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 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.

Introduction to cross-valdation in Machine Learning, with coding examples for supervised classification.

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.

Comprehensive Machine Learning Techniques: Metrics, Classifiers, and Evaluation

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

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.

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.

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

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.

Learning Machine Learning Through Data

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

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

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

Model-Validation-Methods