This project develops a predictive model for customer attrition in the telecom industry using advanced machine learning techniques to identify high-risk customers and enable proactive retention strategies.

• Introduction
• Objectives
• Methodology
• Dataset
• Model Building
• Results and Conclusion

The telecommunications sector is essential to the global economy, providing various services from voice calls to internet services. Customer retention is crucial due to intense competition and high acquisition costs.

• Develop a high-accuracy predictive model for customer attrition.
• Identify key factors contributing to customer turnover.
• Formulate targeted retention strategies based on model insights.

1. Data Collection and Preprocessing: Clean and transform customer data.
2. Feature Engineering: Identify and create influential features.
3. Model Development and Training: Train models on historical data.
4. Handling Imbalanced Data: Use SMOTE to balance the dataset.
5. Model Evaluation and Selection: Assess models using various metrics.
6. Deployment and Integration: Implement the model within the telecom operator's system.
7. Result Interpretation: Use predictions to inform retention strategies.

The dataset includes customer demographics, service usage, billing history, and interactions, with 7,043 entries and 21 columns.

Develop and train models such as Random Forest, SVM, Logistic Regression, and XGBoost for attrition prediction. Optimize parameters to enhance predictive accuracy.

• With SMOTE: Improved recall, better identification of high-risk customers.
• Feature Importance: Key factors include tenure, total charges, and contract type.

• Conclusion: Random Forest combined with SMOTE provides strong predictive accuracy.

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