Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

🤖 An automated machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers). Python 3.6 required.

Threat Intel Platform for T-POTs

Build tensorflow keras model pipelines in a single line of code. Now with mlflow tracking. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.

🍅 A dynamic brain-responsive pomodoro timer

Stress classifier with AutoML

This project is about predictive maintenance with machine learning. It's a final project of my Computer Science AP degree.

RapidML is a smart Python framework for rapidly prototyping Machine Learning APIs for the Web!

Occupancy detection of an office room from light, temperature, humidity and CO2 measurements

tpot is a simple tool to access teleport web UI from terminal

This project has 3 goals: To find out the best machine learning pipeline for predicting ASD cases using genetic algorithms, via the TPOT library. (Classification Problem) Compare the accuracy of the accuracy of the determined pipeline, with a standard Naive-Bayes classifier. Saving the classifier as an external file, and use this file in a Flask…

Computational experiments for the paper "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost" (IJCNN 2021)

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use …

Benchmark of current ML automation frameworks

Benchmark for some usual automated machine learning, such as: AutoSklearn, MLJAR, H2O, TPOT and AutoGluon. All visualized via a Dash Web Application

Semi-automated machine learning pipelines

We will discuss the Hyper Parameter Tuning for different Machine Learning Algorithm

Regress using many metalearning packages including caret and TPOT from one script. Also artificial data generator, comparison script and several expiraments. Messy.