ModelScout 🤖

Intelligent ML Model Recommendation System

An automated machine learning tool that analyzes your dataset and recommends the best-fitting ML models. ModelScout uses Auto-sklearn to intelligently search through a vast hyperparameter space and identifies optimal models for your specific data.

🎯 Features

• Automated Problem Detection: Automatically detects classification, regression, or clustering tasks
• Smart Model Selection: Uses Auto-sklearn to find the best models for your data
• Comprehensive Analysis: Provides detailed dataset analysis and insights
• Multiple Formats: Generates reports in text, JSON, and table formats
• REST API: Flask-based REST API for easy integration
• Support for All ML Tasks: Classification, Regression, Time-series, and more

📋 Project Structure

ModelScout/
├── agent/                    # Core ML engine
│   ├── data_analyzer.py     # Dataset analysis module
│   ├── model_selector.py    # Model recommendation using Auto-sklearn
│   ├── reporter.py          # Report generation
│   ├── orchestrator.py      # Main pipeline orchestrator
│   └── __init__.py
├── api/                      # REST API
│   ├── main.py              # Flask API endpoints
│   └── __init__.py
├── data/                     # Sample datasets
├── models/                   # Trained models storage
├── outputs/                  # Generated reports
├── requirements.txt         # Python dependencies
├── demo.py                  # Demo script with examples
└── README.md

🚀 Quick Start

1. Installation

# Clone or navigate to the project directory
cd ModelScout

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

# Install dependencies
pip install -r requirements.txt

2. Basic Usage

from agent.orchestrator import ModelScout

# Initialize
scout = ModelScout(auto_train_time=300)

# Run complete pipeline
result = scout.run_full_pipeline(
    data_path='your_data.csv',
    target='target_column',
    report_path='outputs/report.txt'
)

# Access results
print(result['recommendations']['best_model_name'])
print(result['recommendations']['test_score'])
print(result['report'])

3. Step-by-Step Usage

from agent.orchestrator import ModelScout
import pandas as pd

scout = ModelScout()

# Load data
df = scout.load_data('data.csv')

# Analyze data
analysis = scout.analyze_data(df, target='label')
print(f"Problem Type: {analysis['target_analysis']['type']}")

# Get recommendations
recommendations = scout.recommend_models(df, 'label')
print(f"Best Model: {recommendations['best_model_name']}")
print(f"Test Score: {recommendations['test_score']}")

# Generate report
report = scout.generate_report(output_format='text', output_path='report.txt')

🔧 API Endpoints

Health Check

GET /health

Analyze Dataset

POST /api/analyze
Content-Type: application/json

{
    "file_path": "path/to/data.csv",
    "target": "target_column"
}

Get Recommendations

POST /api/recommend
Content-Type: application/json

{
    "file_path": "path/to/data.csv",
    "target": "target_column",
    "time_limit": 300
}

Generate Report

POST /api/report
Content-Type: application/json

{
    "file_path": "path/to/data.csv",
    "target": "target_column",
    "format": "text"
}

Full Pipeline

POST /api/pipeline
Content-Type: application/json

{
    "file_path": "path/to/data.csv",
    "target": "target_column",
    "time_limit": 300
}

🎮 Run Demo

python demo.py

The demo script:

1. Creates sample datasets (Iris, Breast Cancer, Regression)
2. Runs ModelScout on each dataset
3. Generates comparison reports
4. Demonstrates both classification and regression

📊 What ModelScout Analyzes

Data Characteristics

• Dataset size and shape
• Missing values and data quality
• Feature types and counts
• Memory usage

Target Variable

• Problem type (Classification/Regression)
• Class distribution (for classification)
• Value range (for regression)
• Class imbalance ratio

Feature Statistics

• Numeric: mean, std, min, max, missing count
• Categorical: unique values, missing count

🤖 How It Works

1. Data Loading: Supports CSV, Excel, JSON formats
2. Analysis: Comprehensive dataset profiling
3. Problem Detection: Auto-detects ML task type
4. Model Search: Auto-sklearn searches optimal models
5. Evaluation: Train/test split and performance metrics
6. Reporting: Generates detailed recommendations

📦 Dependencies

• pandas: Data manipulation
• scikit-learn: ML algorithms
• auto-sklearn: Automated ML model selection
• numpy: Numerical computing
• matplotlib/seaborn: Visualization
• flask: REST API
• xgboost, lightgbm, catboost: Advanced models
• imbalanced-learn: Class imbalance handling

🔍 Example Output

======================================================================
  ___  ___           _      _    ____  ___  _   _ ___
 |  \/  |          | |    | |  / ___ \/ _ \| | | |_  |
 | .  . | ___    __| | ___| | / /   \/ /_\ \ | | | / /
 | |\/| |/ _ \  / _` |/ _ \ | \ \   |  _  | | | |/ /
 | |  | | (_) || (_| |  __/ |  \ \__| | | | |_| / /
 |_|  |_|\___/  \__,_|\___|_|   \___/_| |_|\___/___/

======================================================================

DATA OVERVIEW
======================================================================
Dataset Shape: (150, 5) (rows, columns)
Memory Usage: 0.00 MB
Missing Values: 0 (0.00%)
Numeric Features: 4
Categorical Features: 0

TARGET VARIABLE ANALYSIS
----------------------------------------------------------------------
Problem Type: CLASSIFICATION
Unique Values: 3
Missing Values: 0
Class Imbalance Ratio: 1.00:1
Class Distribution:
  0: 50 (33.3%)
  1: 50 (33.3%)
  2: 50 (33.3%)

MODEL RECOMMENDATIONS
======================================================================
Best Model: RandomForestClassifier
Problem Type: CLASSIFICATION
Train Score: 1.0000
Test Score: 0.9333
Data Shape Used: (150, 4)
Number of Classes: 3

======================================================================

🛠️ Configuration

You can customize behavior by modifying parameters:

scout = ModelScout(
    auto_train_time=600  # Increase for more thorough search (seconds)
)

📝 License

This project is for educational and portfolio purposes.

🤝 Contributing

Feel free to extend ModelScout with:

• Additional models
• More data preprocessing options
• Visualization enhancements
• Performance optimizations

📞 Support

For issues or questions, refer to the demo.py script for usage examples.

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Happy Model Scouting! 🎯