From Data Dumpster to Sales Supercharger: A 5-Step Adventure in AI

In e-commerce, raw data is like lead: heavy, chaotic, and seemingly worthless. But what if you could turn it into gold?

This isn't a theoretical guide; it's a real-world chronicle of data alchemy. We took the messy "Big Basket Products Dataset" from Kaggle and, using the conversational power of theTerno-AI  platform , transformed it into a high-impact, revenue-generating recommendation engine.

for clearer Picture Please go through the chats with Terno-AI . Join us on this journey to see exactly how we turned catalog chaos into pure conversion gold.

The Blueprint for Our Adventure

Every great quest needs a map. This five-step workflow was our guide, taking us from digital wilderness to a land of clear insights and intelligent tools.

Step 1: The Great Unification - Assembling the Puzzle Pieces

Our adventure started with the "Big Basket Products Dataset" from Kaggle. The catch? The data was on a digital scavenger hunt, scattered across 157 separate files.

So, what do you do with 157 puzzle pieces? You use a little magic. We gave Terno-AI the data source and a JSON "cheat sheet" defining our perfect, final dataset. With a single command, it stitched everything together into one massive 57,013-product tapestry.

Prompt:

"I want to Create a Product Recommendation System Based on Big Basket Product Dataset this is kaggle address of that dataset "gauranggujrati/big-basket-products-dataset" i am also attaching the json these are the list of features the merged csv will have "Category, Sub-Category, Sub-sub-Category, Product Link, EAN Code, Image Link, Brand, SKU Name, SKU Size, MRP, About the Product" just load and merge the dataset for now and display few instances to me in tabular form"

Insight:

But as we looked closer, we saw our newly assembled beast had some flaws:

• It had over 3,900 identical twins (duplicate listings) cluttering the place up.
• It was full of holes! Nearly 19% of products were missing a price, and a whopping 75% were missing a size.

Prompt:

"tell me about the merged dataset"

Insight:

Step 2: The Digital Detox - From Grime to Gleam

You can't build a castle on a foundation of quicksand. Before we could get to the fun stuff, we had to perform a serious digital detox. We prompted Terno-AI to roll up its sleeves and get cleaning.

First, we evicted all 3,914 duplicate squatters. Then, we tackled the missing data by

filling in over 53,000 potholes—intelligently patching over 10,500 missing prices with the median value so we didn't throw off the numbers.

Prompt:

"handle missing and duplicated instances"

Insight:

The result? A squeaky-clean, fully complete dataset with zero missing entries. The foundation was now rock solid.

Step 3: Finding the Treasure Map - What the Data Was Hiding

With our data gleaming, it was time for the interrogation phase. We prompted Terno-AI to build us a "mission control" dashboard to make the data talk.

Prompt:

"build an interactive dashboard to answer key managerial questions using only the cleaned dataset"

Insight:

Prompt:

"do further analysis"

Insight:

And talk it did! We instantly uncovered top-secret intel:

• The King of Categories: "Beauty & Hygiene" rules the kingdom with nearly 25,000 products.
• The Secret Weapon: Big Basket's own "bb Combo" is the most common brand, proving their in-house bundling strategy is a massive success.
• The Pricing DNA: The business is a high-volume hero, with the vast majority of its products priced for the everyday shopper.

Step 4 & 5: Building the Brains - The AI Sales Assistant

Now for the main event: turning our insights into an intelligent, money-making machine.

Feature Engineering: Teaching Our AI to Read

First, we had to teach our AI to understand what each product is. We created a "master profile" for each item by combining its name, brand, category, and description. Then, using a clever technique called

TF-IDF, we taught the AI how to spot the "magic words" that make a product unique, ignoring the boring filler.

Modeling: Unleashing the Super-Smart Personal Shopper

Our goal was to build a recommender that could act like a super-smart personal shopper. But here’s where a genius move happened. Terno-AI realized that comparing every single product to every other product would create a digital traffic jam. So, it

automatically switched to a faster, nimbler K-Nearest Neighbours (KNN) model. This ensures recommendations are delivered in a snap.

Prompt:

"Create a system that takes a product name as input and recommends 5 other similar products."

Insight:

But we didn't stop there. We also created a "satellite view" of the entire market using Product Clustering. This interactive map shows all the product "continents" and "islands," giving leaders a bird's-eye view of their digital shelf space.

Prompt:

"create a cluster plot of products based on the similarity give me a clean and clear plot"

Insight:

 Conclusion: Your Adventure Awaits

This end-to-end project, from unifying a chaotic dataset to deploying an intelligent recommendation engine, was driven by a systematic workflow of conversational prompts. This highlights the core advantage of Terno-AI: radical efficiency.

• Effortless Data Prep: What could have been a painstaking engineering task—consolidating 157 fragmented files—was automated, saving significant effort.
• Intelligent Cleaning: The rigorous process of dropping 3,914 duplicates and strategically imputing over 10,500 missing prices was handled with simple commands.
• Automatic Optimization: When building the recommender, Terno-AI didn't just build a model; it intelligently identified a computational bottleneck and automatically pivoted to a more scalable and efficient KNN approach, ensuring the final solution was fast and resource-efficient.

This case study exemplifies how Terno-AI accelerates the journey from raw data to a tangible, revenue-generating business asset. It transforms complex data science into a series of clear, conversational steps, making it possible to turn your data into your most powerful asset with unprecedented speed and ease.

Ready to decode your data? Try Terno-AI now and see your insights come alive.