The process to find a project took more time than I expected. At the first moment I didn`t know where to find interesting projects, and I decide to search for them on google. Most of the results were large projects, like pandas and scikit-learn, that had serious hard problems to solve at this instance and with a lot of people disputing the issues.
I discovered a site called Up for Grabs, that showed me a lot of projects that I could have some good participation. To decide the project I had 3 points that had to be reached: it should be in machine learning field (my field of interest), it had to be feasable in the time I had (by difficult level) and the third one is that it should have had a merge with less than 2 months (because then I could see if it would be possible to better interation with the community).
Then the project that I found in this sence was 100DaysofMLCode, that consists in different machine learning algorithms and uses presented in a simple and praticle way, in a way that is great for a beginner in this field to have a good overrview of what exists and how to implement it.
## Contributing
At the first moment, to contribute, I followed the recommendation of another friend to read the text "studying a little as to how to contribute to an open source project" and I forked the project to my repository.
Meanwhile, I posted an issue in the git GitHub, for the project about the idea I had to help them to make the project even more useful in the context of Dimensionality Reduction. After this, I started working on the project. I decide to add the t-SNE (t-distributed stochastic neighbor embedding) algorithm in the Dimensionality Reduction folder that they had, as part of the idea of the project to offer some structural and powerful ML algorithms for the community. As used this type of algorithm in another application in my life, and it was really useful, I thought it would be great to put an example of the usability and outputs of this method. So I use the scikit-learn in the application and also implemented a plotting method.
In the end, I've opened the pull request. Until now I didn't receive an answer about the merging or not.
## Conclusion
After this project I think I was able to explore and learn a lot about the process of contributing to an open source project. Previously, I didn't know what that process would be like and I understood how the interaction with the project happens and how I can contribute to one. Also, understanding more about the reality of open source projects was something that stood out. I hope my contribution will be useful for the next ones and I hope to be part of open source projects again.
