🤖 multimodel: A Comprehensive Machine Learning Model Comparison Package

multimodel is a Python package designed to compare multiple supervised and unsupervised machine learning models for both classification and regression tasks. It automates model training, evaluation, visualization, and selects the best-performing model based on standard metrics.

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🚀 Key Features

• ✅ Supports Supervised and Unsupervised learning
• 🧠 Works for Classification and Regression problems
• 📊 Automatically generates comparison plots and evaluation reports
• 🏆 Identifies and returns the best-performing model
• 💡 Simple API for fast integration

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🔍 Supported Models

🔷 Supervised Learning

Classification

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• K-Nearest Neighbors
• Support Vector Machine (SVM)
• Gradient Boosting Classifier
• XGBoost Classifier

Regression

• Linear Regression
• Lasso / Ridge Regression
• Decision Tree Regressor
• Random Forest Regressor
• Gradient Boosting Regressor
• XGBoost Regressor

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🔶 Unsupervised Learning

Clustering

• KMeans
• DBSCAN
• Agglomerative Clustering

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📦 Installation

pip install multimodel

(Coming soon to PyPI)

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🧪 Usage

🔹 Classification Example

from multimodel import MultiModelClassifier

mm = MultiModelClassifier(X, y)
mm.run_all()
mm.get_summary()
mm.plot_comparison()

🔹 Regression Example

from multimodel import MultiModelRegressor

mm = MultiModelRegressor(X, y)
mm.run_all()
mm.get_summary()
mm.plot_comparison()

🔹 Clustering Example

from multimodel import MultiModelCluster

mm = MultiModelCluster(X)
mm.run_all()
mm.plot_comparison()

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📈 Output Highlights

• 🧾 Accuracy, Precision, Recall, F1-score (for classification)
• 📉 MSE, RMSE, R² score (for regression)
• 📊 Comparison plots, confusion matrices, and error analysis
• 🥇 Automatically highlights the best-performing model

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📚 Dependencies

• scikit-learn
• xgboost
• matplotlib
• seaborn
• pandas
• numpy

Install all dependencies:

pip install -r requirements.txt

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🛠 Roadmap

• Hyperparameter tuning integration
• SHAP/feature importance plots
• Export best model (.pkl or .joblib)
• Web-based GUI (using Streamlit)

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🤝 Contributing

Contributions are welcome! Please check out the CONTRIBUTING.md for contribution guidelines.

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📜 License

Licensed under the MIT License – see LICENSE for details.

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👤 Author

Uditya Narayan Tiwari 📍 VIT Bhopal University

🔗 GitHub 🔗 LinkedIn 🌐 Portfolio

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