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.

A Decision Tree; multi-class classification problem with continuous feature/attribute values

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

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

Machine Learning Decision Tree Classifier with Gini Algorithm written from scratch

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

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.

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

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

machine learning

Comparative Study of KNN and Decision Tree Models for Predicting Students' Adaptability Level in Online Education

Decision Tree Implementation.