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.

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:

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

Logistic Regression with Ridge and Lasso

My submission for Machine Learning course, University of Arkansas

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

machine learning regression

A summative coursework for MAS8404 Statistical Learning for Data Science