ML Project whats-cooking

After a brief hiatus from Machine learning generally ,this year I have decided to start looking at doing more ML.Learn from old kaggle competitions and participate in new ones.I plan to spend a few hours every week looking at old competitions just to hone my pretty rusty ML skills. For the first one this year I start out of with the WHATS COOKING competition.

In this competition we are asked to predict the category/cuisine of a dish from its ingredients. First step is EDA. I do a count of the ingredients to see if some cuisines are generally more verbose than others .Some do seem verbose potential here for a feature. I clean the data and stem it remove stop words remove commonly occuring words etc.On further investigation I find that when certain ingredients occur together than there probability of it being a certain cuisine increases drastically.So I find 2 words and 3 word combinations for every ingredient in the list.This could potentially be an interesting feature for the model.

After adding our 2 ingredient features I see that we have potentially too many features. We run a LInearSVC on this feature set since this is reasonably resistant to over-fitting. The final model gave me a score of 0.80882

Code