have a question about the type of model which I should use for a dataset I have.
I have use 2 data-sets for my project. After hypothesis testing, I me
Out of the 7 input variables, 6 of them are categorical and 1 is a date column. Now I have encoded the categorical columns using label encoding and converted them into numerical values. Now I’ve used a simple linear regression model on this dataset and achieved a normalized RMSE value of 0.11.
If I want to improve this accuracy, how do I go about doing it? How can I derive upon the kind of models that I can use considering the data set I have?
The data is mostly about forecast revenues for each product group. That's why I have those categorical columns which represent the hierarchy of the product groups.