This repository contains Python scripts for calculating the Gini Impurity measure for each feature in a relational dataset, great for feature selection, data preprocessing, decision tree construction, binary classification tasks.

This notebook can be downloaded, tested and modified with Google Colab and aims at explainable how a Decision Tree is built. It is also coupled with a Medium article.

Machine Learning Decision Tree Classifier with Gini Algorithm written from scratch

Implemented decision tree algo. from scratch by calculating gini impurity and information gain for performing splits

Interactive loan prediction system built with Streamlit and Decision Tree Classifier. Implements mathematical models like entropy, Gini impurity, and information gain with real-time visualization. Combines ML, statistics, and feature engineering for interpretable financial analytics.

This is the work for Privacy-Preserving Random Forest Implementation. NOTE: This repo is still under construction, please use at your own risk. :)

Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment.

Exploration of Decision Trees and KNN classifiers with focus on entropy, gini impurity, and information gain. Features custom implementations, preprocessing, and evaluation.

Decision Tree Classifier | Jupyter Notebook | Supervised Learning | Machine Learning | Python | Scikit library

Crowdfunding campaign risk classifier using custom Decision Trees, Random Forest & Logistic Regression with Gini/entropy feature selection. FastAPI backend + responsive frontend. ~75% accuracy.

A pure Python implementation of a Decision Tree Classifier built from scratch using NumPy. Features manual calculation of Entropy & Gini Impurity for node splitting, with a comparative analysis against Scikit-Learn.

Decision Tree Implementation.

a Decision Tree algorithm from scratch, applied to the Covertype dataset (581,012 samples, 54 features, 7 classes). The project includes data preprocessing, quartile-based discretization, manual tree construction with pruning, and comprehensive visualizations (e.g., Confusion Matrix, Feature Importance, Decision Tree Graph).

Pattern Recognition homework3 in NYCU. This project is to implement the Decision Tree, AdaBoost and Random Forest algorithm by using only NumPy.

Implementation of decision tree from scratch along with analysis of its performance with different types of impurity measures

Tree Predictors for Binary Classification for Secondary Mushroom dataset

Comparative study of Gini Impurity criteria and Shannon Entropy in Decision Trees

This repository contains an implementation of the Decision Tree algorithm from scratch using various impurity methods such as Gini index, entropy, misclassification error, etc.

Prediction of delivery times for DoorDash deliveries. Performed feature engineering (creation, encoding), feature selection using (multi)collinearity analysis, Gini importance and PCA. Applied 6 ML models to perform regression analysis on delivery time prediction.

Second step in decision tree building