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https://github.com/alphacrypto246/old-car-price-prediction
The Old Car Price Prediction project predicts used car prices using features like age, mileage, and fuel type. It includes data preprocessing, model training, and visualization of trends, with easy customization for additional features or models.
https://github.com/alphacrypto246/old-car-price-prediction
machine-learning numpy pandas scikit-learn scikitlearn-machine-learning
Last synced: about 18 hours ago
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The Old Car Price Prediction project predicts used car prices using features like age, mileage, and fuel type. It includes data preprocessing, model training, and visualization of trends, with easy customization for additional features or models.
- Host: GitHub
- URL: https://github.com/alphacrypto246/old-car-price-prediction
- Owner: alphacrypto246
- Created: 2024-11-03T13:56:03.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-06T09:17:09.000Z (2 months ago)
- Last Synced: 2024-12-18T09:19:15.437Z (about 2 months ago)
- Topics: machine-learning, numpy, pandas, scikit-learn, scikitlearn-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 736 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Old Car Price Prediction
This project aims to predict the resale price of used cars based on various features such as age, mileage, fuel type, and more. The model uses machine learning techniques to provide accurate price predictions, helping buyers and sellers make informed decisions.
## Features
- **Data Preprocessing**: Handles missing values, categorical variables, and feature scaling.
- **Machine Learning Model**: Trained to predict car prices based on historical data.
- **Visualization**: Displays trends and relationships between features and car prices.
- **Customizable**: Easily extendable to include additional features or use different models.
## Libraries Used
The following libraries are used in this project:
- **NumPy**: For numerical computations.
- **Pandas**: For data manipulation and analysis.
- **Matplotlib**: For data visualization.
- **Seaborn**: For enhanced data visualization.
- **scikit-learn**: For implementing and evaluating machine learning models.
## Requirements
To run the project, you need the following dependencies:
```bash
numpy
pandas
matplotlib
seaborn
scikit-learn
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/alphacrypto246/Old-Car-Price-Prediction.git
```
2. Navigate to the project directory:
```bash
cd Old-Car-Price-Prediction
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Open the Jupyter Notebook file `main.ipynb`.
2. Load the dataset by following the instructions in the notebook.
3. Run the cells sequentially to preprocess the data, train the model, and evaluate its performance.
4. Visualize the results and interpret the predictions.
## File Structure
- `main.ipynb`: The main notebook containing the implementation of the model.
- `data/`: Folder to store the dataset (not included in the repository).
- `README.md`: Project documentation.
- `requirements.txt`: List of Python dependencies.
## Dataset
Ensure you have access to a dataset with features like:
- Car Age
- Mileage
- Fuel Type
- Transmission Type
- Selling Price
You can source datasets from platforms like Kaggle or other automotive datasets.
## Results
The project demonstrates the ability of machine learning models to accurately predict used car prices based on input features. Visualizations of feature importance and price trends are included.
## Contributing
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature-branch-name
```
3. Make your changes and commit them:
```bash
git commit -m "Description of changes"
```
4. Push to your branch:
```bash
git push origin feature-branch-name
```
5. Open a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Acknowledgments
Special thanks to the developers of the libraries used in this project and the open-source community for providing resources to build machine learning applications.
---
Feel free to raise issues or feature requests via the [Issues](https://github.com/alphacrypto246/Old-Car-Price-Prediction/issues) tab.