🏒 NHL Salary Predictor

A Python-based machine learning application that predicts an NHL player’s estimated salary using historical draft and performance data . The project combines data preprocessing, linear regression modeling, and a lightweight Tkinter desktop interface for interactive predictions.

This project was built as an exploratory ML + Python UI application and later cleaned up to follow better software engineering practices.

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📌 Project Overview

The NHL Salary Predictor estimates player salary based on five core features:

• Draft Year
• Draft Round
• Overall Draft Position
• Games Played
• Goals Scored

A Linear Regression model is trained on historical NHL data https://www.kaggle.com/datasets/camnugent/predict-nhl-player-salaries, evaluated using Mean Absolute Error (MAE), and then saved locally for reuse. Users can interact with the model through a simple GUI that validates inputs and displays predicted salary in real time.

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🧠 How It Works

1. Data Preparation

• Training data is loaded from train.csv
• Features and target variables are explicitly defined
• Data is split into training and testing sets (75% / 25%)

2. Model Training

• A LinearRegression model from scikit-learn is trained
• Model performance is evaluated using Mean Absolute Error (MAE)
• Results are printed to the console

3. Model Persistence

• The trained model is serialized using joblib
• Saved as a binary file (salary_model.joblib)
• Reloaded at prediction time to avoid retraining

4. User Interface

• Built using Tkinter
• Numeric spinboxes enforce valid input ranges
• Predict button is disabled until inputs are valid
• Estimated salary is displayed in a read-only panel

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📊 Features Used

Feature	Description
DftYr	Draft year
DftRd	Draft round
Ovrl	Overall draft position
GP	Games played
G	Goals scored
Salary	Target variable (USD)

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🖥️ Application UI

The desktop UI allows users to:

• Enter player draft and performance data
• Receive instant salary predictions
• View clearly formatted output
• Avoid invalid inputs through real-time validation

The UI is intentionally minimal to keep focus on model behavior rather than styling.

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📈 Model Performance

Example output after training:

• Training MAE: ~1.29M USD
• Test MAE: ~1.31M USD

These results reflect the complexity and variability of NHL salaries and demonstrate the limitations of using a simple linear model for real-world compensation prediction.

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📁 Project Structure

NHL-Salary-Predictor/
│
├── view.py                # Model training, prediction logic, and UI
├── train.csv              # Training dataset
├── salary_model.joblib    # Serialized ML model (generated at runtime)
├── environment.yml        # Conda environment definition
├── README.md              # Project documentation
└── .gitattributes

⚠️ Limitations

Uses a basic linear regression model

Does not account for:

• Player position

• Assists, points, or ice time

• Contract structure or inflation

• Salary cap era differences

• Predictions should be treated as educational estimates, not real salary evaluations