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.

⚡ Parallel LASSO Regression in Go | Efficient feature selection with L1 regularization | Thread-safe implementation | MIT Licensed

Multi term Polynomial Regression with Learnable Exponents and Coefficients (+L1 Regularisation for Term Pruning & Coefficient/Exponent Based Feature augmentation)

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:

A Machine Learning project to predict bike-sharing counts using regression models, including linear, polynomial, and regularized techniques

Vegetation classification using ML: GAMs, SVM, PCA, Random Forests & Q-Learning-based feature selection.

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

This repository contains a collection of Machine Learning tasks, showcasing implementations of various algorithms, techniques, and concepts. From foundational methods like Linear Regression to advanced approaches using Scikit-Learn. Perfect for students and enthusiasts aiming to deepen their understanding of ML.

A summative coursework for MAS8404 Statistical Learning for Data Science

UCI Breast Cancer Dataset - ML Pipeline Project

Results from empirical assessment of LASSO CV, ElasicNetCV, and RidgeCV from scikit-learn

Cyclic Coordinate Descent for Logistic Regression with Lasso regularization

House prices prediction using various regression models.

This repository consists of pipeline of experiments for research in field of connectivity inference based on calcium imaging.

Logistic Regression with Ridge and Lasso

Exploring World Development Indicators: Identifying relationship between Health Indicators using Linear Regression & Classification of Income Group based on Health Indicators using Logistic Regression.

Predicte weekly sales for 20 Walmart stores using historical data. : Deal with missing values and train a linear regression model, avoiding overfitting

Predicting medical insurance charges using Multiple Linear Regression enhanced with Lasso feature selection. The workflow included data preprocessing, feature encoding, multicollinearity check (VIF), LassoCV for feature selection, and model evaluation.

My submission for Machine Learning course, University of Arkansas