Predict Future Sales using Ensembled Random Forests

TL;DR

This paper presents an ensemble-based Random Forest approach with feature engineering for predicting future sales, achieving high accuracy and ranking 5th on Kaggle's leaderboard.

Contribution

It introduces a simple yet effective ensemble method for sales prediction that outperforms many conventional techniques.

Findings

Final score of 0.88186 RMSE

Achieved 5th place on Kaggle leaderboard

Ensemble learning improved prediction accuracy

Abstract

This is a method report for the Kaggle data competition 'Predict future sales'. In this paper, we propose a rather simple approach to future sales predicting based on feature engineering, Random Forest Regressor and ensemble learning. Its performance turned out to exceed many of the conventional methods and get final score 0.88186, representing root mean squared error. As of this writing, our model ranked 5th on the leaderboard. (till 8.5.2018)