K-Nearest Neighbors, Decision Tree, Naive Bayes, Random Forests and Ada-boost.

Machine learning projects solved with the help of various classification model and finding the best accuracy out of all.

A Tic Tac Toe game that uses a Decision Tree Module to study older moves and decise the next move

The goal of this internship project was to create an educational program about artificial intelligence. This program is composed of activities and practical exercises. This repository contains the solutions of the main activities I created. During this project, I developed my knowledge of artificial intelligence and the Python language.

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

Given a dataset of sales in auctions, we are about to classify and predict the "Stage" value which is either Won or Lost using scikit-learn library and algorithms such as decision tree, random forest, naïve Bayes, and KNN.

A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

This repository contains a Python implementation of a decision tree algorithm. The decision tree is a popular machine learning algorithm used for both classification and regression tasks. This implementation provides classes for building decision trees for classification and regression purposes.

Scripts that do my MLG381 work for me, mostly to show me the calculations as they're made

"This repository serves as a comprehensive resource for understanding and applying Regression techniques in achine learning and statistical modeling."

🌳 Multi Split Decision Tree

Machine learning project to predict population based on 'Salmon' dataset using transcriptomic features

Package for removing the black-box around decision trees

Simple machine learning model to predict the house price based on 1990 California Housing Prices dataset

A decision Tree I wrote in Andrea Thomaz's CS 3600 intro to AI

KNN analysis and prediction for Wine Quality

4 machine learning models applied to 2 seperate binary classification problems each. These models include decision trees, neural networks, random forest bagging, and k-nearest neighbor.

Machine Learning with Streamlit Python