Implement Naive Bayes

naive.md

Implement Naive Bayes Classifier

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes' theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.

Use the above dataframe as reference and build a Naive Bayes Classifier using python. Follow the guidelines.

1. Build a production ready classifier following the API interfaces.

2. Class should be reusable using new dataframes.

3. fit method should take data which is complete dataframe X list of only feature names and y target name not complete nd.array.

4. predict method should accept X only one example as list or tuple and return predicted probability.

   class NB:
   
       def __init__(self):
           pass
   
   
       def fit(self, data,  X, y):
           pass
   
   
       def predict(self, X):
           pass