This repository contains an implementation of the linear regression algorithm using only the Numpy library, without the use of specialized machine learning libraries such as Scikit-learn or TensorFlow. The aim of this project is to provide a hands-on approach for those looking to understand the basics of linear regression algorithm.
The project includes step-by-step coding in a Jupyter notebook, covering the following topics:
- Data and Library Loading
- Data Visualization
- Cost Function Computation
- Gradient Descent
- Cost Function Visualization
- Convergence Plotting
- Linear Regression Fit with Training Data
- Inference using Optimized Theta Values
This project is based on a guided project from Coursera's Linear Regression with NumPy and Python course https://www.coursera.org/projects/linear-regression-numpy-python.