he discussion board assignment for this week is about fitting a Prophet model to the imputed sales data. You may skip the model training and testing steps and proceed directly to fitting a Prophet model to each SKU using the entire imputed time series.

Follow these steps to complete the assignment:

Part I: Data Preparation

Begin by creating a new Jupyter Notebook to execute the required Python code. Ensure to incorporate comments for clarity.

Write a Python statement to import the data file Data Analytics Forecasting.xlsx Download Data Analytics Forecasting.xlsx$,$ which serves as the dataset for the data analytics project. Make sure you import the necessary Python libraries.

Subset the data file to retain specific SKUs: S$41948,$ S$1$A$174,$ S$1$A$0$A$2,$ S$42048,$ and S$42049.$

For each SKU, employ the deletion method to eliminate the initial few years of data where a substantial number of entries contain missing values. Consider generating a time series plot to aid in determining the appropriate point to split the data.

If necessary, apply the linear imputation method to fill in the remaining missing values for each SKU.

For each SKU, save the imputed data to an Excel file. This imputed data will be used for building a Prophet model in Part II$.$