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.

• UpdatedFeb 19, 2025
• Python

This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods. The implementation is based on the common theoretic framework presented by Gavin Brown. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided.

• UpdatedMay 5, 2022
• Scala

All Relevant Feature Selection

• UpdatedFeb 7, 2025
• Python

An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).

• UpdatedApr 1, 2022
• C++

This is a complete guide on how to do Pyradiomics based feature extraction and then, build a model to calculate the grade of glioma.

• UpdatedMay 9, 2020
• Jupyter Notebook

Implementations of various feature selection methods

• UpdatedNov 30, 2020
• Python

This is an App developed in Python to implement the algorithm for minimum redundancy maximum ralevance. The formulation was based on a research paper from Chris Ding and Hanchuan Peng (Minimum Redundancy Feature Selection from Microarray Gene Expression Data).

• UpdatedJun 9, 2018
• Python

Implementation of various feature selection methods using TensorFlow library.

• UpdatedNov 21, 2022
• Python

Maximum Relevance Minimum Redundancy for big datasets

• UpdatedSep 30, 2021
• Python

• UpdatedDec 28, 2021
• Python

Conformal Inference tools using python

• UpdatedApr 16, 2020
• Python

Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM

• UpdatedDec 24, 2023
• Python

Feature selection in Apache Spark using Minimum Redundancy and Maximum Relevance

• UpdatedNov 19, 2017
• Python

scikit-learn compatible MRMR feature selection

• UpdatedSep 29, 2024
• Jupyter Notebook

Master MVA - Time Series Project

• UpdatedMay 16, 2021
• Jupyter Notebook

Feature engineering, selection and XGBoost modeling for the Kaggle House Prices Regression competition.

• UpdatedSep 22, 2022

• UpdatedMay 28, 2024
• Jupyter Notebook

A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.

• UpdatedAug 19, 2022
• Python

Cardiovasular Disease Detection using Naive Bayes, Logistic Regression, Random Forest & Support Vector Machine, while comparing the Naive Bayes models with the rest. LIME was also used to explain the predictions of the model.

• UpdatedDec 13, 2023
• Jupyter Notebook

Some Hybrid Machine Learning Algorithms 🤖 that I developed during my 4th Semester 📓

• UpdatedMay 17, 2023
• Jupyter Notebook