2) Classification Task MNIST digit dataset: Use keras ML library to download dataset. It is already separated into train and test if you download from keras. If the data is not separated, please divide the data into train [60,000] and test set [10,000]. from keras.datasets import mnist (x_train,y_train),(x_test,y_test)=mnist.load_data() It is a digit dataset. Input is the image feature and Target is the digit class from 0 to 9. The model will be trained to predict the corresponding label for any input image X.

The input image has a dimension of 28*28 matrix size. You may have to change it to vector to train the model. Apply the following classification models: a) Logistic regression classifier b) Nave Bayes classifier Report the performance of testing results and compare the performance between the two algorithms. Submit a report, write down the procedure and corresponding code for each section, and summarize the results and performance.