Customer Purchase Behavior Analysis and Visualization Using Big Data Analytics: A PySpark-Based Apache Spark Framework

Understanding customer purchase behaviour is critical for effective business decision-making and targeted marketing. The exponential growth of transactional and behavioural data challenges traditional analytics methods due to high volume, velocity, and variety. This study presents a scalable framework for analysing and visualizing customer purchase behaviour using PySpark (Apache Spark based Python API). Leveraging distributed system, the framework efficiently processes large-scale datasets to identify patterns across product categories, geographic regions, and customer segments. The methodology combines exploratory data analysis, aggregation, and visual analytics techniques to deliver actionable insights for marketing strategies, inventory optimization and operational planning. Experimental evaluation on both simulated and real-world datasets demonstrates the framework’s scalability, performance, and capability in uncovering meaningful purchase behaviour trends. This approach advances big data analytics by integrating high-performance distributed processing with intuitive visualization techniques to enhance customer behaviour intelligence.