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uefa-champions-leauge-predictor's Introduction

UEFA Champions League Predictor

Based on last 5 years of UEFA Champions League group stage records, the machine learning model tried to predict the outcome of some teams participating in the League based on their performance in the Group Stage only.

The model predicted stage, which each team can reach in the season 2019-20. Simple Random Forest Classification Model is used in this project.


Predictions for the season 2019-20

Stage Stage
Real Madrid Round of 16
Barcelona Quarter Finals
Bayern Munich Semi Finals
Tottenham Hotspur Finals
Manchester City Quarter Finals
Juventus Quarter Finals
Atlético Madrid Finals
Liverpool Quarter Finals
Borussia Dortmund Round of 16
Chelsea Quarter Finals
Paris Saint-Germain Finals

About the data

All the datasets used in this project were made manually (well, actually using web scraping) and, can be found here.

Data consists of Group Stage information from the past 5 years in UEFA Champions League, of some selected high-rated teams only. Datasets are from the seasons 2014-15, 2015-16, 2016-17, 2017-18 and 2018-19.


Following teams were selected for the project

  • Real Madrid
  • FC Barcelona
  • Bayern Munich
  • Tottenham Hotspur
  • Manchester City
  • Juventus
  • Atlético Madrid
  • Liverpool
  • Borussia Dortmund
  • Chelsea
  • Paris Saint-Germain

Standing of each of these teams in the past years were also collected and, converted into a number using the following table

Stage Integer Representation
Group Stage 1
Round of 16 2
Quarter Finals 3
Semi Finals 4
Finals 5

Steps to setup the project locally

  • Clone the repository
  • Create a python virtual environment, run python3 -m virtualenv venv && source venv/bin/activate
  • Install all the required dependencies, run pip install -r requirement.txt
  • Input the details of whatever team you want to predict future for in src/prediction.py and, simply run the python script python3 src/prediction.py.
  • Make sure you print the predictions or else you won't see the result of the model.

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