1. Classification using Nave Bayes (7 points) For this question, you should be working with the data set called affiliation. This data set includes votes of each of the U.S. House of Representatives (or Congressmen/women) on the 16 key votes identified as 16 different attributes in the data set. We are going to use a portion of this data set (marked for training) to train our classification model. The goal of the classification is simple: given the stands (votes) of an individual congressman/woman, can we predict his/her party affiliation? As to be expected with any real-world dataset, there are several records with NULL values. However, we have taken a subset of the dataset with only those records that do not have a NULL value. The table training-no-NULL should be used to train (build) classification models, and the table testing-no-NULL should be used to test the results. The appropriate data files are provided on Canvas. You can use Excel for parts (a), (b) and (c). Use Python for part (d). (a) Prepare a contingency table or frequency (count) chart for the data set and populate it based on the training data. See examples covered in class. A frequency chart shows cross-tab