To submit your work, go to "File", then "Save and export notebook as...". And export your code including the output to HTML.

Download the file shirts.csv. The data set contains information on shirt sales.

In the following questions, write code that would work on a file shirt.csv containing any number of rows.

Use pandas to preprocess the data set following the steps outlined in the notes preprocessing_steps.txt (found in Modules). Print the data set after each step.

feature selection: Delete the column titled 'id'. ordinal values: Map the size values to numbers. feature transformation: In the column 'collection', change 2023 to the value 1 (indicating a new collection), and the previous years to 0 (indicating an old collection). a. categorical values: Replace the column 'fabric' by three columns titled 'fabric_cotton', 'fabric_wool', and 'fabric_polyester'. b. categorical values: Replace the values in the column 'color' by their frequencies. feature aggregation: Combine the columns 'price' and 'tax' into one column 'total_price', by adding the price to the tax. For example, in the first row, the values 13 and 1.3 are replaced with 14.3.

id	size	collection	fabric	color	price	tax
0	L	2023	cotton	orange	13	1.3
1	L	2022	wool	black	16	1.6
2	M	2023	wool	red	20	2
3	S	2021	polyester	white	12	1.2
4	M	2021	polyester	orange	14	1.4
5	S	2023	cotton	blue	20	2
6	S	2022	polyester	yellow	20	2
7	L	2021	polyester	white	14	1.4
8	M	2021	polyester	pink	17	1.7
9	L	2023	cotton	white	15	1.5
10	L	2022	cotton	black	13	1.3