naivebayes

I am finding it hard to understand the process of Naive Bayes, and I was wondering if someone could explain it with a simple step by step process in English. I understand it takes comparisons by times occurred as a probability, but I have no idea how the training data is related to the actual dataset. Please give me an explanation of what role the training set plays. I am giving a very simple example for fruits here, like banana for example training set--- round-red round-orange oblong-yellow round-red dataset---- round-red round-orange round-red round-orange oblong-yellow round-red round

I am implementing Gaussian Naive Bayes Algorithm: # importing modules import pandas as pd import numpy as np # create an empty dataframe data = pd.DataFrame() # create our target variable data["gender"] = ["male","male","male","male", "female","female","female","female"] # create our feature variables data["height"] = [6,5.92,5.58,5.92,5,5.5,5.42,5.75] data["weight"] = [180,190,170,165,100,150,130,150] data["foot_size"] = [12,11,12,10,6,8,7,9] # view the data print(data) # create an empty dataframe person = pd.DataFrame() # create some feature values for this single row person["height"] = [6]