Football Players Market Value Prediction

This project is a machine learning task aimed at predicting the market value of football players based on their attributes and performance data. We utilized a comprehensive dataset of football players to train, test, and validate our models.

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Project Overview

Football clubs invest heavily in players, and accurately estimating a player's market value can provide significant advantages in terms of transfers, negotiations, and financial planning. By leveraging machine learning, we aim to provide a data-driven approach to market value prediction.

Key Features:

• Dataset: Contains detailed information on football players, including attributes like age, position, club, nationality, stats (e.g., goals, assists), and more.
• Goal: Predict the market value of players using machine learning models.
• Results: Visualizations and performance metrics of the trained model.

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Dataset

The dataset used for this project includes the following key features:

• Player attributes: Age, Height, etc.
• Performance metrics: Goals scored, Assists, Matches played, etc.
• Additional details: Club, Nationality, League, Position, etc.

Source:

The dataset was sourced from [kaggle] and preprocessed to clean and normalize the data for training.

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Approach

1. Data Preprocessing:

   • Handling missing values.
   • Encoding categorical features.
   • Feature scaling.

2. Exploratory Data Analysis (EDA):

   • Visualized key trends and correlations in the dataset.

3. Modeling:

   • Trained multiple machine learning models, including Linear Regression, Random Forest, and Gradient Boosting.

4. Evaluation:

   • Evaluated models using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R².

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Results