Team Renegades final submission
We (Team Renegades) made a systematic approach while dealing with the dataset that needed the output of rank for the next three days. We proposed and tried feature engineering to understand the dataset's gist by removing null values and applying sentiment analysis on the dataset. We analyzed certain features and derived some new features from the dataset, thus adding a new dimension. Followed by that, we tried out various models like knn, xgboost, randomforest and more to find which of them had the least RMSE. Our prediction found that knn had the best result.