Summer Analytics Hackathon - Final Submission

Firstly, I have label encoded the three categorical columns, then I have dropped all the columns that are highly imbalanced, then removes some extreme outliers in a few features, then i have dropped one of the columns that have pearson correlation more than 0.8, all this left me with 16 features. Then I have Trained various models and bagged them together to get the final output.