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.

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.

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

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.

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

Feature Selection Examples

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.

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

SVM classification, RFE feature selection

Computer Intelligence subject final project at UPC.

Bike Sharing in Washington D.C.

HR Analytics Dataset