Decision Tree Classification Overview This project demonstrates the use of Decision Trees for classification tasks. Decision Trees are intuitive and interpretable machine learning models that partition data based on feature splits to predict categorical outcomes.

Key Features Implements Decision Tree Classifier for supervised learning. Preprocessing of data for feature selection and scaling. Performance evaluation using metrics such as accuracy, precision, recall, and f1-score. Visualization of the decision tree structure.

Dataset The project utilizes a dataset for training and evaluating the decision tree model. Typical datasets include labeled samples with multiple features and a target class. Technologies and Libraries Python: Programming language for implementation. scikit-learn: For Decision Tree classifier, preprocessing, and evaluation. pandas: For data manipulation. numpy: For numerical computations. matplotlib/seaborn: For data visualization.

Future Enhancements Hyperparameter tuning to optimize tree depth, splitting criteria, and leaf nodes. Comparison with other classifiers like Random Forest or Gradient Boosted Trees. Visualization of feature importance to interpret model predictions. Exploration of ensemble methods for improved performance.