ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

iFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, iFeatureOmega supplies the largest number of feature extraction and analysis approaches for most molecule types compared to other pipelines. Three ver…

iFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, iFeatureOmega supplies the largest number of feature extraction and analysis approaches for most molecule types compared to other pipelines. Three ver…

This repository contains a Python code script for performing emotion classification using EEG (Electroencephalogram) data. Emotion classification from EEG signals is an important application in neuroscience and human-computer interaction. The code leverages deep learning techniques to analyze EEG data and predict emotional states.

The Internet Movie Database (IMDb) is a website that serves as an online database of world cinema. This website contains a large number of public data on films such as the title of the film, the year of release of the film, the genre of the film, the audience, the rating of critics, the duration of the film, the summary of the film, actors, dire…

A Python cheatcheet for Machine Learning visualizations.

Having fun making a football machine learning app that will predict defensive play calls. See the app link for details on how this was done.

This project fits and tunes several regression models to predict Parkinson's symptom severity scores from voice recordings.

A machine learning project using different feature analysis and cross validation and NLP.

ML-Premier-League-Wins-Predictor is my first machine learning project that predicts the number of wins for each team in the Premier League using linear regression. Explore the key factors that contribute to becoming a champion in one of the world's most competitive football leagues. Jupyter Notebook and code included.

Feature selection project for a student competition. Analyzing data for chemists at University of Southampton.

Training a model using AutoGluon to predict bike sharing demand

A deep learning project implementing neural network models to accurately predict body fat percentage from anthropometric measurements. Features both comprehensive and reduced-input models, with detailed analysis of feature importance and model performance.

A comprehensive implementation and analysis of handwritten digit recognition using multiple neural network architectures on the MNIST dataset. Features basic MLP, optimized feature-selected model, and deep CNN approaches with detailed performance comparisons and visualizations.

Performed Data Cleaning and Data Analysis of the Electric Vehicles Dataset to find the relationship between the features in the dataset and visualized the findings using matplotlib and seaborn.

Perform Feature Analysis with Yellowbrick!

Analysing different text representations for genre identification. I parse CONLL-u files and extract various representations of a text (running text, lemmas, part-of-speech), then train a Fasttext model on each to see which representation is the most beneficial for the genre identification task.

Impact of Car Features Analysis using Excel

Evaluate Machine Learning Models with Yellowbrick