Kaggle Rossman Store Sales

Predicting Daily Store Sales. Problem description is available at https://www.kaggle.com/c/rossmann-store-sales/overview/description

Documentation

• rossman_data_prep.ipynb Downloads and prepares the data for downstream modeling. The bulk of the data cleaning and feature engineering is done in this notebook. [nbviewer][html]

• rossman_gbt.ipynb Trains a boosted tree using lightgbm that serves as a baseline model. gbt_module and config are helper class and configurations that are used in this notebook. [nbviewer][html]

• rossman_deep_learning.ipynb Trains a fastai deep learning model that showcase the use of embeddings for categorical features. [nbviewer][html]

Results

Private Leaderboard Score, Root Mean Square Percentage Error (RMSPE):

• boosted tree: 0.1226

• deep learning: 0.1137

• leaderboard score 50th place: 0.1120

• leaderboard score 1st place: 0.1002

Note that the model here is not tuned extensively and no blending/stacking was used.