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.

A collection of demos showcasing automated feature engineering and machine learning in diverse use cases

Woodwork is a Python library that provides robust methods for managing and communicating data typing information.

An R interface to the Python module Featuretools

A simplified version of featuretools for Spark

Exemplary, annotated machine learning pipeline for any tabular data problem.

Predict the poverty of households in Costa Rica using automated feature engineering.

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Automated creation of EntitySets from relational data stored in SQL databases

An add-on for Alteryx open source that automatically checks for the latest updates and warnings you when an Alteryx package is out of date.

Classification and Oversampling Algorithms Comparison, using Deep Feature Synthesis and Feature Selection with RFE

Automl with Featuretools generate features and use tpot to select model

Kaggle competition

Files and Notebooks for Kaggle Titanic

Automated feature engineering

MLOps project to predict 30-day hospital readmissions for BPJS patients in Indonesia and includes an interactive dashboard for regional and demographic analysis.

Automated approach from feature engineering to modeling on the Kaggle Home Credit Default Risk competition dataset

Web-based AutoML platform for California housing price prediction with FeatureTools integration

Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

berserkr converts retailer data and performs machine learning modeling at scale