pocket-ml 0.1.3

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pocket_ml

A lightweight and user-friendly machine learning library designed to simplify ML workflows.

Table of Contents

• Features
• Installation
• Quick Start
• Package Structure
• Documentation
• Contributing
• License

Feature

pocket_ml offers a range of features to streamline your machine learning projects:

• Simple and Intuitive API: Provides easy-to-use classes like Classifier, DataPreprocessor, and Visualizer for common ML tasks, reducing boilerplate code.
• Automated Data Preprocessing: Includes the DataPreprocessor class to handle essential preprocessing steps like scaling, encoding categorical features, and handling missing values (functionality may vary based on implementation details).
• Easy Model Training and Evaluation: Train various classification and regression models with a consistent .fit() and .predict() interface. Evaluate model performance using standard metrics.
• Built-in Visualization Tools: The Visualizer class helps in understanding data and model results through plots like confusion matrices, feature importance plots, etc. (specific plots depend on implementation).
• Comprehensive Documentation and Examples: Access detailed guides and usage examples to get started quickly.

Installation

pip install pocket_ml

Quick Start

Here's a basic example of how to use pocket_ml:

from pocket_ml import Classifier, DataPreprocessor, Visualizer

# Assume X is your feature matrix (e.g., pandas DataFrame or NumPy array)
# Assume y is your target vector (e.g., pandas Series or NumPy array)
# Assume new_data is the data you want to make predictions on

# 1. Prepare your data
preprocessor = DataPreprocessor() # Initialize the preprocessor
X_processed = preprocessor.fit_transform(X) # Apply preprocessing
# Preprocess the new_data similarly (using transform, not fit_transform)
# new_data_processed = preprocessor.transform(new_data)

# 2. Train a model
# Choose a model type (e.g., 'random_forest', 'logistic_regression')
model = Classifier(model_type='random_forest')
model.fit(X_processed, y) # Train the model

# 3. Make predictions
# Ensure new_data is preprocessed using the *same* preprocessor instance
# predictions = model.predict(new_data_processed)

# 4. Visualize results (Example for classification)
# Assuming you have true labels (y_test) and predictions for a test set
# visualizer = Visualizer()
# visualizer.plot_confusion_matrix(y_test, predictions)

Package Structure

The library is organized as follows:

pocket_ml/
  ├── __init__.py         # Makes pocket_ml a package
  ├── algorithms/         # ML algorithms implementation
  │   ├── __init__.py
  │   ├── classification/ # Classification algorithms
  │   └── regression/     # Regression algorithms
  ├── preprocessing/      # Data preprocessing utilities
  │   ├── __init__.py
  │   └── data_preprocessor.py
  └── visualization/      # Data visualization tools
      ├── __init__.py
      └── visualizer.py

Documentation

For detailed documentation and examples, visit our documentation page. (Note: The provided link points to version 0.1.2, ensure documentation matches the installed version).

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Refer to the project's contribution guidelines if available.

License

This project is licensed under the MIT License - see the LICENSE file for details (if included in the repository).