Customer Sales Predication Using Artifical Intelligence

• Author(s):

  M.Sheeba | k.Srivalli | Y.Venkataprasanna | CH.Devaki

• Keywords:

  Customer Churn Prediction, Machine Learning, Artificial Intelligence,XGBoost, Predictive Analytics, Customer Retention

• Abstract:

  Customer Churn Prediction Is An Important Research Problem In Business Analytics That Helps Organizations Identify Customers Who Are Likely To Discontinue Their Services. This Paper Proposes A Machine Learning-based Churn Prediction Framework Using Decision Tree, Random Forest, Logistic Regression, And XGBoost Algorithms. The Dataset Is Preprocessed Through Missing-value Handling, Categorical Encoding, Normalization, And Feature Selection Techniques To Improve Prediction Accuracy. Experimental Results Show That The XGBoost Model Provides Higher Performance Compared With Other Algorithms. The Proposed System Supports Organizations In Identifying High-risk Customers At An Early Stage And Enables Proactive Retention Strategies That Improve Customer Satisfaction And Organizational Profitability. This Paper Focuses On Customer Churn Prediction Using Artificial Intelligence And Machine Learning Techniques To Identify Customers Who Are Likely To Discontinue Services In Advance. The Proposed System Uses The Telco Customer Churn Dataset And Applies Machine Learning Algorithms Such As Decision Tree, Random Forest, Logistic Regression, And XGBoost To Analyze Customer Behavior Based On Attributes Like Tenure, Contract Type, Monthly Charges, And Payment Methods. Data Preprocessing Techniques Including Handling Missing Values, Encoding Categorical Variables, And Feature Selection Are Performed To Improve Prediction Accuracy. Among All The Algorithms, XGBoost Achieved The Highest Performance, Making It The Most Effective Model For Churn Prediction. The Developed System Helps Organizations Take Proactive Retention Strategies, Reduce Customer Loss, And Improve Business Decision-making Through Data-driven Insights.

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Other Details

• Paper id:

  IJSARTV12I5105444

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-22

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