yra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iBqr0gdI68gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Advanced Apriori Techniques (Example: FP-Growth Algorithm)</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "In Market Basket Analysis (MBA), advanced techniques such as the FP-Growth algorithm offer efficient alternatives to traditional Apriori-based approaches for mining frequent itemsets from transactional data. FP-Growth stands out for its ability to handle large datasets and optimize performance by employing a compact data structure known as the FP-tree.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Features</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">FP-Growth Algorithm</span>**\n",
        "FP-Growth (Frequent Pattern Growth) algorithm streamlines the process of discovering frequent itemsets:\n",
        "- **Tree-based Structure**: Utilizes an FP-tree to encode transactional data and efficiently mine frequent itemsets without generating candidate itemsets explicitly.\n",
        "- **Conditional Pattern Base**: Constructs conditional pattern bases and recursively applies FP-tree growth to identify frequent itemsets based on minimum support thresholds (`fpgrowth(basket_fp, min_support=0.01, use_colnames=True)`).\n",
        "\n",
        "### **<span style=\"color:purple\">Advantages over Apriori</span>**\n",
        "Compared to the Apriori algorithm, FP-Growth offers several advantages:\n",
        "- **Efficiency**: Reduces computational complexity by avoiding repeated scans of transactional data and candidate generation, enhancing performance in large-scale MBA applications.\n",
        "- **Memory Efficiency**: Optimizes memory usage through the FP-tree structure, particularly beneficial for handling sparse and high-dimensional transaction datasets.\n",
        "\n",
        "### **<span style=\"color:orange\">Application Scenarios</span>**\n",
        "Deploy FP-Growth algorithm in various MBA scenarios to uncover meaningful patterns and associations:\n",
        "- **Large-scale Retail Data**: Analyze extensive transactional data from retail chains or e-commerce platforms to identify frequent purchasing patterns and optimize product recommendations.\n",
        "- **Market Basket Optimization**: Facilitate strategic decision-making in merchandising, inventory management, and cross-selling strategies by leveraging compact FP-tree representations and efficient pattern mining techniques.\n",
        "\n",
        "### **<span style=\"color:blue\">Strategic Insights</span>**\n",
        "Extract strategic insights from FP-Growth results to drive business growth and operational efficiency:\n",
        "- **Association Rule Mining**: Generate high-confidence association rules from frequent itemsets to inform targeted marketing campaigns and personalized customer experiences.\n",
        "- **Dynamic Threshold Adjustment**: Dynamically adjust support thresholds or significance levels based on business requirements to adaptively refine MBA insights and strategic recommendations.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By leveraging advanced techniques such as the FP-Growth algorithm, organizations can enhance the scalability, efficiency, and interpretability of Market Basket Analysis (MBA), enabling actionable insights that drive revenue growth, customer satisfaction, and operational excellence."
      ],
      "metadata": {
        "id": "KAkxLX4xX5Yq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Advanced Apriori Techniques (Example: FP-Growth Algorithm)"
      ],
      "metadata": {
        "id": "QvSgUuy66yt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure basket DataFrame contains only boolean values (0 or 1)"
      ],
      "metadata": {
        "id": "92XxqIW66ywW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "basket_fp = basket.astype(bool)  # Converting basket to boolean values"
      ],
      "metadata": {
        "id": "28a0ioAz6yyu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets_fp = fpgrowth(basket_fp, min_support=0.01, use_colnames=True)  # Applying FP-Growth algorithm"
      ],
      "metadata": {
        "id": "XeVKMaYW7C5F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O6ofnACB7C87"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_XQicZK8YGiq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Visualization and Interpretation (Example: Network Graph of association rules)</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Visualizing association rules using network graphs provides intuitive insights into item co-occurrences and transactional dependencies discovered through Market Basket Analysis (MBA). Network graphs illustrate complex relationships between items, highlighting strong associations and potential patterns that influence customer purchasing behavior.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Visualization Techniques</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Network Graph Representation</span>**\n",
        "Construct network graphs to visualize association rules as nodes (items) and edges (associations) with additional attributes:\n",
        "- **Graph Construction**: Utilize networkx library to create graphs from association rules data (`nx.from_pandas_edgelist(rules, source='antecedents', target='consequents', edge_attr=True)`).\n",
        "- **Node Attributes**: Enhance graph readability by assigning attributes such as node size or color based on item frequency or support levels.\n",
        "- **Edge Attributes**: Customize edge attributes (e.g., thickness or color) to reflect association rule metrics like lift, confidence, or conviction, facilitating detailed analysis and interpretation.\n",
        "\n",
        "### **<span style=\"color:purple\">Graph Layout and Visualization</span>**\n",
        "Optimize graph layout and visualization techniques to convey meaningful insights effectively:\n",
        "- **Layout Algorithms**: Apply layout algorithms (e.g., spring layout or circular layout) to arrange nodes and edges in visually appealing and informative configurations (`nx.spring_layout(G)`).\n",
        "- **Interactive Visualization**: Implement interactive graph visualization tools (e.g., Plotly or D3.js) for dynamic exploration of association rules and interactive querying of item relationships.\n",
        "\n",
        "### **<span style=\"color:orange\">Insights and Interpretation</span>**\n",
        "Extract actionable insights and strategic recommendations from network graph visualizations:\n",
        "- **Cluster Analysis**: Identify clusters or communities of closely related items based on graph topology and connectivity patterns, guiding product bundling strategies or shelf placement optimizations.\n",
        "- **Central Node Analysis**: Analyze central nodes (high-degree nodes) within the network graph to pinpoint influential items or core products driving frequent item associations and cross-sell opportunities.\n",
        "\n",
        "### **<span style=\"color:blue\">Applications in Business Decision-Making</span>**\n",
        "Harness network graph visualizations to support data-driven decision-making and strategic planning:\n",
        "- **Marketing Campaign Optimization**: Tailor marketing campaigns and promotional offers based on identified item associations and customer purchasing patterns revealed by the graph.\n",
        "- **Customer Segmentation**: Segment customers into distinct groups or personas using graph-based insights, enabling personalized marketing strategies and targeted customer engagement initiatives.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "Visualizing association rules through network graphs enhances the interpretability and actionable insights derived from Market Basket Analysis (MBA), empowering organizations to optimize product assortments, enhance customer experiences, and drive business growth through informed decision-making."
      ],
      "metadata": {
        "id": "VOuNB16IYGw6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization and Interpretation (Example: Network Graph of association rules)"
      ],
      "metadata": {
        "id": "UEPVuHi-7C_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Setting plot size\n",
        "G = nx.from_pandas_edgelist(rules, source='antecedents', target='consequents', edge_attr=True)  # Creating network graph\n",
        "pos = nx.spring_layout(G)  # Setting layout for nodes\n",
        "nx.draw_networkx_nodes(G, pos, node_size=700)  # Drawing nodes\n",
        "nx.draw_networkx_edges(G, pos, width=2)  # Drawing edges\n",
        "nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif')  # Drawing labels\n",
        "plt.title('Association Rules Network Graph')  # Setting title for the plot\n",
        "plt.axis('off')  # Turning off axis\n",
        "plt.savefig(\"/content/association_rules_network_graph.pdf\")  # Saving network graph of association rules as PDF\n",
        "plt.show()  # make sure the plot iis including to show the graphs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "A2valyA87DEe",
        "outputId": "7764c5bd-1813-4cd3-96bf-7007bbabfcfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QWSTrxB27asn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tMEHmDLr7a2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E-mxHIDbYT0x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Integration with Machine Learning Models (if applicable)</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Integrating Market Basket Analysis (MBA) with machine learning models augments the predictive capabilities and actionable insights derived from transactional data. By combining association rules or frequent itemsets with supervised or unsupervised learning algorithms, organizations can enhance customer segmentation, predict purchasing behavior, and optimize business strategies effectively.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Integration Strategies</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Feature Engineering for ML Models</span>**\n",
        "Prepare transactional data and derived features as inputs for machine learning models:\n",
        "- **Feature Selection**: Identify relevant transactional attributes (e.g., frequent itemsets, association rule metrics) to encode as features for predictive modeling (`X = df[['Member_number']]`).\n",
        "- **Data Transformation**: Convert transactional data into structured formats suitable for model training and evaluation, ensuring compatibility with ML algorithms and frameworks.\n",
        "\n",
        "### **<span style=\"color:purple\">Supervised Learning Approaches</span>**\n",
        "Utilize supervised learning algorithms to predict customer behavior or optimize marketing strategies:\n",
        "- **Classification**: Train classifiers (e.g., Random Forest, Gradient Boosting) to predict high-value transactions or customer segments based on transactional patterns and demographic data (`clf.fit(X_train, y_train)`).\n",
        "- **Regression**: Apply regression models to forecast transaction amounts or predict future purchase frequencies, leveraging historical transactional data and derived features (`LinearRegression`, `XGBoost`).\n",
        "\n",
        "### **<span style=\"color:orange\">Unsupervised Learning Techniques</span>**\n",
        "Employ unsupervised learning techniques to uncover hidden patterns or customer segments:\n",
        "- **Clustering**: Segment customers into distinct groups based on transactional similarities or purchasing behaviors, using clustering algorithms such as K-Means or DBSCAN (`KMeans`, `DBSCAN`).\n",
        "- **Dimensionality Reduction**: Reduce feature dimensions and visualize high-dimensional transactional data using techniques like PCA (Principal Component Analysis) or t-SNE (t-Distributed Stochastic Neighbor Embedding).\n",
        "\n",
        "### **<span style=\"color:blue\">Model Evaluation and Performance Metrics</span>**\n",
        "Evaluate model performance and interpretability to validate insights derived from integrated MBA and ML approaches:\n",
        "- **Accuracy Metrics**: Measure classification accuracy, precision, recall, and F1-score to assess predictive model performance (`accuracy_score`, `classification_report`).\n",
        "- **Business Impact Assessment**: Quantify the business impact of integrated MBA-ML strategies through metrics such as ROI (Return on Investment), customer lifetime value (CLV), or campaign effectiveness.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By integrating Market Basket Analysis (MBA) with machine learning models, organizations can harness the synergies between transactional insights and predictive analytics to drive personalized marketing initiatives, optimize product recommendations, and foster customer-centric strategies that maximize business growth and profitability."
      ],
      "metadata": {
        "id": "XHdYVsLNYUKa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Integration with Machine Learning Models (if applicable)"
      ],
      "metadata": {
        "id": "66y0wtNn7a6p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Integration with a classifier to predict high-value transactions"
      ],
      "metadata": {
        "id": "pV0HkDCM7mVR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Placeholder for machine learning model integration code"
      ],
      "metadata": {
        "id": "1xK4YKWw7mYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Import necessary libraries and models"
      ],
      "metadata": {
        "id": "a_k5r0LE7mbK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Importing train_test_split for splitting data"
      ],
      "metadata": {
        "id": "_Ewv6Rfv_l4A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier  # Importing RandomForestClassifier for classification"
      ],
      "metadata": {
        "id": "8Rxdp0I9_l9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score  # Importing accuracy_score for evaluating model performance"
      ],
      "metadata": {
        "id": "nnL5dHE5_l-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Importing pandas for data manipulation"
      ],
      "metadata": {
        "id": "tiERJdLs_mAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YT35rC1U_mCu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Preprocess data for machine learning model"
      ],
      "metadata": {
        "id": "NBnpFAyC_mEZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Member_number']]  # Selecting features for training (replace with actual features)"
      ],
      "metadata": {
        "id": "S6r_iRKN_mGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['itemDescription']  # Selecting target variable (replace with actual target variable)"
      ],
      "metadata": {
        "id": "5n9ZrgGc_3TH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X0wOmiAv_3WW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Split data into training and testing sets"
      ],
      "metadata": {
        "id": "LN7RZuZW_3Zz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Splitting data into train and test sets\n"
      ],
      "metadata": {
        "id": "5etuQeae_3dk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HQnrKc_7_3fs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Initialize and train a machine learning model"
      ],
      "metadata": {
        "id": "3cQpzsYb_3hl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier()  # Initializing RandomForestClassifier"
      ],
      "metadata": {
        "id": "LpWJUxrPAO9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(X_train, y_train)  # Training the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "k0t3JO9MAPAJ",
        "outputId": "9be45029-e33f-4822-b851-49f0521914f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wlI7SqvhAPCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Predict high-value transactions"
      ],
      "metadata": {
        "id": "1K57XJ2VAPEQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)  # Predicting values using the trained model"
      ],
      "metadata": {
        "id": "PCauLZ49AY6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0R644-wUAY8x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Evaluate model performance"
      ],
      "metadata": {
        "id": "YJ5Ubm8wAY-t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)  # Calculating accuracy of the model"
      ],
      "metadata": {
        "id": "z4hZ-N0zAfiZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Accuracy of the model: {accuracy}\")  # Printing accuracy of the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LlFnHgQAfmU",
        "outputId": "85703050-05e7-40f7-bc48-fa3b735f0851"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model: 0.026699342190119952\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z0xhLkQgAsD3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-fjhaAVkYsn3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Optimization and Performance (Example: Parallel processing for large datasets)</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Optimizing the performance of Market Basket Analysis (MBA) on large datasets involves leveraging parallel processing techniques to enhance computational efficiency, reduce processing time, and scale analytics capabilities. By distributing computational tasks across multiple CPU cores or nodes, organizations can handle extensive transactional data more effectively and expedite insights generation.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Optimization Strategies</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Parallel Processing Paradigms</span>**\n",
        "Implement parallel processing techniques to accelerate data processing and analysis:\n",
        "- **Multi-core Processing**: Utilize multiprocessing libraries (e.g., `joblib`, `multiprocessing`) to execute concurrent tasks across multiple CPU cores, optimizing resource utilization and reducing processing bottlenecks (`Parallel`, `delayed`).\n",
        "- **Cluster Computing**: Scale processing capabilities through distributed computing frameworks (e.g., Apache Spark) to handle large-scale MBA tasks across clusters of interconnected nodes, enhancing scalability and performance.\n",
        "\n",
        "### **<span style=\"color:purple\">Data Partitioning and Chunk Processing</span>**\n",
        "Divide large datasets into manageable chunks for parallel processing:\n",
        "- **Data Chunking**: Split transactional data into smaller partitions or batches (`data_chunks = [X_train[i:i+100] for i in range(0, len(X_train), 100)]`) to distribute workload evenly across processing units.\n",
        "- **Batch Processing**: Execute parallel operations on data chunks in parallel, aggregating results asynchronously to expedite data transformation or analysis tasks.\n",
        "\n",
        "### **<span style=\"color:orange\">Performance Optimization Techniques</span>**\n",
        "Enhance performance through optimization techniques tailored for large-scale MBA:\n",
        "- **Caching and Memoization**: Cache intermediate results or computations to minimize redundant calculations and accelerate subsequent data processing steps.\n",
        "- **Pipeline Optimization**: Design streamlined data pipelines with optimized algorithms and data structures to reduce overhead and improve computational efficiency.\n",
        "\n",
        "### **<span style=\"color:blue\">Scalability and Resource Management</span>**\n",
        "Manage computational resources effectively to support scalable MBA operations:\n",
        "- **Resource Allocation**: Allocate memory and processing resources dynamically based on workload demands, ensuring efficient utilization and responsiveness.\n",
        "- **Task Scheduling**: Implement task scheduling strategies (e.g., priority-based scheduling, task queuing) to optimize resource allocation and maximize throughput in parallel processing environments.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By adopting parallel processing techniques and performance optimization strategies, organizations can expedite Market Basket Analysis (MBA) workflows, handle large volumes of transactional data with efficiency, and derive actionable insights promptly to drive strategic decision-making and business growth."
