Abstract :

Customer Behavior Analysis Has Become An Essential Component For Modern Businesses To Understand Consumer Preferences, Improve Customer Satisfaction, And Enhance Decisionmaking Processes. Traditional Analytical Methods Often Struggle To Handle Large-scale And Dynamic Customer Data Generated Through Online Platforms, Transactions, And Social Media Interactions. This Project Presents An Intelligent Customer Behavior Analysis System Using Machine Learning Techniques To Analyze, Predict, And Classify Customer Activities Effectively. The Proposed System Collects Customer-related Data Such As Purchasing History, Browsing Patterns, Demographic Details, And Feedback Information. Data Preprocessing And Feature Engineering Techniques Are Applied To Improve The Quality And Relevance Of The Dataset. Various Machine Learning Algorithms, Including Decision Tree, Random Forest, Support Vector Machine, And K-Nearest Neighbor, Are Utilized To Identify Customer Behavior Patterns And Predict Future Actions. The System Also Incorporates Data Visualization Techniques To Provide Meaningful Insights For Business Management And Marketing Strategies. Experimental Results Demonstrate That The Proposed Model Improves Prediction Accuracy And Supports Personalized Recommendations, Customer Segmentation, And Targeted Marketing. This Intelligent Approach Helps Organizations Enhance Customer Retention, Optimize Sales Performance, And Achieve Better Business Growth Through Data-driven Decision-making

Published:

22-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1341-1347

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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