🔮 MultiRegal - Automatic Multivariable Regression Analysis Calculator

An intelligent tool that automatically identifies the most important factors affecting your outcomes through AI-powered regression analysis.

✨ Features

• 🤖 AI-Powered Factor Identification: Uses multiple statistical methods and LLM intelligence to identify the most important variables
• 📊 Interactive Web Interface: Beautiful, user-friendly Streamlit interface for easy data analysis
• 🧹 Advanced Data Cleaning: Automatically handles dirty data including missing values, duplicates, outliers, mixed data types, and inconsistent formatting
• 🔍 Comprehensive Analysis: Performs correlation analysis, feature importance ranking, and regression modeling
• 📐 Formula Generation: Creates both precise and simplified mathematical formulas
• 📈 Rich Visualizations: Interactive charts, correlation heatmaps, and model comparisons
• 💡 Smart Insights: LLM-generated recommendations and actionable insights
• 📋 Detailed Reports: Complete cleaning and analysis reports with step-by-step documentation
• 💾 Export Capabilities: Download results in JSON and CSV formats

🚀 Quick Start

Installation

1. Install dependencies:

   pip install -r requirements.txt

2. Install the package in development mode:

   pip install -e .

Launch Web Interface

Option 1: Use the launch script (Recommended)

python run_app.py

Option 2: Direct Streamlit command

streamlit run app.py

The web interface will open at http://localhost:8501

📱 Web Interface Usage

1. Upload Data

• Upload a CSV file with your dataset
• Or use the demo data for quick testing
• Data preview and validation happens automatically

2. Configure Analysis

• Select your target variable (what you want to predict)
• Choose input features (or use all available)
• Set analysis parameters (max features, test size)

3. Run Analysis

• Click "🚀 Run Analysis" to start the process
• Watch real-time progress as the analysis runs
• Results appear in organized tabs

4. Explore Results

• 🎯 Key Findings: Top factors and importance rankings
• 📊 Feature Analysis: Correlation heatmaps and detailed analysis
• 🤖 Model Comparison: Performance metrics across different models
• 📐 Formulas: Generated mathematical formulas
• 💡 Insights: AI-powered recommendations and insights
• 🧹 Data Cleaning Report: Detailed report of all cleaning steps performed

5. Export Results

• Download comprehensive reports in JSON format
• Export factor rankings as CSV
• Save results for further analysis

🧹 Data Cleaning Capabilities

MultiRegal automatically handles messy, real-world data! It can clean:

• 🔄 Duplicate Records: Automatically removes identical rows
• 💰 Currency & Number Formatting: Handles "$1,234", "1,234.56", "€123", percentages
• ❌ Missing Values: Smart imputation using mean/median based on data distribution
• 🔤 Mixed Data Types: Converts "TRUE/FALSE", "Yes/No" to numeric, handles text in number columns
• 📊 Statistical Outliers: Removes extreme values using IQR or Z-score methods
• 🏷️ Column Names: Standardizes messy column names (spaces, special characters)
• 🔢 Data Type Issues: Converts text representations to proper numeric formats
• 📏 Inconsistent Formats: Handles spaces in numbers ("12 34" → 1234)

Try the "Dirty Demo Data" option to see cleaning in action!

📋 Data Requirements

• Format: CSV file
• Minimum Size: At least 10 rows (after cleaning)
• Columns: At least 2 numeric columns (after conversion)
• Missing Data: Up to 50% missing values (will be cleaned automatically)
• Variable Types: Any data types (will be converted to numeric where possible)

🛠️ Programmatic Usage

You can also use the analysis tools programmatically:

from regression_analyzer.analysis_tools import (
    load_and_preprocess_data,
    identify_top_factors,
    perform_regression_analysis,
    generate_formula_and_insights
)
import pandas as pd

# Load your data
df = pd.read_csv("your_data.csv")

# Run analysis
top_factors = identify_top_factors(df, target_column="your_target")
model_results = perform_regression_analysis(df, "your_target", list(top_factors.keys()))
formulas = generate_formula_and_insights(df, "your_target", model_results)

print("Top factors:", top_factors)
print("Best model R²:", max([r['r2_score'] for r in model_results.values()]))

🎯 Use Cases

• Business Analytics: Identify factors affecting sales, revenue, or performance
• Scientific Research: Analyze experimental data and variable relationships
• Financial Analysis: Understand factors influencing financial metrics
• Healthcare: Analyze factors affecting patient outcomes
• Marketing: Determine which factors drive customer behavior
• Operations: Optimize processes by understanding key variables

🧠 How It Works

1. Data Preprocessing: Handles missing values, validates data structure
2. Factor Identification: Uses multiple methods:
   • Pearson correlation analysis
   • F-test statistical significance
   • Random Forest feature importance
   • Recursive feature elimination
3. Model Training: Tests multiple regression models:
   • Linear Regression
   • Ridge Regression (L2 regularization)
   • Lasso Regression (L1 regularization)
4. Formula Generation: Creates mathematical representations
5. Insight Generation: LLM-powered analysis and recommendations

📊 Example Output

Top Factors Identified:

1. square_footage    0.8234
2. location_score    0.7891
3. age_of_building  -0.6543
4. num_bedrooms      0.5321
5. amenities_score   0.4567

Generated Formula:

price = 245.67 + 156.23 * square_footage + 89.45 * location_score - 12.34 * age_of_building

Key Insights:

• Square footage has the strongest positive impact on price
• Location score is the second most important factor
• Building age negatively affects price
• Model achieves 89.4% accuracy (R² = 0.894)

🤖 Google ADK Integration

This tool integrates with Google's Agent Development Kit for enhanced LLM capabilities:

# To use with ADK web interface
adk serve

# To use with ADK CLI
adk run regression_analyzer

🔧 Development

Project Structure

multiregal/
├── regression_analyzer/          # Core analysis package
│   ├── __init__.py
│   ├── agent.py                 # LLM agent integration
│   └── analysis_tools.py        # Statistical analysis functions
├── utils/                       # Web interface utilities
│   ├── __init__.py
│   └── interface_helpers.py     # Streamlit helper functions
├── app.py                       # Main Streamlit application
├── run_app.py                   # Launch script
├── demo.py                      # Command-line demo
├── test_installation.py         # Installation tester
├── sample_data.csv              # Demo dataset
└── requirements.txt             # Dependencies

Running Tests

python test_installation.py

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📄 License

This project is open source and available under the MIT License.

🆘 Support

If you encounter any issues:

1. Check the data requirements section
2. Ensure all dependencies are installed correctly
3. Try the demo data first to verify the installation
4. Check the console/terminal for error messages

For additional help, please open an issue in the repository.