The problem ML-Crate solves

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

When I started learning ML, I was not able to find out as many projects and datasets that will be useful for me to deploy new ideas and new topics. I have also asked the same to other students whether they felt the same or not, and I have got 90% of them faced the same issue. From this, I thought of creating a repo which will contain different types of projects and different genres of projects with the difficulty level from 0 to 10 (beginner to advanced). Eventually this will help the learners of ML who are new to the field of ML or wanting to start their journey through ML.

ML-Crate is a project repo which consists various ML projects from beginner level to advanced level. This project have been part of three open source programs, and has 140+ projects in it. All these projects have been done by the participants of the different open source programs. The motto is, “For the contributors, by the contributors”. All the projects are being arranged in the proper manner, following a README file, a Dataset folder, a folder to store the Images and a folder to store the Model for the Machine Learning project. Each project repo is designed in such a way that anyone can understand what's inside that and how it works.

Programs: SWOC 2022, JWOC 2022, CSI RAIT OpenCode 2022, HSOC 2022, KWOC.

Challenges I ran into

ML-Crate project repo has gone through lots of challenges before achieving the heights. Initially people were hesitate about Machine Learning domain, and even don't wanted to contribute as much as other open source projects. After taking few sessions on machine learning, people became interested about this domain, and right now this is one of the best open source project repo from my side.

Few achievements are,
1️⃣ Recognized as the "🥇 TOP PROJECT" for SWOC 2.0 for the year 2021-22. (49 Pull Requestes have been merged!)
2️⃣ Recognized as the "TOP MENTOR" and "TOP PA" for the project 'ML-Crate' in SWOC 2.0.
3️⃣ Recognized as the "🥇 BEST MENTOR" of JGEC Winter of Code 2022, for mentoring students to contribute in the project repo "ML-Crate".
4️⃣ Recognized as the "🥇 BEST MENTOR" of CSI RAIT OpenCode Open Source Program 2022, for mentoring students to contribute in the project repo "ML-Crate".
5️⃣ Recognized as the "🥇 TOP PROJECT ADMIN" of Hack Club RAIT Summer of Code 2022, for mentoring students to contribute in the project repo "ML-Crate".
6️⃣ Got special mention from the organizing team of JWOC for mentorship and community building.
7️⃣ Consecutively and successfully two times participated in the KWOC event.