📊 Autonomous BI Suite Pro - Universal Edition

An executive-ready business intelligence dashboard that works with ANY dataset type - sales, sports, HR, and more!

🌟 NEW - Universal Dataset Support!

🎨 Customizable Colors

• Choose your own color theme with color pickers
• Quick preset buttons (Purple, Blue, Green, Red)
• All charts and metrics use your custom colors

📊 Works with ANY Dataset

• ✅ Sales Data (Revenue, Customers, Transactions)
• ✅ Sports Data (IPL Players, Runs, Wickets, Teams)
• ✅ HR Data (Employees, Salary, Departments)
• ✅ Education Data (Students, Grades, Courses)
• ✅ ANY Custom Data with numeric and categorical columns

🔧 Flexible Column Mapping

• Manual column selection for Primary Value and Category
• Auto-detection with 20+ common column patterns
• Dataset type selection (auto/sales/sports/hr/generic)
• Dynamic KPI labels based on your data

🎉 What's New in Version 2.0

🎨 Modern UI Overhaul

• Dark Mode - Beautiful light/dark theme toggle
• Glassmorphism Design - Frosted glass effect cards and modern styling
• Animated Gradients - Dynamic, eye-catching backgrounds
• Enhanced Metrics - Hover effects and smooth animations
• Tabbed Interface - Organized navigation across 5 sections

🚀 Core Features

• 🤖 AI-Powered Data Processing: Automatic column normalization using Groq AI
• 📊 Executive Dashboard: Enhanced KPI cards with 8+ metrics
• 📈 Interactive Visualizations: 8+ chart types with drill-down capability
• 🔄 Period Comparisons: Month/Quarter/Year-over-Year analysis
• 💾 Saved Filters: Save and reuse filter combinations
• 🚨 Alert System: Automated threshold notifications
• 🎲 Sample Data: Instant demo with 5,000+ transactions

📊 Advanced Analytics Suite

• 📈 RFM Analysis: Customer segmentation (Champions, Loyal, At Risk, Lost)
• 💰 Customer Lifetime Value: Predictive CLV calculations
• 📅 Cohort Analysis: Retention tracking by acquisition cohort
• 🚨 Anomaly Detection: Statistical outlier identification (Z-score)
• 🔮 Revenue Forecasting: Prophet-based predictions (7-90 days)

📤 Export Capabilities

• 📊 Excel Export: Multi-sheet workbooks with KPIs and statistics
• 📄 PDF Reports: Executive summary documents
• 📥 CSV Download: Filtered data export
• 📈 Forecast Export: Prediction data download

🤖 AI-Powered Insights

• 🎯 Quick Questions: Pre-built analysis prompts
• 💡 Custom Questions: Ask anything about your data
• 📋 Structured Insights: Action plans, root causes, and impact assessments

📦 Installation

1. Clone or download this project

2. Install dependencies:

   pip install -r requirements.txt

3. Configure Groq API (Optional but Recommended):

   • Copy .env.example to .env
   • Get your API key from Groq Console
   • Add your key to .env:

     GROQ_API_KEY=your_actual_api_key_here
     

4. Run the application:

   streamlit run app.py

5. Access the dashboard:

   • Opens automatically in your browser
   • Or navigate to: http://localhost:8501

🚀 Quick Start

🚀 Quick Start

Option 1: Use Sample Data (Fastest)

1. Launch the app
2. In sidebar, select "Use Sample Data"
3. Click "Load Sample Data"
4. Start exploring immediately with 5,000+ sample transactions

Option 2: Upload Your Own Data

1. Click "Upload File" in sidebar
2. Upload CSV or Excel file
3. Data will be automatically cleaned and normalized
4. Apply filters and explore

💡 Usage Guide

📊 Tab 1: Dashboard

• View KPIs: Revenue, Growth, AOV, Transactions at the top
• Period Comparison: Select Month/Quarter/Year comparison
• Visualizations: 4 auto-selected charts based on your data
• Filters: Date range and category filters in sidebar

