Forecasting Customer Turnover Using Machine Learning

• Author(s):

  Sowndarya .M | MS.G.P Angeline Pearl

• Keywords:

  Customer Churn Prediction, Machine Learning, SVM, XGBoost, Data Preprocessing, Classification And Customer Retention.

• Abstract:

  Customer Churn Prediction Is An Essential Task For Telecommunication Companies To Reduce Customer Loss And Improve Retention.A Machine Learning System Is Proposed In This Paper To Predict Customer Churn By Using Customer Usage History. Front End Is Configured Using HTML, CSS And JavaScript, While The Back End Of The System Is Based On Python With Flask Framework. Data Preprocessing Techniques Such As Removal Of Irrelevant Attributes, Encoding Of Categorical Variables, And Normalization Of Numerical Data Are Applied To Enhance Model Performance.Two Machine Learning Algorithms, Support Vector Machine (SVM) And Extreme Gradient Boosting (XGBoost), Are Used To Build Classification Models. These Models Analyze Historical Data To Models Is Evaluated Using Accuracy, And A Comparison Is Made To Identify The Better-performing Algorithm.These Results Indicate That The Proposed Model Indicates A Comparatively Good Prediction For Customer Churn. This Enables Organizations To Implement Strategies Like Enhanced Service, Customized Plans, Etc. That Could Lead To Improved Customer Retention And Business Continuity.

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Other Details

• Paper id:

  IJSARTV12I6105732

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-26

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