Model Saver

Model Saver is a Python package that provides a simple and convenient way to save and load machine learning models using pickle. It is designed specifically for models trained using the Scikit-learn library (sklearn) but can be used with other models as well.

Installation

You can install Model Saver using pip:

pip install model-saver

Usage

To use Model Saver, first import the ModelSaver class:

from model_saver import ModelSaver

Saving a Model

To save a trained machine learning model, create an instance of the ModelSaver class and call the save_model method:

from sklearn.linear_model import LinearRegression

# Train a linear regression model
X_train = ...
y_train = ...
model = LinearRegression()
model.fit(X_train, y_train)

# Save the trained model
saver = ModelSaver()
saver.save_model(model, "linear_regression")

This will save the trained LinearRegression model as a binary file with the name "linear_regression.model" in the current working directory.

Loading a Model

To load a saved machine learning model, create an instance of the ModelSaver class and call the load_model method:

# Load the saved model
loaded_model = saver.load_model("linear_regression")

# Use the loaded model to make predictions
X_test = ...
y_pred = loaded_model.predict(X_test)

This will load the saved LinearRegression model from the binary file named "linear_regression.model" in the current working directory using the ModelSaver class, and use the loaded model to make predictions.

Contributing

Contributions to Model Saver are welcome! To contribute, please fork the repository, make your changes, and submit a pull request.

License

Model Saver is licensed under the MIT License. See the LICENSE file for more information.