This repository contains my solutions for various projects assigned in my university’s Artificial Intelligence course. The projects cover a wide range of AI topics and techniques, providing practical applications of AI theories learned in class.

Simulations for the paper "Inter node Hellinger Distance based Decision Tree by Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib"

Output-Constrained Decision Trees (OCDT)

Contains the implementation of machine learning algorithms

This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

Predicting bank churn rates with machine learning models (decision trees, random forest, & xgboost)

Evolutionary decision tree classifier

Predicting University Admission Chances, where we explore the likelihood of admission for prospective students based on various factors. Leveraging machine learning, we have employed two powerful algorithms, Decision Tree and Random Forest, to predict the chances of admission.

A simple playground app to showcase the mechanisms of Machine Learning

"oxayavongsa/projects" is a public GitHub repository serving as a diverse AI/ML Project Portfolio. Managed by Outhai Xayavongsa (Ms. Thai), using Python coding and Juptyer notebook.

Instruction decoder generator

The aim is to build a predictive model that can accurately classify whether the employee is likely to leave or the employee is likely to stay in the company. This allows companies to take proactive measures, such as improving working conditions, offering promotions, or addressing dissatisfaction, to retain valuable employees.

Kali Linux sanal makinesi kullanarak DDoS saldırılarının simülasyonunu gerçekleştirip, oluşturulan veri seti üzerinde makine öğrenme algoritmaları ile saldırı tespiti ve normal trafikten ayırma.

Dive into the world of Machine Learning in this immersive lab course, exploring open-source tools and algorithms such as random forest, SVM, linear regression, PCA, K-means, LDA, KNN, decision tree, and more. Engage in real-world ML projects and deploy your models, gaining practical experience in the forefront of AI technology.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation