Write Python code(s) to do:
- Ask the user to enter the: NAME (A, B, C, D, E, F, G, H, I, J), AGE, GPA (out of 4) and the MAJOR ( CS, CET, CIS, CE). Then print these information as follows: My name starts with the letter (NAME) and I am (AGE) years old. I study (MAJOR) and my GPA is: (GPA).
- Repeat step 1 for 10 users with different values (Names can’t be repeated).
- Store the entered information in the file students.CSV.
- Plot the 4 features (NAME, AGE, GPA, and MAJOR) on separate figures using the seaborn library functions.
- Calculate the mean() of the GPAs and Ages.
- Define a function to evaluate the Root Mean Squared Error (RMSE) according to the following equation:
- Calculate the mean() of the GPAs, and consider it to be the predicted value for all students () and consider the original GPA of the student to be .
- Compute the RMSE for each student according to the above equation (the squared root of the difference between the predicted and original GPA values). The value of m is 10 (number of the samples)
- Store the computed RMSE values in the student.CSV file (as a new column entitled RMSE)
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