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

1. Load Data: Use provided functions to load synthetic or real-world datasets.
2. Preprocess Data: Utilize preprocessing functions for data cleaning and setup.
3. Train Models: Select and train models using the modular functions provided.
4. 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.