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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 versions (i.e. iFeatureOmega-Web, iFeatureOmega-GUI and iFeatureOmega-CLI) of iFeatureOmega have been made available to cater to both experienced bioinformaticians and biologists with limited programming expertise. iFeatureOmega also expands its functionality by integrating 15 feature analysis algorithms (including ten cluster algorithms, three dimensionality reduction algorithms and two feature normalization algorithms) and providing nine types of interactive plots for statistical features visualization (including histogram, kernel density plot, heatmap, boxplot, line chart, scatter plot, circular plot, protein three dimensional structure plot and ligand structure plot). iFeatureOmega is an open-source platform for academic purposes. The web server can be accessed through http://ifeature2.erc.monash.edu and the GUI and CLI versions can be download at: https://github.com/Superzchen/iFeatureOmega-GUI and https://github.com/Superzchen/iFeatureOmega-CLI, respectively.
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 versions (i.e. iFeatureOmega-Web, iFeatureOmega-GUI and iFeatureOmega-CLI) of iFeatureOmega have been made available to cater to both experienced bioinformaticians and biologists with limited programming expertise. iFeatureOmega also expands its functionality by integrating 15 feature analysis algorithms (including ten cluster algorithms, three dimensionality reduction algorithms and two feature normalization algorithms) and providing nine types of interactive plots for statistical features visualization (including histogram, kernel density plot, heatmap, boxplot, line chart, scatter plot, circular plot, protein three dimensional structure plot and ligand structure plot). iFeatureOmega is an open-source platform for academic purposes. The web server can be accessed through http://ifeature2.erc.monash.edu and the GUI and CLI versions can be download at: https://github.com/Superzchen/iFeatureOmega-GUI and https://github.com/Superzchen/iFeatureOmega-CLI, respectively.
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, directors and much more. Faced with the large amount of data available on this site, I thought that it would be interesting to analyze the movies data on the IMDb website between the year 2000 and the year 2017.
A Python cheatcheet for Machine Learning visualizations.
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.
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.
gradient boosting classifier prediction model to predict one's employability and skill recommendation
Training a model using AutoGluon to predict bike sharing demand
Impact of Car Features Analysis using Excel
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.
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.
Evaluate Machine Learning Models with Yellowbrick
Perform Feature Analysis with Yellowbrick!