Overview
This project focuses on analyzing telecom customer churn data to understand the factors that influence customer churn and to build predictive models to identify customers who are likely to churn. The goal is to enable the telecom company to take proactive measures to retain customers.
What We Did
Data Preprocessing:
Data Cleaning: We cleaned the dataset by converting relevant columns to appropriate data types and handling missing values. For example, the TotalCharges column was converted to numeric, and rows with missing values were dropped.
Feature Engineering: We encoded categorical variables using label encoding and one-hot encoding to make them suitable for machine learning models.
Exploratory Data Analysis (EDA):
Feature Importance: We computed mutual information scores to determine the importance of categorical variables in predicting churn. This helped in understanding which features have the most influence on customer churn.
Model Building:
Data Splitting: The dataset was split into training and testing sets to evaluate the performance of the models.
Model Training: We trained multiple machine learning models including Logistic Regression, Decision Tree, Random Forest, and XGBoost. We used cross-validation to ensure robust model performance.
Hyperparameter Tuning: For models like XGBoost, we performed hyperparameter tuning to optimize model performance.
Model Evaluation:
Classification Report: We evaluated the models using various metrics such as precision, recall, and F1-score to understand their performance in predicting churn.
Confusion Matrix and ROC Curve: We plotted confusion matrices and ROC curves to visualize model performance and the trade-offs between true positive and false positive rates.
Why We Did It
The primary objective was to identify the key factors contributing to customer churn and build accurate predictive models. By understanding these factors, the telecom company can develop targeted strategies to reduce churn rates. Predictive models enable the company to identify high-risk customers early and take proactive measures to retain them, thereby improving customer satisfaction and reducing revenue loss.
Results
Feature Importance: Mutual information scores revealed that features like Contract, InternetService, and PaymentMethod are significant predictors of customer churn.
Model Performance:
Random Forest and XGBoost: These models showed high accuracy and performed well in predicting churn, with XGBoost slightly outperforming others after hyperparameter tuning.
Evaluation Metrics: The models were evaluated using precision, recall, and F1-score, with XGBoost achieving the best balance between these metrics.
Visualizations: Confusion matrices and ROC curves provided insights into the models' performance, confirming their effectiveness in distinguishing between churn and non-churn customers.
What's Next
Further Model Optimization: Explore additional feature engineering techniques and advanced algorithms to further improve model accuracy and performance.
Model Deployment: Implement the best-performing model in a real-world setting to monitor and predict customer churn continuously.
Integration with Business Strategies: Use the insights from the model to inform customer retention strategies, such as targeted marketing campaigns and personalized offers.
Continuous Improvement: Regularly update the model with new data to maintain its accuracy and adapt to changing customer behavior patterns.
Customer Feedback: Collect feedback from customers who were identified as high-risk and engaged through retention strategies to refine the model and retention approaches further.
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