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.

• UpdatedFeb 1, 2025
• Python

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…

• UpdatedAug 16, 2023
• Python

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…

• UpdatedMay 23, 2022
• Python

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.

• UpdatedSep 4, 2023
• Jupyter Notebook

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…

• UpdatedMay 25, 2020
• Jupyter Notebook

A Python cheatcheet for Machine Learning visualizations.

• UpdatedJul 3, 2023
• Jupyter Notebook

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

• UpdatedMar 30, 2022
• Jupyter Notebook

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.

• UpdatedNov 18, 2020
• HTML

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

• UpdatedNov 1, 2021
• Jupyter Notebook

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.

• UpdatedJul 19, 2023
• Jupyter Notebook

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

• UpdatedJul 3, 2020
• R

• UpdatedNov 16, 2021
• HTML

【This repo runs on caffeine and stars - fuel us up! ⭐️】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.

• UpdatedJan 15, 2025
• Jupyter Notebook

Training a model using AutoGluon to predict bike sharing demand

• UpdatedApr 28, 2024
• HTML

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.

• UpdatedNov 1, 2024
• Jupyter Notebook

【Sprinkle some star dust on this repo! ⭐️ It's good karma!】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.

• UpdatedJan 7, 2025
• Jupyter Notebook

Perform Feature Analysis with Yellowbrick!

• UpdatedJun 27, 2020
• Jupyter Notebook

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.

• UpdatedAug 18, 2022
• Jupyter Notebook

Impact of Car Features Analysis using Excel

• UpdatedJun 4, 2023

Evaluate Machine Learning Models with Yellowbrick

• UpdatedJun 28, 2020
• Jupyter Notebook