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🧠 What is MLCompare?

Machine Learning experimentation often involves repeating the same workflow for multiple algorithms:

Prepare Data → Select Model → Train → Evaluate → Compare

Doing this manually for every model can lead to repetitive code and slower experimentation.

MLCompare is designed to simplify this process by providing a reusable interface for comparing multiple Machine Learning models for Classification and Regression tasks.

Goal: Reduce repetitive ML boilerplate and make model comparison simple, fast, and beginner-friendly.

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✨ Features

• 🎯 Classification & Regression support
• 🤖 Multiple model comparison in a single workflow
• 📊 Performance evaluation and result comparison
• 🏆 Best model selection
• 💾 Save trained models
• 📂 Load saved models
• 🔮 Prediction support
• 🧩 Simple and beginner-friendly API
• 🐍 Built with the Scikit-Learn ecosystem

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📦 Installation

Install from PyPI

pip install mlcompare-dev

Install locally for development

git clone https://github.com/Developer-Manish007/mlcompare.git
cd mlcompare
pip install -e .

🔗 Important Links

Resource	Link
📦 PyPI Package	https://pypi.org/project/mlcompare-dev/
💻 GitHub Repository	https://github.com/Developer-Manish007/mlcompare

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🚀 Quick Start

from mlcompare import MLCompare
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load dataset
X, y = load_iris(return_X_y=True)

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42
)

# Create MLCompare object
mc = MLCompare()

# Train models
mc.fit(X_train, y_train)

# Compare models
results = mc.compare()

print(results)

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💾 Save a Model

Save a trained model for later use:

mc.save("best_model.pkl")

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📂 Load a Model

from mlcompare import load_model

model = load_model("best_model.pkl")

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📊 Supported Models

🔵 Classification

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• Gaussian Naive Bayes

🟢 Regression

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor
• K-Neighbors Regressor
• Support Vector Regressor

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🔄 MLCompare Workflow

              ┌─────────────────┐
              │     Dataset     │
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │   Data Setup    │
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │  Model Training │
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │    Evaluation   │
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │ Model Comparison│
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │   Best Model    │
              └─────────────────┘

The workflow is designed to make it easier to experiment with multiple models without rewriting the same comparison logic repeatedly.

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🧪 Example Use Case

Suppose you have a classification dataset and want to test several algorithms.

Instead of creating separate training and evaluation code for every model, MLCompare provides a common workflow where the models can be trained and compared together.

This is especially useful for:

• 📚 Learning Machine Learning
• 🔬 Model experimentation
• 📊 Exploratory model comparison
• 🧑‍💻 Data Science projects
• ⚡ Rapid prototyping

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📁 Project Structure

mlcompare/
│
├── mlcompare/
│   ├── core.py
│   ├── models.py
│   ├── metrics.py
│   ├── save.py
│   ├── utils.py
│   ├── version.py
│   └── __init__.py
│
├── examples/
├── README.md
├── setup.py
├── requirements.txt
├── LICENSE
└── .gitignore

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🛣️ Roadmap

v0.2.0

• StandardScaler
• MinMaxScaler
• RobustScaler
• Cross Validation
• More Evaluation Metrics

v0.3.0

• Hyperparameter Tuning
• Feature Importance
• Model Explainability

v1.0.0

• AutoML Workflow
• Pipeline Support
• SHAP Integration
• XGBoost
• LightGBM
• CatBoost

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🤝 Contributing

Contributions, feature requests, improvements, and bug reports are welcome!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test your changes
5. Open a Pull Request

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📄 License

MLCompare is released under the MIT License.

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👨‍💻 Author

Manish Kumar

Aspiring Data Scientist & Machine Learning Engineer

• 💻 GitHub: https://github.com/Developer-Manish007
• 📦 PyPI: https://pypi.org/project/mlcompare-dev/

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