DataStorm

---

---

Overview

DataStorm is a comprehensive retail analytics and demand forecasting platform designed to provide actionable insights for FMCG (Fast-Moving Consumer Goods) and Fashion retail businesses. The platform combines advanced data analytics, machine learning-based demand forecasting, and interactive visualizations to help businesses make data-driven decisions.

---

Features

• Sales Analytics Dashboard: Real-time visualization of net sales, unit sold, and revenue metrics
• Demand Forecasting: 7-day ML-powered demand prediction for inventory optimization
• Multi-Currency Support: Analysis across USD, EUR, CNY, and GBP
• Geographic Analysis: Sales comparison across multiple countries and stores
• Channel Analytics: Performance tracking across different sales channels
• Inventory Optimization: Stock alerts and safety stock recommendations
• Pricing and Discount Analysis: Impact assessment of promotional strategies
• Supplier Performance: Vendor evaluation and cost analysis
• Weather Correlation: Analysis of weather impact on sales patterns
• Interactive Maps: Geographic visualization of sales data

---

Tech Stack

Frontend

• Next.js 16
• React 19
• Tailwind CSS 4
• Recharts
• MapLibre GL
• Zustand (State Management)
• Framer Motion

Backend

• FastAPI
• SQLAlchemy
• PostgreSQL
• Upstash Redis (Caching)
• Google Generative AI

AI/ML Backend

• Python
• Pandas
• Scikit-learn
• Custom ML Pipeline for Demand Forecasting

---

Project Structure

datastorm/
├── frontend/          # Next.js frontend application
│   ├── app/           # App router pages and components
│   ├── components/    # Reusable UI components
│   └── lib/           # Utility functions
├── backend/           # FastAPI backend service
│   ├── app/           # Application code
│   │   ├── model/     # Database models
│   │   └── utils/     # Utility functions
│   └── requirements.txt
├── ai_backend/        # ML forecasting service
│   ├── core.py        # ML pipeline
│   ├── server.py      # FastAPI server
│   └── models/        # Trained model storage
└── data/              # Dataset directory
    ├── Fashion/       # Fashion retail dataset
    └── FMCG/          # FMCG sales dataset

---

Getting Started

Prerequisites

• Node.js 18+
• Python 3.10+
• PostgreSQL
• Redis (optional, for caching)

Installation

1. Clone the Repository

git clone https://github.com/DomTechnology/datastorm.git
cd datastorm

2. Frontend Setup

cd frontend
npm install
npm run dev

The frontend will be available at http://localhost:3000

3. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload

The backend API will be available at http://localhost:8000

4. AI Backend Setup

cd ai_backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn server:app --reload --port 8001

The AI/ML API will be available at http://localhost:8001

---

API Documentation

Backend API

The main backend provides endpoints for:

Category	Endpoints
General	/information, /country, /store/list, /city/list
Sales	/net_sales/daily, /unit_sold/daily, /net_sales/category
SKU	/sku/top, /sku/list
Stock	/stock_alerts
Analytics	/analytics/revenue, /analytics/profit, /analytics/kpi
AI/ML	Demand forecasting endpoints

Full API documentation available at http://localhost:8000/docs

AI Backend API

Endpoint	Description
POST /train	Train the forecasting model
POST /forecast	Get 7-day demand forecast

Full API documentation available at http://localhost:8001/docs

---

Datasets

Fashion Retail Dataset

• 4M+ sales records
• 7 countries coverage
• 35 distinct stores
• Customer demographics and behavior data

FMCG Dataset

• Product sales data
• Inventory levels
• Promotional data

---

Environment Variables

Frontend (.env.local)

NEXT_PUBLIC_API_URL=http://localhost:8000

Backend (.env)

DATABASE_URL=postgresql://user:password@localhost:5432/datastorm
UPSTASH_REDIS_REST_URL=your_redis_url
UPSTASH_REDIS_REST_TOKEN=your_redis_token
GOOGLE_API_KEY=your_google_api_key

---

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

---

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

Acknowledgments

• Thanks to all contributors who have helped build this project
• Special thanks to our sponsors and organizers for their support

---

Contact

For questions or support, please open an issue on GitHub or contact the maintainers.