Grocery Store Analytics Dashboard

A comprehensive analytics dashboard for grocery store data analysis built with Streamlit and Python. This dashboard provides real-time insights into sales patterns, customer behavior, marketing effectiveness, and operational efficiency.

🌟 Features

📊 Sales & Revenue Analytics

• Daily sales trends and transaction volume
• Product category performance
• Store location comparisons
• Payment method distribution

🎯 Marketing & Promotions

• Discount impact analysis
• Promotional effectiveness
• Customer segment response
• Holiday performance tracking

🔄 Customer Retention & Churn

• Purchase frequency patterns
• Customer recency analysis
• Churn risk identification
• High-value customer tracking

🗺️ Geographic Analysis

• Regional product preferences
• Urban vs suburban behavior
• Store location performance
• Payment method variations by region

💰 Financial Analytics

• Product category margins
• Discount impact on profitability
• Volume vs margin analysis
• Daily profit trends

🚨 Anomaly Detection

• Unusual transaction patterns
• Price anomalies
• Quantity outliers
• High-value transaction monitoring

🚀 Getting Started

Prerequisites

• Python 3.8+
• pip

Installation

1. Clone the repository

git clone https://github.com/imzeeshaan/grocery-analytics.git
cd grocery-analytics

2. Create and activate virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages

pip install streamlit pandas plotly numpy

Running the Application

Local Development

1. Generate sample data

python grocery_data_generator.py

2. Launch the dashboard

streamlit run grocery_analysis_app.py

Streamlit Cloud Deployment

1. Fork this repository
2. Go to Streamlit Cloud
3. Create a new app
4. Select your forked repository
5. Set the main file path as: grocery_analysis_app.py
6. Deploy!

📊 Data Structure

The application uses transaction data with the following schema:

{
    'transaction_id': str,          # Unique transaction identifier
    'transaction_datetime': datetime,# Date and time of transaction
    'store_location': str,          # Store location (Downtown, Suburb North, etc.)
    'customer_id': str,             # Unique customer identifier
    'product_id': str,              # Unique product identifier
    'product_name': str,            # Product name
    'product_category': str,        # Product category
    'quantity': int,                # Number of items purchased
    'unit_price': float,            # Price per unit
    'total_price': float,           # Total transaction amount
    'payment_method': str,          # Payment method used
    'loyalty_member': bool,         # Customer loyalty status
    'discount_applied': float       # Discount percentage (0-1)
}

🛠️ Built With

• Streamlit - Web application framework
• Plotly - Interactive visualization library
• Pandas - Data manipulation library
• NumPy - Numerical computing library

📝 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📧 Contact

Project Link: https://github.com/imzeeshaan/grocery-analytics

🙏 Acknowledgments

• Inspired by real-world retail analytics needs
• Streamlit community for excellent documentation and examples
• Contributors who help improve this project