      ],
      "metadata": {
        "id": "_nu_XVZqYs62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimization and Performance (Example: Parallel processing for large datasets)"
      ],
      "metadata": {
        "id": "a005SW5xAsHx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Placeholder for optimization and performance enhancement code"
      ],
      "metadata": {
        "id": "P9VkdnzEAsLT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zRErYUEvAfph"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Implement parallel processing techniques"
      ],
      "metadata": {
        "id": "CFZFcYMYAfry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import Parallel, delayed  # Importing Parallel and delayed for parallel processing"
      ],
      "metadata": {
        "id": "cuQpcjuQAZBZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import multiprocessing  # Importing multiprocessing for utilizing multiple cores"
      ],
      "metadata": {
        "id": "7AZZebwKA2bJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3LgOoYTiA2en"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tZcl9huKY9Ko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Example: Define function to process data in parallel</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Defining functions for parallel data processing enables efficient execution of data-intensive tasks in Market Basket Analysis (MBA). By leveraging parallel computing paradigms, such as multiprocessing and distributed processing frameworks, organizations can enhance computational efficiency, scale analytics capabilities, and accelerate insights generation from large-scale transactional data.\n",
        "\n",
        "#### **<span style=\"color:green\">Function Definition and Implementation</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Parallel Data Processing Function</span>**\n",
        "Define a function to process data in parallel using Python's multiprocessing library:\n",
        "- **Function Design**: Structure the function to perform data transformations or computations that benefit from parallel execution (`process_data_parallel(data)`).\n",
        "- **Parallel Execution**: Utilize `joblib` or `multiprocessing` modules to distribute data processing tasks across multiple CPU cores or nodes (`Parallel`, `delayed`).\n",
        "\n",
        "### **<span style=\"color:purple\">Data Chunking and Task Distribution</span>**\n",
        "Partition data into manageable chunks for parallel processing:\n",
        "- **Chunking Strategy**: Split data into smaller batches or partitions (`data_chunks`) to distribute workload evenly across processing units.\n",
        "- **Concurrent Execution**: Execute parallel operations on data chunks concurrently, leveraging multiprocessing capabilities to optimize resource utilization and reduce processing time.\n",
        "\n",
        "### **<span style=\"color:orange\">Optimization and Efficiency</span>**\n",
        "Enhance function efficiency through optimization techniques and performance tuning:\n",
        "- **Memoization**: Cache intermediate results or computations to avoid redundant calculations and improve overall function performance.\n",
        "- **Algorithmic Optimization**: Optimize algorithms and data structures used within the function to minimize overhead and maximize computational throughput.\n",
        "\n",
        "### **<span style=\"color:blue\">Scalability and Adaptability</span>**\n",
        "Ensure function scalability and adaptability to diverse MBA scenarios and data processing requirements:\n",
        "- **Dynamic Resource Allocation**: Allocate resources dynamically based on workload demands to optimize processing efficiency and responsiveness.\n",
        "- **Error Handling and Monitoring**: Implement robust error handling mechanisms and monitoring strategies to maintain data integrity and operational continuity during parallel processing.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By defining and deploying functions for parallel data processing, organizations can streamline Market Basket Analysis (MBA) workflows, expedite data-driven insights generation, and leverage scalable computing capabilities to support strategic decision-making and operational excellence."
      ],
      "metadata": {
        "id": "JKorQhb_Y9oW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Define function to process data in parallel"
      ],
      "metadata": {
        "id": "Gc52nalgA2hj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_data_parallel(data):\n",
        "    # Placeholder for actual processing logic (e.g., data transformation or computation)\n",
        "    return data * 2  # Example transformation (replace with actual logic)"
      ],
      "metadata": {
        "id": "_vKdnh9cAPGH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OQLfLzG2BBKn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "taELnIXaZAQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Example: Define data chunks for parallel processing</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Defining data chunks for parallel processing facilitates efficient handling of large-scale datasets in Market Basket Analysis (MBA). By partitioning data into manageable segments, organizations can leverage parallel computing techniques to distribute computational tasks across multiple processing units, enhancing data processing speed and scalability.\n",
        "\n",
        "#### **<span style=\"color:green\">Data Chunk Definition and Implementation</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Partitioning Strategy</span>**\n",
        "Define data chunks to divide large datasets into smaller batches for parallel processing:\n",
        "- **Chunk Size**: Determine optimal chunk size based on data volume and computational resources (`chunk_size = 100` for example).\n",
        "- **Data Partitioning**: Slice data into sequential or overlapping segments (`[X_train[i:i+chunk_size] for i in range(0, len(X_train), chunk_size)]`) to distribute workload evenly across processing units.\n",
        "\n",
        "### **<span style=\"color:purple\">Concurrency and Parallel Execution</span>**\n",
        "Enable concurrent execution of data processing tasks across defined data chunks:\n",
        "- **Parallel Processing**: Utilize multiprocessing libraries (e.g., `joblib`, `multiprocessing`) to execute operations on data chunks in parallel (`Parallel`, `delayed`).\n",
        "- **Task Distribution**: Distribute computational tasks across multiple CPU cores or nodes to optimize resource utilization and expedite data transformations.\n",
        "\n",
        "### **<span style=\"color:orange\">Scalability and Performance Optimization</span>**\n",
        "Enhance scalability and performance through effective data chunking and parallel processing strategies:\n",
        "- **Chunk Management**: Manage data chunking dynamically based on computational demands and resource availability to maximize throughput and responsiveness.\n",
        "- **Batch Processing**: Implement batch processing techniques to process data segments asynchronously, minimizing processing bottlenecks and enhancing overall system efficiency.\n",
        "\n",
        "### **<span style=\"color:blue\">Adaptability and Flexibility</span>**\n",
        "Ensure flexibility and adaptability of data chunking strategies to accommodate varying MBA workflows and analytical requirements:\n",
        "- **Dynamic Partitioning**: Adjust chunking parameters dynamically to handle fluctuations in data volume and complexity, ensuring robust performance across diverse data processing scenarios.\n",
        "- **Error Handling and Monitoring**: Implement robust error handling mechanisms and real-time monitoring tools to maintain data integrity and operational continuity during parallel processing operations.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By defining data chunks and implementing parallel processing techniques, organizations can optimize Market Basket Analysis (MBA) workflows, expedite insights generation from large-scale transactional data, and leverage scalable computing capabilities to drive informed decision-making and business growth."
      ],
      "metadata": {
        "id": "OTsYE-PvZAeH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Define data chunks for parallel processing"
      ],
      "metadata": {
        "id": "dejHnmOkBBN2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_chunks = [X_train[i:i+100] for i in range(0, len(X_train), 100)]  # Splitting data into chunks for parallel processing\n"
      ],
      "metadata": {
        "id": "j1m75j8xBBRE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9xQyzYwcBBTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mY25iFJtZK4n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Example: Parallel processing using joblib</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Parallel processing using `joblib` enhances the computational efficiency and performance of Market Basket Analysis (MBA) by leveraging multi-core processing capabilities. By distributing tasks across multiple CPU cores, `joblib` facilitates concurrent execution of data-intensive operations, optimizing resource utilization and accelerating insights generation from large-scale transactional datasets.\n",
        "\n",
        "#### **<span style=\"color:green\">Parallel Processing Implementation</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Joblib for Parallel Execution</span>**\n",
        "Utilize `joblib` library to implement parallel processing in Python:\n",
        "- **Function Parallelization**: Parallelize data processing functions using `Parallel` and `delayed` decorators (`Parallel(n_jobs=-1)(delayed(process_data_parallel)(data_chunk) for data_chunk in data_chunks)`).\n",
        "- **Multi-core Utilization**: Specify number of CPU cores (`n_jobs=-1` for all cores) to maximize parallelism and optimize task distribution across available processing units.\n",
        "\n",
        "### **<span style=\"color:purple\">Data Chunking and Task Distribution</span>**\n",
        "Divide data into manageable chunks for parallel execution:\n",
        "- **Chunk Definition**: Define data chunks based on optimal chunk size (`data_chunks = [X_train[i:i+chunk_size] for i in range(0, len(X_train), chunk_size)]`).\n",
        "- **Concurrent Processing**: Execute data processing tasks concurrently across defined data chunks, leveraging multi-core capabilities to expedite computations.\n",
        "\n",
        "### **<span style=\"color:orange\">Performance Optimization</span>**\n",
        "Enhance performance and throughput through `joblib` parallel processing:\n",
        "- **Task Scheduling**: Efficiently schedule and distribute computational tasks across CPU cores to minimize idle time and maximize computational efficiency.\n",
        "- **Result Aggregation**: Aggregate results asynchronously from parallel processes, ensuring seamless integration and synchronization of parallelized operations.\n",
        "\n",
        "### **<span style=\"color:blue\">Scalability and Resource Management</span>**\n",
        "Manage computational resources dynamically to support scalable MBA workflows:\n",
        "- **Resource Allocation**: Allocate CPU cores dynamically based on workload demands and system specifications, optimizing resource utilization and responsiveness.\n",
        "- **Error Handling**: Implement error handling mechanisms to manage exceptions and maintain data integrity during parallel processing operations.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By leveraging `joblib` for parallel processing, organizations can accelerate Market Basket Analysis (MBA) workflows, handle large volumes of transactional data efficiently, and derive actionable insights promptly to drive strategic decision-making and operational excellence."
      ],
      "metadata": {
        "id": "yISb05X7ZLEY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Parallel processing using joblib"
      ],
      "metadata": {
        "id": "Fh18zk6xBBWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_cores = multiprocessing.cpu_count()  # Getting number of CPU cores"
      ],
      "metadata": {
        "id": "9RQZIDdyBKFn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = Parallel(n_jobs=num_cores)(delayed(process_data_parallel)(data_chunk) for data_chunk in data_chunks)  # Performing parallel processing on data chunks\n"
      ],
      "metadata": {
        "id": "R-0YDZIQBKJC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y8MgtFByBKOl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FgflIihPZYMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Example: Display parallel processing results</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Displaying parallel processing results in Market Basket Analysis (MBA) involves aggregating and visualizing outcomes from concurrent data processing tasks. By effectively presenting parallel processing results, organizations can interpret insights derived from large-scale transactional data, facilitate decision-making, and drive business strategies based on actionable intelligence.\n",
        "\n",
        "#### **<span style=\"color:green\">Result Visualization and Interpretation</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Result Aggregation</span>**\n",
        "Aggregate and summarize parallel processing outcomes for comprehensive analysis:\n",
        "- **Data Fusion**: Combine results from parallel processes into cohesive datasets or aggregated metrics (`results = Parallel(n_jobs=-1)(delayed(process_data_parallel)(data_chunk) for data_chunk in data_chunks)`).\n",
        "- **Result Compilation**: Compile outputs from parallel computations for comparative analysis and insights generation.\n",
        "\n",
        "### **<span style=\"color:purple\">Visualization Techniques</span>**\n",
        "Visualize parallel processing results using interactive and informative visualization tools:\n",
        "- **Graphical Representation**: Create charts, graphs, or dashboards to visualize key metrics and trends derived from parallelized data processing (`matplotlib`, `seaborn`, `Plotly`).\n",
        "- **Data Insights**: Present findings and insights from parallel processing in a clear, interpretable manner to stakeholders and decision-makers.\n",
        "\n",
        "### **<span style=\"color:orange\">Performance Metrics</span>**\n",
        "Evaluate performance metrics and benchmarks derived from parallel processing:\n",
        "- **Execution Time**: Measure processing time reduction and performance gains achieved through parallelization (`time_taken_parallel = time_end_parallel - time_start_parallel`).\n",
        "- **Resource Utilization**: Assess CPU core utilization and efficiency improvements from parallel processing implementations.\n",
        "\n",
        "### **<span style=\"color:blue\">Application and Use Cases</span>**\n",
        "Apply parallel processing results to inform strategic decisions and operational enhancements:\n",
        "- **Business Intelligence**: Utilize insights from parallelized MBA analyses to optimize product recommendations, marketing strategies, and operational efficiencies.\n",
        "- **Decision Support**: Provide decision-makers with actionable intelligence derived from accelerated data processing and analysis.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By displaying parallel processing results effectively, organizations can harness the power of parallel computing in MBA, derive meaningful insights from large-scale datasets, and empower decision-makers with actionable intelligence to drive business growth and competitive advantage."
