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

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

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

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

This Repository Contains Different Machine Learning and Important Concepts

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…

ElasticNet Linear Regression on Solar Power Generation

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

Code for Master Thesis

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

By leveraging pipelines, artifacts, logging, EDA, exception handling, and other components, the Diamond Price Prediction project provides a robust and scalable solution for predicting diamond prices, empowering stakeholders in the diamond industry to make data-driven decisions with confidence.

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

ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection

Wine Quality Prediction using ElasticNet Regression using MLFlows

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

Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app