10.8 AMLS Designer: Data Preparation Background: The idea behind this project is

10.8 AMLS Designer: Data Preparation

Background:

The idea behind this project is to improve the ML pipeline you likely generated in the last assessment by performing a variety of data preparation tasks. 

Data Source:

Use the data file provided nba_salaries.csv containing the salaries of all nba players for the 2022-23 season. The details of this dataset can be found here: https://www.kaggle.com/datasets/jamiewelsh2/nba-player-salaries-2022-23-season. It includes:

Player Information: Player name, team(s) played for during the season.

Per Game Statistics: A wide array of per-game statistics, including points scored (PPG), assists (APG), rebounds (RPG), steals (SPG), blocks (BPG), and more.

Shooting Efficiency: Metrics like field goal percentage (FG%), three-point percentage (3P%), two-point percentage (2P%), and free throw percentage (FT%) for assessing scoring efficiency.

Advanced Statistics: A wide array of advanced metrics such as value over replacement player (VORP), win shares (WS) and true shooting percentage (TS%)

Salaries: The financial aspect of the dataset includes player salaries for the 2022-23 season, offering insights into player earning

Parameters:

Complete the instructions found within each question below.

There are two attempts on this assessment and the highest score will keep.

Points: 35

Resource Files:1 file

nba_salaries.csv

I will upload the file for question 19