Case Study 2

Introduction Football, as we all know, is an international sport that is particularly popular in Europe. The market value of footballers is one of the most intriguing aspects of football when it comes to federations, clubs, and players. The projection of market valuations of players has always been one of the most important considerations for a football club, and it can be influenced by a variety of factors. Our analysis includes a variety of athlete characteristics such as age, height, left or right foot, number of goals per season, and international impact.

This is a web application designed to show the project structure for a machine learning model deployed using flask. This project features a machine learning model that has been trained to predict the value of players.

Installation

First clone the repo locally.

git clone https://github.com/Pranshu1993/Transfer_Market_Values_Prediction.git

Create a new virtual environment in the project directory.

python3 -m venv ./venv

Activate the virtual environment.

source venv/bin/activate

While in the virtual environment, install required dependencies from requirements.txt.

pip install -r ./requirements.txt

Now we can deploy the web application via

python app.py

and navigate to http://127.0.0.1:5000/ to see it live. On this page, a user can then submit text into the text field and receive predictions from the trained model and determine how much the player is worth with your own stats.

The application may then be terminated with the following commands.

$ ^C           # exit flask application (ctrl-c)
$ deactivate   # exit virtual environment