How I Built ShopeE-Mate (An E-commerce Product Recommendation /Comparison Using Python and Machine Learning)

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🛍️ Project Overview

During my final year, I created a multi-platform e-commerce product recommendation system called ShopeE-Mate. This tool helps users find the best deals across Amazon, Flipkart, and JioMart — all in one place.

💡 The Problem

Users waste a lot of time switching between apps and websites just to find the best price for the same product. I wanted to solve that using automation and ML/AI.

🛠️ Tools and Technologies Used

  • 🔎 Web Scraping: RPA UiPath, Selenium

  • 📊 Data Processing: Pandas, NumPy , Google colab

  • 📦 Machine Learning: Scikit-learn, Tokenization(NLP)

  • 🧠 Recommendation Models: Content-based, Collaborative, Hybrid 

  • 🌐 Web Framework: Flask 

  • 🛢Database system : MySql XAMPP server

⚙️ How It Works

  1. Step 1: Product data is scraped in real-time using automation tools

  2. Step 2: Data is cleaned and structured using Pandas

  3. Step 3: ML models rank the products based on user preference, rating, and discount

  4. Step 4: Flask displays the final recommended products in a simple user interface with real-time price analysis.

  5. Extra : User can also browse through trendy items Of all 3 platform if they don't really want recommendation of certain type of product.

📷 Screenshots of whole Web application ShopeE-Mate

{Click on image for broad clear view}


                              Before Searching 



Here after Buy now we will also get the real time pricing analysis 


Here Trendy items page if they don't want recommendation of any thing

Extra Add to cart for future views

{User can search as many items from any niche they want the top performer from our backend algorithm will show up to them which will the great product doing well on reviews/ratings with pricing of all 3 platform of same listed products}

💡 Challenges I Faced

  • ⚠️ Handling websites with dynamic content (especially Flipkart)

  • ⏱️ Real-time scraping and slow response times

  • ⚠️ Automating RPA task 

  • ⚖️ Balancing recommendation quality with performance

🎯 Key Learnings

  • How to build end-to-end systems with real-world, messy data and solve real-world problems

  • How to combine automation and Machine Learning for business value

  • Importance of user interface in presenting ML results effectively

🔗  View Code

GitHub Link:  ShopeE-Mate Project

🙋‍♀️ Final Words

This was one of my most fulfilling projects. I can still do most of future enhancement and i am willing to do it , It not only helped me improve my technical skills but also helped me understand how real users interact with AI tools. I hope to build more such systems in the future! 




Hi, I’m Heena Shaikh — a Data enthusiast with a passion for analytics, storytelling, and machine learning. I enjoy exploring data to uncover patterns, solve problems, and make meaningful decisions. With skills in Python, MySQL, R, Data Visualization , Machine Learning and automation tools, I'm constantly learning and building towards a career in data science.

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How i built ShopeE-Mate

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Welcome to Decode With Heena

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I'm Heena Shaikh , a Data Science graduate with a passion for turning raw data into meaningful insights. Through this blog, I share my real-world projects, virtual internship experiences, and tutorials in data analytics, machine learning, and business reporting. Whether you're a recruiter, a fellow learner, or someone just curious about data — I'm glad you're here! 👉 Feel free to explore my About Me and Get my Resume , check out my Projects , check out my Blog Posts , or Contact Me anytime.
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