I am working on a dataset to predict the sales for the next 6 weeks of a retail store. You can get the datasets from here .
There are two datasets ‘train’ and ‘store’. The dataset ‘store’ has information about different stores owned by the company. There are 1115 stores in total. And each store is significant here with sales.
The dataset train has information about the sales records, number of customers, date, whether it was open on that day, whether it had launched any promotional schemes that day, etc.
Here, the variable ‘store’ has 1115 levels. While applying algorithms like linear regression, it is advisable to convert the variables into one hot encoding, so that they can be easily interpreted by the code. But here, since there are a lot of levels and every one is significant, I am in a confusion. How should I deal with this variable? Should I convert it into one-hot encoding or leave it as it is in the model?
Help would be really appreciated.