This exercise is a homework assignment that I have in Data Mining course.

This is the link given on the question : https://archive.ics.uci.edu/ml/datasets/optical+recognition+of+handwritten+digits

PROBLEM 3 In this problem you are required to apply various classification techniques on a benchmark dataset: Optical Recognition of Handwritten Digits Data Set, from the UCl repository (https://archive. ics.uci.edu/ml/datasets/optical+recognition + of + handwritten+digits). This is a dataset of optical character recognition of numeric digits from processed pixel data. Each instance of the dataset represents a different 3232 pixel image of a handwritten numeric digit (from 0 to 9 ). Each image was partitioned into 64 partitions of dimensions 44 and the number of pixels with non-background color were counted in each segment. These 64 values (ranging from 016 ) are the first 64 attributes in the data set, and the 65th attribute is the class label, i.e. the number from 09 that is represented by the image. The dataset is already provided in two components: the training component (optdigits.tra) and the testing component (optdigits.tes). Based on the given training and testing components, apply all the classification techniques that we have studied in our course: i. Decision trees ii. KNN iii. Support Vector Machines iv. Logistic Regression v. Naive Bayes For each classification technique that is applied, the confusion matrix and the classification report should also be printed. Briefly compare the results