So, I've been trying to implement my first algorithm to predict the (sales/month) of a single product, I've been using linear regression since that was what were recommended to me. I'm using data from the past 42 months, being the first 34 months as training set, and the remaining 8 as validation.
I've been trying to use 4 features to start:
- Month number(1~12)
- Average price that the product was sold during that month
- Number of devolutions previous month
- Number of units sold previous month
Here are images with graphs comparing the Real Data x Predicted Data and a Error x number of elements graph:
So far the results are not good at all (as shown in the images above), the algorithm can't even get the training set right. I tried to use higher degrees polynomials, and the regularization parameter, it seems to make it worse.
Then, I would like to know if there is a better approach for this problem, or what could I do to improve the performance.
Thanks a lot in advance!