MLGenerator - A simple web app to generate starter template code for different machine learning tasks

Hello,

I have created a simple web-app to generate starter template code for different machine learning tasks. Currently, MLGenerator supports anomaly detection (LOF, iForest, and kNN anomaly detectors included) and classification (SVM, Random Forest, Decision Tree, kNN, Logistic Regression classifiers included) tasks. In future, I will be adding Clustering and Dimensionality Reduction algorithms as well.

It is worth to mention that, MLGenerator is motivated from Traingenerator (https://traingenerator.jrieke.com).
This project is open source and available on GitHub.

MLGenerator GitHub : GitHub - durgeshsamariya/MLgenerator: MLgenerator is a web app which help you to generate machine learning starter code with ease.

Web : https://ml-generator.herokuapp.com/

I really appreciate all kind of contributions.

Check out Blog post on MLGenerator : Generate Machine Learning Code in Few Clicks Using Machine Learning Code Generator | by Durgesh Samariya | Towards AI | Feb, 2021 | Medium

4 Likes

Hello @durgeshsamariya, welcome to the community :slight_smile:

Nice app :+1: it reminds me a bit of https://traingenerator.jrieke.com/ actually, did you have a look at it?

Always happy to see apps that generate code to help other users bootstrap their projects. I’ll be waiting for the GPT-3 version of code generation now.

Cheers,
Fanilo

1 Like

hi @andfanilo,

Thanks for your warm welcome.

Yeah, MLGenerator is bit similar to traingenerator. Our motivation is somewhat similar but the goal is different. As far as I can see, Traingenerator provide code template for the specific applications – image classification and object classification. While MLGenerator provides code for core machine learning tasks – classification, clustering, anomaly detection dimensionality reduction.

We can say something MLGenerator motivated for Traingenerator.

Thanks for pointing this out. I will add this in my README file.

Cheers,
Durgesh

3 Likes