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

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

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

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.

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

machine learning

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

Decision Tree Implementation.

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

The practical works (TP) of SD201 - Mining of Large Datasets course at Télécom Paris.