This repository contains a detailed exploration of the mathematical concepts behind Logistic Regression, implemented in Google Colab. The primary goal is to provide a comprehensive understanding of how logistic regression works.

• Introduction
• Mathematical Background
  • Probability Theory and Logistic Function
  • Maximum Likelihood Estimation (MLE)
  • Gradient Descent for Logistic Regression
  • Regularization Techniques
• Implementation
• Usage
• Contributing
• License

Logistic Regression is a popular statistical method used for binary classification problems. This repository aims to break down the mathematical principles behind logistic regression, making them accessible and understandable.

• Introduction to probability theory
• Explanation of the logistic function and its properties
• Mathematical formulation of the logistic function

• Concept of likelihood in statistical modeling
• Derivation of the likelihood function for logistic regression
• Maximizing the likelihood function to obtain model parameters

• Introduction to optimization and gradient descent
• Derivation of the gradient for the logistic regression cost function
• Implementation of gradient descent algorithm to optimize logistic regression parameters

• Importance of regularization in logistic regression
• Explanation of L1 (Lasso) and L2 (Ridge) regularization
• Incorporating regularization into the logistic regression cost function

The implementation is done in Python using Google Colab to facilitate interactive learning. All mathematical derivations are accompanied by code examples to illustrate the concepts.

To use the notebooks, follow these steps:

1. Clone the repository:

   git clone https://github.com/geethasagarb/Logistic-Regression-Math.git

2. Navigate to the repository directory:

   cd Logistic-Regression-Math

3. Open the notebook using Google Colab:
   • Go to Google Colab
   • Click on File > Open notebook
   • Select the GitHub tab and enter the repository URL: https://github.com/geethasagarb/Logistic-Regression-Math
   • Open the Math_behind_Logistic_Regression.ipynb notebook

Contributions are welcome! If you have any suggestions or improvements, please feel free to create a pull request or open an issue.

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