{"id":15105244,"url":"https://github.com/rohitpawar001/regression_model","last_synced_at":"2026-01-05T10:52:29.331Z","repository":{"id":257570436,"uuid":"858676373","full_name":"RohitPawar001/regression_model","owner":"RohitPawar001","description":"This repository contains a machine learning model for predicting house prices. 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The model is deployed as a web application, providing an easy-to-use interface for making predictions.\n\n## Live Demo\n\nYou can try out the live demo of our House Price Prediction API here:\n[ML API on PythonAnywhere](https://rohitpawar001.pythonanywhere.com)\n\n## Repository Structure\n\n\n- `regression_model .ipynb/`: Contains the trained regression model\n- `app.py/`: Source code for the web application\n- `data/`: Dataset used for training (if publicly available)\n- `notebooks/`: Jupyter notebooks for data analysis and model development\n\n## Getting Started\n\n### Prerequisites\n\n- Flask\n- pickle\n- numpy\n- pandas\n- sklearn\n- Python 3.7+\n- pip\n\n### Installation\n\n1. Clone the repository:\n   ```\n   git clone https://github.com/RohitPawar001/regression_model.git\n   cd regression_model\n   ```\n\n2. Install the required packages:\n   ```\n   pip install -r requirements.txt\n   ```\n\n### Usage\n\n\n1. Run the web application:\n   ```\n   python app.py\n   ```\n2. Open your web browser and navigate to `http://localhost:5000`\n\n## Model Information\n\nLinear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship, represented by the equation \\( y = mx + b \\), where \\( m \\) is the slope and \\( b \\) is the intercept. The goal is to find the best-fit line that minimizes the sum of squared differences between observed and predicted values. It's widely used for predictive analysis and identifying trends. Linear regression is simple yet powerful, making it a fundamental tool in data science and machine learning.\n\n## Deployment\n\nThis project is deployed on PythonAnywhere. For details on how to deploy your own version, please refer to the [PythonAnywhere documentation](https://help.pythonanywhere.com/pages/DeployExistingDjangoProject/).\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\n\nThis project is licensed under the Apache License Version 2.0, January 2004 - see the [LICENSE](LICENSE) file for details.\n\n\n## Contact\n\nRohit Pawar - [rppawar491@gmail.com]\n\nProject Link: [https://github.com/RohitPawar001/regression_model](https://github.com/RohitPawar001/regression_model)","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frohitpawar001%2Fregression_model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frohitpawar001%2Fregression_model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frohitpawar001%2Fregression_model/lists"}