@aliraza

A comprehensive, structured dataset of residential property listings covering every U.S. state, sourced from Zillow listing data. Each record captures pricing, valuation, physical attributes, location coordinates, listing status, and media availability — making it ready for market analysis, price prediction models, and investment research without heavy cleaning. What's inside (45+ fields): Location — full address, street, city, state, ZIP code, unit, latitude/longitude, state ID Pricing & valuation — list price, price label, price change amount, price reduction flag, date of price change, Zestimate, Rent Zestimate, tax assessed value Property attributes — bedrooms, bathrooms, living area (sq ft), lot area value and unit, home type (single-family, condo, townhouse, multi-family, land) Listing intelligence — home status, status text, marketing status, listing sub-type, days on Zillow, time on Zillow Agent & broker — broker name, agent name, detail URL Media & flags — primary image, has image, 3D model, video walkthrough, showcase listing, Zillow-owned, new construction, open house Record tracking — createdat, updatedat timestamps Who this is for: Real estate investors screening markets, data analysts and BI professionals, machine learning practitioners building home-price prediction models, proptech developers, market researchers, and students working on housing economics projects. Example use cases: Compare median list price vs. Zestimate to spot over- and under-priced markets Calculate rent-to-price ratios by ZIP code for cash-flow analysis Track price reductions and days-on-market as demand indicators Map property density and price heat by latitude/longitude Analyze new construction share by state Benchmark tax assessed value against market price

Stop scraping the "paint" (HTML) and start intercepting the "data packages" (API responses). This guide introduces the Network Interception strategy using Python and Playwright. Learn how to bypass BeautifulSoup entirely, listen to background network traffic, and capture raw, structured JSON data directly from the server—even for complex infinite-scroll sites.

🚀 The 7-Day Price Volatility Dataset: Market Dynamics Tracker 🔍 Dataset Description & Fields This dataset is designed to capture short-term market liquidity and volatility by comparing the initial price of an item to its price exactly one week later. It is a powerful tool for analyzing pricing strategies, predicting short-term market trends, and identifying products with significant price instability. Interpretation: A positive percentage (e.g., ) indicates the item has experienced appreciation (price increase) over the week, potentially signaling rising demand or reduced supply. A negative percentage (e.g., ) indicates depreciation (price decrease), possibly due to overstocking, new competition, or sales events. A value close to signifies strong price stability. 💡 Advanced Analytical Uses The simple structure of this data facilitates a wide range of analytical tasks: Market Timing: Determine the optimal days or conditions under which prices are most likely to drop (or surge) over a 7-day window. Product Segmentation: Group products by their volatility index to understand which market segments are most sensitive to change. Pricing Strategy Audit: Assess the effectiveness of promotional campaigns by observing the seven-day rebound or stabilization after an initial price change. Risk Assessment: Use the change percentage as a risk indicator for inventory management or investment decisions. First time time it was scraped at 01-12-2025 and next time at 08-12-2025.

Food Beverages Tobacco – Trustpilot Dataset 📘 Dataset Description This dataset contains structured information collected from Trustpilot under the “Food Beverages Tobacco” category. It includes publicly available details from company profile pages to help with analysis, lead research, market insights, or automation workflows. 📂 What’s Inside Company Name – Official name listed on Trustpilot Website URL – Link to the company’s homepage (if available) TrustScore – Overall rating based on customer reviews Total Reviews – Number of reviews submitted by users Categories / Sub-categories – Classification under the “Public Local Service” sector Location Information – Country / address if publicly displayed Contact Presence – Indicators of email, phone, or social links if visible Profile URL – Direct link to the Trustpilot company profile 🎯 Purpose This dataset is suitable for: Market research and competitor analysis Lead generation and outreach targeting local services Data-driven insights for automation or RPA workflows Trend analysis based on public ratings and reviews ⚠️ Notes All data is sourced from publicly accessible pages on Trustpilot. No private or login-restricted information is included. Dataset accuracy depends on the availability and completeness of the company’s public profile.

Learn how to build an advanced Python web scraping script using Requests and BeautifulSoup to automatically detect repeated page structures and cluster content intelligently. This topology-based scraping approach extracts clean text, links, images, and headings while removing noise like scripts and navigation, making it ideal for large-scale data extraction, RPA workflows, and structured web data mining.