Exercise 1: Logistic Regression Write a scikit-learn based application to predict the secondary school student performance using a logistic regression model. The dataset is present in file student.cleaned.data.csv. The features to be taken into account are traveltime, studytime, failures, famrel, freetime, gout, health. The target should be G3. In G3 column, assume the values less than 10 to be 0, and the values equal to or more than 10 to be 1. Evaluate the accuracy of the model. (5 marks)

Exercise 2: Support Vector Machines Write a scikit-learn based application to classify MNIST digits using a Support Vectors Machine (SVM) model. The dataset is from http://yann.lecun.com/exdb/mnist/. You must use a tensorflow function to just fetch the data. The description about this tensorflow function is in this page: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data Rest of the functionality must be accomplished using scikit-learn library. Train the model using the top 60 rows out of 60000 rows of the training data (present in x_train; see below how to obtain the training data in x_train). Test the model using top 10 rows out of 10000 rows of test data (present in x_test; see below how to obtain the test data in x_test). Evaluate the accuracy of the model.