| # Kaggle M5 Forecasting |
| Source - https://www.kaggle.com/c/m5-forecasting-accuracy. |
| |
| This is an example of how one might use Hamilton using the M5 Forecasting Kaggle |
| challenge as an example. The code here is based on Ratan Rohith's notebook |
| that can be [found here](https://www.kaggle.com/code/ratan123/m5-forecasting-lightgbm-with-timeseries-splits). |
| The notebook is a beginner level notebook that does some feature engineering, and uses LightGBM to fit |
| a model over folds that then predicts the future. The notebook is a good starting point for anyone want to know |
| the basics of an approach to time series forecasting. |
| |
| # Set up |
| 1. Set up the python virtual environment and activate it, then install the required python dependencies. |
| `pip install -r requirements.txt`. |
| 2. [Download the data](https://www.kaggle.com/competitions/m5-forecasting-accuracy/data) -- |
| you will need to log in to Kaggle to download the data. |
| 3. Decompress the data into the same folder as the code. |
| 4. Run `run.py`. `python run.py`. |
| |
| # Notes |
| Here's what this code executes: |
|  |
