## Welcome to the BDACC's internal hackathon!! 

With the same goal to give the NITW community a variety of fairly light-weight challenges ,this competition aims to learn and sharpen skills in different aspects of machine learning and data science. Again, this competition uses fairly light-weight datasets that are synthetically generated from real-world data, and will provide an opportunity to quickly iterate through various model and feature engineering ideas, create visualizations, etc. 

## Synthetically-Generated Datasets

Using synthetic data allows us to strike a balance between having real-world data (with named features) and ensuring test labels are not publicly available. This allows us to host competitions with more interesting datasets than in the past. While there are still challenges with synthetic data generation, the state-of-the-art is much better now than when we started the Tabular Playground Series two years ago, and that goal is to produce datasets that have far fewer artifacts. Please feel free to give us feedback on the datasets for the different competitions so that we can continue to improve!