Logistic Regression

This module contains an implementation of Logistic Regression, a popular algorithm for binary classification.

Usage

To use Logistic Regression, follow these steps:

1. Import the LogisticRegression class.
2. Create an instance of the class, specifying parameters such as learning rate and number of iterations.
3. Fit the model to your training data using the fit method.
4. Make predictions using the predict method.

Example:

from LogisticRegression import LogisticRegression

logistic_model = LogisticRegression(learning_rate=0.01, n_iterations=1000)
logistic_model.fit(X_train, y_train)
predictions = logistic_model.predict(X_test)

Parameters

• learning_rate: Step size for gradient descent.
• n_iterations: Number of iterations for gradient descent.

Installation

To use this module, make sure you have the required dependencies installed:

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