-> Overview This project implements a Decision Tree Classifier to predict customer behavior in a banking context. Specifically, it predicts whether a client will subscribe to a term deposit (y) based on various demographic and marketing attributes.

-> Machine Learning Pipeline

• Preprocessing: Categorical variables are transformed using Label Encoding.
• Modeling: A DecisionTreeClassifier is trained with a maximum depth of 5 to ensure interpretability and prevent overfitting.
• Evaluation:
  • Accuracy: Measured on a 20% test split.
  • Cross-Validation: 5-fold CV is used to ensure model consistency.
  • Metrics: Includes a full Classification Report and Confusion Matrix.

-> Visualizations

• The Decision Tree: A visual representation of the first 3 levels of the model's logic.
• Feature Importance: A chart ranking the top 10 attributes that most influence the prediction (e.g., duration, balance).

-> Requirements

• Scikit-Learn, Pandas, Matplotlib