Implementation of different types of machine learning algorithm and there performance comparison on a same dataset

This Repository Contains Different Machine Learning and Important Concepts

Algoritmos de regressão na linguagem python utilizando bibliotecas como sklearn e pandas.

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…

This project was our submission for the Data Challenge by the Undergraduate Statistics Club @ UW-Madison where we explore regularized least square regression models like the Lasso and Ridge along with ElasticNet for Price Prediction in a Grocery Store

Demonstrating Regularization techniques like Lasso, Ridge and Elastic Net to solve Linear Regression and it's relative performance with OLS

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

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

Regression models determining the socioeconomic factors impacting "Generosity" within a given country

House Price Prediction

This project is about a Bike Rental facility located in South Korea, We built different regression models in order to predict the future demand for the rental bikes depending upon the other conditional and non-conditional features in the dataset.

Practical Implementation of Linear Regression on Boston Housing Price Prediction

Practical Implementation of Linear Regression on Algerian Forest Fire Dataset.

ElasticNet Linear Regression on Solar Power Generation

Machine learning regression model to predict energy consumption and GHG emission

Penalized linear regression modeling in R and application to life expectancy data

Code for Master Thesis

EPS forecast with Lasso, Elastic Net and multiple linear regression

IMDB Reviews Text Categorization Using Machine Learning Classifiers (Logistic Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, KNN)

Seoul bike sharing demand prediction is a project that uses machine learning to predict the demand for rental bikes in Seoul, South Korea. The project aims to provide insights for the city's bike-sharing system to better manage the supply of bikes and ensure their availability to the public at the right time.