Variable Importance Plots (VIPs)

I used six classification techniques, artificial neural network (ANN), Support Vector Machine (SVM), Decision tree (DT), random forest (RF), Logistics Regression (LR) and Naïve Bayes (NB)

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

Verifying suitability of dysphonia measurements for diagnosis of Parkinson’s Disease using multiple supervised learning algorithms.

Supervised classification to predict rock facies and a T-test flow to evaluate the prediction performance.

With unbalanced outcome distribution, which ML classifier performs better? Any tradeoff?

Registered Software. Official code of the published article "Automatic design of quantum feature maps". This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for tabular data.

Comprehensive portfolio showcasing AI/ML applications in fraud detection, including foundational EDA, transaction fraud, identity fraud, and KYC/AML compliance systems.

This repository contains practical implementations of core machine learning algorithms and techniques, created for learning and practice purposes.

this project aims to be an easy and reusable way to use supervised machine learning techniques

This project uses a Machine learning approach to detect whether the patient has diabetes or not using different machine learning algorithms.

This repository contains machine learning programs in the Python programming language.

Just a simple implementation of K-Nearest Neighbour algorithm.

It consists of basic concepts of Machine-Learning with its algorithms.

ECN 5090- Machine Learning in Economics and Finance (Python)

This repository provides a comprehensive implementation of supervised machine learning models using PyTorch and Scikit-learn. It includes end-to-end workflows for both classification and regression tasks, covering data preprocessing, model training, evaluation, and comparison between traditional ML models

This is the framework for supervised algorithms in mechine learning

Complete lecture slides for Machine Learning (ES-442) at GIK Institute, Fall 2025. Covers Supervised Learning (Decision Trees, SVM, Neural Networks), Unsupervised Learning (Clustering, SOM), and Reinforcement Learning (MDPs, Q-Learning, Deep RL).

Hear All Solution In R Language