In the platform of your choice, solve the below problem and send us the link to the repository with the solution.
The purpose is to predict the weekly sales (demand) in physical retail stores.
You'll be provided with a solution after you submit your solution.
This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file stores_sales.csv. Within this file you will find the following fields:
- store: the store number
- date: the week of sales
- weekly_sales: The total sales for the given store
- holiday_flag: whether the week is a special holiday week (1 – Holiday week, 0 – Non-holiday week)
- temperature: Temperature on the day of sale in Fahrenheit
- fuel_price - Cost of fuel in the region
- cpi: Prevailing consumer price index
- unemployment: Prevailing unemployment rate
Holiday Events:
- Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13
- Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13
- Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13
- Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13
Build a model that predicts the weekly_sales for each store. A store is identified by the combination of store and date. The model should be able to predict the weekly_sales for a given store and date.
You can use any machine learning library you want.
The evaluation metric is the Root Mean Squared Error (RMSE).
Send us the link to the repository with the solution.
- Explain the model you built and why you chose it.
- Explain how you would deploy the model in production.
- Explain how you would monitor the model in production.
- Explain how you would retrain the model over time.
- Explain how you would evaluate the model over time.
- Explain how you would improve the model over time.
- The code is well structured and easy to understand
- The notebook / repository is well documented
- Best practices in Python are followed
- Strong understanding of mathematical concepts in machine learning