Abishek-kk

Abishek-kk / AutoMart-AI

Public

Developed an AI-powered supermarket analytics system using LSTM, K-Means, and Apriori to predict sales, segment customers, recommend products, and optimize inventory through an agent-based architecture with a Streamlit dashboard.

19
0
100% credibility
Found Apr 29, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AutoMart-AI is a retail analytics tool that processes supermarket sales data to deliver insights on customer segments, inventory management, profit analysis, product recommendations, and sales forecasts through an interactive web dashboard.

How It Works

1
🛒 Discover AutoMart-AI

You stumble upon this free tool designed to help supermarket owners get smart insights from their sales data.

2
💻 Set it up easily

Download the files to your computer and follow simple steps to get everything ready in minutes.

3
🚀 Launch the dashboard

Open the app and instantly see a colorful screen filled with charts, numbers, and helpful alerts about your store.

4
📊 Review sales and stock

Check daily trends, top earning products, low stock warnings, and what sells big during festivals.

5
👥 Understand your customers

See groups of shoppers like high-spenders or bargain hunters, plus ideas on rewards and deals.

6
🔮 Forecast tomorrow

Click a button to predict next day's sales and plan your orders ahead of time.

🎉 Run your store smarter

With clear insights on profits, stock, customers, and recommendations, you make better decisions to grow your business.

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AI-Generated Review

What is AutoMart-AI?

AutoMart-AI is an AI-powered supermarket analytics platform that predicts next-day sales with LSTM, segments customers using K-Means, uncovers product bundles via Apriori, and flags inventory issues—all wrapped in an agent-based architecture. It delivers actionable insights on profit, demand, and recommendations through a Streamlit dashboard or CLI runner, solving the pain of manual retail data analysis for managers. Built in Python with PyTorch and scikit-learn, it runs out-of-the-box on dummy sales data.

Why is it gaining traction?

Its agent-based setup layers business logic over raw ML outputs for ready-to-use insights like restock alerts and marketing strategies, standing out from bare ML notebooks. The polished Streamlit UI with charts, predictions, and optional LLM enhancements via Google Gemini hooks devs wanting quick prototypes over fragmented tools. Low setup friction—pip install and streamlit run—makes it a fast win for automart analytics demos.

Who should use this?

Retail data analysts prototyping dashboards for sales forecasting and customer segmentation. ML engineers building agent-based AI-powered apps for inventory optimization. Indie devs or students creating PoCs around Apriori recommendations and LSTM predictions in supermarket scenarios.

Verdict

Solid starter for retail ML experiments with excellent docs and modular design, but at 19 stars and 1.0% credibility, it's early-stage—add tests and real data before production. Fork it if you need a battle-tested analytics architecture baseline.

(198 words)

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