Real Estate Data Scraping and Visualization Platform
Prashant Patil
Project Title
Real Estate Data Scraping and Visualization Platform
Objective
Develop a modern platform that scrapes real estate data from multiple listing websites, analyzes key metrics (e.g., price trends, property demand), and visualizes insights on an interactive web application. The tool will enable users to make data-driven decisions about real estate investments.
Key Features
1. Data Scraping
Extract property details such as:
Property Type (e.g., Residential, Commercial)
Location (City, State, Zip Code, Neighborhood)
Price and Size (e.g., Square Footage, Number of Rooms)
Listing Date and Agent Information
Amenities and Features (e.g., Pool, Parking, Balcony)
Handle both static and dynamic websites using Scrapy and Selenium.
Integrate proxy rotation and CAPTCHA handling to bypass restrictions on real estate sites.
2. Data Analysis
Perform the following analytics:
Price Trends: Analyze property prices over time for different regions.
Demand Hotspots: Identify high-demand areas based on listing counts and price movement.
Comparative Analysis: Compare properties based on price per square foot, amenities, and location.
Leverage Python libraries such as Pandas, NumPy, and Scikit-learn for advanced analytics.
3. Interactive Visualizations
Create dynamic, user-friendly visualizations:
Line charts for price trends.
Heatmaps for regional demand and pricing.
Bar charts for property comparisons.
Geo-spatial visualizations using Leaflet.js or Plotly.
4. Search and Filter Options
Enable users to search and filter properties by:
Location (City, State, Zip Code)
Price Range
Property Type and Features
5. User Authentication and Alerts
Allow users to create accounts and save favorite searches.
Set price alerts for specific properties or regions.
Send email or push notifications for new listings that match user preferences.
6. Modern Web Interface
Develop a responsive and intuitive front-end using React.js:
Dashboard with real-time data updates.
Map integration for location-based property visualization.
Data export options (CSV, Excel).
Technology Stack
Back-End
Django: RESTful API and data management.
Django REST Framework (DRF): For API creation.
PostgreSQL: Database for structured storage of scraped data.
Front-End
React.js: Build an interactive and dynamic user interface.
Axios: For API integration.
Redux/Context API: For state management.
Scraping Tools
Scrapy: For efficient and scalable web scraping.
Selenium/Playwright: For handling dynamic content.
Proxy Services: Rotate IPs using tools like ScraperAPI or BrightData.
Visualization and Analysis Tools
Matplotlib/Plotly/Seaborn: For data visualization.
Leaflet.js/GeoPandas: For geo-spatial analysis.
Deployment and Hosting
Docker: Containerized deployment.
AWS (EC2, S3, RDS): For scalable cloud infrastructure.
Nginx + Gunicorn: Web server and application server for Django.
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Posted Dec 11, 2024
Develop a modern platform that scrapes real estate data from multiple listing websites, analyzes key metrics (e.g., price trends, property demand)
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