Predicting Football Player Market Values Using LSTM Neural Networks

This repository contains an LSTM-based model designed to predict football players' market values using historical performance data and relevant variables.

Structure

• football_data_loader.py: Python script for loading and preprocessing football dataset.
• football_player_analysis.ipynb: Jupyter notebook for data analysis and exploration.
• lstm.ipynb: Jupyter notebook implementing LSTM model for market value prediction.
• lstm_market_value_predictor.pth: Saved PyTorch model file.
• requirements.txt: List of Python packages required to run the project.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.6+
• pip

Installation

Clone the repository:

git clone https://github.com/yourusername/football-market-value-forecast.git
cd football-market-value-forecast

Install the required packages:

pip install -r requirements.txt

Dataset

The dataset used in this project contains information about football players, including:

• Player demographics (age, position, etc.)
• Performance statistics
• Historical market values
• League and club information

License

This project is open-source and available under the MIT License.