Customer Purchasing Behaviour Observation: Using Machine Learning Algorithms And Python Implementation

Abstract

Understanding customer purchasing behavior is paramount for businesses aiming to refine their marketing strategies and enhance customer satisfaction. Traditional data analysis methods often fall short in capturing the complexities of purchasing patterns. This research proposes a novel approach utilizing machine learning algorithms implemented in Python to observe and analyze customer purchasing behavior comprehensively. The primary objectives include developing a robust framework for analyzing purchasing data, evaluating various machine learning algorithms to identify the most effective techniques, and creating predictive models for future behavior forecasting.

Expected outcomes include validated machine learning models capable of accurately analyzing and predicting purchasing behavior, providing businesses with a scalable and practical framework. The findings aim to uncover significant patterns and features in customer behavior, ultimately aiding businesses in optimizing their marketing strategies and improving customer engagement. This research bridges the gap between advanced analytics and practical application, offering a data-driven approach to understanding customer purchasing behaviour.