FOOTBALL-ENGLISH_PREMIER_LEAGUE

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 dataset provided

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 task is:

Use Seaborn to investigate the data and present your findings (20 marks)
Build models using all the algorithms above to predict market_value (15 marks)
Tune the hyperparameters and build the most accurate model (20 marks)
Use model selection approaches discussed in class to choose the best model (10 marks)
Implement a Genetic Algorithm for learning attribute weights for the Nearest Neighbour Algorithm. Implement at least one mechanism for maintaining Diversity within the Population (25 marks)
Deploy your model as a RESTful Web Service (10 marks)

For web app model deployment refer to master branch