🎯 jackofalltrades

A jack of all trades is a master of none, but oftentimes better than a master of one.

🌟 Overview

jackofalltrades is a comprehensive, beginner-friendly machine learning library designed to make ML accessible to everyone. Whether you're a student learning the fundamentals, a researcher prototyping new ideas, or a developer integrating ML into your applications, jackofalltrades provides the tools you need.

✨ Key Features

• 🚀 One-line dataset loading - Access popular datasets instantly
• 🧠 Complete ML pipeline - From data loading to model evaluation
• 🔧 Easy-to-use APIs - Intuitive interfaces for all skill levels
• 📊 Built-in evaluation - Comprehensive metrics and error analysis
• 🎨 Advanced models - GANs, VAEs, and deep learning models
• 💡 Educational focus - Perfect for learning and teaching ML concepts

🎯 What Can You Do?

• Regression Tasks: Linear, Ridge, Adaptive, and MLP regression models
• Classification: Logistic regression and image classification
• Generative Models: GANs and Variational Autoencoders
• Data Analysis: Load and explore real-world datasets
• Model Evaluation: Comprehensive metrics and performance analysis

🔧 Installation

Prerequisites

• Python 3.7 or higher
• pip package manager

Install from PyPI

pip install jackofalltrades

Install from Source

git clone https://github.com/sanepunk/jackofalltrades.git
cd jackofalltrades
pip install -e .

Development Installation

For developers who want to contribute:

git clone https://github.com/sanepunk/jackofalltrades.git
cd jackofalltrades
pip install -e ".[dev]"

⚡ Quick Start

🏠 Real Estate Price Prediction

from jackofalltrades.datasets import get_real_estate
from jackofalltrades.Models import LinearRegression
from jackofalltrades.Errors import Error

# Load dataset
X, y = get_real_estate()

# Train model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict(X)

# Evaluate performance
evaluator = Error(y_true=y, y_predicted=predictions)
print(f"R² Score: {evaluator.RSquared():.3f}")
print(f"MSE: {evaluator.MSE():.3f}")

🖼️ Image Classification

from jackofalltrades.Models import ImageClassification
import numpy as np

# Create sample data (28x28 grayscale images)
X = np.random.rand(1000, 28, 28, 1)
y = np.random.randint(0, 10, 1000)

# Initialize and train model
model = ImageClassification(input_shape=(28, 28, 1), num_classes=10)
model.fit(X, y)

# Make predictions
predictions = model.predict(X[:10])

🎲 Generate Images with GANs

from jackofalltrades.Models.GAN import GAN
import torch

# Create GAN
gan = GAN(noise_dim=100, image_channels=1)

# Train (assuming you have a data_loader)
# gan.train(data_loader, epochs=10)

# Generate images
noise = torch.randn(16, 100)  # Generate 16 images
generated_images = gan.generator(noise)

📚 Documentation Structure

This package includes comprehensive documentation:

• Models Guide - Detailed guide to all available models
• Datasets Reference - Complete dataset documentation
• Evaluation Metrics - Guide to error analysis and metrics
• Examples - Real-world usage examples

🏗️ Package Architecture

jackofalltrades/
├── datasets.py          # Dataset loading utilities
├── Errors.py           # Evaluation metrics and error analysis
├── Models/             # Core ML models
│   ├── __init__.py
│   ├── GAN/           # Generative Adversarial Networks
│   └── VAE/           # Variational Autoencoders
└── utils/             # Utility functions

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Inspired by scikit-learn's ease of use
• Built for the machine learning community
• Thanks to all contributors and users

📞 Support

• 📧 Email: patil.ojas.250202@gmail.com
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

Made with ❤️ for the machine learning community