Primera

A Machine Learning Framework for Training Models from CSV Files

Description

This project is a machine learning framework that provides tools for loading datasets, preprocessing data, training machine learning models, and making predictions. It utilizes the Random Forest Classifier from the scikit-learn library to perform classification tasks.

Installation Instructions

To set up the project, ensure you have Python installed, then install the required packages using pip:

pip install pandas scikit-learn

Usage

1. Import the framework:

   from primera import MLFramework

2. Create an instance of the framework:

   ml = MLFramework('path_to_your_dataset.csv')

3. Load the data:

   data = ml.load_data()

4. Preprocess the data:

   processed_data = ml.preprocess_data()

5. Select features and target:

   X, y = ml.feature_selection('target_column_name')

6. Train the model:

   ml.train_model(X, y)

7. Save the model:

   ml.save_model('model_filename.pkl')

8. Load the model:

   ml.load_model('model_filename.pkl')

9. Make predictions:

   predictions = ml.predict(X)

10. Evaluate the model:

    accuracy = ml.evaluate_model(X, y)
    print(f'Accuracy: {accuracy}')

Datasets

This project uses datasets in CSV format. Ensure that your dataset is structured correctly for the framework to process it.

Contributing

Contributions are welcome! Please submit a pull request or open an issue for any suggestions or improvements.

License

This project is licensed under the MIT License.