📊 Retail Demand Analytics App

A production-ready retail analytics dashboard that transforms raw sales data into actionable business insights.
Built with Python, Pandas, Streamlit, and Plotly, the app supports dynamic client data uploads, automatic schema detection, and end-to-end analysis — with no bundled sample datasets.

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🚀 Key Highlights

• 🔄 Client-driven data uploads (CSV / Excel)
• 🧠 Automatic column mapping & schema normalization
• 📈 Time-series trend and growth analysis
• 🛍️ Product & category performance insights
• 🔮 Demand forecasting
• 💡 Strategic business recommendations
• 🎨 Modern, executive-grade UI/UX

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🎯 Features

📥 Data Ingestion

• Upload CSV or Excel files directly through the UI
• Automatic detection of column names (schema-agnostic)
• Built-in data validation and cleaning
• Revenue auto-calculated if missing

📊 Trend Analysis

• Daily, weekly, and monthly revenue trends
• Growth rate analysis
• Seasonality detection
• Moving-average forecasting

🛍️ Product Intelligence

• Top and bottom product rankings
• Category-level revenue analysis
• Price sensitivity and performance scoring
• Product growth classification (Growing / Stable / Declining)

💡 Strategic Recommendations

• Inventory optimization suggestions
• Pricing strategy insights
• Marketing and operational recommendations
• Priority-based action items (High / Medium / Low)

🧭 Interactive Dashboard

• Executive KPI cards
• Plotly-powered interactive charts
• Filterable data explorer
• Exportable filtered datasets

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🏗️ Project Structure

retail-demand-analytics/ ├── src/ │ ├── data_loader.py │ ├── trend_analysis.py │ ├── inventory_analysis.py │ ├── recommendations.py │ └── init.py ├── tests/ ├── app.py ├── cli.py ├── README.md ├── requirements.txt └── .gitignore

⚠️ No bundled datasets — the app runs entirely on user-uploaded data.

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📂 Expected Data Format

The app automatically maps column names, but the following logical fields are required:

Logical Field	Required	Notes
product_id	✅	SKU / Item ID
product_name	✅	Product name
category	✅	Product category
date	✅	Order / sales date
unit_price	✅	Price per unit
quantity_sold	✅	Units sold
revenue	❌	Auto-calculated if missing

Column names do not need to match exactly — the app auto-detects common variants.

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🚀 Quick Start

1️⃣ Create Virtual Environment

python -m venv venv

2️⃣ Activate Environment

Windows

venv\Scripts\activate

macOS / Linux

source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the App

streamlit run app.py

Open the browser link shown in the terminal.

🧠 How the App Works

User uploads a sales dataset

Columns are auto-detected and normalized

Data is validated and cleaned

Analytics engines compute trends, forecasts, and insights

Interactive dashboards and recommendations are rendered

🛠 Tech Stack

Python

Pandas & NumPy

Streamlit

Plotly

Matplotlib (optional, used for advanced table styling)

📌 Project Status

✅ Core analytics complete

✅ Production-ready UI/UX

✅ Dynamic client data support

🚧 Advanced forecasting models (planned enhancement)

🎓 Use Cases

Retail sales analysis

Business intelligence dashboards

Data analytics portfolios

Interview & internship projects

Internal analytics tools

📄 License

This project is intended for educational and portfolio use. You are free to extend and adapt it for personal or academic projects.

⭐ Final Note

This project is designed to reflect real-world analytics workflows, not toy datasets. It emphasizes data robustness, modular design, and business relevance.