Description
in python please7.15 LAB: Warm up: People’s weights (Lists)(1) Prompt the user to enter four numbers, each corresponding to a person’s weight in pounds. Store all weights in a list. Output the list. (2 pts)Ex:Enter weight 1:
236.0
Enter weight 2:
89.5
Enter weight 3:
176.0
Enter weight 4:
166.3
Weights: [236.0, 89.5, 176.0, 166.3]
(2) Output the average of the list’s elements with two digits after the decimal point. Hint: Use a conversion specifier to output with a certain number of digits after the decimal point. (1 pt)(3) Output the max list element with two digits after the decimal point. (1 pt)Ex:Enter weight 1:
236.0
Enter weight 2:
89.5
Enter weight 3:
176.0
Enter weight 4:
166.3
Weights: [236.0, 89.5, 176.0, 166.3]
Average weight: 166.95
Max weight: 236.00
(4) Prompt the user for a number between 1 and 4. Output the weight at the user specified location and the corresponding value in kilograms. 1 kilogram is equal to 2.2 pounds. (3 pts) Ex:Enter a list location (1 – 4):
3
Weight in pounds: 176.00
Weight in kilograms: 80.00
(5) Sort the list’s elements from least heavy to heaviest weight. (2 pts) Ex:Sorted list: [89.5, 166.3, 176.0, 236.0]
Output the average and max weights as floating-point values with two digits after the decimal point, which can be achieved as follows:print(f'{your_value:.2f}’)
![2a. Write a Python program to perform supervised classification on the Iris and Indian Pines datasets using Naive Bayes, Support Vector Machines (with RBF kernel), and Multi-layer Perceptron (MLP) classifiers for training sizes ={10%,20%,30%,40%,50%} for each of the below cases: i) With dimensionality reduction - Reduce data based on your choice of 'K' dimensions from 1a using each of the dimensionality reduction methods (PCA) followed by supervised classification by the listed classifiers. ii) Without dimensionality reduction - Data is followed by supervised classification using the listed classifiers. iii) Provide the plots for overall training accuracy, and overall classification accuracy versus the training size for all methods (classification schemes). Tabulate the classwise classification accuracies (i.e. extension of the sensitivity and specificity values) only for 30% training size over all methods for each dataset for case ii) i.e. without dimensionality reduction. Please note that the point distribution is as follows: - Total 4 plots, i.e. 2 plots for each dataset [case i and ii, overall training, and testing accuracy] - 2 tables (classwise accuracy), i.e, 1 table per dataset - Total points available: 30 points (5 points per plot/table)](https://gotit-pro.com/wp-content/uploads/2023/10/638be90e-4e3d-41db-a9d6-5279466b2f80.jpg)
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