Implement the first image using simple Python code, not any machine learning aspects. The code has to have conditional statements(if, elif, for loops) for calculating the classification accuracy, and produces an output similar to the one shown in the second image.

Problem 2) Threshold-based Classifier - We have a two-class classification problem (i.e., Cl and C2). Each data sample is represented by two attributes (x, y). The three data samples in class Ci 1 are {(1, 1), (3, 2), (2, 3)} and {(1, 2), (2, 2), (2, 1)} in class C2. Perform the followings in Python: a) Plot the data samples. The data points in classes C1 and C2 must be in two different colors and shapes. Label the axes and add legends as appropriate. b) The code asks the user to enter two thresholds thx and thy. c) Your code calculates and prints the classification accuracy based on the user-entered thresholds. To do so, assume that for any data point (x, y) with x> thx and y> thy, the data sample belongs to class C1, and C2 if otherwise. Using this rule and the user- entered thresholds, the code calculates the classification accuracy for the six data samples. The classification accuracy is defined as the number of correctly classified data points over the total number of data points (6 in here). d) Use a for loop to repeat part c for a total of three times and enter different sets of thresholds each time. e) Based on your observation from the results above, report a suitable set of thresholds that will give the highest possible accuracy. Report your suggested thresholds and the corresponding classification accuracy. un 5 Class 1 Class 2 4 3 y feature 2 N 1 0 -1 0 1 3 4 un 2 5 x: feature 1 Enter the threshold values, TH_x and TH_y Enter the TH_x value: 0 Enter the Th_y value: 0 The classifier accuracy for the selected thresholds is: 8.50 Enter the threshold values, TH_x and TH_y Enter the TH_x value: 5 Enter the Th_y value: 5 The classifier accuracy for the selected thresholds is: 6.50 Enter the threshold values, TH_x and TH_y Enter the TH_x value: -5 Enter the Th_y value: -5 The classifier accuracy for the selected thresholds is: 0.50