This is a responsive web application for House Price Prediction.
HousePricePredictor for Bangalore Welcome to the HousePricePredictor project! This project aims to predict house prices in Bangalore using machine learning models and provides a responsive web interface built with Python Flask.
Table of Contents Introduction Features Installation Usage Models API Endpoints Contributing License Introduction HousePricePredictor leverages machine learning models to predict house prices based on various features like location, size, number of bedrooms, etc. The project uses popular regression techniques such as Linear Regression, Lasso, and Ridge Regression to provide accurate predictions. The predictions are accessible via a web interface built using Python Flask, ensuring a responsive and user-friendly experience.
Features Predict house prices based on input features Uses multiple regression models: Linear Regression, Lasso, and Ridge Responsive web interface built with Flask Easy-to-use API for integration Installation Prerequisites Python 3.7+ Flask Scikit-learn Pandas NumPy Jinja2 (for templating in Flask) Steps Clone the repository:
bash Copy code git clone https://github.com/Kumaraniketank/HousePricePredictor.git cd HousePricePredictor Create and activate a virtual environment (optional but recommended):
bash
Copy code
python -m venv env
source env/bin/activate # On Windows use env\Scripts\activate
Install dependencies:
bash Copy code pip install -r requirements.txt Run the Flask app:
bash Copy code flask run Access the web interface: Open your browser and go to http://127.0.0.1:5001
Usage Web Interface Open the web interface in your browser. Enter the required details (location, size, number of bedrooms, etc.). Click on the 'Predict' button to get the predicted house price. API You can also access the prediction functionality via the API.
Endpoint: /predict Method: POST Parameters: JSON object with house features (e.g., location, size, etc.) Example Request bash Copy code curl -X POST http://127.0.0.1:5001/predict -H "Content-Type: application/json" -d '{"location": "Whitefield", "sqft": 1200,"bhk":4, "bathrooms": 3}' Example Response json Copy code { "predicted_price": 8500000 } Models The project uses the following machine learning models:
Linear Regression: A basic regression technique to model the relationship between the dependent and independent variables. Lasso Regression: A regression analysis method that performs both variable selection and regularization to enhance prediction accuracy. Ridge Regression: A technique used to analyze multiple regression data that suffer from multicollinearity. The models are trained on a dataset of Bangalore house prices and can be easily retrained with new data.
API Endpoints /: Home page of the web application. /predict: Endpoint to get the house price prediction. Contributing Contributions are welcome! Please follow these steps to contribute:
Fork the repository. Create a new branch (git checkout -b feature-branch). Make your changes. Commit your changes (git commit -m 'Add new feature'). Push to the branch (git push origin feature-branch). Open a Pull Request. License This project is licensed under the MIT License. See the LICENSE file for more details.
Thank you for using HousePricePredictor! If you have any questions or feedback, please open an issue on GitHub.