CUSTOMER SHOPPING BEHAVIOR ANALYSIS FOR BETTER INSIGHTS

Abstract

The Customer Shopping Behavior Analysis for Better Insights project presents the development of an intelligent data analytics system designed to analyze customer purchasing behavior and extract meaningful business insights from large-scale retail data. In modern business environments, understanding customer shopping behavior is essential for improving marketing strategies, enhancing customer satisfaction, increasing sales performance, and supporting data-driven decision-making. Traditional analytical methods mainly rely on manual observation and basic statistical techniques, which are often inefficient, time-consuming, and unable to process large volumes of customer data effectively. This project addresses these challenges by utilizing machine learning and data analytics techniques to identify hidden patterns, trends, and customer preferences automatically. The proposed system analyzes historical customer data including purchase history, transaction frequency, spending behavior, product preferences, demographic information, and shopping trends. Data preprocessing techniques such as missing value handling, normalization, feature encoding, and feature selection are applied to improve data quality and analytical performance. Multiple machine learning and predictive analytics techniques are implemented to perform customer segmentation, classification, and behavioral pattern recognition. The system utilizes various supervised and unsupervised machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) to analyze customer behavior and predict purchasing trends. These models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix to identify the most effective predictive approach. Experimental analysis indicates that ensemble learning methods, particularly the Random Forest algorithm, provide higher prediction accuracy and more reliable insights into customer shopping behavior compared to other traditional models.