This repository contains the code for the Multi-Layer Support Vector Machine (MLSVM) Bachelor's thesis of the Computing specialization in Informatics Engineering at FIB, UPC.

This project provides a comprehensive framework for evaluating classification models and selecting the best algorithm based on performance metrics. It demonstrates the importance of hyperparameter tuning and model comparison in machine learning workflows.

This project provides a comprehensive guide to implementing PCA from scratch and validating it using scikit-learn's implementation. The visualizations help in understanding the data's variance and the effectiveness of dimensionality reduction.

Machine Learning Algorithms

Enhancing Patient Care through AI-Driven Disease Prediction

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

Statistical inference on machine learning or general non-parametric models

Why should you care about Eigenvectors? A study on the efficacy of using eigenfaces in image classification.

Machine Learning project on Support Vector Machines (SVM) and value prediction.

Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model

Diabetes Prediction using Machine Learning's Support Vector Machine Model

Scripts, figures, and working notes for the participation in ImageCLEFmedical GANs task, part of the 14th CLEF Conference, 2023.

ML algorithms from scratch

Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

The code for the ACL 2023 paper "Linear Classifier: An Often-Forgotten Baseline for Text Classification". The code was originally from Yu-Chen Lin (@JamesLYC88); we cloned it to the lab repo for future maintenance.

Use the SVM model to automatically take attendance in class with high accuracy up to 80%

We have used our skill of machine learning along with our passion for cricket to predict the performance of players in the upcoming matches using ML Algorithms like random-forest and XG Boost

Disease Prediction Model using SVM, GaussianNB and Random Forest Classifiers.

This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.