🎯 Tab 2: Advanced Analytics

RFM Analysis:

• Segment customers by Recency, Frequency, Monetary value
• Identify Champions, Loyal Customers, At Risk, and Lost segments
• View distribution pie chart

Customer Lifetime Value:

• Calculate predicted CLV for each customer
• View top customers by lifetime value
• Analyze CLV distribution

Cohort Analysis:

• Track retention by acquisition month
• View retention heatmap
• Identify loyalty patterns

Anomaly Detection:

• Adjust sensitivity slider (1.0-3.0 sigma)
• Detect unusual revenue patterns
• Investigate spikes and drops

🔮 Tab 3: Forecasting

1. Select forecast period (7-90 days)
2. Click "Generate Forecast"
3. View Prophet model predictions with confidence intervals
4. Download forecast data as CSV

📊 Tab 4: Reports

• Excel Report: Download comprehensive multi-sheet workbook
• PDF Summary: Generate executive summary document
• CSV Export: Download filtered data

🤖 Tab 5: AI Insights

1. Select a quick question or enter custom question
2. Click "Generate AI Insights"
3. Review structured recommendations:
   • Key insights
   • Root causes
   • Action plans
   • Expected impact
   • Risk considerations

🎨 UI Features

Dark Mode

• Toggle in sidebar control panel
• Smooth theme transitions
• Optimized for both light and dark viewing

Metric Cards

• Gradient backgrounds
• Hover animations
• Delta indicators
• Icon labels

Alerts

• Success: Growth exceeding 50%
• Warning: Revenue below threshold
• Error: Declining revenue >10%

Saved Filters

1. Apply desired filters
2. Enter filter name
3. Click "Save Current Filters"
4. Load anytime from dropdown

📋 Data Requirements

Minimum Required

• At least 2 columns
• Preferably includes:
  • Date/timestamp column
  • Amount/revenue column
  • Category column (optional)
  • Customer ID (optional)

Optimal Dataset

For full feature functionality:

• Date Column: Transaction dates
• Revenue Column: Sales amounts
• Customer Column: Customer identifiers
• Category Column: Product/service categories
• Quantity Column: Units sold (optional)

Supported Formats

• CSV (.csv)
• Excel (.xlsx, .xls)

🔧 Advanced Features

AI Column Normalization

When Groq API is configured:

• Automatically renames columns to business-friendly terms
• Detects column purposes from sample data
• Converts abbreviations to full terms

Automatic Data Cleaning

• Removes duplicate rows
• Handles missing values intelligently:
  • Numeric: Median imputation
  • Dates: Forward fill
  • Categorical: Mode or 'Unknown'
• Detects and converts date formats
• Identifies numeric columns in text format

Smart Visualization

Algorithm selects best 2 charts based on:

• Available column types
• Data characteristics
• Business relevance

Priority order:

1. Time series (if dates available)
2. Category breakdown (if categories available)
3. Distribution analysis (if numeric data)
4. Top performers
5. Correlations

📊 Analytics Explained

RFM Scoring

• Recency (R): Days since last purchase (1-5, 5 = recent)
• Frequency (F): Number of purchases (1-5, 5 = frequent)
• Monetary (M): Total spend (1-5, 5 = high value)
• RFM Score: Combination like "555" = Champion

CLV Calculation

CLV = Avg Order Value × Purchase Rate × Customer Lifespan (months)

Anomaly Detection

Uses Z-score statistical method:

Z-score = (Value - Mean) / Standard Deviation

Flags points where |Z| > threshold (default: 2.0)

Forecasting

Prophet Model (preferred):

• Additive/multiplicative seasonality
• Trend changepoint detection
• Holiday effects (configurable)
• Confidence intervals