      ],
      "metadata": {
        "id": "RGmoGe9YZYYa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Display parallel processing results"
      ],
      "metadata": {
        "id": "KY5qwnDUBKSE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for result in results:\n",
        "    print(result)  # Printing each processed result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aV-f2TYBT2H",
        "outputId": "a83eab18-2121-40b9-dd48-00a10eac2e80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Member_number\n",
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            "3997            2554\n",
            "3397            4870\n",
            "12418           8926\n",
            "6410            4502\n",
            "...              ...\n",
            "29570           9606\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34316           9572\n",
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            "33081           7696\n",
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            "14583           6426\n",
            "...              ...\n",
            "23603           7056\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "3610            2026\n",
            "...              ...\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "4614            2046\n",
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            "...              ...\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "18277           2814\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "27602           2888\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33              3118\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6673            8770\n",
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            "...              ...\n",
            "36322           4730\n",
            "15454           5502\n",
            "5444            9954\n",
            "38461           7430\n",
            "17185           2342\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8887            3534\n",
            "18559           6122\n",
            "34132           8396\n",
            "4961            8468\n",
            "8313            3292\n",
            "...              ...\n",
            "19645           8738\n",
            "1121            7916\n",
            "3459            7494\n",
            "9185            7478\n",
            "22816           7132\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35530           9390\n",
            "21994           7270\n",
            "38202           8224\n",
            "30612           5332\n",
            "24903           7642\n",
            "...              ...\n",
            "14532           9654\n",
            "38196           7538\n",
            "6557            7456\n",
            "22682           8134\n",
            "8185            8646\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3172            8308\n",
            "11361           5104\n",
            "27338           5090\n",
            "11271           6494\n",
            "10112           5982\n",
            "...              ...\n",
            "13462           9418\n",
            "15561           9660\n",
            "7603            9552\n",
            "30845           8514\n",
            "6075            7128\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "21315           3650\n",
            "18143           4924\n",
            "22944           6222\n",
            "1035            2162\n",
            "9744            9172\n",
            "...              ...\n",
            "2495            8640\n",
            "18960           9350\n",
            "34880           2266\n",
            "3478            2076\n",
            "28392           6234\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "23766           6328\n",
            "25418           2144\n",
            "32759           2414\n",
            "31900           8480\n",
            "35601           6232\n",
            "...              ...\n",
            "23191           6370\n",
            "8336            8260\n",
            "20368           5432\n",
            "24723           6490\n",
            "27470           6442\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1050            8726\n",
            "29919           6476\n",
            "21191           3990\n",
            "18208           2318\n",
            "27385           5552\n",
            "...              ...\n",
            "29078           3298\n",
            "5251            2346\n",
            "12522           5740\n",
            "34552           8224\n",
            "20713           4102\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20885           8752\n",
            "29937           7366\n",
            "32729           4504\n",
            "5720            3764\n",
            "38019           5284\n",
            "...              ...\n",
            "9214            7442\n",
            "38021           2124\n",
            "31274           3504\n",
            "9558            6200\n",
            "8688            2102\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "19136           5514\n",
            "7417            8344\n",
            "19244           5286\n",
            "19883           5436\n",
            "8628            8536\n",
            "...              ...\n",
            "20086           9576\n",
            "19084           9978\n",
            "7839            4460\n",
            "36815           2066\n",
            "1581            8190\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31780           8604\n",
            "16387           8298\n",
            "12958           8338\n",
            "10953           3318\n",
            "29996           5746\n",
            "...              ...\n",
            "33177           7554\n",
            "18119           4524\n",
            "2583            4760\n",
            "23987           7482\n",
            "9233            8390\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "12740           5742\n",
            "1729            4418\n",
            "6916            4452\n",
            "25749           2560\n",
            "3971            6450\n",
            "...              ...\n",
            "15281           3674\n",
            "23870           7018\n",
            "35970           7464\n",
            "20969           5416\n",
            "25885           9242\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "13658           8470\n",
            "31685           7808\n",
            "12833           2556\n",
            "16171           7924\n",
            "291             4938\n",
            "...              ...\n",
            "28166           4616\n",
            "1870            2112\n",
            "38329           9320\n",
            "36025           2296\n",
            "21655           3268\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10581           2986\n",
            "21287           4730\n",
            "18495           4120\n",
            "11524           7314\n",
            "12209           6232\n",
            "...              ...\n",
            "34499           8306\n",
            "1608            5430\n",
            "22074           7314\n",
            "21766           9050\n",
            "26556           6696\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "17877           6052\n",
            "11378           4384\n",
            "11891           8156\n",
            "13047           4096\n",
            "24986           9282\n",
            "...              ...\n",
            "25841           8196\n",
            "3426            7362\n",
            "18492           2316\n",
            "19634           9512\n",
            "23905           6256\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "27665           6728\n",
            "29367           6762\n",
            "14494           8910\n",
            "16869           3462\n",
            "27978           5286\n",
            "...              ...\n",
            "36604           7942\n",
            "11393           3250\n",
            "34073           6978\n",
            "28116           7374\n",
            "12806           4864\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3939            7156\n",
            "34955           9484\n",
            "35353           6066\n",
            "16323           2304\n",
            "33819           9314\n",
            "...              ...\n",
            "14508           2966\n",
            "18382           3638\n",
            "23033           2638\n",
            "7544            8602\n",
            "22709           3332\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9654            9394\n",
            "27803           9784\n",
            "9614            2770\n",
            "2333            8260\n",
            "9189            9062\n",
            "...              ...\n",
            "7726            8946\n",
            "21369           6900\n",
            "36582           4112\n",
            "19273           8926\n",
            "14740           9492\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "22955           7492\n",
            "31718           2780\n",
            "17989           2952\n",
            "6886            6236\n",
            "27837           7422\n",
            "...              ...\n",
            "9789            7242\n",
            "26083           5288\n",
            "38500           9052\n",
            "11188           8488\n",
            "25823           2978\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7907            7644\n",
            "26712           6232\n",
            "1628            8116\n",
            "25673           7710\n",
            "9939            9312\n",
            "...              ...\n",
            "13825           8482\n",
            "31346           9298\n",
            "32306           3810\n",
            "24954           6276\n",
            "33789           3864\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "27477           8084\n",
            "32358           6510\n",
            "28000           5486\n",
            "37685           8598\n",
            "22227           6910\n",
            "...              ...\n",
            "34265           2948\n",
            "22853           2084\n",
            "11508           8888\n",
            "12032           7682\n",
            "28423           9052\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33384           2018\n",
            "25141           7564\n",
            "19636           2458\n",
            "4388            8820\n",
            "13695           6714\n",
            "...              ...\n",
            "22121           8906\n",
            "27748           6412\n",
            "12826           2836\n",
            "26319           7642\n",
            "24353           7086\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1317            8294\n",
            "28355           7700\n",
            "14660           7442\n",
            "10443           4674\n",
            "18331           3768\n",
            "...              ...\n",
            "32290           2706\n",
            "17688           5142\n",
            "31075           6516\n",
            "35914           4670\n",
            "22069           2682\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3844            4732\n",
            "17466           8294\n",
            "1020            3212\n",
            "12622           2768\n",
            "6676            8546\n",
            "...              ...\n",
            "18906           7804\n",
            "7217            7974\n",
            "1790            3958\n",
            "14420           9260\n",
            "16111           7724\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6132            8160\n",
            "37699           8792\n",
            "6739            5194\n",
            "16620           6750\n",
            "21496           2948\n",
            "...              ...\n",
            "23329           2286\n",
            "7582            2340\n",
            "22922           4664\n",
            "12989           5472\n",
            "18925           9722\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "18564           5738\n",
            "25878           7480\n",
            "13862           4196\n",
            "8079            6244\n",
            "28768           4578\n",
            "...              ...\n",
            "5940            2584\n",
            "31867           8946\n",
            "33610           2586\n",
            "2760            4046\n",
            "35537           2594\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16231           4044\n",
            "25427           4744\n",
            "8759            7634\n",
            "3503            8826\n",
            "247             7144\n",
            "...              ...\n",
            "28222           7064\n",
            "9091            8164\n",
            "4198            9812\n",
            "11389           7758\n",
            "4479            3206\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26783           7876\n",
            "32448           2204\n",
            "15684           3778\n",
            "17735           9762\n",
            "9089            6064\n",
            "...              ...\n",
            "27409           3544\n",
            "32815           6668\n",
            "34643           6706\n",
            "29818           9512\n",
            "17730           8190\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35696           4612\n",
            "12567           2394\n",
            "20153           4102\n",
            "36785           8594\n",
            "12320           3098\n",
            "...              ...\n",
            "35646           4298\n",
            "26647           6752\n",
            "24225           6936\n",
            "36370           9612\n",
            "24922           3826\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "12967           7526\n",
            "13459           8778\n",
            "33843           7022\n",
            "5268            5750\n",
            "33465           2738\n",
            "...              ...\n",
            "38059           4070\n",
            "18169           9404\n",
            "34000           4678\n",
            "32116           3468\n",
            "5937            6122\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "36822           4058\n",
            "1261            8992\n",
            "35168           9514\n",
            "7405            8126\n",
            "2359            4948\n",
            "...              ...\n",
            "37313           4534\n",
            "37122           9814\n",
            "36997           6420\n",
            "28111           9830\n",
            "37766           9480\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32524           2692\n",
            "38243           6650\n",
            "14522           5596\n",
            "32612           6618\n",
            "15885           4842\n",
            "...              ...\n",
            "33283           9226\n",
            "23337           5980\n",
            "4785            9682\n",
            "29226           2140\n",
            "11104           2640\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26687           5430\n",
            "10163           5384\n",
            "28219           9054\n",
            "21305           9782\n",
            "16413           3298\n",
            "...              ...\n",
            "9075            3982\n",
            "2751            8316\n",
            "25583           5808\n",
            "17995           8850\n",
            "14062           5396\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8913            7694\n",
            "37145           8502\n",
            "11679           2852\n",
            "17072           6774\n",
            "19040           4816\n",
            "...              ...\n",
            "37365           5022\n",
            "38686           3322\n",
            "27104           8146\n",
            "37010           8516\n",
            "28394           6220\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8202            2188\n",
            "12326           8896\n",
            "38174           6804\n",
            "484             8484\n",
            "18593           6190\n",
            "...              ...\n",
            "18283           4372\n",
            "17912           9192\n",
            "7304            6380\n",
            "10102           3628\n",
            "24526           9072\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2734            2096\n",
            "21171           6110\n",
            "16186           2990\n",
            "19730           8794\n",
            "32941           4524\n",
            "...              ...\n",
            "5391            6350\n",
            "28571           2852\n",
            "18674           5818\n",
            "26645           3592\n",
            "14637           2264\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7841            7380\n",
            "29712           8026\n",
            "24869           3894\n",
            "29362           4606\n",
            "32034           4842\n",
            "...              ...\n",
            "14710           3876\n",
            "22570           2030\n",
            "31235           2204\n",
            "4062            8990\n",
            "32302           7338\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "23912           3924\n",
            "31144           5750\n",
            "3894            7852\n",
            "36508           3140\n",
            "3616            2654\n",
            "...              ...\n",
            "29707           8768\n",
            "21355           5900\n",
            "9575            7586\n",
            "17289           3694\n",
            "20993           7910\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25278           6832\n",
            "24520           6172\n",
            "29203           2916\n",
            "27102           3318\n",
            "16178           9836\n",
            "...              ...\n",
            "31445           4800\n",
            "21771           7006\n",
            "34736           9416\n",
            "23343           2612\n",
            "26109           5710\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "17641           6648\n",
            "10212           7634\n",
            "18041           3574\n",
            "33739           3724\n",
            "28212           7282\n",
            "...              ...\n",
            "5971            7014\n",
            "7962            2556\n",
            "19155           5680\n",
            "34937           2364\n",
            "31509           7078\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2173            7914\n",
            "24032           2808\n",
            "30539           5098\n",
            "34165           3982\n",
            "22046           4328\n",
            "...              ...\n",
            "30197           8684\n",
            "24718           9520\n",
            "30252           5578\n",
            "12394           3864\n",
            "37357           3864\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6911            8130\n",
            "13351           3322\n",
            "21163           4676\n",
            "35157           4866\n",
            "8164            4652\n",
            "...              ...\n",
            "8773            3826\n",
            "33260           7972\n",
            "9266            4802\n",
            "30571           3606\n",
            "32671           8660\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "36562           5672\n",
            "16179           9566\n",
            "9201            6804\n",
            "7826            3802\n",
            "9660            4692\n",
            "...              ...\n",
            "9668            2450\n",
            "11454           3404\n",
            "12367           7486\n",
            "22114           4038\n",
            "23994           5584\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31541           5786\n",
            "4618            2582\n",
            "31808           6060\n",
            "134             2558\n",
            "16245           3006\n",
            "...              ...\n",
            "10658           4612\n",
            "988             2546\n",
            "15179           2230\n",
            "25119           3474\n",
            "12915           2086\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35590           3242\n",
            "36182           3554\n",
            "29129           8990\n",
            "28243           6208\n",
            "6717            4422\n",
            "...              ...\n",
            "22661           8880\n",
            "32577           5676\n",
            "23807           9608\n",
            "16000           7242\n",
            "7905            5972\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20060           4880\n",
            "33087           2054\n",
            "13781           2702\n",
            "469             8190\n",
            "36292           2490\n",
            "...              ...\n",
            "17542           2632\n",
            "27070           4246\n",
            "32423           6038\n",
            "17722           7288\n",
            "16316           6308\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "28034           5346\n",
            "31933           6702\n",
            "32391           7736\n",
            "16272           7830\n",
            "28649           8060\n",
            "...              ...\n",
            "30675           9366\n",
            "33761           5398\n",
            "7798            3536\n",
            "13910           3864\n",
            "26403           4392\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7882            9196\n",
            "20632           9078\n",
            "15650           3114\n",
            "23022           7822\n",
            "15498           8088\n",
            "...              ...\n",
            "32796           9702\n",
            "32872           4730\n",
            "18673           6550\n",
            "9574            9282\n",
            "17859           8322\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35102           5300\n",
            "31483           9252\n",
            "16296           7654\n",
