featuremind 3.1.0

Universal AutoML + Feature Engineering + Explainability Library

🧠 featuremind v3.1.0

Universal AutoML · Feature Engineering · Reliability Framework

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📌 What is featuremind?

featuremind is a one-line AutoML library that handles the complete machine learning pipeline — from raw CSV to production-ready model — with built-in reliability checking, leakage detection, and feature engineering.

import featuremind as fm
fm.analyze("data.csv")

That's it. One line. Full analysis, model selection, feature suggestions, SHAP importance, leakage check, and HTML report — all automated.

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🧪 Tested Datasets

featuremind v3.1 has been verified on:

Dataset	Type	Score	Notes
Telecom Churn (7,043 rows)	Classification	85.7% F1	✅ Stable, well-balanced
Credit Card Fraud (284,807 rows)	Classification	~99% F1	⚠️ High score due to PCA-transformed separable data
Heart Failure Medical	Classification	~80% Accuracy	✅ Works
House Prices	Regression	R² reported	✅ Works
Generic CSVs	Auto-detected	Auto-detected	✅ Works

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🚀 Key Features

🤖 Auto ML Pipeline

• Loads and cleans any CSV automatically
• Detects target column, task type (classification / regression), and data issues
• Trains 6 models: LogisticRegression, RandomForest, GradientBoosting, XGBoost, LightGBM, CatBoost
• Picks best model using cross-validation
• Auto hyperparameter tuning (RandomizedSearchCV)

🛡️ Leakage Guard (Core Feature)

• Detects if any feature formula references the target column
• Flags columns with suspiciously high correlation with target (>0.95)
• Smart ID detection (non-generalizable columns)
• Warns user before model training (no silent failures)

🔍 Reliability Engine

• Detects unrealistic scores (>0.98)

• Adjusts confidence level automatically:

  • 0.99 → Low confidence ❌

  • 0.98 → Medium ⚠️

• Highlights possible issues:

  • Data leakage
  • Overfitting
  • Sampling bias

⚖️ Class Imbalance Handling

• Detects imbalance automatically
• Applies SMOTE (if available)
• Falls back to class weights
• Switches evaluation metric to F1 when needed

📊 SHAP Explainability

• Computes SHAP values for model explainability
• Displays top features influencing predictions
• Helps identify real business drivers

🔬 Feature Engineering (Multi-layer)

• Domain-aware features: Telecom · Medical · Real Estate · Finance · HR
• Interactions, ratios, log transforms, polynomial features
• Only surfaces features that improve performance

🏗️ Production Pipeline

• Save trained model + preprocessing pipeline
• Load and predict on new/unseen data
• Handles missing columns and unseen categories

🏆 Experiment Tracking

• Logs every run automatically
• Leaderboard of models and scores
• Export results to CSV

🌐 REST API (Optional)

• FastAPI-based prediction server
• Ready-to-use endpoints for deployment

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🆚 Why featuremind?

Capability	featuremind	Typical AutoML Tools
One-line usage	✅	❌
Leakage detection	✅	❌
Reliability scoring	✅	❌
SHAP explainability	✅	⚠️
Production pipeline	✅	✅

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📦 Installation

# Install from local
pip install -e .

# (Recommended) Install advanced ML libraries
pip install xgboost lightgbm catboost shap imbalanced-learn

# Optional API support
pip install fastapi uvicorn python-multipart

🔜 PyPI release planned:

pip install featuremind

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🚀 Quick Start

import featuremind as fm

# Analyze dataset
fm.analyze("data.csv")

# Leakage check
fm.check_leakage("data.csv", target="Churn")

# Train + save pipeline
pipeline = fm.train("data.csv", target="Churn")
pipeline.save("churn_pipeline")

# Load + predict
pipeline = fm.load_pipeline("churn_pipeline")
results = pipeline.predict_df(new_data)

# Experiment tracking
fm.get_tracker().leaderboard()

# Optional API
fm.serve("churn_pipeline/", port=8000)

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🏢 Example Use Case

A company can use featuremind to:

1. Upload raw dataset (CSV)
2. Run fm.analyze()
3. Identify key drivers using SHAP
4. Validate model reliability
5. Deploy pipeline via API

➡️ Reduces manual ML workflow from days to minutes.

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📁 Project Structure

featuremind_project/
│
├── featuremind/
│   ├── analyzer.py
│   ├── feature_engineer.py
│   ├── evaluator.py
│   ├── leakage_guard.py
│   ├── importance.py
│   ├── reporter.py
│   ├── html_reporter.py
│   ├── insights.py
│   ├── pipeline.py
│   ├── tracker.py
│   └── api.py
│
├── setup.py
├── requirements.txt
├── test.py
└── README.md

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⚠️ Notes

• High accuracy (>0.98) may indicate:

  • Data leakage
  • Highly separable datasets
  • Sampling bias

• Always validate models on unseen data.

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📊 Output Files

• featuremind_report.html → Full analysis report
• featuremind_report.png → Feature visualization
• enhanced_data.csv → Dataset with engineered features
• featuremind_experiments.csv → Experiment logs
• pipeline/ → Saved production model

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💡 Use Cases

• Telecom churn prediction
• Fraud detection
• Healthcare predictions
• Real estate pricing
• HR analytics
• Any tabular ML problem

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🔮 Roadmap

• Time-series support
• Deep learning integration
• Streamlit dashboard
• Cloud deployment

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📄 License

MIT License

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👩‍💻 Author

Niveditha — Aspiring Data Scientist & ML Engineer

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