Customer Churn Prediction

Algorithms Tried and Their Justifications
1. LightGBM (LGBMClassifier)
• Reason: LightGBM is known for its efficiency and speed, especially with large datasets. It supports various advanced features like categorical feature handling and model complexity control, making it suitable for tabular data like customer churn.
2. CatBoost
• Reason: CatBoost is particularly effective for datasets with categorical features, which are common in customer churn datasets. It provides automatic handling of categorical variables, reducing preprocessing efforts and enhancing model performance.
3. GridSearchCV
• Reason: To fine-tune hyperparameters and improve the model’s performance. GridSearchCV exhaustively searches over a specified parameter grid, helping in finding the optimal parameters for the given model.
Lessons Learned
1. Feature Engineering is Crucial:
• Proper handling of categorical variables and creating meaningful features
significantly impact model performance.
• Techniques like label encoding and standard scaling can enhance model accuracy and convergence speed.
2. Model Selection and Hyperparameter Tuning:
• Trying different algorithms and tuning their hyperparameters is essential for finding the best-performing model.
• Automated tools like GridSearchCV can streamline the process and lead to better results.
3. Data Splitting and Validation:
• Properly splitting the data into training and testing sets is critical for evaluating model performance.
• Using techniques like cross-validation helps in assessing the model’s robustness and generalizability.

4. Visualization for Insights:
• Visualizing data distributions and relationships using tools like Matplotlib and Seaborn can provide valuable insights.
• Feature importance plots can help identify the most influential features, guiding further feature engineering.
Potential Improvements
1. Explore Additional Algorithms:
• Why: Different algorithms might capture different aspects of the data. Algorithms like XGBoost, Random Forest, or even neural networks could be explored for potentially better performance.
• Example: Trying XGBoost could provide a balance between model interpretability and performance.
2. Advanced Feature Engineering:
• Why: Creating new features or transforming existing ones can provide more information to the model, leading to better predictions.
• Example: Creating interaction terms or polynomial features might capture non-linear relationships in the data.
3. Ensembling Methods:
• Why: Combining predictions from multiple models can often lead to improved performance by leveraging the strengths of each model.
• Example: Using techniques like stacking, bagging, or boosting with different base models could enhance prediction accuracy.
4. More Comprehensive Hyperparameter Tuning:
• Why: Fine-tuning the model’s parameters can significantly impact performance. A more extensive search space or different optimization techniques (e.g., RandomizedSearchCV) could be employed.
• Example: Implementing Bayesian optimization for hyperparameter tuning might provide better results with fewer iterations.
5. Handling Class Imbalance:• Why: Customer churn datasets often have an imbalance between churned and non-churned classes. Properly addressing this can improve model performance, especially for the minority class.
• Example: Using techniques like SMOTE (Synthetic Minority Over sampling Technique) or adjusting class weights in the models.