Explore the vibrant real estate market of Bangalore through our House Price Prediction project. This repository hosts a comprehensive solution utilizing machine learning to forecast property prices accurately. Whether you're a data enthusiast or a prospective homeowner, dive into our project to gain valuable insights into Bangalore's housing trends.
Python for data cleaning, preprocessing, and modeling.
Flask for developing the web application interface.
Scikit-Learn for implementing machine learning models.
Pandas for data manipulation and analysis.
NumPy for numerical computing.
Matplotlib for data visualization.
Seaborn for statistical data visualization.
&
for front-end development.
for dynamic elements in the web interface.
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Data Collection & Preprocessing: Gathered and cleaned real estate data from Bangalore's diverse neighborhoods using Python and Pandas.
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Machine Learning Models: Employed advanced regression models including Random Forest and Decision Trees to predict house prices.
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Web Application Development: Developed an interactive web interface with Flask, allowing users to input property details and receive price estimates instantly.
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Visualization: Utilized Matplotlib and Seaborn to create insightful visualizations of housing market trends and model performance metrics.
- Clone the repository:
git clone https://github.com/yourusername/bangalore-house-price-prediction.git - Install dependencies:
pip install -r requirements.txt - Run the Flask application:
python app.py - Access the web interface at
http://localhost:5000and start exploring house price predictions!
- Incorporate more robust feature engineering techniques.
- Expand model capabilities to handle larger datasets.
- Deploy the application on cloud platforms for wider accessibility.
Explore more about this project and connect with me on LinkedIn.
This project is licensed under the MIT License - see the LICENSE file for details.