So I am new to regression and I have a basic doubt:
Let's say I have 100 unique products(product id) which have a lot of other features that contribute in calculating the product_price(dependent variable). Some of the features worth mentioning are Quantity_sold, Region, Supplier etc.
My aim is to calculate the price of each of the product at a
"Product_id, Quantity_sold,Region,Supplier" level
i.e. the ideal output of the predicted price should be as follows:
Product_id Quantity_sold Region Supplier Product_price 1023 25 Texas AMC 20
Now my question is should i first filter the data for specific product_id, quantity, region,supplier and then run linear regression or should i run the linear regression on the whole data set containing all the product id, quantity,region, supplier.
In case I should run the regression on the entire data set, then should i convert the region(more than 100 unique values), supplier(more than 300 unique values) etc into dummy variables or remove them all together.
This question may sound very novice to you, but I would really appreciate some explanation as to how do we usually go about solving such problems in regression.