Hitters Salary Prediction

This project is a machine learning application aimed at predicting employee salaries based on various features such as experience, education level, location, etc. By using different models and techniques, the project seeks to present an optimized model for salary predictions.

Table of Contents

• Project Structure
• Features
• Installation
• Usage
• Results
• Contributing
• License
• Contact

Project Structure

• catboost_info/: Information and log files related to the CatBoost model.
• data/: Directory containing the datasets.
• output/: Project outputs, result files, and graphs.
  • mse_comparison_plot.png: Comparison plot of different models.
  • result.csv: File containing the prediction results.
• .gitignore: File to exclude unnecessary files from being included in Git.
• config.py: Configuration file used throughout the project.
• data_loader.py: Helper classes for loading and processing data.
• data_preprocessor.py: Functions and classes for data preprocessing.
• feature_selector.py: Code for feature selection.
• hyperparameter_tuner.py: Functions for hyperparameter optimization.
• main.py: Main execution file of the project.
• model_trainer.py: Code for training and evaluating models.
• research.py: Code for research and model experiments.
• visualization.py: Code for visualizing data and results.

Features

• Data Loading and Processing: Loading, preprocessing, and analyzing data.
• Feature Selection: Selecting the best features for the model.
• Model Training: Training models with various algorithms like CatBoost.
• Hyperparameter Optimization: Adjusting hyperparameters for optimal model performance.
• Visualization: Graphically presenting the model results.

Installation

To run the project, follow these steps:

Requirements

• Python 3.x
• You can use the requirements.txt file to install the necessary Python packages (if available):

pip install -r requirements.txt

Running the Project

1. Clone the repository:

   git clone https://github.com/Furk4nBulut/Salary-Prediction.git
   cd Salary-Prediction

2. Load and preprocess the data:

   python data_preprocessor.py

3. Train the model:

   python model_trainer.py

4. Evaluate and visualize the results:

   python visualization.py

Results

• MSE Comparison: The performance of different models is visualized in output/mse_comparison_plot.png.
• Prediction Results: The predicted salaries are stored in output/result.csv.

Contributing

If you would like to contribute to this project, please open an issue first. Any contributions or suggestions are welcome.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions about this project, feel free to reach out:

• Email: [email protected]
• GitHub: Furk4nBulut