ml_project_fifa

Project Description 📄

❄️ Built a Random Forest Regressor model using scikit learn on European SOccer Database to predict 'overall_rating' based on other dependent variable.

Jupyter Notebook: (https://github.com/shiv0112/ml_project_fifa/blob/master/research)

Data:

:European Soccer Database
25k+ matches, players & teams attributes for European Professional Football

About Dataset
The ultimate Soccer database for data analysis and machine learning
What you get:

+25,000 matches
+10,000 players
11 European Countries with their lead championship
Seasons 2008 to 2016
Players and Teams' attributes* sourced from EA Sports' FIFA video game series, including the weekly updates
Team line up with squad formation (X, Y coordinates)
Betting odds from up to 10 providers
Detailed match events (goal types, possession, corner, cross, fouls, cards etc…) for +10,000 matches
*16th Oct 2016: New table containing teams' attributes from FIFA !

You can easily find data about soccer matches but they are usually scattered across different websites. A thorough data collection and processing has been done to make your life easier. I must insist that you do not make any commercial use of the data. The data was sourced from:

http://football-data.mx-api.enetscores.com/ : scores, lineup, team formation and events

http://www.football-data.co.uk/ : betting odds. Click here to understand the column naming system for betting odds:

http://sofifa.com/ : players and teams attributes from EA Sports FIFA games. FIFA series and all FIFA assets property of EA Sports.

I trained this model using Random Forest:

Selected features

Metrics of all models used:

The Accuracy of the model:

The comparison of Actual value and predicted value by our model

Index page of Website:

Data Input from user:

Finally prediction displayed: