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.

Contains the implementation of machine learning algorithms

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

This project employs data preprocessing, visualization, and machine learning techniques, including Decision Tree Classifier implementation, data splitting, and performance evaluation with feature selection for enhanced model interpretability and accuracy.

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

Statistical Learning Algorithm Implementation.

Practice Assignments for Data Science Coursework

Connected components on discrete and continuous multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants; periodic boundaries (4, 8, & 6)

Desenvolvimento de um pipeline de machine learning para o conjunto de dados de pacientes com Carcinoma Hepatocelular (HCC) de modo a prever a sobrevivência dos pacientes 1 ano após o diagnóstico.

The final experiment of machine learning 24, spring in HUST.

Recursive Partitioning for Structural Equation Models

Master thesis project - a hybrid Neural Network-Decision Tree system and dataset for classical music form recognition and analysis.

ML based Smart Crop Recommendation System with Disease Identification, utilizing CNNs. It aids farmers in selecting crops, managing diseases, and boosts productivity by integrating weather and geolocation APIs.

This project aims to provide web application for house price prediction. EDA, model comparison through Pycaret, hyper-parameter tuning and making pipeline model are included. The result is shown as a web application.

Machine Learning Algorithms in Fortran

Classification of heart diseases with different artificial intelligence models