Project README

Overview

This project is inspired by the principles of the R tidymodels framework but implemented in Python. The primary goal is to provide a cohesive and streamlined approach to data analysis and modeling in Python, leveraging the tidy principles that have made tidymodels so popular in the R community.

Features

Tidy Data Principles: Emphasizing the importance of tidy data where each variable is a column, each observation is a row, and each type of observational unit forms a table.
Modular Design: Components are designed to work together seamlessly, allowing users to mix and match parts according to their needs.
Consistent API: A consistent and intuitive API across all modules to facilitate ease of learning and use.
Extensible: Easy to extend with new models, preprocessing steps, and utilities.
Integration with Popular Libraries: Built to work well with popular Python libraries such as pandas, scikit-learn, and others.

Installation

To install the package, use pip:

pip install topmodels

Getting Started

Here is a basic example to illustrate how to use this package:

import mypackage as mp

# Load data
data = mp.load_data('my_dataset.csv')

# Preprocess data
preprocessor = mp.Preprocessor()
data_clean = preprocessor.fit_transform(data)

# Split data
train, test = mp.split_data(data_clean, test_size=0.2)

# Define model
model = mp.models.LinearRegression()

# Train model
model.fit(train)

# Evaluate model
evaluation = model.evaluate(test)

print(evaluation)

Documentation

Detailed documentation is available here, covering all modules and functions, with examples and tutorials to help you get the most out of the package.

Contributing

We welcome contributions from the community! Please see our CONTRIBUTING.md file for more information on how to get involved.

License

Acknowledgments

This project was inspired by the R tidymodels framework. We thank the creators and maintainers of tidymodels for their innovative work in making data analysis and modeling more accessible and organized.

Contact

For questions or feedback, please contact us at email@example.com.

By following the tidy principles and leveraging the power of Python, we aim to provide a robust and user-friendly tool for data scientists and analysts. We hope you find this package useful and look forward to your feedback and contributions.

Note: This is a fictional example, and the package name mypackage and other details should be replaced with the actual names and links relevant to your project.

AnthonyTedde/topmodels