The purpose of this project is to develop a predictive model that can identify customers who are likely to churn, i.e., those who are at risk of leaving the bank. By identifying these customers early on, the bank can take proactive measures to retain them, thereby reducing customer attrition and maintaining a healthy customer base.
The project utilizes historical data collected from Kaggle. The dataset includes various features such as customer demographics, account information, transaction history, and any other relevant variables that may influence customer churn. (link)[https://www.kaggle.com/datasets/shantanudhakadd/bank-customer-churn-prediction]
Data Preprocessing: The dataset undergoes preprocessing steps to handle missing values, encode categorical variables, and scale numerical features. Feature Engineering: Additional features may be derived or selected based on domain knowledge to improve the predictive power of the model. Model Selection: Several machine learning algorithms are considered for building the churn prediction model, including logistic regression, decision trees, random forests, support vector machines, and gradient boosting techniques like XGBoost or LightGBM. Model Training: The selected algorithms are trained on a portion of the dataset and evaluated using appropriate performance metrics such as accuracy, precision, recall, and F1-score. Hyperparameter Tuning: Hyperparameters of the chosen models are fine-tuned using techniques like grid search or randomized search to optimize performance. Model Evaluation: The performance of the final model is assessed using cross-validation techniques to ensure its robustness and generalizability.
Once the churn prediction model is trained and evaluated, it can be deployed into the bank's system for real-time predictions. Here's how to use the model effectively:
- Input Data: Provide the relevant customer data including demographics, account information, and transaction history.
- Prediction: The model will output a probability score indicating the likelihood of churn for each customer.
- Action: Based on these predictions, the bank can take appropriate actions to retain customers at risk of churning, such as offering personalized incentives, providing better customer * * service, or targeted marketing campaigns.