https://github.com/goldsharon/logistic-regression-from-scratch

A Logistic Regression model built from scratch in Python using NumPy, without ML libraries. It includes gradient descent, binary classification, and adjustable learning rates, demonstrating training, predictions, and weight updates with sigmoid activation.
https://github.com/goldsharon/logistic-regression-from-scratch

binary-classification data-science from-scratch gradient-descent logistic-regression machine-learning python sigmoid-activation

Last synced: about 1 month ago
JSON representation

A Logistic Regression model built from scratch in Python using NumPy, without ML libraries. It includes gradient descent, binary classification, and adjustable learning rates, demonstrating training, predictions, and weight updates with sigmoid activation.

• Host: GitHub
• URL: https://github.com/goldsharon/logistic-regression-from-scratch
• Owner: GoldSharon
• Created: 2024-10-30T12:53:45.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-30T13:17:42.000Z (3 months ago)
• Last Synced: 2024-10-30T13:41:01.010Z (3 months ago)
• Topics: binary-classification, data-science, from-scratch, gradient-descent, logistic-regression, machine-learning, python, sigmoid-activation
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Logistic Regression from Scratch

## Description

This project implements Logistic Regression from scratch using Python and NumPy, with no external machine learning libraries. The code demonstrates a basic understanding of gradient descent, probability predictions, and binary classification. Logistic Regression is a foundational algorithm in supervised machine learning, particularly used for binary classification problems.

## Features

- Built without any machine learning libraries (like scikit-learn) to illustrate fundamental concepts

- Includes functions for model training, predictions, and weight updates using gradient descent

- Implements sigmoid activation and binary cross-entropy loss calculation

- Customizable learning rate and number of iterations

## Getting Started

### Prerequisites

- Python 3.x

- NumPy

- Pandas (if using a dataset for testing)

### Installation

1. Clone the repository:

   ```bash

        git clone https://github.com/your-username/logistic-regression-from-scratch.git

2. Install the dependencies:

   ```bash

      pip install numpy pandas

### Usage

1.Import the Logistic Regression class and load your dataset.

2.Fit the model on your data.

3.Use the model to make predictions.