An extension of sklearn's Lasso/ElasticNet/Ridge model to allow users to customize the penalties of different covariates. Works similar to penalty.factor parameter in R's glmnet.

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.

It was a competition on KAGGLE for prediction on the most sales products on bikes via their features

Machine learning applications in volleyball (python, scikit-learn)

Machine learning model to forecast the sales of each Rossmann store for any given date.

This Repository Contains Different Machine Learning and Important Concepts

This repository is the third project of the master's degree in AI Engineering that I am following. It aims toto optimize real estate price valuation through the use of advanced regularisation techniques in linear regression models by implementing Lasso, Ridge and Elastic Net in order to obtain accurate and stable price predictions.

Practical Implementation of Linear Regression on Boston Housing Price Prediction

We explored various approaches to deal with high-dimensional data in this study, and we compared them using simulation and soil datasets. We discovered that grouping had a significant impact on model correctness and error reduction. For the core projection step, we first looked at the properties of all the algorithms and how they function to com…

A small project addressing a regression problem explains implementation of multiple linear regression techniques, hyperparameter tuning, collinearity, model overfitting and complexity using LASSO, Ridge and Elastic net

The dataset used for this project is taken from the official UCI Machine Learning Repository.

ElasticNet Linear Regression on Solar Power Generation

I constructed a machine learning model to predict the quality of wine

Code for Master Thesis

This project develops a machine learning model to predict the salaries of baseball players based on their past performance.

Nesse trabalho vou explorar uma conhecida base, boston dataset. Nela encontramos informações sobre algumas características de casas. Queremos estudar o comportamento dos preços desses imóveis para futuramente conseguirmos prever seus preços

Practical Implementation of Linear Regression on Algerian Forest Fire Dataset.

CyberSoft Machine Learning 03 - Overview

Analysis pipeline for the PAH biomarker study by Sonnweber T et al.