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https://github.com/faizantkhan/regression-project-bangalore-property-price-prediction

🏠 Bangalore Property Price Prediction is a comprehensive project designed to accurately predict property prices in Bangalore. Leveraging advanced regression techniques and a dataset sourced from Kaggle, the model undergoes meticulous feature engineering, data cleaning, and parameter tuning to ensure high accuracy.
https://github.com/faizantkhan/regression-project-bangalore-property-price-prediction

backend-api css data-cleaning data-science data-visualization eda flask html javascript machine-learning-algorithms numpy pandas project project-repository property python regression-models server

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🏠 Bangalore Property Price Prediction is a comprehensive project designed to accurately predict property prices in Bangalore. Leveraging advanced regression techniques and a dataset sourced from Kaggle, the model undergoes meticulous feature engineering, data cleaning, and parameter tuning to ensure high accuracy.

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README 

        
🏠 Bangalore Property Price Prediction

Welcome to the Bangalore Property Price Prediction repository! This project is dedicated to predicting property prices in Bangalore using advanced regression techniques. The repository includes client-side components developed with HTML, CSS, and JavaScript, alongside a robust prediction model and a Flask-powered server-side backend. With meticulous feature engineering, data cleaning, parameter tuning, and seamless integration, this project offers a comprehensive solution for accurate property price predictions in Bangalore.



🌐 Client Side

The client-side interface is crafted with HTML, CSS, and JavaScript, providing an intuitive platform for users to interact with the prediction model. Through a user-friendly interface, users can input relevant parameters and receive accurate predictions for property prices in Bangalore. The client-side component ensures a seamless and engaging user experience, enhancing accessibility and usability.



🧮 Model

The prediction model utilized in this project is trained on a comprehensive dataset sourced from Kaggle, encompassing various features relevant to property prices in Bangalore. Leveraging sophisticated feature engineering techniques, meticulous data cleaning processes, and rigorous parameter tuning, the model ensures high accuracy and reliability in predictions. By encapsulating intricate market dynamics, the model empowers users with valuable insights for informed decision-making.



🖥️ Server Side

The server-side component of this project is powered by Flask, a lightweight and efficient web framework for Python. Flask facilitates seamless integration between the prediction model and the client-side interface, enabling smooth data exchange and efficient handling of requests. With Flask, the prediction model is deployed as a web backend, ensuring scalability, reliability, and optimal performance.



🚀 Getting Started

To get started with the Bangalore Property Price Prediction project, follow these steps:



Clone the Repository: Clone this repository to your local machine using git clone https://github.com/yourusername/bangalore-property-price-prediction.git.

Install Dependencies: Install the required dependencies by running pip install -r requirements.txt.

Run the Server: Navigate to the server directory and run the Flask server using python app.py.

Access the Client Interface: Open the client interface by navigating to http://localhost:5000 in your web browser.

Input Parameters: Input relevant parameters into the client interface to receive predictions for property prices in Bangalore.

Explore Results: Explore the predicted property prices and leverage the model's insights for informed decision-making.

🛠️ Contributors

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.



🙏 Acknowledgements

Kaggle for providing the dataset used in this project.

Flask for providing a robust framework for building the server-side component.

Bootstrap for enhancing the visual appeal of the client-side interface.

Feel free to customize this README according to your project's specific requirements and add any additional information you deem necessary. Happy predicting!