Salary-Prediction-with-Machine-Learning

1. Business Problem

Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

2. Dataset Story

• This dataset was originally taken from the StatLib library at Carnegie Mellon University.
• The data set is part of the data used in the 1988 ASA Graphics Section Poster Session.
• Salary data originally taken from Sports Illustrated, April 20, 1987.
• 1986 and career statistics, Collier Books, Macmillan Publishing Company.

3. Variables

• AtBat: Number of hits with a baseball bat during the 1986-1987 season
• Hits: the number of hits in the 1986-1987 season
• HmRun: Most valuable hits in the 1986-1987 season
• Runs: The points he earned for his team in the 1986-1987 season
• RBI: The number of players a batter had jogged when he hit
• Walks: Number of mistakes made by the opposing player
• Years: Player's playing time in major league (years)
• CAtBat: The number of times the player hits the ball during his career
• CHits: The number of hits the player has made throughout his career
• CHmRun: The player's most valuable number during his career
• CRuns: The number of points the player has earned for his team during his career
• CRBI: The number of players the player has made during his career
• CWalks: The number of mistakes the player has made to the opposing player during his career
• League: A factor with levels A and N, showing the league in which the player played until the end of the season
• Division: a factor with levels E and W, indicating the position played by the player at the end of 1986
• PutOuts: Helping your teammate in-game
• Assits: The number of assists the player made in the 1986-1987 season
• Errors: the number of errors of the player in the 1986-1987 season
• Salary: The salary of the player in the 1986-1987 season (over thousand)
• NewLeague: a factor with levels A and N indicating the league of the player at the beginning of the 1987 season

TASK

Salary using data preprocessing and feature engineering techniques develop a forecasting model