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https://github.com/mohini1403/house-price-prediction
https://github.com/mohini1403/house-price-prediction
Last synced: 7 days ago
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- Host: GitHub
- URL: https://github.com/mohini1403/house-price-prediction
- Owner: MOHINI1403
- Created: 2024-03-28T12:15:10.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-03-28T12:18:19.000Z (8 months ago)
- Last Synced: 2024-03-28T13:39:14.866Z (8 months ago)
- Language: Jupyter Notebook
- Size: 5.58 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Below is a sample README file for your house price prediction project to be deployed on GitHub:
---
# House Price Prediction Project
This project implements a machine learning model for predicting house prices based on various features such as the number of bedrooms, bathrooms, living area, lot area, and other factors. The model is trained on a dataset containing information about houses in a specific area.
## Features
- Predicts house prices based on multiple features.
- Uses a machine learning model trained on historical data.
- Provides accurate price estimates for houses in the given area.
## Getting Started
To get started with the project, follow these steps:
1. Clone the repository to your local machine:
```
git clone https://github.com/MOHINI1403/house-price-prediction.git
```
2. Install the necessary dependencies:
```
pip install -r requirements.txt
```
3. Run the Flask application:
```
python deploy.py
```
4. Open a web browser and go to `http://localhost:5000` to access the application.
5. Enter the relevant features for a house and click "Predict" to get the estimated price.
## Project Structure
- `deploy.py`: Main Flask application file containing routes for rendering the input form and processing predictions.
- `templates/`: Directory containing HTML templates for rendering web pages.
- `index.html`: HTML template for the input form page.
- `result.html`: HTML template for displaying the prediction result page.
- `static/`: Directory containing static files such as CSS stylesheets and images.
- `styles.css`: CSS stylesheet for styling the HTML pages.
- `background.jpg`: Background image used in the HTML pages.
- `trained_model.pkl`: Serialized trained machine learning model for house price prediction.
- `requirements.txt`: File containing the Python dependencies required for the project.
## Data
The dataset used for training the model is not included in this repository due to its size and licensing restrictions. However, you can use your own dataset or obtain a similar dataset from sources such as Kaggle or UCI Machine Learning Repository.
## Contributing
Contributions to the project are welcome! Feel free to open issues for any bugs or feature requests, and submit pull requests for improvements or new features.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
Feel free to customize this README file with more specific details about your project, such as additional features, deployment instructions, or information about the dataset used.