30 days of ML was a machine learing competition conducted on Kaggle to enable ML enthusiasts to kickstart their kaggle journey. This contest involved learning the concepts of machine learning as well as applying them through code. I learnt some of the most important building blocks of data science which included data exploration, random forests, various types of encodings, cross validation, xgboost and many more as I progressed through the competition.
Towards the end of competition, we had to make predictions on two datasets, housing price prediction and the competition dataset itself. I have included my code in this repository in which I applied the concepts that I learnt during the competition.