Fallback: 7-day moving average projection

🆘 Troubleshooting

"Prophet Not Available"

pip install prophet

"PDF Export Requires ReportLab"

pip install reportlab

AI Features Not Working

• Check .env file exists and has valid GROQ_API_KEY
• Verify API key at https://console.groq.com
• Check internet connection

Slow Performance

• Apply date/category filters to reduce data size
• Use sample data for testing
• Close other browser tabs
• Increase Streamlit server resources

Charts Not Appearing

• Verify data has required columns
• Check for all-null columns
• Try sample data to verify installation

📚 Additional Resources

• Full Features Guide: See FEATURES_GUIDE.md
• Groq API Docs: https://console.groq.com/docs
• Streamlit Docs: https://docs.streamlit.io
• Prophet Docs: https://facebook.github.io/prophet/

🎯 Use Cases

E-commerce

• Track daily revenue trends
• Identify top products
• Segment customers by value
• Forecast holiday sales

SaaS

• Monitor MRR/ARR
• Calculate customer LTV
• Track cohort retention
• Predict churn

Retail

• Analyze store performance
• Identify seasonal patterns
• Optimize inventory with forecasts
• Target high-value customers

Marketing

• Measure campaign ROI
• Segment audience by engagement
• Predict future conversions
• Allocate budget to best channels

🔐 Security & Privacy

• All data processing happens locally
• No data stored on external servers
• API calls use encrypted HTTPS
• Optional API usage (works offline without AI features)

🛠️ Tech Stack

• Frontend: Streamlit
• Visualizations: Plotly
• Data Processing: Pandas, NumPy
• Analytics: Scikit-learn, SciPy
• Forecasting: Prophet
• AI: Groq LLM (llama-3.3-70b)
• Export: ReportLab (PDF), XlsxWriter (Excel)

📝 Version History

Version 2.0 (February 2026)

• ✨ Complete UI overhaul with modern design
• 🎨 Dark mode support
• 📊 Advanced analytics suite (RFM, CLV, Cohort)
• 🔮 Revenue forecasting with Prophet
• 🚨 Anomaly detection
• 📤 Export to Excel/PDF
• 💾 Saved filters
• 🚨 Alert system
• 🎲 Sample data generator
• 🗂️ Tabbed interface

Version 1.0 (Initial Release)

• Basic KPI dashboard
• AI column normalization
• Simple visualizations
• AI insights

🤝 Contributing

Suggestions and improvements welcome!

📄 License

MIT License - Free to use and modify

👤 Author

Management Analytics Team

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🎊 Get Started Now!

pip install -r requirements.txt
streamlit run app.py

Explore the future of business intelligence! 🚀

• Avg Order Value: Average transaction size

4. Analyze Visualizations

• Distribution Chart: Histogram showing value distribution
• Categorical Chart: Bar chart comparing categories

5. Get AI Insights

• Scroll to "Autonomous Root Cause Analysis"
• Enter a question (e.g., "Why is revenue declining?")
• Click "Generate Insights"
• Review bulleted insights and prescriptive action plan

Architecture

Data Pipeline (process_data)

• Detects file type (CSV/Excel)
• Removes duplicates automatically
• Normalizes column names using Grok AI
• Auto-detects date columns
• Handles missing values intelligently

State Management

• Uses st.session_state to persist processed data
• Dashboard only re-renders when filters change
• Efficient data handling for large datasets

AI Integration

• Column Normalization: Converts technical names to business terms
• Prescriptive Insights: Generates actionable recommendations
• Root Cause Analysis: Analyzes patterns and provides specific action plans

Example Questions for AI Insights

• "What factors are driving high-value transactions?"
• "Why is revenue declining in recent periods?"
• "Which categories show the strongest growth?"
• "What actions should we take to improve performance?"

Requirements

• Python 3.8+
• Streamlit 1.31+
• Grok API key (from xAI)

Notes

• The dashboard is designed for management-level presentations
• All insights are prescriptive (actionable) rather than just descriptive
• Modular code structure for easy customization
• Automatic data type detection and handling