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A sneaker reseller was losing deals because they couldn't monitor eBay listings fast enough manually. I built a Python bot that continuously monitors eBay search results for specific sneaker keywords and size filters, detects new listings or price drops below a set threshold, and sends real-time Telegram alerts the moment something matches.
The bot stores seen listings in a local SQLite database to avoid duplicate alerts, and runs on a configurable check interval (every 2–5 minutes).
Stack: Python, requests, BeautifulSoup, python-telegram-bot, schedule, sqlite3
Result: Eliminated manual hourly checks. Client received instant deal alerts and landed 4 profitable flips within the first two weeks of using the bot.
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I had the opportunity to work on another live platform for a business client.
🌐 https://tmsfurniture.in
This project involved building and supporting a multi-store ecommerce and lead management platform designed to help customers explore furniture collections and connect directly with nearby stores as easy management by seperate dashboard for store owner - who controls everything and manager - controls specific store.
Some of the areas I worked on:
• Multi-store product catalogue system - Products with variants, category, banners, lead updations
• Customer enquiry & lead management
• Admin dashboard for store operations - Store Owner and Store Manager
• Deployment and hosting setup - AWS EC2 and S3
• Continuous technical support and monitoring
The platform has now been live in production for the past 4 months, running smoothly with stable performance and no major issues, which has been a great experience in maintaining real-world systems.
It was rewarding to see the client happy with the outcome and how the platform helps improve their online visibility and customer enquiries.
ProData AI — Automated Data Science Platform
Built ProData AI, an automated data science platform developed entirely with Streamlit.
It is designed to help users transform raw datasets into actionable business insights in seconds.
🚀 What it does
One-Click Mode
Upload any CSV or Excel file and the full pipeline runs automatically in under 30 seconds:
Data cleaning & preprocessing
AutoML (6 models trained simultaneously)
30-day forecasting using Prophet
Business driver analysis with Explainable AI (XAI)
AI-generated insights using Anthropic Claude
PDF report generation
Manual Mode
Provides full control over each stage of the data science workflow for advanced users.
🛠 Tech Stack
Streamlit — complete UI and app framework
scikit-learn — AutoML pipeline
Prophet — time-series forecasting
Anthropic Claude API — AI insights & chat
Plotly — interactive visualizations
fpdf2 — PDF report generation
Ideal for:
business analysts
startups
small businesses
automated reporting workflows
freelance analytics projects
Open to freelance collaborations and custom dashboard / AI reporting solutions.
Great work
GEO: Optimising for AI Search 🚀
SEO is no longer just for humans. With the rise of LLMs, we now need to focus on GEO (Generative Engine Optimisation).
I’m now implementing llms.txt as a standard for my builds. It’s a markdown file hosted at the root directory that provides a clean, structured roadmap for Large Language Models.
Why it matters:
- Clarity: It strips away HTML clutter so AI agents see only the core content.
- Accuracy: It reduces AI hallucinations by providing direct data anchors for products and services.
- Discovery: It ensures a site is easily cited in AI-generated answers.
In technical SEO, Bland = Readable. Since implementing this on my Shopify builds, I’m already seeing AI agents pull cleaner product data without getting snagged on the site’s underlying code overhead.:
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