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I built a robust automated web scraper using Python, Selenium, and BeautifulSoup to extract large-scale product data from complex e-commerce websites. The bot is designed to navigate dynamic pages, bypass bot-detection mechanisms (like CAPTCHAs), and collect detailed product information, including prices, reviews, and stock levels. The extracted data is cleaned and exported into structured formats like CSV and Excel. This tool significantly reduced the client's manual data collection time by 90% and provided valuable market insights
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Architected a global-scale automation framework capable of managing thousands of concurrent bots. The system features a centralized control dashboard for monitoring deep matrix logic and real-time execution flows. Built with a focus on high availability and scalability for enterprise-level web scraping and automation tasks."
Architected a global-scale automation framework capable of managing thousands of concurrent bots. The system features a centralized control dashboard for monitoring deep matrix logic and real-time execution flows. Built with a focus on high availability and scalability for enterprise-level web scraping and automation tasks."
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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AI-Powered Email Processing & Business Data Extraction (n8n)
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The system runs on a scheduled trigger (every 30 minutes) and processes customer data in a fully automated pipeline. When a user logs in, their data is stored with a pending status. The workflow then retrieves these records, analyzes recent business emails, and extracts key information such as sender domain and transactional details.
Using domain-based logic, the system cross-checks retailer data from a centralized database (Google Sheets). If retailer information is incomplete, the workflow automatically performs web scraping and enrichment to fetch return policies, business details, and relevant metadata before continuing execution.
Once validated, the system parses emails, extracts structured data, triggers external APIs, and marks processed emails accordingly. It ensures idempotent processing by updating statuses and avoiding duplicate handling.
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Key Highlights
Fully automated email parsing & classification system
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Domain-based retailer validation logic
Seamless integration with APIs, databases, and Google Sheets
Built for scalability, reliability, and minimal manual intervention
🚗 Driver Insights Dashboard
Power BI | Data Analytics | Ride-Hailing Industry
Project Overview
A client in the ride-hailing space had 76M in revenue and 148K+ bookings — but zero visibility into WHY rides were failing. No one could explain the cancellations, the incomplete trips, or the growing gap between driver and customer satisfaction.
I built a 5-page interactive Power BI dashboard that answered all of it.
The Problem
The client's data had serious blind spots:
→ Thousands of cancelled rides with no breakdown of fault
→ Auto & Go Mini vehicles had the highest incomplete ride rate (2.3K and 1.8K)
→ Driver ratings (4.23 avg) consistently below customer ratings (4.41) across all 7 vehicle types — with no system to catch it
→ Seasonal revenue dips (Q1 → Q2) were going unnoticed and unplanned
→ Go Sedan held 24.92% of total booking value — but had no dedicated tracking
What I Delivered
📊 5-page Power BI Dashboard with full interactivity
Overview — Revenue, Booking Ratio, CR by Customer & Driver at a glance
Booking Ratio — VTAT trends across all vehicle types
Revenue by Time — Month-wise & quarter-wise booking price analysis
Cancelling — Cancelled rides breakdown by vehicle type & reason
Cancelling Rides — Incomplete ride root causes + booking value vs distance scatter plot
Plus: Dynamic slicers for Booking Status and Vehicle Type — so the client's team could self-serve their own filters without needing a data analyst every time.
Results & Impact
✅ Operations team can now flag high-cancellation drivers proactively
✅ Vehicle maintenance triggers identified through incomplete ride spikes
✅ CX team has exact cancellation reasons — wrong address, AC issues, driver-asked-to-cancel — with counts, not guesses
✅ Revenue seasonality now visible and plannable quarter by quarter
✅ Fleet resource allocation backed by data (Go Sedan priority confirmed)
Tools Used
• Power BI Desktop
• DAX (custom measures & KPIs)
• Power Query (data cleaning & transformation)
Looking for similar work?
If you have messy data and need a dashboard that actually tells a story — let's talk. I specialize in turning raw operational data into clear, decision-ready visuals.
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