Overview:
This project focuses on predicting customer reactions to opening a bank account, based on historical data. The system extracts important customer features and trains a model using a Decision Tree algorithm to classify potential customers’ responses. The model’s performance is validated using the AUC (Area Under the Curve) metric to measure its accuracy in classifying customer behavior.
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Key Features:
1. Decision Tree Model:
The Decision Tree algorithm is used to classify customer responses based on a variety of customer-related features, providing a clear, interpretable decision-making path.
2. Feature Extraction:
Key customer attributes such as age, income, and banking history are extracted to improve the accuracy of predictions regarding their likelihood to open a bank account.
3. AUC Validation:
The model’s performance is evaluated using the Area Under the Curve (AUC) metric, which helps assess its ability to differentiate between customers likely to respond positively or negatively.
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