Real Estate Market Data Scraping Project
Overview:
This project showcases my ability to collect, process, and visualize real estate data from Realtor.ca. Utilizing tools like Pandas, Selenium WebDriver, and Beautiful Soup, I extracted valuable market insights and presented them in a visually appealing format using Matplotlib, Seaborn, and Plotly.
Check out my project in detail on
https://github.com/codeadvance/Toronto-Real-Estate-/tree/main
Project Highlights:
- Data Collection: Automated navigation and data scraping from Realtor.ca using Selenium WebDriver.
- Data Parsing: Employed Beautiful Soup and Selenium to parse and extract relevant data fields.
- Data Cleaning & Manipulation: Utilized Pandas for cleaning, structuring, and manipulating the data for analysis.
- Data Visualization: Created insightful visualizations to present real estate market trends using Matplotlib, Seaborn, and Plotly.
Detailed Breakdown:
- Environment Setup:
- Installed necessary Python packages, including Selenium, Beautiful Soup, and Pandas.
- Web Scraping with Selenium:
- Launched a Selenium WebDriver to navigate Realtor.ca.
- Automated search queries and navigated through multiple pages to gather data.
- Data Extraction with Beautiful Soup:
- Parsed HTML content fetched by Selenium.
- Extracted relevant data points such as property prices, locations, and features.
- Data Cleaning with Pandas:
- Cleaned and formatted the extracted data using Pandas.
- Handled missing values and standardized data formats.
- Data Visualization:
- Created graphs and charts to display real estate market trends.
- Visualizations include price distribution, market trends over time, and geographical data representation.
Real Estate Market Graphs:
