- I am working on a project for displaying products to customer by context, based on a search query. For example, I don't want customers to have to enter a specific product name, instead searching based on functionality (e.g., "walls do not heat much" would return product names such as "Whirlpool NEO IC355 ROY 3S 340 L Double Door Refrigerator")
- I have a training set comprised of the functionality associated products. I am planning to use Logistic Regression to train a model on these data. How do I process this data in Python or extract features to feed into logistic regression? I have heard of "Bag of words model", but not sure how to use this, or is it even applicable here?
I know there are plenty of NLTK libraries available. But, I want to implement it from the scratch or using minimum external libraries possible.
Please help or are there any resources to refer to?