linear-model-Surya-Sudhaharan 0.1.0

A lightweight Python library for Linear Regression with multiple training methods and regularization options.

📦 linear_model

A lightweight and easy-to-use Python library for Linear Regression.
Supports multiple training methods, regularization options, and sample weighting.

✨ Features:

• 📐 Normal Equation
• 🔁 Gradient Descent
• 🛡️ Ridge Regression (L2 regularization)
• ✂️ Lasso Regression (L1 regularization)
• ⚖️ Sample weights for weighted regression
• 🎯 Optional intercept fitting

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🚀 Installation

Clone the repository and install in editable mode:

git clone https://github.com/<your-username>/linear_model.git
cd linear_model
pip install -e .

Or install directly from GitHub (no clone needed):

pip install git+https://github.com/<your-username>/linear_model.git

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📌 Quick Start

import numpy as np
from linear_model import LinearRegression

# Training data
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

# Initialize model
model = LinearRegression(method="ridge", alpha=0.01, lambd=0.1)

# Fit model
model.fit(X, y, sample_weight=None)

# Predictions
print("Predictions:", model.predict([[5], [6]]))

# Model details
print("Coef:", model.coef_())
print("Intercept:", model.intercept_())

# R² score
print("R²:", model.score(X, y))

⚙️ API Overview

• fit(X, y, sample_weight=None) → Train the model
• predict(X) → Predict target values
• score(X, y, sample_weight=None) → Compute R² score
• coef_() → Get feature coefficients
• intercept_() → Get model intercept

Parameters

• method: "normal_equation" | "gradient_descent" | "ridge" | "lasso"
• fit_intercept: True (default) | False
• n_iter: Number of iterations for gradient methods (default: 1000)
• alpha: Learning rate for gradient methods (default: 0.01)
• lambd: Regularization strength (default: 0.1)

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🧪 Testing

Run unit tests with:

pytest tests/

📂 Project Structure

linear_model/
│── __init__.py
tests/
│── test_linear_regression.py
setup.py
README.md
LICENSE

📜 License

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