ML_Assignments

ML Assignment1 on Linear Regression

Simple Linear Regression with Gradient Descent

Overview

• Implements a Simple Linear Regression model using Gradient Descent.
• Tasks include training on a dataset, exploring learning rates, and comparing optimization methods.

Features

• Batch Gradient Descent implementation.
• Learning rate comparison (0.005, 0.5, 5).
• Cost function vs iteration plots.
• Stochastic and mini-batch gradient descent.
• Visualizations of regression results.

Requirements

• numpy
• matplotlib
• pandas

How to Run

• Clone the repository and navigate to the project directory:

  git clone <repository_url>
  cd simple-linear-regression

• Install dependencies:

  pip install -r requirements.txt

• Open the Jupyter Notebook:

  jupyter notebook ML_LinearRegression_Assignment1.ipynb

Outputs

• Cost function vs iteration plots.
• Fitted regression line visualizations.
• Analysis of learning rates and optimization methods.

Project Structure

• README.md: Documentation.
• ML_LinearRegression_Assignment1.ipynb: Jupyter Notebook.
• requirements.txt: Dependencies.
• data/: Dataset folder.
• plots/: Output plots folder.