Predictive Analytics for Customer Retention: A CatBoost Model for Churn Detection

Competitive marketplaces make customer retention a priority since businesses spend more on acquiring new customers than on keeping current ones. The research uses CatBoost as its algorithm because it represents a gradientboosting model that excels at processing categorical data to improve churn prediction accuracy. An analysis of the 440,834entry customer churn dataset used our team to complete thorough preprocessing alongside feature engineering and model training activities. The CatBoost model demonstrated superior performance when compared to logistic regression, random forest and XGBoost because it achieved higher accuracy levels and lower computational resources and overfitting. The examine of metrics shows outstanding success where the training accuracy matched validation accuracy at 99.89 % and 99.88 % respectively and precision scored 100 % and the F1-score achieved 99.90 %. CatBoost's powerful categorical feature abilities together with its automated hyperparameter execution maintained a validation loss at a minimal 0.71 %. This paper demonstrates why CatBoost functions well with real-life customer retention problems since it delivers tangible solutions that help companies prevent customer attrition. The research contributes to academic analysis of machine learning models along with real-world CRM strategies due to optimized predictive processes and superior accuracy achievement.