TextSenseAI Advanced Text Classification and Analysis

Overview

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.

Features

• 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.

Requirements

• Python 3.x
• sklearn
• pandas
• numpy
• matplotlib
• seaborn

Usage

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.

Example

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')

Contributing

Contributions to improve the script or extend its capabilities are welcome. Please ensure to follow coding standards and add appropriate tests for new features.

License

This project is open-sourced under the MIT license.