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Clone the repository.\n2. Install the required Python packages listed in `requirements.txt`.\n3. Download and preprocess the dataset using the provided scripts.\n\n## Data Preprocessing 📊\n- Data normalization and cleaning to handle missing values and outliers.\n- Encoding of categorical features to numeric values for model training.\n- Data split into training, validation, and test sets for model evaluation.\n\n## Model Training 🚀\n- Models with different configurations (MLP1, MLP2, MLP3) are trained to find the best architecture.\n- Uses Adam and Adagrad optimizers with varying learning rates to optimize the models.\n- Saves the best model based on validation performance.\n\n## Results and Evaluation 📊\n- Evaluation on test data to measure MSE and R2 Score.\n- Visualization of model performance through loss and R2 Score plots.\n- Detailed comparison of predicted vs. actual prices on test samples.\n\n## Contributing 🤝\nContributions to improve the project and implement new features are welcome. Please fork the repository, make your changes, and submit a pull request.\n\n## License 📜\nThe project is licensed under the MIT License. See the LICENSE file for more details.\n\n## Acknowledgements 🙌\n- Dataset providers for making the car price dataset publicly available.\n- The PyTorch team for providing a powerful and user-friendly deep learning framework.\n\nFor more information and to view the source code, visit the [GitHub repository](https://github.com/MJAHMADEE/MLP_Regression/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjahmadee%2Fmlp_regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmjahmadee%2Fmlp_regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjahmadee%2Fmlp_regression/lists"}