Building a Real Estate Price Tracker with Python

In today's fast-paced real estate market, staying ahead of price trends can be the difference between a smart investment and a costly mistake. This tutorial guides you through building a real estate price tracker using Python, combining web scraping, data analysis, and visualization. Prerequisites pip install requests beautifulsoup4 pandas matplotlib seaborn schedule plotly Step 1: Scraping Real Estate Listings import requests from bs4 import BeautifulSoup import pandas as pd def scrape_real_estate_data ( url ): headers = { " User-Agent " : " Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 " } response = requests . get ( url , headers = headers ) if response . status_code != 200 : return pd . DataFrame () soup = BeautifulSoup ( response . text , " html.parser " ) properties = [] for item in soup . select ( " .property-listing .item " ): title = item . select_one ( " .title " ). text . strip () price = item . select_one ( " .price " ). text . strip () properties . append ({