Decision Tree Project

Welcome to the Decision Tree Project! This repository contains code and resources for building and analyzing decision tree models for classification and regression tasks. Decision trees are fundamental machine learning algorithms known for their simplicity, interpretability, and effectiveness on diverse datasets.

Features

• Implementation of decision tree algorithms from scratch
• Support for classification and regression tasks
• Visualizations of tree structures and decision boundaries
• Evaluation metrics and performance comparison
• Customizable splitting criteria (Gini, entropy, etc.)
• Documented code with clear structure

Installation

1. Clone the repository:

   git clone https://github.com/Coderamrish/decision-tree-project.git
   cd decision-tree-project

2. (Optional) Set up a Python virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

• Explore main algorithms and modules in the repository (see /src or main script).

• Example for training and evaluating a decision tree:

  from decision_tree import DecisionTreeClassifier
  
  # Load your dataset (e.g., X, y)
  tree = DecisionTreeClassifier(max_depth=5)
  tree.fit(X_train, y_train)
  predictions = tree.predict(X_test)

• For more detailed instructions, see the Usage Guide (create this file if needed).

Project Structure

decision-tree-project/
│
├── src/                   # Source code for algorithms
├── data/                  # Example datasets
├── notebooks/             # Jupyter notebooks for experiments
├── docs/                  # Documentation and usage guides
├── tests/                 # Unit tests
├── requirements.txt       # Python dependencies
└── README.md              # Project overview

Contributing

Contributions, issues, and feature requests are welcome!
Feel free to check issues and submit PRs.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/MyFeature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/MyFeature)
5. Open a pull request

License

This project is licensed under the MIT License.

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Author: Coderamrish
Repository: Coderamrish/decision-tree-project