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?

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

Just a simple implementation of K-Nearest Neighbour algorithm.

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.

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

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

This is the framework for supervised algorithms in mechine learning

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

Basic templates of codes for quick ML

Hear All Solution In R Language

Minimax Classification with 0-1 Loss and Performance Guarantees

This Repository Consists All Courses, Projects and Online Learning Done in Context of Machine learning, Data Sceince And Deep Learning From Various Sources like Youtube, Coursera, Udemy And WEbsites like Scikit, Keras

Unsupervised Learning (PCA) on Vehicle dataset

Supervised Machine Learning project with KNN, decision tree, random forest and adaboost algorithms