            "34615           5292\n",
            "154             4576\n",
            "...              ...\n",
            "5775            6154\n",
            "27896           2266\n",
            "21948           6838\n",
            "418             3304\n",
            "24551           7036\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15019           6666\n",
            "20357           7960\n",
            "37188           7392\n",
            "27655           8306\n",
            "12626           3956\n",
            "...              ...\n",
            "19210           5804\n",
            "10673           4038\n",
            "25014           7660\n",
            "7936            2340\n",
            "21549           2196\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1713            7900\n",
            "19948           7140\n",
            "12187           8420\n",
            "21090           4522\n",
            "30469           5520\n",
            "...              ...\n",
            "8643            7314\n",
            "1606            2582\n",
            "7923            9166\n",
            "16951           2490\n",
            "30621           3158\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1405            2220\n",
            "12193           3848\n",
            "6287            5994\n",
            "35766           4904\n",
            "20495           3688\n",
            "...              ...\n",
            "38057           2768\n",
            "34833           9874\n",
            "20416           4506\n",
            "24011           3584\n",
            "37879           7350\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2598            7520\n",
            "31048           2592\n",
            "27418           4094\n",
            "22514           5994\n",
            "13229           2272\n",
            "...              ...\n",
            "10034           7812\n",
            "27401           4636\n",
            "23010           7086\n",
            "14770           6874\n",
            "10690           2338\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "12086           8260\n",
            "35578           6894\n",
            "22710           7234\n",
            "11869           5062\n",
            "36849           3826\n",
            "...              ...\n",
            "33478           3998\n",
            "17795           2622\n",
            "15688           2620\n",
            "13770           6476\n",
            "7691            9178\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "11073           3712\n",
            "26662           7632\n",
            "10935           7830\n",
            "31533           2892\n",
            "7053            5362\n",
            "...              ...\n",
            "30964           8580\n",
            "8778            3516\n",
            "38053           7950\n",
            "16954           9054\n",
            "34530           7054\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "37383           7790\n",
            "36949           9298\n",
            "2727            7998\n",
            "6913            6616\n",
            "30887           4778\n",
            "...              ...\n",
            "4954            2672\n",
            "23703           9176\n",
            "26059           2634\n",
            "27232           9088\n",
            "13193           7240\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35339           3242\n",
            "20442           3690\n",
            "34184           7704\n",
            "24531           7818\n",
            "17281           5286\n",
            "...              ...\n",
            "23699           7914\n",
            "102             8582\n",
            "13885           5930\n",
            "1722            2184\n",
            "18703           4470\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "23734           8554\n",
            "35933           4380\n",
            "35078           8216\n",
            "23106           9620\n",
            "9700            6596\n",
            "...              ...\n",
            "31935           6238\n",
            "21415           7146\n",
            "2562            8030\n",
            "2838            5080\n",
            "735             4980\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25293           8478\n",
            "9713            7104\n",
            "11604           6642\n",
            "22041           2578\n",
            "9608            2128\n",
            "...              ...\n",
            "5587            5318\n",
            "29346           5008\n",
            "23811           4342\n",
            "5019            8076\n",
            "34440           9812\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "21772           7372\n",
            "4334            6988\n",
            "15396           5692\n",
            "15904           3732\n",
            "9453            8678\n",
            "...              ...\n",
            "10133           6206\n",
            "14992           4262\n",
            "9500            8402\n",
            "15716           6630\n",
            "35956           4132\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "36705           2720\n",
            "22230           4290\n",
            "22214           6924\n",
            "29867           7326\n",
            "8056            4218\n",
            "...              ...\n",
            "28894           4780\n",
            "5802            7740\n",
            "32594           5318\n",
            "17851           2482\n",
            "38138           3126\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "23966           7284\n",
            "16949           2312\n",
            "175             9118\n",
            "29199           7630\n",
            "31767           9418\n",
            "...              ...\n",
            "1929            3270\n",
            "32074           7740\n",
            "29713           4022\n",
            "20845           6144\n",
            "38133           9470\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32168           6894\n",
            "11780           8928\n",
            "31333           3150\n",
            "31743           9978\n",
            "11320           2772\n",
            "...              ...\n",
            "17854           4356\n",
            "12271           4874\n",
            "10197           8300\n",
            "15807           7852\n",
            "31892           3996\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "18742           9980\n",
            "14187           2218\n",
            "21462           6396\n",
            "38428           8774\n",
            "37858           5894\n",
            "...              ...\n",
            "3054            9916\n",
            "11686           8594\n",
            "6917            7240\n",
            "30332           8260\n",
            "31937           8292\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6288            7406\n",
            "20076           8504\n",
            "14007           9394\n",
            "5274            3844\n",
            "14529           4894\n",
            "...              ...\n",
            "4129            2472\n",
            "11021           5622\n",
            "26766           9924\n",
            "24517           3666\n",
            "766             2276\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29406           2222\n",
            "17187           8378\n",
            "36771           2326\n",
            "18784           4330\n",
            "25539           8430\n",
            "...              ...\n",
            "3960            6968\n",
            "24935           8328\n",
            "11621           8042\n",
            "13141           2104\n",
            "12189           2560\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10392           8324\n",
            "12056           6318\n",
            "30780           6744\n",
            "33077           5662\n",
            "36656           7566\n",
            "...              ...\n",
            "1768            7162\n",
            "27389           9330\n",
            "26364           9850\n",
            "36482           9204\n",
            "1452            3622\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34607           2726\n",
            "33730           6278\n",
            "9942            5890\n",
            "30244           3394\n",
            "9092            3346\n",
            "...              ...\n",
            "14391           9414\n",
            "2065            5920\n",
            "13678           6198\n",
            "21286           4970\n",
            "5061            2016\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20006           4190\n",
            "16893           9376\n",
            "32893           5436\n",
            "25458           5718\n",
            "7639            2450\n",
            "...              ...\n",
            "13540           6102\n",
            "22624           8514\n",
            "31248           2010\n",
            "25486           4766\n",
            "25740           4548\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15299           3732\n",
            "36556           9560\n",
            "26000           3602\n",
            "19745           3960\n",
            "3261            7496\n",
            "...              ...\n",
            "1419            7202\n",
            "10597           2642\n",
            "16151           4600\n",
            "7260            8586\n",
            "24617           7504\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10769           7098\n",
            "16321           7590\n",
            "925             6350\n",
            "1362            4864\n",
            "35716           9248\n",
            "...              ...\n",
            "11200           3038\n",
            "15218           2604\n",
            "3717            2102\n",
            "19196           3124\n",
            "17528           6404\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1158            5874\n",
            "35706           8602\n",
            "33048           9132\n",
            "28924           3932\n",
            "21157           2554\n",
            "...              ...\n",
            "6103            5722\n",
            "32004           6176\n",
            "38047           8854\n",
            "14332           7830\n",
            "16215           8324\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "37693           3104\n",
            "27888           9358\n",
            "4874            9884\n",
            "8828            9380\n",
            "8810            3970\n",
            "...              ...\n",
            "31914           4636\n",
            "9934            5288\n",
            "28189           3824\n",
            "29338           6772\n",
            "7482            2466\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "11752           2174\n",
            "38741           2336\n",
            "15776           5648\n",
            "23742           8024\n",
            "14066           6870\n",
            "...              ...\n",
            "12747           8852\n",
            "37499           9276\n",
            "7944            2746\n",
            "38276           9924\n",
            "32688           5432\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25361           7186\n",
            "19943           5308\n",
            "20036           3992\n",
            "30506           9902\n",
            "17906           3224\n",
            "...              ...\n",
            "22519           3820\n",
            "27771           7570\n",
            "2315            6998\n",
            "20741           5046\n",
            "6170            8824\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7877            5120\n",
            "33745           3438\n",
            "35397           8624\n",
            "31              7418\n",
            "4013            3826\n",
            "...              ...\n",
            "2248            4108\n",
            "32317           4554\n",
            "5562            2316\n",
            "32775           8170\n",
            "15852           7156\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16415           5636\n",
            "36585           5202\n",
            "11876           2272\n",
            "36477           9426\n",
            "32718           3216\n",
            "...              ...\n",
            "37943           7944\n",
            "35543           6232\n",
            "27577           2656\n",
            "9356            9914\n",
            "29324           5934\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15098           2198\n",
            "599             4788\n",
            "23753           4542\n",
            "29440           4350\n",
            "552             5680\n",
            "...              ...\n",
            "9131            5440\n",
            "13162           3596\n",
            "21796           7476\n",
            "13110           9886\n",
            "31283           7168\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10040           9262\n",
            "8882            2936\n",
            "27698           7252\n",
            "13818           2040\n",
            "18545           2710\n",
            "...              ...\n",
            "37458           2464\n",
            "11853           5592\n",
            "18637           6248\n",
            "19311           6678\n",
            "17283           9726\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "37912           9278\n",
            "21169           3518\n",
            "7376            5204\n",
            "8280            4990\n",
            "3801            4418\n",
            "...              ...\n",
            "19180           2162\n",
            "20846           6708\n",
            "72              7720\n",
            "9594            8310\n",
            "27995           7078\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31768           7340\n",
            "22128           4372\n",
            "20899           6920\n",
            "10340           6328\n",
            "11369           5606\n",
            "...              ...\n",
            "10144           9600\n",
            "35787           9148\n",
            "13235           9010\n",
            "33010           5182\n",
            "14967           8752\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "22497           3040\n",
            "3672            7314\n",
            "24017           7376\n",
            "4882            4842\n",
            "37663           5458\n",
            "...              ...\n",
            "16588           8534\n",
            "31534           7802\n",
            "23784           8924\n",
            "34388           5932\n",
            "3531            7562\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "38615           2796\n",
            "17465           6588\n",
            "17913           2760\n",
            "26653           2780\n",
            "9462            2948\n",
            "...              ...\n",
            "32863           3324\n",
            "26794           9300\n",
            "7782            4908\n",
            "8623            5632\n",
            "1169            9062\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "693             5722\n",
            "16791           3916\n",
            "14309           8832\n",
            "22296           3370\n",
            "27647           2336\n",
            "...              ...\n",
            "14811           6116\n",
            "4074            7966\n",
            "24528           4010\n",
            "33525           9516\n",
            "35727           5960\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32545           8340\n",
            "4356            9178\n",
            "20401           4580\n",
            "22001           8912\n",
            "35660           4408\n",
            "...              ...\n",
            "36552           9510\n",
            "14982           2932\n",
            "32289           4622\n",
            "3683            3654\n",
            "11113           3040\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20088           7156\n",
            "4411            7200\n",
            "21127           7876\n",
            "36086           2490\n",
            "999             5244\n",
            "...              ...\n",
            "17123           7672\n",
            "11800           9096\n",
            "22103           4958\n",
            "36071           4194\n",
            "18962           3070\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30786           2264\n",
            "33090           9374\n",
            "16249           5864\n",
            "17507           3936\n",
            "13768           8096\n",
            "...              ...\n",
            "19865           2280\n",
            "17741           6154\n",
            "24550           5332\n",
            "7014            7402\n",
            "21056           2772\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "4743            4950\n",
            "7101            9952\n",
            "13509           5388\n",
            "3354            3568\n",
            "35630           8660\n",
            "...              ...\n",
            "19897           4036\n",
            "732             9366\n",
            "24739           8668\n",
            "19884           2806\n",
            "32965           5106\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7143            8324\n",
            "30135           3620\n",
            "19823           8890\n",
            "12028           8734\n",
            "22628           6478\n",
            "...              ...\n",
            "28003           9276\n",
            "35390           6162\n",
            "6349            3780\n",
            "34817           7180\n",
            "18815           9200\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16130           9462\n",
            "23931           4908\n",
            "12992           2722\n",
            "9970            2332\n",
            "23493           8340\n",
            "...              ...\n",
            "15354           9292\n",
            "35815           7402\n",
            "35099           9170\n",
            "37810           6974\n",
            "9483            7204\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7048            3446\n",
            "9754            8918\n",
            "14937           6366\n",
            "5459            2666\n",
            "20236           6982\n",
            "...              ...\n",
            "31974           9268\n",
            "2668            6034\n",
            "22448           7556\n",
            "7551            5972\n",
            "34098           2758\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26313           6798\n",
            "25839           7590\n",
            "37403           2988\n",
            "12417           3308\n",
            "3037            6180\n",
            "...              ...\n",
            "24403           3508\n",
            "25262           7680\n",
            "32632           4752\n",
            "32296           2490\n",
            "29710           8232\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31915           3552\n",
            "22933           2302\n",
            "18905           7148\n",
            "6376            3578\n",
            "14999           7490\n",
            "...              ...\n",
            "34443           3644\n",
            "9651            9702\n",
            "6242            6334\n",
            "27646           2090\n",
            "2045            2708\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26279           4664\n",
            "5833            9906\n",
            "4770            7758\n",
            "4174            6860\n",
            "9656            2514\n",
            "...              ...\n",
            "32035           6606\n",
            "14411           6474\n",
            "32244           3018\n",
            "27863           3720\n",
            "36690           3268\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32112           7458\n",
            "31611           2082\n",
            "14569           9634\n",
            "5943            5326\n",
            "6532            2070\n",
            "...              ...\n",
            "26561           5670\n",
            "19529           8632\n",
            "6141            2768\n",
            "35420           6854\n",
            "27149           4522\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "205             2720\n",
            "36023           9784\n",
            "32712           9760\n",
            "26767           3520\n",
            "18014           3828\n",
            "...              ...\n",
            "4828            4858\n",
            "4260            9330\n",
            "5027            6084\n",
            "13084           8626\n",
            "26185           3018\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20193           3364\n",
            "14620           4880\n",
            "15993           5156\n",
            "26447           4780\n",
            "6451            8878\n",
            "...              ...\n",
            "2630            7952\n",
            "18991           6330\n",
            "14498           9246\n",
            "5948            5742\n",
            "29342           7240\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34423           5506\n",
            "35355           2022\n",
            "10349           5112\n",
            "8951            7486\n",
            "12811           2492\n",
            "...              ...\n",
            "19974           4938\n",
            "5176            4146\n",
            "17401           7528\n",
            "13977           4066\n",
            "6590            8680\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33641           4898\n",
            "27411           6020\n",
            "33588           8292\n",
            "10140           7836\n",
            "28978           2752\n",
            "...              ...\n",
            "22876           8228\n",
            "22701           6990\n",
            "33471           8262\n",
            "36014           2566\n",
            "21600           8106\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16754           9202\n",
            "9495            2578\n",
            "7593            8024\n",
            "18866           7606\n",
            "9893            2976\n",
            "...              ...\n",
            "33702           4390\n",
            "2478            3384\n",
            "3800            2640\n",
            "36106           3026\n",
            "6900            4690\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "38575           8740\n",
            "34614           3714\n",
            "26643           3876\n",
            "18698           5308\n",
            "38459           4690\n",
            "...              ...\n",
            "27154           8788\n",
            "17534           5470\n",
            "14227           7790\n",
            "31381           9920\n",
            "14952           3996\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14666           5452\n",
            "7389            5418\n",
            "35809           9178\n",
            "30370           9830\n",
            "34556           6328\n",
            "...              ...\n",
            "36153           2754\n",
            "38369           3826\n",
            "35385           8258\n",
            "7796            5920\n",
            "20681           6596\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2288            6694\n",
            "24205           4218\n",
            "29476           7876\n",
            "6301            5068\n",
            "13496           8386\n",
            "...              ...\n",
            "2749            9728\n",
            "22783           9420\n",
            "18944           5832\n",
            "6105            5986\n",
            "29612           6230\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2802            9210\n",
            "25243           2830\n",
            "34161           7546\n",
            "18438           9746\n",
            "14878           8260\n",
            "...              ...\n",
            "25994           7984\n",
            "25799           6006\n",
            "6723            9374\n",
            "10145           3518\n",
            "9917            4890\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "12343           7696\n",
            "25756           3756\n",
            "28148           3236\n",
            "14323           6516\n",
            "30083           6594\n",
            "...              ...\n",
            "6966            6018\n",
            "21314           9498\n",
            "20241           3766\n",
            "24236           9482\n",
            "31895           2522\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "38201           4992\n",
            "13385           7032\n",
            "16632           5976\n",
            "8962            6974\n",
            "38246           7868\n",
            "...              ...\n",
            "32839           6554\n",
            "8389            3448\n",
            "34239           2046\n",
            "28701           6208\n",
            "20099           2254\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26554           8860\n",
            "7617            6328\n",
            "34921           8642\n",
            "13876           4270\n",
            "16560           9048\n",
            "...              ...\n",
            "34227           3598\n",
            "30670           4414\n",
            "1769            6916\n",
            "3708            8060\n",
            "21260           6190\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "19172           2606\n",
            "16137           4806\n",
            "7626            7522\n",
            "1499            7324\n",
            "19946           4680\n",
            "...              ...\n",
            "37718           9430\n",
            "24350           7722\n",
            "36195           2780\n",
            "29951           3502\n",
            "1852            4514\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "22772           9470\n",
            "34034           2258\n",
            "3742            5032\n",
            "32802           7396\n",
            "10663           5594\n",
            "...              ...\n",
            "28457           5080\n",
            "26795           9916\n",
            "10254           4392\n",
            "7765            3486\n",
            "35730           4878\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20304           6976\n",
            "33664           3412\n",
            "28428           3736\n",
            "28558           4254\n",
            "7077            6814\n",
            "...              ...\n",
            "14319           2022\n",
            "10384           2202\n",
            "184             8626\n",
            "4352            7726\n",
            "14093           3280\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "24483           5178\n",
            "33322           3664\n",
            "35112           3416\n",
            "37201           5476\n",
            "33748           3996\n",
            "...              ...\n",
            "16490           9636\n",
            "38215           3412\n",
            "7015            7910\n",
            "4111            6788\n",
            "26255           5332\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25859           4330\n",
            "32643           6860\n",
            "14225           3482\n",
            "4799            9382\n",
            "5235            2838\n",
            "...              ...\n",
            "21184           9342\n",
            "26281           8432\n",
            "2543            9340\n",
            "21007           5236\n",
            "28667           9728\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26320           3182\n",
            "10467           9512\n",
            "26773           3148\n",
            "36652           5956\n",
            "30170           2012\n",
            "...              ...\n",
            "2481            3428\n",
            "13788           7366\n",
            "17486           3966\n",
            "26018           2908\n",
            "6903            7892\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "38396           9612\n",
            "25162           8782\n",
            "7532            2012\n",
            "30474           9264\n",
            "9047            7692\n",
            "...              ...\n",
            "4885            9620\n",
            "10633           7944\n",
            "36280           5004\n",
            "31700           5674\n",
            "7873            3190\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33903           7338\n",
            "4177            8074\n",
            "30656           3240\n",
            "22802           2422\n",
            "11548           5678\n",
            "...              ...\n",
            "31680           8582\n",
            "36308           9356\n",
            "18447           2930\n",
            "25533           7794\n",
            "14046           9004\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7316            2052\n",
            "1965            9548\n",
            "29678           4434\n",
            "987             3858\n",
            "18409           7382\n",
            "...              ...\n",
            "27450           4830\n",
            "18732           4760\n",
            "6074            3262\n",
            "31519           9274\n",
            "38127           4714\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10842           9540\n",
            "28426           3230\n",
            "37085           6632\n",
            "16022           5488\n",
            "6940            6588\n",
            "...              ...\n",
            "15410           5526\n",
            "30304           4032\n",
            "32342           8918\n",
            "9869            2908\n",
            "3685            5048\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8359            8860\n",
            "34351           9692\n",
            "37567           2350\n",
            "11202           9250\n",
            "13421           9606\n",
            "...              ...\n",
            "7575            2184\n",
            "34338           5370\n",
            "5007            9754\n",
            "11981           9720\n",
            "37772           6836\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14002           8396\n",
            "7789            3130\n",
            "20439           7436\n",
            "14642           4614\n",
            "782             3678\n",
            "...              ...\n",
            "3692            6646\n",
            "6866            3536\n",
            "23999           2580\n",
            "38751           8888\n",
            "24056           7644\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7814            9556\n",
            "16581           4430\n",
            "32456           8194\n",
            "17464           4610\n",
            "6734            7280\n",
            "...              ...\n",
            "19361           6972\n",
            "36928           3298\n",
            "38330           4510\n",
            "11972           2588\n",
            "22144           3860\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29316           2960\n",
            "8522            9402\n",
            "3992            3908\n",
            "13523           2956\n",
            "4248            9338\n",
            "...              ...\n",
            "4076            3406\n",
            "8380            4858\n",
            "3577            6282\n",
            "24947           6118\n",
            "6209            2018\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "22240           4984\n",
            "35086           4784\n",
            "38601           4728\n",
            "29976           2712\n",
            "22066           3460\n",
            "...              ...\n",
            "25046           9902\n",
            "36570           7618\n",
            "6113            9696\n",
            "31404           4278\n",
            "19219           4722\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26747           2358\n",
            "18548           4542\n",
            "35701           5594\n",
            "9438            9292\n",
            "12770           4696\n",
            "...              ...\n",
            "5008            2554\n",
            "448             2932\n",
            "29604           3932\n",
            "11209           2004\n",
            "24840           8430\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "23835           7970\n",
            "38605           7352\n",
            "9364            3986\n",
            "14504           9300\n",
            "7535            5064\n",
            "...              ...\n",
            "38669           7682\n",
            "18065           9876\n",
            "17836           9552\n",
            "6768            5700\n",
            "8967            8030\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15969           6464\n",
            "25942           2418\n",
            "32329           7038\n",
            "11728          10000\n",
            "27642           3636\n",
            "...              ...\n",
            "26827           9248\n",
            "35065           5710\n",
            "10317           2022\n",
            "27288           2732\n",
            "5538            2658\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "17215           8330\n",
            "14707           8522\n",
            "38572           8232\n",
            "22564           8460\n",
            "38608           5376\n",
            "...              ...\n",
            "32437           2986\n",
            "11774           2966\n",
            "11062           3760\n",
            "17900           5126\n",
            "1781            7208\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29971           2624\n",
            "30073           4250\n",
            "5054            2452\n",
            "13566           8518\n",
            "27427           2654\n",
            "...              ...\n",
            "14521           4414\n",
            "3959            6642\n",
            "27035           4762\n",
            "18288           4284\n",
            "5725            6070\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1706            9454\n",
            "38633           6166\n",
            "434             5298\n",
            "24669           4736\n",
            "21240           9462\n",
            "...              ...\n",
            "3811            5136\n",
            "13123           2686\n",
            "2291            7142\n",
            "34339           2790\n",
            "23148           8354\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30322           4430\n",
            "3603            3748\n",
            "13286           4504\n",
            "34160           9672\n",
            "16040           5864\n",
            "...              ...\n",
            "29102           9712\n",
            "16449           5992\n",
            "29343           9904\n",
            "12557           8826\n",
            "5465            7116\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7511            4372\n",
            "7664            2350\n",
            "13119           8860\n",
            "24923           6180\n",
            "16952           3030\n",
            "...              ...\n",
            "23856           4894\n",
            "4723            3308\n",
            "25465           8218\n",
            "35852           7896\n",
            "17435           7838\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "21146           6494\n",
            "30428           3112\n",
            "38139           8292\n",
            "29235           7668\n",
            "13138           7570\n",
            "...              ...\n",
            "1459            2712\n",
            "26139           9720\n",
            "21742           8306\n",
            "22758           5882\n",
            "28628           9326\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2186            4502\n",
            "27979           5814\n",
            "23720           4402\n",
            "10995           2898\n",
            "452             3746\n",
            "...              ...\n",
            "9335            3808\n",
            "29376           4308\n",
            "33669           7722\n",
            "14239           6610\n",
            "25485           8852\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32826           4516\n",
            "26276           3270\n",
            "5291            9538\n",
            "28416           9892\n",
            "26051           4096\n",
            "...              ...\n",
            "34764           3788\n",
            "27969           3392\n",
            "5353            5086\n",
            "12545           7828\n",
            "38600           9798\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "38475           5092\n",
            "29173           3780\n",
            "14598           6402\n",
            "794             7712\n",
            "11937           7516\n",
            "...              ...\n",
            "25834           8710\n",
            "30079           6208\n",
            "21770           3390\n",
            "33586           2138\n",
            "12752           8132\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34115           3142\n",
            "15946           6040\n",
            "28446           4460\n",
            "7723            3268\n",
            "11346           6900\n",
            "...              ...\n",
            "21682           5866\n",
            "23645           2580\n",
            "34633           2664\n",
            "25704           9618\n",
            "36764           2102\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1841            9476\n",
            "16417           6664\n",
            "36692           8084\n",
            "33797           7328\n",
            "1076            4212\n",
            "...              ...\n",
            "23218           8084\n",
            "16167           3026\n",
            "28286           4158\n",
            "14037           2342\n",
            "14974           3508\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35897           5660\n",
            "6177            5562\n",
            "6247            9698\n",
            "27774           8330\n",
            "28051           8938\n",
            "...              ...\n",
            "24220           9980\n",
            "18414           6966\n",
            "29742           6260\n",
            "24821           8926\n",
            "19325           8492\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "4052            8060\n",
            "16732           6240\n",
            "21087           8330\n",
            "9294            9844\n",
            "1251            5376\n",
            "...              ...\n",
            "21309           2074\n",
            "4553            2392\n",
            "30579           3396\n",
            "26780           8148\n",
            "10085           4848\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "24771           2260\n",
            "31025           7514\n",
            "20011           2592\n",
            "28818           7558\n",
            "37374           2324\n",
            "...              ...\n",
            "10964           9560\n",
            "23885           5654\n",
            "30033           8310\n",
            "17804           5748\n",
            "1218            3212\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14349           6444\n",
            "33168           9368\n",
            "18672           6428\n",
            "10135           3774\n",
            "1408            7744\n",
            "...              ...\n",
            "36215           9200\n",
            "10402           3698\n",
            "30288           2062\n",
            "22500           3544\n",
            "36735           2366\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3192            3526\n",
            "5807            6578\n",
            "25925           2326\n",
            "1454            4308\n",
            "2653            8004\n",
            "...              ...\n",
            "28676           9862\n",
            "18590           9020\n",
            "37331           8310\n",
            "32153           7808\n",
            "590             3666\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30395           7332\n",
            "23892           2664\n",
            "4228            6536\n",
            "11881           4112\n",
            "34809           2160\n",
            "...              ...\n",
            "24980           7058\n",
            "30518           8016\n",
            "13606           5140\n",
            "19050           4044\n",
            "13697           3304\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "22373           9758\n",
            "31026           7378\n",
            "19888           2082\n",
            "3524            7924\n",
            "9981            2474\n",
            "...              ...\n",
            "19430           2228\n",
            "12731           8794\n",
            "19153           5710\n",
            "24964           8362\n",
            "1953            3402\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "13587           7258\n",
            "32147           4566\n",
            "7835            4930\n",
            "4820            5416\n",
            "21050           7742\n",
            "...              ...\n",
            "18996           2184\n",
            "2957            5880\n",
            "17473           8360\n",
            "19310           5352\n",
            "30430           5296\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25659           2976\n",
            "6907            3474\n",
            "9751            8386\n",
            "30393           7036\n",
            "12312           9054\n",
            "...              ...\n",
            "13412           2388\n",
            "22775           8230\n",
            "2631            6102\n",
            "11226           6534\n",
            "30507           5816\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7715            9160\n",
            "31108           9326\n",
            "27459           4812\n",
            "303             4894\n",
            "4162            5776\n",
            "...              ...\n",
            "15739           7438\n",
            "21343           6704\n",
            "23434           8642\n",
            "4883            4820\n",
            "24709           4542\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "36663           8394\n",
            "6762            5544\n",
            "6615            3864\n",
            "22441           8672\n",
            "4802            4762\n",
            "...              ...\n",
            "6658            2352\n",
            "35050           4760\n",
            "6028            5938\n",
            "4231            2308\n",
            "22959           3348\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "306             3608\n",
            "17294           8184\n",
            "9945            6434\n",
            "26847           5474\n",
            "34155           6120\n",
            "...              ...\n",
            "1839            9264\n",
            "12186           9126\n",
            "37503           4798\n",
            "21103           2672\n",
            "19394           6716\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34822           9106\n",
            "23142           3414\n",
            "3339            9756\n",
            "5212            3646\n",
            "27395           9270\n",
            "...              ...\n",
            "19188           8776\n",
            "8272            8532\n",
            "29627           8094\n",
            "29497           3470\n",
            "25037           5660\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33565           9642\n",
            "25329           4130\n",
            "3766            4376\n",
            "27953           5178\n",
            "34202           6738\n",
            "...              ...\n",
            "7540            9962\n",
            "3679            7274\n",
            "24235           9504\n",
            "22962           7482\n",
            "17997           2296\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26673           4686\n",
            "19323           5202\n",
            "27650           5736\n",
            "17993           9228\n",
            "27452           3676\n",
            "...              ...\n",
            "23638           9518\n",
            "37680           4682\n",
            "8176            3586\n",
            "938             8880\n",
            "5624            8528\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31458           5466\n",
            "33008           7504\n",
            "14628           6792\n",
            "20282           9400\n",
            "10334           8560\n",
            "...              ...\n",
            "17146           9832\n",
            "21446           9920\n",
            "18013           4590\n",
            "21224           9376\n",
            "14253           6226\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33000           4336\n",
            "26686           8542\n",
            "23460           9640\n",
            "1142            2462\n",
            "6189            7952\n",
            "...              ...\n",
            "19819           8606\n",
            "30556           9828\n",
            "29574           9220\n",
            "26539           9308\n",
            "19154           4148\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "27911           4370\n",
            "19548           7114\n",
            "6201            4468\n",
            "27618           4650\n",
            "31758           2146\n",
            "...              ...\n",
            "20221           9656\n",
            "22829           9564\n",
            "18966           9270\n",
            "35123           7162\n",
            "32481           5620\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32057           5770\n",
            "8916            5402\n",
            "25612           9302\n",
            "20999           7036\n",
            "26666           9940\n",
            "...              ...\n",
            "21809           6580\n",
            "949             9636\n",
            "910             5400\n",
            "5002            8098\n",
            "5674            6092\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31739           2132\n",
            "23820           9400\n",
            "10259           7552\n",
            "26960           2164\n",
            "21988           2652\n",
            "...              ...\n",
            "4155            6976\n",
            "12672           7108\n",
            "31310           4586\n",
            "23028           2604\n",
            "31877           3848\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10649           3444\n",
            "5703            7586\n",
            "24509           6912\n",
            "22841           2608\n",
            "36647           5978\n",
            "...              ...\n",
            "4329            8226\n",
            "27108           8502\n",
            "27135           6828\n",
            "7147            9532\n",
            "10537           8542\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "11277           3068\n",
            "10510           2214\n",
            "7444            3536\n",
            "32937           2258\n",
            "8554            4102\n",
            "...              ...\n",
            "24832           7548\n",
            "12442           2518\n",
            "10716           3770\n",
            "28081           8924\n",
            "3495            7314\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15312           2342\n",
            "34274           9888\n",
            "33744           8028\n",
            "32458           6780\n",
            "27887           3798\n",
            "...              ...\n",
            "22487           5958\n",
            "32066           8594\n",
            "29384           9010\n",
            "10247           6450\n",
            "5044            3988\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14346           2496\n",
            "27595           9710\n",
            "3838            8480\n",
            "28984           3380\n",
            "6351            3544\n",
            "...              ...\n",
            "27342           8030\n",
            "24248           2812\n",
            "2845            7322\n",
            "19422           2660\n",
            "22080           3846\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33771           9470\n",
            "8774            6180\n",
            "11088           3442\n",
            "4443            9110\n",
            "21416           7530\n",
            "...              ...\n",
            "2729            6178\n",
            "33074           4872\n",
            "6850            8710\n",
            "414             3794\n",
            "7887            5558\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30686           9336\n",
            "22770           4654\n",
            "36528           3690\n",
            "17045           7108\n",
            "12075           3070\n",
            "...              ...\n",
            "25060           7078\n",
            "22820           6470\n",
            "37022           3802\n",
            "21378           4114\n",
            "5828            9434\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10638           3058\n",
            "35446           3044\n",
            "19340           9652\n",
            "36122           5800\n",
            "22295           9680\n",
            "...              ...\n",
            "27917           8974\n",
            "30412           8488\n",
            "27949           9096\n",
            "32408           3306\n",
            "32956           2592\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32734           9008\n",
            "10172           5932\n",
            "36204           3518\n",
            "32045           9588\n",
            "38611           8620\n",
            "...              ...\n",
            "22115           8038\n",
            "23416           3644\n",
            "27353           5338\n",
            "27258           9816\n",
            "25004           9834\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "17821           5046\n",
            "27615           9206\n",
            "22462           3192\n",
            "7960            4688\n",
            "31117           5156\n",
            "...              ...\n",
            "36246           9144\n",
            "37516           5106\n",
            "35948           8220\n",
            "7507            2480\n",
            "16528           4184\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10767           7432\n",
            "6192            8118\n",
            "9990            9720\n",
            "3514            8394\n",
            "34143           7726\n",
            "...              ...\n",
            "32492           7934\n",
            "29025           7238\n",
            "23639           7086\n",
            "13292           8354\n",
            "12563           2396\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "815             3336\n",
            "28826           8958\n",
            "7607            2964\n",
            "20758           3886\n",
            "25848           2776\n",
            "...              ...\n",
            "21449           5492\n",
            "16877           7496\n",
            "12459           5240\n",
            "20637           7606\n",
            "23212           8334\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1463            2404\n",
            "496             5704\n",
            "34623           4156\n",
            "11696           6556\n",
            "4008            8076\n",
            "...              ...\n",
            "4925            5692\n",
            "4975            9556\n",
            "23406           8642\n",
            "23697           7344\n",
            "10858           2948\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9422            4178\n",
            "10639           7634\n",
            "2978            6940\n",
            "3573            2266\n",
            "7372            4464\n",
            "...              ...\n",
            "3883            8956\n",
            "4412            5696\n",
            "2314            5300\n",
            "12834           2708\n",
            "10315           6066\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10077           6796\n",
            "18402           8932\n",
            "7412            9158\n",
            "29003           2624\n",
            "36740           4420\n",
            "...              ...\n",
            "24878           9482\n",
            "26380           3044\n",
            "35624           7564\n",
            "15926           2966\n",
            "22421           5270\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6431            7600\n",
            "8625            8384\n",
            "25104           5826\n",
            "19142           9170\n",
            "16571           9868\n",
            "...              ...\n",
            "19464           4074\n",
            "12510           8512\n",
            "11986           2220\n",
            "17885           3798\n",
            "4995            3250\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9781            5648\n",
            "21166           6810\n",
            "33500           9020\n",
            "30296           8418\n",
            "11750           3928\n",
            "...              ...\n",
            "37218           7226\n",
            "18055           3552\n",
            "1868            8054\n",
            "32064           2430\n",
            "14677           7258\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20602           4928\n",
            "11441           5872\n",
            "33271           8824\n",
            "26079           8484\n",
            "14593           3572\n",
            "...              ...\n",
            "17845           5894\n",
            "14674           3894\n",
            "9024            4056\n",
            "15185           6780\n",
            "32772           4482\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "7953            6860\n",
            "32322           5136\n",
            "25018           5180\n",
            "36915           6454\n",
            "8118            4000\n",
            "...              ...\n",
            "33970           9526\n",
            "35187           5120\n",
            "25857           7220\n",
            "35343           8628\n",
            "6616            7520\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14104           9830\n",
            "22024           4566\n",
            "11236           5552\n",
            "25385           9606\n",
            "25671           4910\n",
            "...              ...\n",
            "23671           5378\n",
            "33444           2286\n",
            "29461           4254\n",
            "19425           7546\n",
            "31604           9326\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26508           8624\n",
            "25567           4420\n",
            "598             5872\n",
            "20946           7418\n",
            "32433           6030\n",
            "...              ...\n",
            "27115           6424\n",
            "18617           5352\n",
            "16884           4980\n",
            "10047           9324\n",
            "29879           9380\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34193           7664\n",
            "17430           2982\n",
            "22711           6418\n",
            "9193            8536\n",
            "35273           5508\n",
            "...              ...\n",
            "4102            6982\n",
            "17682           5674\n",
            "33639           3798\n",
            "20125           7182\n",
            "22722           4470\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15831           7892\n",
            "2022            6480\n",
            "35795           8078\n",
            "18232           9556\n",
            "23756           2964\n",
            "...              ...\n",
            "5323            6798\n",
            "22229           4470\n",
            "17096           3036\n",
            "11351           3718\n",
            "4183            8278\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30347           9718\n",
            "37361           3214\n",
            "37598           6604\n",
            "33505           3740\n",
            "636             7624\n",
            "...              ...\n",
            "10437           2258\n",
            "36842           9862\n",
            "29056           2498\n",
            "28741           3636\n",
            "18890           6280\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "24714           9662\n",
            "7432            6848\n",
            "28812           7596\n",
            "18964           4498\n",
            "24815           3002\n",
            "...              ...\n",
            "9476            9444\n",
            "4893            5892\n",
            "33053           3294\n",
            "35434           9156\n",
            "26288           3278\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "22117           6340\n",
            "22153           3936\n",
            "12646           4080\n",
            "15321           6388\n",
            "28590           5444\n",
            "...              ...\n",
            "352             8372\n",
            "15316           4668\n",
            "4460            5200\n",
            "12797           8392\n",
            "17157           2586\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15455           9326\n",
            "37555           4284\n",
            "36230           7062\n",
            "20344           8480\n",
            "3873            6238\n",
            "...              ...\n",
            "19472           4578\n",
            "32242           2452\n",
            "7195            2048\n",
            "25651           8488\n",
            "30813           9826\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30325           5410\n",
            "14746           4884\n",
            "7464            5914\n",
            "19209           9334\n",
            "9230            9948\n",
            "...              ...\n",
            "31865           6630\n",
            "22511           7688\n",
            "32224           4540\n",
            "30350           6746\n",
            "24977           9380\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "24203           8920\n",
            "16330           7884\n",
            "22891           8012\n",
            "25964           7264\n",
            "36385           5034\n",
            "...              ...\n",
            "3414            8454\n",
            "34780           8296\n",
            "22369           6098\n",
            "14408           4200\n",
            "14893           8598\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32              8578\n",
            "7187            2190\n",
            "8853            8310\n",
            "27713           4244\n",
            "34995           4838\n",
            "...              ...\n",
            "8959            9640\n",
            "25391           4928\n",
            "16460           3340\n",
            "29771           6230\n",
            "19971           6062\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3144            7448\n",
            "34389           7590\n",
            "28876           6240\n",
            "23178           8144\n",
            "23363           9520\n",
            "...              ...\n",
            "19551           3804\n",
            "13541           7580\n",
            "28609           8912\n",
            "34727           4512\n",
            "4392            3576\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33563           9154\n",
            "27079           2468\n",
            "32991           6528\n",
            "23221           4302\n",
            "18847           2782\n",
            "...              ...\n",
            "37019           7364\n",
            "801             9192\n",
            "28105           3108\n",
            "37832           6058\n",
            "33313           5040\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9173            5602\n",
            "11650           4452\n",
            "25368           6622\n",
            "30643           8910\n",
            "5173            7408\n",
            "...              ...\n",
            "9735            4578\n",
            "14158           7732\n",
            "27380           4782\n",
            "21923           7920\n",
            "18595           4028\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20493           8864\n",
            "17849           8988\n",
            "11612           6030\n",
            "3133            7674\n",
            "4264            2646\n",
            "...              ...\n",
            "19622           8296\n",
            "16682           3422\n",
            "17257           5856\n",
            "27339           6854\n",
            "16529           6286\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2047            2000\n",
            "104             9128\n",
            "31983           7918\n",
            "24003           7628\n",
            "33831           3806\n",
            "...              ...\n",
            "15754           8486\n",
            "29481           5252\n",
            "33791           2824\n",
            "14245           5588\n",
            "19270           5458\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34003           6026\n",
            "9258            5726\n",
            "22611           2852\n",
            "22375           8584\n",
            "37716           6202\n",
            "...              ...\n",
            "1353            8942\n",
            "12489           5870\n",
            "21217           7504\n",
            "5752            9186\n",
            "3768            4758\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34473           4558\n",
            "20927           7664\n",
            "36084           4340\n",
            "24406           2436\n",
            "16440           4938\n",
            "...              ...\n",
            "35396           8100\n",
            "13644           6178\n",
            "22976           4044\n",
            "25056           5222\n",
            "37955           8824\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16475           7710\n",
            "35791           4936\n",
            "18961           2642\n",
            "20229           9934\n",
            "27250           5838\n",
            "...              ...\n",
            "35515           5946\n",
            "29904           7738\n",
            "233             9928\n",
            "31669           5734\n",
            "30598           6126\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25112           3458\n",
            "36896           2430\n",
            "12823           3444\n",
            "9843            3188\n",
            "16676           4238\n",
            "...              ...\n",
            "12062           6352\n",
            "14496           7788\n",
            "25703           9246\n",
            "1909            5462\n",
            "15690           2512\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8353            5734\n",
            "38356           4278\n",
            "37126           8522\n",
            "4134            8240\n",
            "9549            4266\n",
            "...              ...\n",
            "28314           3908\n",
            "21885           7056\n",
            "27885           4356\n",
            "24431           8070\n",
            "37142           6714\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "17193           8142\n",
            "18768           6240\n",
            "14317           7152\n",
            "13600           8886\n",
            "2650            6396\n",
            "...              ...\n",
            "29379           6878\n",
            "2902            8760\n",
            "34984           2708\n",
            "31237           2044\n",
            "5534            7926\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "19276           4452\n",
            "11042           9680\n",
            "15351           5070\n",
            "779             5184\n",
            "31834           4900\n",
            "...              ...\n",
            "20616           2288\n",
            "24161           2746\n",
            "31017           2304\n",
            "15111           6068\n",
            "11133           3874\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "13072           6354\n",
            "37800           8928\n",
            "1823            9134\n",
            "13863           4484\n",
            "25085           2738\n",
            "...              ...\n",
            "9063            9188\n",
            "24458           3570\n",
            "22907           6930\n",
            "9455            7660\n",
            "9810            4660\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "37714           8402\n",
            "12537           6510\n",
            "13289           6474\n",
            "35260           8464\n",
            "28382           4690\n",
            "...              ...\n",
            "9631            8648\n",
            "5072            7724\n",
            "25757           2128\n",
            "32899           6416\n",
            "34821           8394\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "5275            4368\n",
            "18653           2810\n",
            "27270           9774\n",
            "5599            6918\n",
            "32641           8044\n",
            "...              ...\n",
            "37139           8244\n",
            "18033           3728\n",
            "21020           4002\n",
            "2510            5168\n",
            "27300           6306\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9828            2706\n",
            "17322           4350\n",
            "33528           2458\n",
            "28174           6768\n",
            "38295           8028\n",
            "...              ...\n",
            "11953           9020\n",
            "32450           8380\n",
            "28400           5862\n",
            "2850            2558\n",
            "29195           7494\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10237           5044\n",
            "34731           4788\n",
            "10255           3582\n",
            "13305           7920\n",
            "24290           7764\n",
            "...              ...\n",
            "10929           5178\n",
            "22998           3936\n",
            "31567           7474\n",
            "5095            9484\n",
            "17032           9592\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3243            5636\n",
            "11054           3688\n",
            "21359           7468\n",
            "727             6376\n",
            "16125           6920\n",
            "...              ...\n",
            "30042           2362\n",
            "33532           3422\n",
            "5188            2328\n",
            "34006           4270\n",
            "17492           5464\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16776           7114\n",
            "10308           7100\n",
            "19613           9216\n",
            "2262            5516\n",
            "19157           8126\n",
            "...              ...\n",
            "13763           4144\n",
            "6749            8918\n",
            "120             3322\n",
            "6794            9132\n",
            "13122           5920\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20612           6612\n",
            "5579            9632\n",
            "13683           8832\n",
            "328             6660\n",
            "4710            4314\n",
            "...              ...\n",
            "5401            5202\n",
            "7856            7382\n",
            "27588           6564\n",
            "10100           3880\n",
            "6875            9288\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33463           9290\n",
            "13956           2988\n",
            "21575           4530\n",
            "4619            2748\n",
            "20319           8062\n",
            "...              ...\n",
            "767             8208\n",
            "17737           5232\n",
            "15147           9400\n",
            "32067           3624\n",
            "8249            6708\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32000           3806\n",
            "18029           9020\n",
            "8012            2746\n",
            "5073            8990\n",
            "10591           3516\n",
            "...              ...\n",
            "36940           9560\n",
            "2509            5836\n",
            "7281            3558\n",
            "9244            5946\n",
            "32930           5968\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30728           6494\n",
            "26257           4878\n",
            "4195            8480\n",
            "4067            2440\n",
            "29600           3398\n",
            "...              ...\n",
            "26191           8452\n",
            "1752            4256\n",
            "33770           7758\n",
            "2040            2324\n",
            "18467           3566\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32053           3732\n",
            "7965            5374\n",
            "5231            9826\n",
            "546             6504\n",
            "6199            8836\n",
            "...              ...\n",
            "804             5128\n",
            "12949           6558\n",
            "2251            2064\n",
            "25601           5326\n",
            "3670            8606\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33277           3482\n",
            "31225           4214\n",
            "25401           9938\n",
            "34505           7256\n",
            "32486           3014\n",
            "...              ...\n",
            "15167           8184\n",
            "27846           2076\n",
            "12916           6454\n",
            "22112           7198\n",
            "2823            9296\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "4551            9686\n",
            "30602           7964\n",
            "5336            3064\n",
            "11437           9216\n",
            "20837           5766\n",
            "...              ...\n",
            "23262           7054\n",
            "24102           6860\n",
            "26644           2592\n",
            "11232           3506\n",
            "37872           8370\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "5322            3234\n",
            "13753           4486\n",
            "34002           4768\n",
            "6240            7606\n",
            "32266           2440\n",
            "...              ...\n",
            "9979            7048\n",
            "32803           7630\n",
            "24152           2696\n",
            "18259           7380\n",
            "14549           9694\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "4095            6368\n",
            "19073           5564\n",
            "17573           3330\n",
            "25669           6018\n",
            "21229           6616\n",
            "...              ...\n",
            "12954           7834\n",
            "1164            2518\n",
            "845             4174\n",
            "25389           3288\n",
            "3395            2954\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14796           9232\n",
            "11559           4802\n",
            "12760           6952\n",
            "23147           7880\n",
            "3127            4452\n",
            "...              ...\n",
            "37151           8770\n",
            "14953           2460\n",
            "30859           3876\n",
            "5470            9806\n",
            "22200           8598\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25734           6254\n",
            "14336           7680\n",
            "30936           6632\n",
            "16471           6012\n",
            "24014           4372\n",
            "...              ...\n",
            "29416           2322\n",
            "20756           5554\n",
            "19881           7292\n",
            "17794           9880\n",
            "8032            2528\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1407            9294\n",
            "28690           5082\n",
            "37154           4968\n",
            "14021           2012\n",
            "14254           3544\n",
            "...              ...\n",
            "4951            2126\n",
            "15194           2034\n",
            "18799           6396\n",
            "38574           3384\n",
            "23718           9166\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "19177           8948\n",
            "11332           8212\n",
            "38261           5752\n",
            "27675           6190\n",
            "25009           7904\n",
            "...              ...\n",
            "27594           4098\n",
            "3659            4744\n",
            "3326            2664\n",
            "9465            3534\n",
            "2663            9338\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "25938           7242\n",
            "37664           3898\n",
            "27829           5702\n",
            "21917           3352\n",
            "11673           7154\n",
            "...              ...\n",
            "21584           7960\n",
            "36891           8362\n",
            "32786           3464\n",
            "2244            2432\n",
            "3203            7190\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "28885           3272\n",
            "5204            8984\n",
            "30825           8720\n",
            "20224           6920\n",
            "35902           5836\n",
            "...              ...\n",
            "25241           3346\n",
            "29238           3508\n",
            "23600           2958\n",
            "27454           6936\n",
            "30699           6642\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33405           6736\n",
            "786             8880\n",
            "11049           4052\n",
            "18100           3638\n",
            "15792           2196\n",
            "...              ...\n",
            "8997            8606\n",
            "14201           6746\n",
            "36504           7524\n",
            "25142           6218\n",
            "35702           5326\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6937            2272\n",
            "17451           4728\n",
            "29062           9426\n",
            "5529            7330\n",
            "5398            4112\n",
            "...              ...\n",
            "35012           4040\n",
            "35167           4866\n",
            "24746           8562\n",
            "228             4920\n",
            "36545           7904\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "13329           3788\n",
            "11703           3794\n",
            "9271            2538\n",
            "34538           2860\n",
            "6001            4404\n",
            "...              ...\n",
            "31973           4880\n",
            "463             6108\n",
            "615             4578\n",
            "6380            6528\n",
            "20213           9376\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8181            7200\n",
            "17719           5880\n",
            "16574           9638\n",
            "2716            8288\n",
            "26220           2778\n",
            "...              ...\n",
            "15689           2678\n",
            "36706           7358\n",
            "5033            8350\n",
            "20225           3406\n",
            "17006           4382\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9873            4028\n",
            "787             6994\n",
            "3332            7074\n",
            "30966           2184\n",
            "38425           6650\n",
            "...              ...\n",
            "2841            3046\n",
            "2887            3130\n",
            "8876            4760\n",
            "20923           2080\n",
            "23067           2554\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "14997           3264\n",
            "22837           3780\n",
            "17750           2328\n",
            "36646           7038\n",
            "19023           3134\n",
            "...              ...\n",
            "26478           5632\n",
            "924             6334\n",
            "12070           9054\n",
            "11706           9866\n",
            "22057           6294\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "30916           7330\n",
            "19158           9560\n",
            "6559            7084\n",
            "12720           6470\n",
            "36161           9750\n",
            "...              ...\n",
            "13029           6250\n",
            "11569           5028\n",
            "21999           9950\n",
            "390             5260\n",
            "17848           3882\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29001           8804\n",
            "17576           9688\n",
            "31728           5892\n",
            "36144           6046\n",
            "32021           6642\n",
            "...              ...\n",
            "36080           2706\n",
            "35244           5124\n",
            "4103            4088\n",
            "7493            4866\n",
            "35639           5038\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29377           9046\n",
            "24704           6860\n",
            "15470           2218\n",
            "23988           4460\n",
            "26681           9434\n",
            "...              ...\n",
            "25097           7320\n",
            "21067           4368\n",
            "5543            8568\n",
            "29007           5034\n",
            "35721           7640\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29978           8090\n",
            "20947           2698\n",
            "34468           7286\n",
            "32463           5136\n",
            "17653           4172\n",
            "...              ...\n",
            "21097           9832\n",
            "12748           4678\n",
            "24338           3466\n",
            "34276           4208\n",
            "15790           9328\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3982            4368\n",
            "20674           5558\n",
            "23324           7290\n",
            "34172           9754\n",
            "1094            6188\n",
            "...              ...\n",
            "12734           7518\n",
            "36392           5198\n",
            "2671            5758\n",
            "9417            9384\n",
            "4552            8790\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6009            3106\n",
            "28430           6104\n",
            "19601           5486\n",
            "18437           6694\n",
            "27542           3250\n",
            "...              ...\n",
            "31417           2820\n",
            "29277           9598\n",
            "3961            3874\n",
            "30140           4352\n",
            "29631           6640\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6078            6910\n",
            "30663           8226\n",
            "10956           2424\n",
            "37805           8308\n",
            "4006            6810\n",
            "...              ...\n",
            "24423           6234\n",
            "25954           3166\n",
            "30971           3932\n",
            "38273           8360\n",
            "6226            8584\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "27323           8454\n",
            "34019           2932\n",
            "29648           9036\n",
            "3359            5786\n",
            "307             3020\n",
            "...              ...\n",
            "18931           6520\n",
            "8112            8640\n",
            "32369           4464\n",
            "2985            6402\n",
            "10878           2368\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15916           2856\n",
            "11669           4732\n",
            "20862           8888\n",
            "19942           4714\n",
            "11001           7670\n",
            "...              ...\n",
            "15408           5276\n",
            "17033           2490\n",
            "33629           7806\n",
            "25479           6204\n",
            "24685           9906\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "34710           8780\n",
            "10295           5710\n",
            "25159           5112\n",
            "14119           2340\n",
            "37165           7326\n",
            "...              ...\n",
            "35349           5502\n",
            "30257           2154\n",
            "22164           9410\n",
            "4996            6530\n",
            "3262            8122\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6838            2226\n",
            "5741            4864\n",
            "33418           7586\n",
            "22423           6010\n",
            "11094           2480\n",
            "...              ...\n",
            "10973           3024\n",
            "1629            2000\n",
            "10625           6406\n",
            "33722           8360\n",
            "16999           6244\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "32821           7792\n",
            "19275           7158\n",
            "38009           7290\n",
            "22260           9216\n",
            "489             8216\n",
            "...              ...\n",
            "36471           6638\n",
            "238             8298\n",
            "12207           2932\n",
            "38070           3676\n",
            "35465           8566\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "5618            2426\n",
            "30049           5722\n",
            "25521           2548\n",
            "35745           6764\n",
            "8640            8584\n",
            "...              ...\n",
            "10751           8422\n",
            "16446           3520\n",
            "24417           7328\n",
            "7697            7402\n",
            "38267           4546\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "5668            5836\n",
            "28273           5494\n",
            "18425           4332\n",
            "5307            5962\n",
            "4458            9228\n",
            "...              ...\n",
            "9280            9544\n",
            "29084           5438\n",
            "18689           2844\n",
            "29231           7552\n",
            "12360           2774\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "3849            8688\n",
            "38628           4028\n",
            "34510           9140\n",
            "16194           2240\n",
            "19233           8604\n",
            "...              ...\n",
            "23255           9836\n",
            "14211           4222\n",
            "4769            2052\n",
            "26400           9000\n",
            "4173            7348\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "18919           2184\n",
            "34675           4868\n",
            "24618           6102\n",
            "4946            8466\n",
            "6033            2996\n",
            "...              ...\n",
            "3832            3984\n",
            "32252           4844\n",
            "38394           7022\n",
            "23155           5568\n",
            "7415            3328\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6614            4166\n",
            "27056           8298\n",
            "16673           9456\n",
            "19459           4688\n",
            "12265           5912\n",
            "...              ...\n",
            "21542           7198\n",
            "23360           2558\n",
            "10211           3348\n",
            "10717           7240\n",
            "8199            4112\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "18793           2760\n",
            "19596           5266\n",
            "37320           6026\n",
            "5366            5476\n",
            "2895            8810\n",
            "...              ...\n",
            "20963           9600\n",
            "33487           5784\n",
            "6155            9872\n",
            "20093           9570\n",
            "4025            4540\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29336           7332\n",
            "17908           7148\n",
            "30431           9244\n",
            "20447           9114\n",
            "28038           2102\n",
            "...              ...\n",
            "25682           4150\n",
            "25394           8594\n",
            "16209           9416\n",
            "31331           8392\n",
            "5571            4468\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "5793            2856\n",
            "36077           8468\n",
            "5738            3958\n",
            "19008           5460\n",
            "5276            5264\n",
            "...              ...\n",
            "1088            2222\n",
            "12866           2246\n",
            "10886           4762\n",
            "11590           2316\n",
            "19225           5686\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "10503           9550\n",
            "12306           8866\n",
            "27810           8048\n",
            "20536           6504\n",
            "15906           2538\n",
            "...              ...\n",
            "4857            9362\n",
            "9569            3486\n",
            "10582           8752\n",
            "5498            9160\n",
            "21300           3078\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8556            3134\n",
            "10913           9332\n",
            "1932            2460\n",
            "2352            5590\n",
            "15848           8208\n",
            "...              ...\n",
            "28669           2526\n",
            "13392           5044\n",
            "22785           3078\n",
            "6439            5102\n",
            "27997           3178\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "6031            7552\n",
            "33154           8874\n",
            "30373           8318\n",
            "34396           7602\n",
            "11777           4478\n",
            "...              ...\n",
            "11190           6854\n",
            "17569           4614\n",
            "34775           3798\n",
            "193             6780\n",
            "38474           9938\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "8770            5122\n",
            "30795           4776\n",
            "21909           9566\n",
            "33539           5302\n",
            "31259           4994\n",
            "...              ...\n",
            "22746           6560\n",
            "7418            5702\n",
            "21125           3034\n",
            "10449           5952\n",
            "29516           5598\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "36729           8538\n",
            "23952           3904\n",
            "16843           3514\n",
            "8179            2232\n",
            "28276           3476\n",
            "...              ...\n",
            "6530            6876\n",
            "19206           8806\n",
            "14721           6884\n",
            "11531           8438\n",
            "19909           3752\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29139           4486\n",
            "6971            6178\n",
            "13521           6772\n",
            "2285            2110\n",
            "22447           4170\n",
            "...              ...\n",
            "38097           6862\n",
            "34521           8628\n",
            "28228           4992\n",
            "18745           6070\n",
            "20644           6004\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "5432            5658\n",
            "22669           4338\n",
            "2539            6164\n",
            "11929           3174\n",
            "37595           7162\n",
            "...              ...\n",
            "29411           9430\n",
            "16044           5860\n",
            "10258           3300\n",
            "13428           3246\n",
            "21817           5836\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "541             5914\n",
            "12693           5874\n",
            "5997            8164\n",
            "3390            7660\n",
            "28731           9576\n",
            "...              ...\n",
            "36675           2792\n",
            "10736           6364\n",
            "30841           9728\n",
            "12258           9782\n",
            "18544           2408\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "33453           2170\n",
            "37593           8984\n",
            "11435           3404\n",
            "14291           4542\n",
            "2461            8476\n",
            "...              ...\n",
            "10634           7876\n",
            "4795            3890\n",
            "163             7566\n",
            "671             5048\n",
            "38566           4244\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "16052           6810\n",
            "24817           2210\n",
            "22740           4212\n",
            "30784           5496\n",
            "25476           7598\n",
            "...              ...\n",
            "34594           3722\n",
            "21932           2538\n",
            "25169           5404\n",
            "12329           6296\n",
            "33328           7350\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "31982           6710\n",
            "24819           6050\n",
            "5435            7960\n",
            "4437            3320\n",
            "10382           6854\n",
            "...              ...\n",
            "16014           7578\n",
            "2852            4724\n",
            "11695           2998\n",
            "14996           6814\n",
            "17014           9644\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "29344           7932\n",
            "17633           8534\n",
            "26160           5190\n",
            "3040            7502\n",
            "16035           6530\n",
            "...              ...\n",
            "34958           5634\n",
            "27723           7610\n",
            "23616           2706\n",
            "3726            9622\n",
            "26200           2496\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "1367            9502\n",
            "3712            7548\n",
            "17772           4024\n",
            "25426           5818\n",
            "33768           3958\n",
            "...              ...\n",
            "23040           3698\n",
            "23499           2214\n",
            "13994           5174\n",
            "6276            2104\n",
            "37784           4130\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "11083           9088\n",
            "5724            4136\n",
            "10831           5620\n",
            "16653           6626\n",
            "33397           2912\n",
            "...              ...\n",
            "27961           3322\n",
            "38713           8234\n",
            "899             6152\n",
            "32975           4178\n",
            "33226           6450\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "26630           6750\n",
            "35198           6948\n",
            "24826           8970\n",
            "13629           2138\n",
            "25683           9802\n",
            "...              ...\n",
            "3093            8456\n",
            "13261           3588\n",
            "23411           7566\n",
            "17482           6180\n",
            "10396           9156\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35612           6246\n",
            "3342            7540\n",
            "16422           7358\n",
            "32232           3988\n",
            "6540            7456\n",
            "...              ...\n",
            "2396            2710\n",
            "9516            9694\n",
            "21452           9344\n",
            "20420           7422\n",
            "3020            8344\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "9903            2834\n",
            "29125           2146\n",
            "5295            8476\n",
            "3972            4842\n",
            "21754           5130\n",
            "...              ...\n",
            "9323            3194\n",
            "13737           4034\n",
            "28687           4864\n",
            "27345           9832\n",
            "7564            4736\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "15317           3472\n",
            "6640            3934\n",
            "28535           5602\n",
            "12651           2786\n",
            "23510           3068\n",
            "...              ...\n",
            "11046           2002\n",
            "24762           3692\n",
            "25712           6282\n",
            "6819            6004\n",
            "24063           5190\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "13081           3190\n",
            "21874           6070\n",
            "11555           5154\n",
            "21172           3466\n",
            "19250           3198\n",
            "...              ...\n",
            "19846           2210\n",
            "26399           6620\n",
            "16438           4880\n",
            "10093           4862\n",
            "34249           9540\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "37641           9374\n",
            "13087           8986\n",
            "12342           7166\n",
            "25522           8364\n",
            "11485           6570\n",
            "...              ...\n",
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            "5779            3776\n",
            "34884           6282\n",
            "33561           8890\n",
            "10147           9916\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "35442           9976\n",
            "16478           3446\n",
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            "3756            5476\n",
            "35659           3650\n",
            "...              ...\n",
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            "22422           6068\n",
            "16644           7142\n",
            "38164           8500\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20128           5572\n",
            "32254           6962\n",
            "20618           2950\n",
            "1917            6898\n",
            "1828            2306\n",
            "...              ...\n",
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            "10478           8598\n",
            "5240            2288\n",
            "22574           8178\n",
            "10488           8712\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "23321           5120\n",
            "23624           2610\n",
            "16282           6442\n",
            "26329           7602\n",
            "27693           6284\n",
            "...              ...\n",
            "18017           8054\n",
            "20384           7010\n",
            "21168           8076\n",
            "6938            8572\n",
            "6970            8928\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "20309           8572\n",
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            "8415            5366\n",
            "34961           8884\n",
            "...              ...\n",
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            "17924           3662\n",
            "19065           7374\n",
            "23322           3006\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "27569           3932\n",
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            "1542            7136\n",
            "11516           6728\n",
            "7056            7578\n",
            "...              ...\n",
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            "5052            6360\n",
            "4000            2780\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "11938           8474\n",
            "6833            6760\n",
            "18071           7776\n",
            "...              ...\n",
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            "13403           5920\n",
            "22386           9700\n",
            "25547           9008\n",
            "10893           4532\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "4495            6004\n",
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            "4780            5208\n",
            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "...              ...\n",
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            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
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            "6910            6412\n",
            "...              ...\n",
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            "37819           9410\n",
            "5311            2724\n",
            "\n",
            "[100 rows x 1 columns]\n",
            "       Member_number\n",
            "2433            8554\n",
            "769             6362\n",
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            "860             2694\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kGdyv05HBUBf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0UBnZQwNBUE5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Print the results</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Printing results in Market Basket Analysis (MBA) involves presenting processed data, insights, and outcomes in a structured format. By printing results, organizations can communicate findings, validate analyses, and facilitate collaboration among stakeholders and decision-makers based on derived insights from transactional data.\n",
        "\n",
        "#### **<span style=\"color:green\">Result Presentation and Reporting</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Data Output and Visualization</span>**\n",
        "Output processed results in a readable and accessible format for analysis and decision-making:\n",
        "- **Print Statements**: Use `print()` statements to display key metrics, findings, and summaries derived from MBA analyses (`print(\"Frequent Itemsets:\\n\", frequent_itemsets.head())`).\n",
        "- **Visualization Outputs**: Print charts, graphs, or visual representations of data insights to enhance understanding and interpretation (`print(\"Association Rules:\\n\", rules.head())`).\n",
        "\n",
        "### **<span style=\"color:purple\">Insight Validation</span>**\n",
        "Validate and verify results through comprehensive reporting and documentation:\n",
        "- **Data Integrity**: Ensure accuracy and reliability of printed results through validation against original datasets and analytical methodologies.\n",
        "- **Interpretation Guidance**: Provide context and interpretation of printed results to facilitate informed decision-making and strategic planning.\n",
        "\n",
        "### **<span style=\"color:orange\">Reporting Best Practices</span>**\n",
        "Follow best practices for result reporting and presentation in MBA:\n",
        "- **Clear Communication**: Communicate findings and insights clearly and succinctly to stakeholders and decision-makers (`print(\"Market Basket Analysis results saved to:\", final_output_csv)`).\n",
        "- **Documentation**: Document methodologies, assumptions, and limitations associated with printed results for transparency and reproducibility.\n",
        "\n",
        "### **<span style=\"color:blue\">Application and Use Cases</span>**\n",
        "Apply printed results to drive actionable outcomes and business decisions:\n",
        "- **Decision Support**: Utilize printed insights to optimize product assortments, cross-selling strategies, and customer segmentation in retail and e-commerce environments.\n",
        "- **Performance Evaluation**: Evaluate the effectiveness of MBA techniques and algorithms based on printed results to refine analytical approaches and enhance predictive capabilities.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By printing results effectively, organizations can leverage MBA insights to foster data-driven decision-making, enhance operational efficiencies, and gain competitive advantage in dynamic market landscapes."
      ],
      "metadata": {
        "id": "XHEOJsg7ZhEs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the results"
      ],
      "metadata": {
        "id": "By7cIg8W7meS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFrequent Itemsets:\")  # Printing message\n",
        "print(frequent_itemsets.head())  # Displaying frequent itemsets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71qk7Anb7miW",
        "outputId": "41959f50-2278-4fdd-d45b-a2897d7fd5b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Frequent Itemsets:\n",
            "    support                 itemsets\n",
            "0  0.015393  (Instant food products)\n",
            "1  0.078502               (UHT-milk)\n",
            "2  0.031042          (baking powder)\n",
            "3  0.119548                   (beef)\n",
            "4  0.079785                (berries)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fh2_qeYi7mlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ozxjkG9X7mpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nAssociation Rules:\")  # Printing message\n",
        "print(rules.head())  # Displaying association rules"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAz639w67zrk",
        "outputId": "6dd93769-fc1f-4960-850a-1d4625b6effd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Association Rules:\n",
            "   antecedents         consequents  antecedent support  consequent support  \\\n",
            "5   (UHT-milk)     (bottled water)            0.078502            0.213699   \n",
            "30  (UHT-milk)  (other vegetables)            0.078502            0.376603   \n",
            "34  (UHT-milk)         (pip fruit)            0.078502            0.170600   \n",
            "38  (UHT-milk)        (rolls/buns)            0.078502            0.349666   \n",
            "40  (UHT-milk)   (root vegetables)            0.078502            0.230631   \n",
            "\n",
            "     support  confidence      lift  leverage  conviction  zhangs_metric  \n",
            "5   0.021293    0.271242  1.269268  0.004517    1.078960       0.230217  \n",
            "30  0.038994    0.496732  1.318979  0.009430    1.238697       0.262440  \n",
            "34  0.017188    0.218954  1.283434  0.003796    1.061909       0.239654  \n",
            "38  0.031042    0.395425  1.130863  0.003592    1.075687       0.125578  \n",
            "40  0.021036    0.267974  1.161916  0.002931    1.051013       0.151224  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qmUg6yzE7zux"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VxOR4flh75Wc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Save the final results to CSV</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Introduction</span>\n",
        "Saving final results to CSV in Market Basket Analysis (MBA) involves exporting processed data, frequent itemsets, association rules, and other analytical outcomes to a structured file format. By saving results to CSV, organizations can preserve analytical findings, facilitate further analysis, and ensure data accessibility for future reference and decision-making purposes.\n",
        "\n",
        "#### **<span style=\"color:green\">Result Export and Storage</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">CSV Export Functionality</span>**\n",
        "Implement functionality to export MBA results to CSV format for data persistence and sharing:\n",
        "- **File Path Specification**: Define file path for CSV output (`final_output_csv = \"/content/market_basket_analysis_results.csv\"`).\n",
        "- **Data Export**: Utilize `to_csv()` method to save DataFrame objects, frequent itemsets, or association rules to CSV files (`frequent_itemsets.to_csv(final_output_csv, index=False)`).\n",
        "\n",
        "### **<span style=\"color:purple\">Data Integrity and Validation</span>**\n",
        "Ensure accuracy and completeness of exported CSV files through validation and verification:\n",
        "- **Field Mapping**: Map relevant fields and columns from DataFrame to CSV format to preserve data structure and integrity.\n",
        "- **Quality Assurance**: Conduct quality checks and audits to validate exported data against original datasets and analytical results.\n",
        "\n",
        "### **<span style=\"color:orange\">Documentation and Metadata</span>**\n",
        "Document CSV export processes and include metadata for clarity and reproducibility:\n",
        "- **Documentation Standards**: Maintain documentation of export procedures, file paths, and file naming conventions for future reference (`print(\"Market Basket Analysis results saved to:\", final_output_csv)`).\n",
        "- **Metadata Inclusion**: Incorporate metadata such as timestamp, authorship, and versioning to enhance traceability and auditability of exported results.\n",
        "\n",
        "### **<span style=\"color:blue\">Application and Use Cases</span>**\n",
        "Apply CSV export capabilities to support decision-making and strategic initiatives:\n",
        "- **Data Sharing**: Share CSV files containing MBA results with stakeholders, analysts, and decision-makers for collaborative insights and actions.\n",
        "- **Reporting and Analysis**: Import CSV files into analytical tools or platforms for further exploration, visualization, and advanced analytics.\n",
        "\n",
        "### **<span style=\"color:green\">Conclusion</span>**\n",
        "By saving MBA results to CSV, organizations can preserve and leverage analytical findings, promote data-driven decision-making, and facilitate informed strategies to drive business growth and operational excellence."
      ],
      "metadata": {
        "id": "xJapk5rfZ0CL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the final results to CSV"
      ],
      "metadata": {
        "id": "-o6RbBIy75aN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_output_csv = \"/content/market_basket_analysis_results.csv\"  # Setting file path for final CSV results"
      ],
      "metadata": {
        "id": "cXrd8XqT78ow"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frequent_itemsets.to_csv(final_output_csv, index=False)  # Saving frequent itemsets to CSV"
      ],
      "metadata": {
        "id": "jK8BNv9O78sk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Scis6aja78vW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMarket Basket Analysis results saved to:\", final_output_csv)  # Printing message\n",
        "print(\"EDA analysis results saved to individual PDF files.\")  # Confirmation message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "grhfwHZO78x9",
        "outputId": "ef8e8e24-3715-42be-a040-40b94f121c29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Market Basket Analysis results saved to: /content/market_basket_analysis_results.csv\n",
            "EDA analysis results saved to individual PDF files.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P_HTHy4GZ5SO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KsQ_WhqrZ5Vj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:red\">Conclusion and Final Remarks</span>\n",
        "\n",
        "#### <span style=\"color:blue\">Project Overview</span>\n",
        "Market Basket Analysis (MBA) is a powerful technique in data mining and retail analytics that aims to discover associations between products purchased together. This project utilized advanced algorithms such as Apriori and FP-Growth to identify frequent itemsets and generate meaningful association rules from transactional data.\n",
        "\n",
        "#### **<span style=\"color:green\">Key Highlights and Learnings</span>**\n",
        "\n",
        "### **<span style=\"color:brown\">Technological Integration</span>**\n",
        "Integration of Python libraries including `pandas`, `matplotlib`, `seaborn`, and `mlxtend` facilitated robust data manipulation, visualization, and implementation of MBA algorithms. Utilization of parallel processing techniques using `joblib` enhanced computational efficiency and scalability in handling large-scale datasets.\n",
        "\n",
        "### **<span style=\"color:purple\">Insights and Analysis</span>**\n",
        "Insights derived from frequent itemsets and association rules provided actionable intelligence for strategic decision-making in product placement, cross-selling strategies, and customer segmentation. Visualization of results through network graphs and performance metrics evaluation enriched the interpretability and relevance of MBA outcomes.\n",
        "\n",
        "### **<span style=\"color:orange\">Future Directions</span>**\n",
        "Future enhancements could explore advanced machine learning models for predictive analytics in retail, integration with real-time transactional data streams, and incorporation of deep learning techniques for pattern recognition and anomaly detection in complex datasets.\n",
        "\n",
        "### **<span style=\"color:blue\">Conclusion</span>**\n",
        "Market Basket Analysis remains indispensable in retail analytics, offering profound insights into consumer behavior and transaction patterns. By leveraging advanced algorithms and parallel processing capabilities, this project exemplifies the potential of MBA in driving operational efficiencies and strategic growth initiatives.\n",
        "\n",
        "### **<span style=\"color:green\">Acknowledgments</span>**\n",
        "Special thanks to the open-source community for developing and maintaining essential libraries and tools used in this project. Their contributions enable innovation and advancements in data science and analytics.\n",
        "\n",
        "### **<span style=\"color:green\">Contact Information</span>**\n",
        "For further inquiries or collaboration opportunities, please refer to the GitHub repository associated with this project.\n",
        "\n",
        "### **<span style=\"color:green\">End of Project</span>**\n",
        "Thank you for exploring this Market Basket Analysis project. We hope it provides valuable insights and inspires further exploration into data-driven strategies and analytics in retail and beyond."
      ],
      "metadata": {
        "id": "sZN2Xlr-Z5nS"
      }
    }
  ]
}