lasso-regularization
There are 12 repositories under lasso-regularization topic.
AtharvaKulkarniIT/ParkinsonsTelemonitoringInsights
This R-based data science project on the UCI Parkinson's dataset employs machine learning (Decision tree, Random Forest, SVM, XGBoost) with a focus on hyperparameter tuning and feature selection. This repository showcases insights into Parkinson's disease prediction using effective data science practices.
DeepraMazumder/Bike-Sharing-Count-Prediction
A Machine Learning project to predict bike-sharing counts using regression models, including linear, polynomial, and regularized techniques
smm1999/TFM
This repository serves as a platform to upload new code updates for my Master's Thesis (TFM), focused on the utilization of both supervised and unsupervised models on a dataset extracted from Spotify. It also includes a small fragment of my thesis. For more information, please contact me at:
bharghavs/Customer-Interest-in-Purchasing-Insurance---Logistic-Regression
Logistic Regression with Ridge and Lasso
Brinthat/World-Development-Indicators
Exploring World Development Indicators: Identifying relationship between Health Indicators using Linear Regression & Classification of Income Group based on Health Indicators using Logistic Regression.
pavanreddy2307/Regression_models
Explore various regression models including univariate and multivariate linear regression, along with regularization techniques such as Ridge Regression and Lasso Regression. This repository contains Jupyter Notebook files (.ipynb) demonstrating the implementation and usage of different regression models. Additionally, datasets used for training an
quanmai/UoA-MachineLearning
My submission for Machine Learning course, University of Arkansas
Srking501/mas8404_coursework
A summative coursework for MAS8404 Statistical Learning for Data Science
vicaaa12/simple-regression-Predict-Movie-Rental-Duration
machine learning regression
Develop-Packt/Investigating-Company-Bankruptcy
Investigate the reasons behind bankruptcy and attempt to identify early warning signs. Perform exploratory data analytics using pandas profiling and apply missing value treatments and oversampling
lokk798/House-Prices-Regression
House prices prediction using various regression models.