regressionmadesimple 1.1.1

Minimalist ML toolkit wrapping sklearn for quick prototyping. Just `import rms` and go.

RegressionMadeSimple (RMS)

"A minimalist ML backdoor to sklearn. Just import rms and go."

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🚀 Quickstart

import regressionmadesimple as rms

# Load dataset
df = rms.Preworks.readcsv("./your_data.csv")

# Train a linear regression model
model = rms.Linear(dataset=df, colX="feature", colY="target")

# Predict new values
predicted = model.predict([[5.2], [3.3]])

# Plot prediction vs. test
model.plot_predict([[5.2], [3.3]], predicted).show()

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📦 Features

• 🧠 Wraps sklearn's most-used regression model(s) in a friendly API
• 📊 Built-in Plotly visualizations -- planned later support for matplotlib (? on this)
• 🔬 Designed for quick prototyping and educational use
• 🧰 Utility functions via preworks:
  • readcsv(path) — load CSV
  • create_random_dataset(...) — create random datasets (for demos)

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Project LINK

https://unknownuserfrommars.github.io/regressionmadesimple/

PS: Changelog also can be accessed from there. (still actively developing)

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✅ Installation

pip install regressionmadesimple

Or install the dev version:

git clone https://github.com/Unknownuserfrommars/regressionmadesimple.git
cd regressionmadesimple
pip install -e .

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📁 Project Structure

regressionmadesimple/
├── __init__.py
├── base_class.py
├── linear.py
├── logistic.py        # (soon)
├── tree.py            # (soon)
├── utils_preworks.py

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🧪 Tests

Coming soon under a /tests folder using pytest

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📜 License

MIT License

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🧠 Author

Unknownuserfrommars — built with 💡, ❤️, and while True: in VSCode and PyCharm.

kind note: ignore that last statement :)

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🌌 Ideas for Future Versions

• Logistic() and DecisionTree() models
• .summary() for all models
• rms.fit(df, target="y", model="linear") one-liner
• Export/save models
• Visual explainability (feature importance, SHAP)

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⭐ Star this project if you like lazy ML. No boilerplate, just vibes.

Also: Buy me a coffee (maybe) coming soon.