This is a collection of all the machine learning techniques required in any machine learning project. It contains detailed descriptions, videos, book recommendations, and additional material to properly grasp all the concepts. It also contains implementations in various frameworks.

A tool for performing cross-validation with panel data

Cross-validation methods designed for ensemble learning

This project employs multiple regression analysis to identify key determinants of employee salaries, such as experience and education, using R. Through extensive data analysis and model comparisons between Linear Regression and Random Forest, the study offers insights for effective salary structuring and employee retention strategies.

A machine learning method to determine the age of abalone from physical measurements of size and weight

A high-level machine learning and deep learning library for the PHP language.

University Admission Predictor is a sophisticated Flask-based web application designed to predict the likelihood of admission to graduate programs based on student profiles. It leverages a range of regression techniques to evaluate admission chances.This project showcases the practical application of machine learning in educational forecasting.

Cross-platform vigenere cipher application for node.js

The blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments. See

Time based splits for cross validation

Pegando vários dataset e fazendo a predição

Prediction of students' dropout using classification models. Data visualisation, feature selection, dimensionality reduction, model selection and interpretation, parameters tuning.

Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it

Finding Relationships in Data: UBC Term Project 2024W2 - Predictive Modelling

Python utilities, e.g. for pandas

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)

Modern computational statistics

Aquí compartiré y documentaré mi aprendizaje en el análisis predictivo utilizando ML y prácticas de Python. Variando desde simples ejercicios hasta proyectos prácticos. Cada proyecto incluye archivos de soporte y notebooks con código y análisis y las prácticas sus enunciados comentado al inicio.

This repo contains my cross-validation ML analysis for training and testing co-occurrence network inference algorithms