Shopper’s Serendipity : Know Why They Buy ‘That’ with ‘This’ via K-NN on Graph

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What is this article about
✨ Real world application of graphs and the K-NN algorithm to decode why customers buy certain products together. Think of it like predicting the perfect side dish for your main course (their original purchase)!

Why Read It
🧠 Understand how the K-NN algorithm, a cool machine learning tool, works in a practical business setting.
🔮 Learn to build a visual roadmap that turns complex data into actionable insights for your sales team
🎯 For anyone who loves the intersection of tech and retail!

The Problem
😕 Customers are unpredictable, and traditional recommendation systems often miss the mark on those "add-on" purchases.
👎 This means lost opportunities for businesses to boost sales and make their customers happier.

The Solution
🕸️ Building a graph network that maps relationships between different products.
🔎 Using Neo4j GDS K-Nearest Neighbors (K-NN) algorithm to find patterns and predict what else customers will like based on past purchases

Why You Can't Miss This
📈 Get a unique, data-driven perspective to personalize the shopping experience for everyone.
🚀 Practical learning of boost sales by anticipating "surprise" purchases that go perfectly with the main item, same concept can be used to may diff business use cases
💕 Know your customers on a deeper level – understand their secret needs and motivations!

Let's go!
🧪 Jump into the fun of simulating an e-commerce network, testing the algorithm, and visualizing the results.

Let's get cooking!
👩‍🍳 Explains the code behind the scenes in a simple, step-by-step way.

Closing thoughts
*💡 This is just the start! Graphs can uncover even more about customer behavior, inventory, and so much more.
🚀 Recommendation systems are getting smarter by the day, and graphs are at the center of it.
👍 This method is surprisingly easy to implement and gives you fantastic results