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README.md

EPL-Player-Market-Value-Prediction

Project Overview

This project focuses on predicting a football player's market value based on their statistical performance. In the competitive world of football, accurately assessing a player's market value is crucial for clubs and scouts. This project aims to provide a data-driven solution for this task.

Models Explored

Two different models were employed to tackle the prediction problem:

  1. Linear Regression: A traditional regression method used to establish a linear relationship between a player's statistics and their market value. This model can provide a baseline for prediction.

  2. XGBoost Regressor: Leveraging the power of gradient boosting, the XGBoost regressor aims to capture complex relationships within the data, potentially leading to improved accuracy.

Model Evaluation

To compare the performance of the models, several key metrics were used:

  • Mean Squared Error (MSE): A measure of the average squared differences between the predicted and actual market values.

  • Mean Absolute Error (MAE): This metric provides the average of the absolute differences between the predicted and actual values, offering a more interpretable measure of error.

  • R-squared (R2): Also known as the coefficient of determination, it quantifies the proportion of the variance in the market value that is predictable by the model.

Results

For Linear Regression:

  • Mean Squared Error (MSE): 34.804
  • Mean Absolute Error (MAE): 4.346
  • R-squared (R2): 0.781

For XGB Regressor:

  • Mean Squared Error (MSE): 30.853
  • Mean Absolute Error (MAE): 3.798
  • R-squared (R2): 0.805

The results indicate that both models show promise in predicting football player market values. The XGBoost Regressor outperforms the Linear Regression model, providing lower error metrics and a higher R-squared value.

Conclusion

In the world of football, accurately predicting a player's market value can lead to better decision-making for clubs and scouts. This project showcases the potential of data-driven models for this task. The XGBoost Regressor, in particular, demonstrates promising results, indicating its suitability for predicting football player market values.