Topic: "feature-analysis"
kochlisGit/ProphitBet-Soccer-Bets-Predictor
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.
Language: Python - Size: 3.47 MB - Last synced at: 27 days ago - Pushed at: 3 months ago - Stars: 389 - Forks: 132
Superzchen/iFeatureOmega-CLI
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.
Language: Python - Size: 8.58 MB - Last synced at: 19 days ago - Pushed at: over 1 year ago - Stars: 32 - Forks: 10
Superzchen/iFeatureOmega-GUI
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.
Language: Python - Size: 23.6 MB - Last synced at: about 2 months ago - Pushed at: almost 3 years ago - Stars: 29 - Forks: 6
Vidhi1290/Deep-Learning-for-EEG-Emotion-Classification
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.
Language: Jupyter Notebook - Size: 1.79 MB - Last synced at: 29 days ago - Pushed at: over 1 year ago - Stars: 17 - Forks: 2
shishir349/Analyzing-the-IMDB-Movie-Dataset
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.
Language: Jupyter Notebook - Size: 699 KB - Last synced at: 9 months ago - Pushed at: almost 5 years ago - Stars: 3 - Forks: 0
alessandro1802/ml-visualizations
A Python cheatcheet for Machine Learning visualizations.
Language: Jupyter Notebook - Size: 1.27 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0
BobbyWilt/PD_Voice_UPDRS
This project fits and tunes several regression models to predict Parkinson's symptom severity scores from voice recordings.
Language: Jupyter Notebook - Size: 2.22 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0
TrentBrunson/Turbo_Learning
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.
Language: HTML - Size: 34.6 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0
ChanMeng666/bodyfat-estimation-mlp
【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.
Language: Jupyter Notebook - Size: 5.55 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 1
ChanMeng666/mnist-handwritten-digit-recognition-project
【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.
Language: Jupyter Notebook - Size: 1020 KB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0
aishwaryagm1999/Electric-Vehicles-Dataset-Data-Analaysis
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.
Language: Jupyter Notebook - Size: 167 KB - Last synced at: about 2 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0
ADA-CTP/EmploymentReady
gradient boosting classifier prediction model to predict one's employability and skill recommendation
Language: Python - Size: 2 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0
dshreesr/impact-car-features
Impact of Car Features Analysis using Excel
Size: 9.67 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0
samsamiczy/eda-competition
Feature selection project for a student competition. Analyzing data for chemists at University of Southampton.
Language: R - Size: 305 KB - Last synced at: 5 months ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 1
MarwaEshra/Evaluate-Machine-Learning-Models-with-Yellowbrick
Evaluate Machine Learning Models with Yellowbrick
Language: Jupyter Notebook - Size: 903 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0
MarwaEshra/Perform-Feature-Analysis-with-Yellowbrick-
Perform Feature Analysis with Yellowbrick!
Language: Jupyter Notebook - Size: 789 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0