🏪 Smart Retail Inventory Management System with AI/ML

A full-stack Django-based Smart Retail Inventory Management System integrated with Machine Learning for anomaly detection. The system helps retailers manage products, inventory, sales, reports, authentication, and AI-powered analytics.

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📌 Features

👤 Authentication

• User Login
• User Registration
• Forgot Password
• Password Reset via Email
• Logout
• Role-Based Access Control (Admin, Manager, Staff)

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📦 Product Management

• Add Products
• Update Products
• Delete Products
• Product Listing
• Product Search

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📊 Inventory Management

• Stock In
• Stock Out
• Inventory Records
• Low Stock Detection

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💰 Sales Management

• Record Sales
• Sales History
• Revenue Calculation
• Sales Analytics

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📑 Reports

• Sales Report (PDF)
• Inventory Report (Excel)
• AI Anomaly Report (PDF)

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🤖 AI Analytics

Three anomaly detection techniques are implemented.

Method 1 – Z-Score

Statistical anomaly detection based on standard deviation.

Method 2 – Isolation Forest

Machine Learning model trained using historical retail sales data.

Method 3 – Local Outlier Factor (LOF)

Density-based anomaly detection algorithm.

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🔒 Role Based Access

Admin

• Full Access

Manager

• Products
• Inventory
• Sales
• Reports
• AI Analytics

Staff

• Products
• Inventory
• Sales

Staff users cannot access:

• Reports
• AI Analytics

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🧠 Machine Learning Workflow

Training Dataset
        │
        ▼
Data Preprocessing
        │
        ▼
StandardScaler
        │
        ▼
Isolation Forest
        │
        ▼
Local Outlier Factor
        │
        ▼
Save Trained Models (.pkl)
──────────────────────────────────
        │
        ▼
Django Application
        │
        ▼
Load Trained Models
        │
        ▼
Predict Anomalies

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🏗️ Project Structure

SmartRetailInventory/

│
├── accounts/
├── analytics_dashboard/
├── api/
├── inventory/
├── products/
├── reports/
├── sales/
│
├── ai_engine/
│   ├── data/
│   ├── models/
│   ├── train_model.py
│   ├── predict.py
│   ├── predict_lof.py
│   ├── database_detector.py
│   ├── database_isolation.py
│   ├── database_lof.py
│   └── anomaly_detector.py
│
├── static/
├── templates/
├── manage.py
└── requirements.txt

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⚙️ Technologies Used

Backend

• Python
• Django
• Django REST Framework

Frontend

• HTML5
• CSS3
• Bootstrap 5
• JavaScript
• Chart.js

Database

• SQLite3

Machine Learning

• Scikit-learn
• Isolation Forest
• Local Outlier Factor
• Z-Score
• StandardScaler
• Joblib
• Pandas
• NumPy

Reporting

• ReportLab
• OpenPyXL

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🚀 Installation

Clone Repository

git clone https://github.com/YOUR_USERNAME/SmartRetailInventory.git

cd SmartRetailInventory

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Create Virtual Environment

Windows

python -m venv myenv

myenv\Scripts\activate

Linux / macOS

python3 -m venv myenv

source myenv/bin/activate

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Install Dependencies

pip install -r requirements.txt

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Apply Migrations

python manage.py makemigrations

python manage.py migrate

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Create Superuser

python manage.py createsuperuser

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Train Machine Learning Models

python ai_engine/train_model.py

This creates:

ai_engine/models/

isolation_forest.pkl

lof.pkl

scaler.pkl

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Run Server

python manage.py runserver

Open

http://127.0.0.1:8000/

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📡 REST API

Example APIs

GET /api/products/

GET /api/inventory/

GET /api/sales/

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📸 Screenshots

Login Page

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Dashboard

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Products

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Inventory

Sales

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Reports

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AI Analytics

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📊 Machine Learning Models

Algorithm	Purpose
Z-Score	Statistical anomaly detection
Isolation Forest	Machine Learning anomaly detection
Local Outlier Factor	Density-based anomaly detection

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🎯 Future Enhancements

• Email Notifications
• Barcode Scanner
• QR Code Support
• Demand Forecasting
• Sales Prediction
• Deep Learning Models
• Cloud Deployment
• PostgreSQL Support

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👨‍💻 Developer

• Anamika Kumari
• Shekhar Kumar Ray
• Ashwini Kumar Sinku

Computer Science and Engineering Student

AI | Machine Learning | Full Stack Django Developer

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⭐ If you like this project

Please consider giving it a ⭐ on GitHub.