ml-model-handler 0.1.0

A simple library to train, manage, and use multiple ML models easily.

ml_model_handler

A lightweight Python package for training, managing, and predicting with multiple machine learning models.
Supports classical ML algorithms, pipelines, ensemble learning (Voting), and user-defined custom estimators.

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Features

• Preconfigured ML models: Logistic Regression, KNN, Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, XGBoost, SVM, SGD.
• Voting Classifier support (soft/hard voting).
• Plug-and-play with custom estimators.
• Built-in pipelines with StandardScaler where useful.
• Easy single prediction and batch predictions.
• Probability predictions (predict_proba) when available.

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Installation

pip install ml-model-handler

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Quick Start

import pandas as pd
from ml_model_handler import MLModelHandler

# Example dataset
X = pd.DataFrame({
    "feature1": [1, 2, 3, 4],
    "feature2": [10, 20, 30, 40]
})
y = [0, 1, 0, 1]

# Initialize handler
model_handler = MLModelHandler()

# Train default models
model_handler.train_models(X, y)

# Single prediction
print(model_handler.predict("logistic", [5, 50]))

# Batch prediction
batch_features = [
    [6, 60],
    [7, 70]
]
model_handler.batch_predict("knn", batch_features)

# Probability prediction
print(model_handler.predict_proba("logistic", [5, 50]))

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Voting Classifier

The voting estimator combines predictions from multiple trained models (soft or hard voting).

# Use Logistic, KNN and Voting
model_handler.train_models(X, y, estimators=["logistic", "knn"], voting_type="soft")

print(model_handler.predict("voting", [7, 70]))

⚠️ If fewer than 2 base models are given with voting, it falls back to all trained models with a warning.

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Custom Estimators

You can extend with your own models:

from sklearn.linear_model import RidgeClassifier

custom = {
    "ridge": RidgeClassifier()
}

model_handler.train_models(X, y, estimators=["ridge"])
print(model_handler.predict("ridge", [8, 80]))

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Project Structure

ml_model_handler/
 ├── __init__.py
 └── main.py          # MLModelHandler class
README.md
setup.py

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Contributing

Contributions are welcome!

• Fork the repo
• Create a feature branch
• Submit a pull request

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License

MIT License