## Description

For Project 2 you will create a regression program and choose a model to predict the women’s

Olympic 100-meter race record time for year 2022. We will code the year of each race as we did

in lecture 2.3. A text file with the data is available on Canvas for the years 1928 through 2008

when the Olympics were held. The first line of the text file indicating there’re m lines of data

and a n number of features (in this case, one).

Your project assignment is to compare three different models, linear, quadratic, and cubic.

hw(x) = w0 + w1x

hw(x) = w0 + w1x + w2x2

hw(x) = w0 + w1x + w2x2 + w3x3

using 5-fold cross validation.

Then you should present a chart, similar to the one in the lecture (see below), of all your test

results and a plot of your training andtest J’s with respect to the polynomial degree.

Linear Quadratic Cubic

1234

5

1235

4

1245

3

1345

2

2345

1

Mean for Training

Mean for Testing

Based on your data and plot, you should then:

• Argue which model (linear, quadratic, or cubic) you expect will best predict the times for

the women’s Olympic 100-meter race in the future.

• Compute weights using the complete data set with your best model.

• Using those weights, write a Python program that takes a year as input, then outputs

the winning women’s Olympic 100-meter race time for that year.

Important Note:

You cannot use python machine learning package that can have the k-fold validation algorithm

as embedded function, for instance, sklearn package.

You are required to submit a project report, including:

• The J value chart as shown in the table above.

• A plot of your training and test J’s with respect to the polynomial degree

• Argue which model (linear, quadratic, or cubic) you will choose

• The final hypothesis function hw(x)

• Predict the women’s Olympic 100-meter race record time for this winter Olympic (2022)

• Full screenshot of your python console.

• A copy of your code

Your report should be named yourlastname_yourfirstname_P2.docx or .doc or .pdf. Your

Python program should be named yourlastname_yourfirstname_P2.py, then zipped together

with your project report and uploaded to Canvas