Bank Customer Churn Prediction (Introduction to Neural Networks – February 2024)

Project Purpose

• Develop a neural network model to predict customer churn for a bank, enabling proactive retention strategies.

Goals

• Design and train a neural network model to analyze customer data and identify churn risk.
• Evaluate model performance using relevant metrics like accuracy and F1 score.
• Provide recommendations for customer segmentation and targeted marketing campaigns.

Challenges

• Data preprocessing: Cleaning and preparing customer data for neural network training
• Hyperparameter tuning: Optimizing neural network architecture and parameters for optimal performance.
• Model bias: Mitigating potential bias in the model that could lead to unfair customer segmentation.

Achievements

• Achieved over 85% accuracy in predicting bank customer churn.
• Utilized feature importance analysis to identify key factors driving churn, such as account activity, product usage, and customer satisfaction levels.
• Fine-tuned the neural network architecture to improve model performance and minimize false positives.