https://github.com/rohitpawar001/regression_model
This repository contains a machine learning model for predicting house prices. The model is deployed as a web application, providing an easy-to-use interface for making predictions.
https://github.com/rohitpawar001/regression_model
flask linear-regression machine-learning postman python pythonanywhere
Last synced: 4 months ago
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This repository contains a machine learning model for predicting house prices. The model is deployed as a web application, providing an easy-to-use interface for making predictions.
- Host: GitHub
- URL: https://github.com/rohitpawar001/regression_model
- Owner: RohitPawar001
- License: apache-2.0
- Created: 2024-09-17T10:41:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-18T10:42:58.000Z (over 1 year ago)
- Last Synced: 2025-03-25T10:11:36.426Z (about 1 year ago)
- Topics: flask, linear-regression, machine-learning, postman, python, pythonanywhere
- Language: Jupyter Notebook
- Homepage: https://rohitpawar001.pythonanywhere.com
- Size: 1010 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# House Price Prediction Model
This repository contains a machine learning model for predicting house prices. The model is deployed as a web application, providing an easy-to-use interface for making predictions.
## Live Demo
You can try out the live demo of our House Price Prediction API here:
[ML API on PythonAnywhere](https://rohitpawar001.pythonanywhere.com)
## Repository Structure
- `regression_model .ipynb/`: Contains the trained regression model
- `app.py/`: Source code for the web application
- `data/`: Dataset used for training (if publicly available)
- `notebooks/`: Jupyter notebooks for data analysis and model development
## Getting Started
### Prerequisites
- Flask
- pickle
- numpy
- pandas
- sklearn
- Python 3.7+
- pip
### Installation
1. Clone the repository:
```
git clone https://github.com/RohitPawar001/regression_model.git
cd regression_model
```
2. Install the required packages:
```
pip install -r requirements.txt
```
### Usage
1. Run the web application:
```
python app.py
```
2. Open your web browser and navigate to `http://localhost:5000`
## Model Information
Linear 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.
## Deployment
This 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/).
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the Apache License Version 2.0, January 2004 - see the [LICENSE](LICENSE) file for details.
## Contact
Rohit Pawar - [rppawar491@gmail.com]
Project Link: [https://github.com/RohitPawar001/regression_model](https://github.com/RohitPawar001/regression_model)