Business Segmenter

A comprehensive business analytics tool for customer segmentation, market basket analysis, and AI-powered marketing campaigns, built with Streamlit.

Features

• Overview Dashboard - Key metrics, revenue trends, and top products
• Smart Bundles - Product association discovery using Apriori algorithm
• Customer Segments - K-Means clustering with RFM analysis
• Marketing Assistant - AI-powered personalized campaign generation

Quick Start

Installation

# Clone the repository
git clone https://github.com/VIDHITTS/Business-Segmenter.git
cd Business-Segmenter

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the application
streamlit run app.py

The app will open at http://localhost:8501

Data Format

Required Columns

Your CSV file must contain ALL of these columns:

Column	Type	Description	Example
Date	datetime	Transaction date (YYYY-MM-DD)	2024-01-15
UserID	string	Customer identifier	CUST001
ProductID	string	Product name or ID	Gaming Mouse
Amount	float	Transaction amount	299.99
TransactionID	string	Unique transaction identifier	TXN001

Example CSV

Date,UserID,ProductID,Amount,TransactionID
2024-01-15,CUST001,Gaming Mouse,299.99,TXN001
2024-01-15,CUST001,Mousepad,49.99,TXN001
2024-01-16,CUST002,Laptop,1299.99,TXN002
2024-01-16,CUST002,HDMI Cable,19.99,TXN002
2024-01-17,CUST001,Keyboard,149.99,TXN003
2024-01-18,CUST003,Gaming Mouse,299.99,TXN004
2024-01-18,CUST003,Mousepad,49.99,TXN004
2024-01-18,CUST003,Keyboard,149.99,TXN004

Usage

1. Upload Data

• Drag and drop your CSV file in the sidebar
• Or use the built-in demo data to explore features

2. Smart Auto Mode (Recommended)

• Toggle "Smart Auto Mode" ON (default)
• Automatically calculates optimal parameters based on your data
• See explanations for each parameter choice

3. Manual Settings (Optional)

• Turn off Smart Auto Mode for manual control
• Adjust Bundle Support (1-20%)
• Adjust Confidence (30-100%)
• Set number of Customer Segments (2-5)

4. Click "Analyze Data"

• Required for uploaded CSV files
• Demo data auto-loads immediately

5. Explore Insights

• Overview - Metrics, trends, and top products
• Smart Bundles - Product recommendations and revenue opportunities
• Customer Segments - Segment profiles and 3D visualization
• Marketing - Generate personalized email campaigns

Project Structure

business-segmenter/
├── app.py                    # Main application entry point
├── requirements.txt          # Python dependencies
├── components/              # UI components
│   ├── sidebar.py           # Sidebar with Smart Auto Mode
│   ├── overview_dashboard.py
│   ├── smart_bundles.py
│   ├── customer_segments.py
│   └── marketing_assistant.py
├── utils/                   # Business logic
│   ├── data_generator.py   # Demo data generation
│   ├── segmentation.py     # K-Means clustering
│   └── market_basket.py    # Apriori algorithm
└── assets/                  # Styling
    └── styles.py

Key Technologies

• Streamlit - Web framework
• Pandas & NumPy - Data processing
• Scikit-learn - Machine learning (K-Means)
• MLxtend - Market basket analysis (Apriori)
• Plotly - Interactive visualizations

Error Handling

The app includes robust CSV validation:

✅ Validates required columns
✅ Checks for empty files
✅ Validates date formats
✅ Catches parser errors
✅ Shows helpful error messages with examples

No more crashes from invalid data!

Troubleshooting

App won't start?

# Ensure dependencies are installed
pip install -r requirements.txt

# Run the app
streamlit run app.py

Port already in use?

streamlit run app.py --server.port 8502

CSV upload errors?

Your CSV must contain ALL required columns: Date, UserID, ProductID, Amount, TransactionID

License

MIT License - Free for personal and commercial use

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Built with Streamlit, Scikit-learn, and MLxtend