🏠 Real Estate Dashboard

A comprehensive real estate price analysis and prediction dashboard built with Streamlit and machine learning.

🌐 Live Demo

🚀 Try the Live Dashboard

Features

• Data Exploration: Interactive visualizations of the real estate dataset
• Price Analysis: Geographic and temporal price analysis
• Machine Learning: Random Forest model for price prediction
• Interactive Prediction: Real-time price prediction based on property features
• Dashboard Navigation: Multi-page dashboard with different analysis views

Dataset

The dashboard uses a real estate dataset with the following features:

• Transaction date
• House age
• Distance to the nearest MRT station
• Number of convenience stores
• Latitude and Longitude
• House price of unit area (target variable)

Installation

1. Clone the repository:

git clone https://github.com/Apc0015/Real_State_Dashboard.git
cd Real_State_Dashboard

2. Install the required dependencies:

pip install -r requirement.txt

Usage

Local Development

Run the Streamlit dashboard locally:

streamlit run app.py

The dashboard will be available at http://localhost:8501

Docker Deployment

Build and run using Docker:

# Build the Docker image
docker build -t real-estate-dashboard .

# Run the container
docker run -p 8501:8501 real-estate-dashboard

Dashboard Pages

1. Overview: Dataset summary and basic statistics
2. Data Exploration: Interactive data visualization and correlation analysis
3. Price Analysis: Geographic and temporal price analysis
4. ML Model: Machine learning model performance and feature importance
5. Price Prediction: Interactive price prediction tool

Deployment

✅ Already Deployed on Streamlit Cloud

This dashboard is live and accessible at: https://realstatedashboard-sqqfjc8adbm7tps8pfujii.streamlit.app/

Deploy Your Own Version

1. Fork this repository
2. Go to share.streamlit.io
3. Connect your GitHub repository
4. Deploy the app

Project Structure

Real_State_Dashboard/
├── app.py                 # Main Streamlit application
├── Real_Estate.csv        # Dataset
├── requirement.txt        # Python dependencies
├── Dockerfile            # Docker configuration
├── README.md             # This file
└── .streamlit/
    └── config.toml       # Streamlit configuration

Technologies Used

• Frontend: Streamlit
• Data Analysis: Pandas, NumPy
• Visualization: Plotly, Matplotlib, Seaborn
• Machine Learning: Scikit-learn (Random Forest)
• Deployment: Docker, Streamlit Cloud

Contributing

Feel free to fork this project and submit pull requests for any improvements.

License

This project is open source and available under the MIT License.