This repository contains a Multilayer Perceptron (MLP) regression model to predict car prices based on various features like make, model, engine size, etc., using the PyTorch framework.
- Implements MLP regression models with varying depths and complexities to predict car prices.
- Uses the CarPrice_Assignment dataset for training and testing the models.
- Includes comprehensive preprocessing, normalization, and encoding of the dataset.
- Provides performance evaluation through Mean Squared Error (MSE) and R2 Score metrics.
- Contains visualization of loss and R2 Score over training epochs.
- Clone the repository.
- Install the required Python packages listed in
requirements.txt. - Download and preprocess the dataset using the provided scripts.
- Data normalization and cleaning to handle missing values and outliers.
- Encoding of categorical features to numeric values for model training.
- Data split into training, validation, and test sets for model evaluation.
- Models with different configurations (MLP1, MLP2, MLP3) are trained to find the best architecture.
- Uses Adam and Adagrad optimizers with varying learning rates to optimize the models.
- Saves the best model based on validation performance.
- Evaluation on test data to measure MSE and R2 Score.
- Visualization of model performance through loss and R2 Score plots.
- Detailed comparison of predicted vs. actual prices on test samples.
Contributions to improve the project and implement new features are welcome. Please fork the repository, make your changes, and submit a pull request.
The project is licensed under the MIT License. See the LICENSE file for more details.
- Dataset providers for making the car price dataset publicly available.
- The PyTorch team for providing a powerful and user-friendly deep learning framework.
For more information and to view the source code, visit the GitHub repository.