Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

Comprehensive Machine Learning Techniques: Metrics, Classifiers, and Evaluation

This project predicts tuition rates for U.S. public and private universities using linear regression with leave-one-out cross-validation. Helping to assess if a college market price, maximizing ROI and minimizing student loan debt.

This project provides a tutorial on performing leave-one-out cross-validation (LOO-CV) using the Pareto-smoothed importance sampling (PSIS) approximation. The tutorial leverages the arviz package and applies these techniques to a synthetic dataset from Welbanks et al. 2023, focusing on exoplanet atmospheric analysis.

This toolbox offers 6 machine learning methods including KNN, SVM, LDA, DT, and etc., which are simpler and easy to implement.

This toolbox offers 7 machine learning methods for regression problems.

This repository aims to explore the Co-Ni deposits samples and develop a model using ordinary kriging technique. The model is further validated by leave one out cross validation strategy.

This project aims to understand and implement all the cross validation techniques used in Machine Learning.

In this project I have extarcted 30 time and frequancy features from EEG signals (of left hand and right hand moving) in an espicific time window. Then using PCA i have decreased the features dimension to 10. Then I have quarried different methdos of ML: KNN(1,3,5,6), SVM(Linear kernel, Gaussian kernel), LDA, Naive bayes on different time windows.

The dataset contains information regarding residential properties which were collected by the US Census Service, the period 2006 to 2010.

1. train_test_split 2.K_fold 3.LeaveoneOut 4.Cross Validation Score 5.Logistic Regression

Model-Validation-Methods

Methodology used to classify breast cancer histopathological images as part of a datachallenge organised at Telecom Paris

Learning Machine Learning Through Data

This repository features a machine learning project utilizing the Pima Indians Diabetes Dataset to predict diabetes risk. It explores data preprocessing, model training, and evaluation using techniques such as Naive Bayes and K-Nearest Neighbors (KNN) . The aim is to highlight the impact of various health factors on diabetes prediction.

The purpose of this project is to analyze some winning factors for a NBA team and predict their win rate using multiple linear regression. Different cross-validation methods were used to evaluate the model's prediction ability.

Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service.