Silver Medal Solution of M5 Forecasting - Accuracy Kaggle Competition

About The Project

Given hierarchical sales data from Walmart, the world’s largest company by revenue, we need to forecast daily sales for the next 28 days.

This is my solution to the M5 Forecasting - Accuracy Kaggle competition. I used a LightGBM to train on the tabular dataset, which was preprocessed to include 7-day and 28-day rolling mean features.

Result: Weighted root mean squared scaled error (RMSSE) score of 0.63730 in the private leaderboard. Ranked 248 out of 5558 teams (Top 5% - silver medal).

Getting Started

To get a local copy up and running, follow these simple example steps.

Installation

Clone the repo

git clone https://github.com/stephenllh/m5_accuracy.git

Change directory
```
cd m5_accuracy
```
Install packages
```
pip install requirements.txt
```

Usage

Change directory
```
cd m5_accuracy
```
Create a directory called input
```
mkdir input
cd input
```
Download the dataset into the folder
- Option 1: Use Kaggle API
  - pip install kaggle
  - kaggle competitions download -c m5-forecasting-accuracy
- Option 2: Download the dataset from the competition website.
Run the training script
```
cd ..
python train.py
```
(Optional) Run the inference script
```
python inference.py
```

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Stephen Lau - Email - Twitter - Kaggle