Decision-Trees-implementation-from-scratch

This repository contains an implementation of the Decision Tree algorithm from scratch using various impurity methods such as Gini index, entropy, misclassification error, etc. The implementation is provided in the form of a Jupyter Notebook file (Decision Tree Implementation.ipynb).

Features

• Decision Tree implementation using different impurity methods
• Visualization of the decision tree
• Comparison of the results with scikit-learn library implementation

Installation

To run the Jupyter Notebook and experiment with the Decision Tree implementation, you need to have the following dependencies installed:

• Python (version >= 3.6)
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

You can install the dependencies using pip:

pip install jupyter numpy pandas matplotlib scikit-learn

Contributions

Any contributions or improvements are always welcome! :)