Step 1

First, we connected to the Snowflake database using Snowpark Python. This lets us easily access all the retail data needed.

Step 2

Then we grabbed a historical sales dataset with info on sales and store demographics from different locations. This dataset had everything we needed to start digging in.

Step 3

We cleaned up the data, got it into a nice Pandas DataFrame, and organized it around the 'Transaction_Date'. This made the data much easier to work with.

Step 4

We decided to use XGBoost since it's great for problems like sales forecasting. We spent some time tuning it to fit our data just right - tweaking things like the learning rate and number of estimators.

Step 5

To streamline training, we set up a Stored Procedure in Snowflake to re-train the model efficiently.

Step 6

Then came the fun part - testing the model on three months of data to see how well it forecasted sales. The results were looking good!

Step 7

Now our XGBoost model is ready to start forecasting sales for the next 6 months. This will give us crucial insights to make better decisions. For detailed instructions, please refer to https://github.com/sree-soundarya/Sales-forecasting/blob/main/forecasting.ipynb.