/Gradient_Descent_Correlation

it is gradient decent technique for checking correalation

Primary LanguageJupyter Notebook

Gradient Descent Optimization

Welcome to the Gradient Descent Optimization repository! This project explores the implementation of the Gradient Descent algorithm for optimizing predictive models in Python.

Overview

Gradient Descent is a fundamental optimization technique used extensively in machine learning and data science. It iteratively adjusts model parameters to minimize a cost function, thereby enhancing model accuracy and efficiency. This repository demonstrates the application of Gradient Descent in a straightforward example using synthetic data.

Tools & Technologies

  • Python: Core programming language for implementation.
  • NumPy: Essential for efficient numerical operations.
  • Matplotlib: Utilized for data visualization.
  • Jupyter Notebook: Provided for interactive experimentation and visualization.

Purpose

The primary goal of this project is to showcase:

  • Implementation: Step-by-step implementation of Gradient Descent.
  • Visualization: Visual representation of algorithmic outputs using Matplotlib.
  • Educational Resource: Serve as an educational resource for understanding Gradient Descent.

Contents

  • Gradient_Descent_Example.ipynb: Jupyter Notebook demonstrating Gradient Descent implementation.
  • README.md: This file providing an overview of the project.
  • requirements.txt: List of Python dependencies for easy setup.

Contact

For questions or feedback, please feel free to reach out: