Market Basket Insights is a data analysis project that provides valuable insights into customer purchase behavior. This Python-based analysis aims to uncover hidden patterns within sales data, enabling businesses to make informed decisions about product placements, promotions, and customer targeting strategies.

Understanding customer behavior is vital for businesses aiming to optimize their operations and enhance customer satisfaction. Market Basket Insights delve deep into transactional data, deciphering connections between products frequently purchased together. By identifying these patterns, businesses can strategize cross-selling, improve inventory management, and personalize marketing campaigns, ultimately boosting sales and customer loyalty.

Before you begin, ensure you have met the following requirements:

• Python 3.6 or higher: You can download and install Python from python.org.

• Jupyter Notebook: Install it using pip:

  pip install jupyter

• Required Packages: Install the necessary Python packages by running:

  pip install -r requirements.txt

Follow these steps to run the analysis:

1. Clone the Repository:

   git clone https://github.com/KavyaSwethaJ/market-basket-insights

2. Navigate to the Project Directory:

   cd market-basket-insights

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open the Jupyter Notebook File:

   • Click on market-basket-insights.ipynb to open the interactive notebook.

5. Run the Code:

   • Execute the cells in the Jupyter Notebook to view the analysis results.

• market-basket-insights.ipynb: Jupyter Notebook containing the analysis code.
• dataset/: Directory with the dataset file (Assignment-1_Data.csv).
• requirements.txt: List of required Python packages.

The dataset (Assignment-1_Data.csv) consists of 7 attributes and 522065 rows, providing a comprehensive foundation for the analysis.

The analysis provides actionable insights for cross-selling and upselling opportunities. By understanding customer purchase patterns, the business can optimize marketing strategies, personalize customer experiences, and ultimately boost sales and customer satisfaction. These insights demonstrate the power of data-driven decision-making in retail operations.