How do you predict a continuous variable when all your independent variables are categorical I am new to data science and ML. Recently I have been given a sales dataset which contains weekly sales of a fashion brand. It has information about the product like category(t shirt, polo shirt, cotton shirts, briefs, jeans, etc.), gender (male, female, unisex), partner stores ( multiple retail outlets), the color of the apparel (some 150 color codes in hex format), MRP, Sold MRP and quantity sold. The data is for two years timeframe.
I added a new column called discount which is deriver from sold mrp/qty and mrp.
Can anyone suggest any kind of predictive modelling scenario for the above kind of data? I have already done time series forecasting and clustering based on high-performing partner stores. I tried Regression but how do you perform regression when most of the independent variables are categorical in nature ?
Any help would be much appreciated.
It depends on the problem statement, have you got some background about the source of this dataset.. if not then predicting sales in the most common type of analysis you can do here.

And regression doesn't really only depend on input values, it is also considering the past output values so do not worry if the input values are all categorical. You can also try gradient boosting regression trees for predicting sales in partner stores.

Aprt from predictive models, its also a good idea to do some data analysis and  find trends and patterns within your data by utilizing graphs