mlcompare-dev 0.1.1

Compare classification and regression ML models

🚀 MLCompare

MLCompare is a beginner-friendly Python library to compare multiple Machine Learning models with just a few lines of code.

Instead of writing repetitive code for training and evaluating models, MLCompare automatically trains multiple algorithms, compares their performance, selects the best model, and allows prediction and model saving.

---

✨ Features

• ✅ Automatic Classification & Regression Detection
• ✅ Train Multiple ML Models
• ✅ Compare Model Performance
• ✅ Automatically Select Best Model
• ✅ Predict Using Best Model
• ✅ Save & Load Trained Model
• ✅ Simple Beginner-Friendly API

---

📦 Installation

pip install mlcompare

Or install locally

pip install -e .

---

📚 Supported Models

Classification

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest
• AdaBoost
• Gradient Boosting
• Extra Trees

Regression

• Linear Regression
• Ridge Regression
• Lasso Regression
• ElasticNet
• Decision Tree Regressor
• Random Forest Regressor
• Gradient Boosting Regressor
• Extra Trees Regressor

---

🚀 Quick Start

from mlcompare import MLCompare
import pandas as pd

df = pd.read_csv("data.csv")

ml = MLCompare(
    data=df,
    target="target"
)

results = ml.compare()

print(results)

---

🏆 Best Model

print(ml.best_model_name)

---

🔮 Prediction

new_data = df.drop(columns=["target"]).head()

predictions = ml.predict(new_data)

print(predictions)

---

💾 Save Model

ml.save("best_model.pkl")

---

📂 Load Model

ml.load("best_model.pkl")

---

📊 Example Output

Model	Accuracy
SVM	0.95
Random Forest	0.94
Extra Trees	0.93
Logistic Regression	0.91

---

📁 Project Structure

mlcompare/
│
├── core.py
├── models.py
├── metrics.py
├── utils.py
├── save.py
├── version.py
└── __init__.py

---

🛠 Requirements

• Python 3.10+
• pandas
• numpy
• scikit-learn
• joblib

---

📌 Roadmap

Upcoming Features

• Hyperparameter Tuning
• Cross Validation
• Feature Importance
• Confusion Matrix
• ROC-AUC Score
• StandardScaler Support
• Missing Value Handling
• Categorical Encoding
• XGBoost
• LightGBM
• CatBoost

---

🤝 Contributing

Contributions are welcome!

Feel free to open an Issue or submit a Pull Request.

---

📄 License

MIT License

---

👨‍💻 Author

Manish Kumar

GitHub: https://github.com/Developer-Manish007