Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

Alignment-free method to identify and analyse discriminant genomic subsequences within pathogen sequences

HR Analytics Dataset

Computer Intelligence subject final project at UPC.

SVM classification, RFE feature selection

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.

Bike Sharing in Washington D.C.

Feature Selection Examples

Data warehouse and analytics project to predict bike theft prediction from TPS data

This framework is a versatile toolkit for data analysis across domains, offering robust data processing, feature selection, predictive modeling, and visualization tools adaptable to various datasets.

[Features extraction method] You can find the new version of CASTOR_KRFE at https://github.com/bioinfoUQAM/CASTOR_KRFE

Machine Learning project for predicting stroke risk using healthcare data. Includes EDA, preprocessing, SMOTE, feature selection (RFE), evaluation of Logistic Regression, Decision Tree, Random Forest, KNN, SVM, and Stacked Ensemble models.