- Here are some key points about churn prediction for bank customers using TensorFlow and feature engineering:
Churn prediction aims to identify customers who are likely to cancel/switch their accounts based on their characteristics and behavior patterns. This helps banks prioritize retention efforts.
TensorFlow is a popular open-source machine learning framework. It can be used to build deep learning models for classification/regression tasks like churn prediction. Common models are CNNs, RNNs, LSTMs to capture customer histories/patterns over time.
Feature engineering is critical. Important features may include demographic info, account details, transaction volumes/values, product holdings, customer service interactions, campaign responses, tenure, etc.
Features need cleaning, encoding, normalization. Text/categorical data require embedding layers in neural networks.
Feature selection/reduction is done to remove irrelevant/noisy features using techniques like correlation