🛒 Smart Checkout Prediction System

🚀 Overview

This project demonstrates an AI-powered decision system for retail checkout optimization.

The goal is not only to analyze transactions, but to anticipate congestion, reduce waiting time, and guide customers in real time.

It simulates a real-world operational environment where:

• customers need fast guidance
• stores need to manage flow efficiently
• systems must translate data into simple decisions

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🎯 Product Vision

Traditional dashboards show data.

👉 This system delivers decisions:

• Which checkout should customers go to?
• What is the expected waiting time?
• Which areas are under operational pressure?
• Where should staff intervene?

This project illustrates a product mindset, not just a data model.

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🧠 Core Features

1. Predictive Models

• ⏱️ Transaction duration prediction (Regression)
• ⚠️ Abandonment risk prediction (Classification)
• 📊 Congestion scoring system

Built using:

• Random Forest models
• Behavioral features (items, retries, help requests, time)

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2. Real-Time Checkout Guidance

The system identifies:

• ✅ Best checkout available
• ⏳ Estimated waiting time
• 📍 Alternative recommended checkouts

👉 Output is simplified for customer-facing display

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3. Operational Decision Layer

Each checkout is evaluated based on:

• predicted duration
• congestion score
• abandon risk

This enables:

• staff allocation
• queue management
• friction detection

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🖥️ Product Interface

🎯 Customer Guidance Screen

👉 Large, simple, actionable:

• recommended checkout
• waiting time
• visual priority

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🧭 Alternative Checkout Suggestions

👉 Helps distribute customer flow across multiple checkouts

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📊 Operational Dashboard

👉 Used by store teams:

• congestion distribution
• checkout performance
• risky transactions

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⚙️ Architecture

Data → Features → ML Models → Scoring → UI

Stack:

• Python
• Pandas / NumPy
• Scikit-learn
• Streamlit

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📊 Results (Simulated)

• Accurate transaction duration estimation
• High detection of friction patterns
• Realistic congestion modeling
• Actionable outputs (not just predictions)

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🏪 Use Cases

• Retail self-checkout systems
• Store operations optimization
• Queue management
• Customer experience improvement

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💡 Key Product Insight

This project demonstrates the shift from:

❌ Data dashboards
➡️
✅ Decision systems

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🧠 Why this project matters (Product Perspective)

As a Product Manager, the challenge is not building models.

👉 It is turning complexity into simple user decisions.

This project shows:

• strong product thinking
• ability to connect AI → business → UX
• operational impact mindset
• system design beyond pure analytics

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🚀 How to run

python main.py
streamlit run app/streamlit_app.py