https://github.com/arpanpramanik2003/car-price-prediction

This project based on car price prediction using a Random Forest model and a Streamlit web app for a user-friendly interface.
https://github.com/arpanpramanik2003/car-price-prediction

car-price-prediction mlpipelines model-evaluation price-prediction python random-forest regression-models rf-classifier sklearn streamlit-webapp

Last synced: 4 months ago
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This project based on car price prediction using a Random Forest model and a Streamlit web app for a user-friendly interface.

• Host: GitHub
• URL: https://github.com/arpanpramanik2003/car-price-prediction
• Owner: arpanpramanik2003
• License: apache-2.0
• Created: 2024-11-15T09:51:22.000Z (11 months ago)
• Default Branch: master
• Last Pushed: 2024-12-29T12:36:57.000Z (10 months ago)
• Last Synced: 2025-03-28T07:45:10.335Z (7 months ago)
• Topics: car-price-prediction, mlpipelines, model-evaluation, price-prediction, python, random-forest, regression-models, rf-classifier, sklearn, streamlit-webapp
• Language: Jupyter Notebook
• Homepage: https://car-price-prediction-arpan.streamlit.app
• Size: 1.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Car-Price-Prediction-Streamlit

![Car-Price-Prediction-Streamlit](image.jpg)

# Car Price Prediction with Machine Learning and Streamlit

This project uses a **Random Forest** model to predict car prices based on various features. The predictions are deployed in an interactive **Streamlit** web app, making it simple for users to input details and view estimated prices.

## Features

- **Data Preprocessing**: The project employs `ColumnTransformer` and `Pipeline` for efficient feature encoding and scaling.

- **Random Forest Model**: A Random Forest model is trained to ensure accurate and reliable price predictions.

- **Streamlit App**: An easy-to-use web interface for real-time predictions based on user-provided inputs.

- **Pickle for Model Storage**: The model and preprocessing pipeline are saved using `pickle` for quick access and deployment.