feature-analysis

There are 0 repository under feature-analysis topic.

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.
soccer statistics feature-analysis deep-neural-networks feature-importance imbalanced-learning interpretability-methods machine-learning-algorithms neural-network random-forest soccer-betting soccer-matches football-data predictions upcoming-games imbalanced-learn data-visualization pandas-dataframe seaborn optuna
Language:Python 269
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.
python feature-engineering biological-sequences-analysis bioinformatics-tool feature-analysis biological-data-visualization
Language:Python 24
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.
python feature-engineering bioinformatics-tool biological-data-visualization feature-analysis biological-sequences-analysis
Language:Python 22
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.
coorelation data-exploration data-preprocessing data-science data-visualization deep-learning deep-learning-algorithms eeg-emotion-recognition emotion-prediction feature-analysis heatmap machine-learning machine-learning-algorithms pie-chart spectral-analysis time-series-visualization egg-signals emotion-distribution human-emotions
Language:Jupyter Notebook 8
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.
movies entertainment media data-analysis data-mining python query data-mining-algorithms machine-learning data-cleaning feature-analysis feature-extraction feature-engineering feature-selection recommendation-system
Language:Jupyter Notebook 3
alessandro1802 / ml-visualizations
A Python cheatcheet for Machine Learning visualizations.
artificial-intelligence classification machine-learning python visualization clustering feature-analysis
Language:Jupyter Notebook 1
BobbyWilt / PD_Voice_UPDRS
This project fits and tunes several regression models to predict Parkinson's symptom severity scores from voice recordings.
machine-learning python jupyter-notebook regression-models feature-engineering feature-analysis data-visualization pandas sklearn hyperparameter-tuning xgboost gradient-boosting-regressor random-forest decision-tree linear-regression regularization seaborn permutation-importance
Language:Jupyter Notebook 1
Sajib003 / SMS-spam
A machine learning project using different feature analysis and cross validation and NLP.
machine-learning cross-validation feature-analysis sms-spam
Language:Jupyter Notebook 1
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.
deep-neural-networks defense feature-analysis football machine-learning prediction-plays predictive-analytics predictive-modeling texas
Language:HTML 1
dshreesr / impact-car-features
Impact of Car Features Analysis using Excel
excel exploratory-data-analysis feature-analysis regression regression-analysis
0
RobertRusev / ML-Premier-League-Wins-Predictor
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.
data-analysis data-science data-visualization feature-analysis football-analytics football-statistics jupyter-notebook linear-regression machine-learning premier-league python regression-analysis soccer sports-data sports-predictions
Language:Jupyter Notebook 0
samsamiczy / eda-competition
Feature selection project for a student competition. Analyzing data for chemists at University of Southampton.
pca chemistry boruta linear-regression feature-analysis feature-selection
Language:R 0
TajaKuzman / Text-Representations-in-FastText
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.
conllu fasttext feature-analysis genre-identification language-processing text-classification
Language:Jupyter Notebook 0
ADA-CTP / EmploymentReady
gradient boosting classifier prediction model to predict one's employability and skill recommendation
data-visualization feature-analysis machine-learning predictive-modeling
Language:Python
Hannah-Susan-Mathew / Feature-Analysis-on-US-Airline-Passenger-Survey
feature-analysis orange-data-mining-tool classification
Language:HTML
MarwaEshra / Evaluate-Machine-Learning-Models-with-Yellowbrick
Evaluate Machine Learning Models with Yellowbrick
yellowbrick poof evaluation-metrics confusion-matrix threshold-plot discrimination-threshold logistic-regression class-imbalance cross-validation classification-report roc-auc manifold-visualization covariance feature-analysis parallel-coordinates-plot radviz scatter-plot anscombesquartet
Language:Jupyter Notebook
MarwaEshra / Perform-Feature-Analysis-with-Yellowbrick-
Perform Feature Analysis with Yellowbrick!
feature-analysis yellowbrick visualization feature-selection scikit-yb scikit-learn radviz anscombesquartet
Language:Jupyter Notebook

feature-analysis

kochlisGit / ProphitBet-Soccer-Bets-Predictor

Superzchen / iFeatureOmega-GUI

Superzchen / iFeatureOmega-CLI

Vidhi1290 / Deep-Learning-for-EEG-Emotion-Classification

shishir349 / Analyzing-the-IMDB-Movie-Dataset

alessandro1802 / ml-visualizations

BobbyWilt / PD_Voice_UPDRS

Sajib003 / SMS-spam

TrentBrunson / Turbo_Learning

dshreesr / impact-car-features

RobertRusev / ML-Premier-League-Wins-Predictor

samsamiczy / eda-competition

TajaKuzman / Text-Representations-in-FastText

ADA-CTP / EmploymentReady

Hannah-Susan-Mathew / Feature-Analysis-on-US-Airline-Passenger-Survey

MarwaEshra / Evaluate-Machine-Learning-Models-with-Yellowbrick

MarwaEshra / Perform-Feature-Analysis-with-Yellowbrick-