Develop a serverless solution on a platform like AWS or Azure that automatically processes uploaded files (such as images, PDFs, or logs). Use cloud functions (e.g., AWS Lambda) to trigger workflows for file validation, transformation, and storage. Integrate monitoring, notifications, and secure access controls. This project demonstrates scalable, efficient cloud architecture with real-world utility.

Explore transactional purchase data to uncover relationships between products frequently bought together. Use association rule mining (such as the Apriori algorithm) to identify product bundles for cross-selling or targeted promotions, and visualize the results to guide business decisions.

Build a model that analyzes historical sales data and predicts future sales trends. Use techniques like ARIMA, Prophet, or LSTM to identify patterns and seasonal effects. Visualize the results and provide actionable insights for business planning and inventory management.

Analyze customer data to identify distinct segments based on demographics, purchase behavior, or engagement history. Use clustering techniques (like K-means) to group customers, and visualize findings to help businesses tailor marketing strategies and improve user retention.