Naive Bayes requires less training data to calculate the problem and need less time to train the model.
Naive Bayes works best when you have small training data set, relatively small features(dimensions). If we have huge feature list, the model may not give you accuracy, because the likelihood would be distributed and may not follow the Gaussian or other distribution. Another condition for Naive Bayes to work is that features should be dependent of each other - if you understand the domain, then try to analyze how each features are related to each other, are they affecting the each others likelihood. if not Naive Bayes can give you good result.
We have 30 rows records which are capturing sales for weekday, weekend and holidays based on discount and offer. Will calculate how user showing interest based on offers towards type of day.
Here are few records below
will seperate records based on each requirment. Here we are considering only four features like Day/Discount/Free delivery and also whether purchased or not. will be having more attributes like names, age aand brand of the shirts, but these are not required for calculating sales.