In this problem you are required to apply various classification techniques on a benchmark dataset: Congressional Voting Records Data Set, from the UCI repository (https://archive.ics.uci.edu/ml/machine-learning-databases/voting-records/house-votes-84.data). This data set includes the class label (republican or democrat) of the U.S. House of Representatives congresspeople and their votes in the 16 key voting sessions. Although in reality there are nine different types of votes, in our case it is simplified into three cases: y, n and ?. So, the cases: voted for, paired for, and announced for are all simplified to y. Cases: voted against, paired against, and announced against are all simplified to n. Cases: voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known are all simplified to an unknown disposition or abstaining, denoted ?. In order to assess the performance of the classification techniques, you are going to apply a simplified version of the k-fold cross validation method. The simplified k-fold cross validation method proceeds in this way: Shuffle the dataset Divide the dataset into k equal partitions For each of the partitions: o Apply the classification model using the union of (k-1) other partitions as training set o Test it on the current partition o Analyze the performance (precision, recall, accuracy) for this case Generate the overall performance report, taking the averages of the found results.

Apply a 5-fold cross validation methodology, as described above, for all the classification techniques that we have studied in our course: i. Decision trees ii. KNN iii. Support Vector Machines iv. Logistic Regression v. Nave Bayes For each classification technique that is applied, the overall performance report should be printed. Briefly compare the results.

Please solve in python