Classify a dataset using different methods

Using a provided dataset of a categories URI, (URI and category), perform three methods to classify the dataset. Code must be in python. Shuffle the dataset, for each category split randomly to 20-80 testing and training, create 8 fold on the training, where each fold have to have URIs from all the categories, group the testing file together. Method 1: use naive bayes Binary classifier. Method2: use naive bayes Binary classifier on each category to determine where the URI is positive (using binary approach to simulate the multiway). Method3: use Naive Bayes Multiway classifier. For each method show using tables the following: 1-(category-precision-recall- fscore) for each fold, with the total score and weighted average. Note the weight average is because the dataset is not balanced. 2-the confusion matrix of each fold. 3-(fold number- total precision-total recall-total fscore- total weight average).