This Python script is a comprehensive tutorial for machine learning, demonstrating classification and regression tasks using various models and datasets. It's designed to be a practical and educational tool for understanding and applying key machine-learning techniques.
- Modular Functions: For data loading, preprocessing, model training, and visualization.
- Various Models: Includes Naive Bayes, Random Forests, Gradient Boosted Decision Trees (GBDT), and Neural Networks.
- Classification and Regression Tasks: Demonstrations using synthetic and real-world datasets.
- Visualization: Integrated visualization for model evaluation and data analysis.
- Reusability and Clarity: Code structured for easy modification and clear understanding.
- Python 3.x
- sklearn
- pandas
- numpy
- matplotlib
- seaborn
- Load Data: Use provided functions to load synthetic or real-world datasets.
- Preprocess Data: Utilize preprocessing functions for data cleaning and setup.
- Train Models: Select and train models using the modular functions provided.
- Evaluate and Visualize: Assess model performance and visualize results using integrated functions.
Here's a simple example of how to use the script:
X_fruits, y_fruits, target_names_fruits = load_fruits_data()
random_forest_classifier(X_fruits, y_fruits, 'Random Forest: Fruits dataset')Contributions to improve the script or extend its capabilities are welcome. Please ensure to follow coding standards and add appropriate tests for new features.
This project is open-sourced under the MIT license.