Predicting the price of a football player using Machine Learning Algorithms
In the English Premier League, May - July represents a lull period due to the lack of club football. What makes up for it, is the intense transfer speculation that surrounds all major player transfers today. An important part of negotiations is predicting the fair market price for a player. You are tasked with predicting this Market Value of a player using the data provided below
The attached data set consists of the following attributes:
name: Name of the player club: Club of the player age : Age of the player position : The usual position on the pitch position_cat : 1 for attackers
2 for midfielders
3 for defenders
4 for goalkeepers
market_value : As on transfermrkt.com on July 20th, 2017 page_views : Average daily Wikipedia page views from September 1, 2016 to May 1, 2017 fpl_value : Value in Fantasy Premier League as on July 20th, 2017 fpl_sel : % of FPL players who have selected that player in their team fpl_points : FPL points accumulated over the previous season region: 1 for England
2 for EU
3 for Americas
4 for Rest of World
nationality new_foreign : Whether a new signing from a different league, for 2017/18 (till 20th July) age_cat club_id big_club: Whether one of the Top 6 clubs new_signing: Whether a new signing for 2017/18 (till 20th July)
You have learned about a number of regression algorithms in your course: Linear Regression, Lasso Regression, Ridge Regression, Nearest Neighbour Regression, Support Vector Regression, Tree Regression, Random Forest Regression and Gradient Boosted Regression.
Your ask is:
Use Seaborn to investigate the data and present your findings Build models using all the algorithms above to predict market_value Tune the hyperparameters and build the most accurate model Use model selection approaches discussed in class to choose the best model Implement a Genetic Algorithm for learning attribute weights for the Nearest Neighbour Algorithm. Implement at least one mechanism for maintaining Diversity within the Population Deploy your model as a RESTful Web Service