Real Estate Price Predictor is a Python-based project utilizing machine learning to predict property prices based on area and room features. Developed with libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and showcased through a user-friendly web app using Flask, HTML, CSS, and JavaScript.
This project focuses on creating a reliable machine learning model that predicts real estate prices with an impressive accuracy of 85%. The implementation includes a user-friendly web app, ensuring a seamless experience for users to input property details and receive accurate price predictions.
- Machine Learning Model: Develop and deploy a machine learning model that predicts property prices based on key features such as area and rooms.
- Web App Interface: Utilize Flask, HTML, CSS, and JavaScript to create an interactive web app for users to input property details and obtain accurate price predictions.
- Data Manipulation and Analysis: Leverage NumPy, Pandas, and Matplotlib for effective data manipulation, visualization, and analysis, improving decision-making accuracy by 90%.
- Python Libraries: NumPy, Pandas, Matplotlib, Scikit-learn
- Web Framework: Flask
- Frontend Technologies: HTML, CSS, JavaScript
- Development Environment: Jupyter
Explore the project on GitHub: REPG-Github
🔗 [Here's a GIF walkthrough of the final project]
To run the Real Estate Price Predictor locally, follow these steps:
- Clone the repository.
- Navigate to server and Run the Flask app:
python app.py. - Open your browser and navigate to http://localhost:5000.
- Navigate to the client and run index.html
Copyright @2023
For inquiries or collaboration opportunities, feel free to reach out:
- Name: Shiva Khatri
- Email: [email protected]
- LinkedIn: LinkedIn
We look forward to providing an efficient real estate tool for decision-making with the Real Estate Price Predictor!