This repository contains the ft_linear_regression project from 42 School, which focuses on implementing a simple linear regression model from scratch using Python and NumPy. The primary goal is to understand and apply the fundamentals of linear regression, including data preprocessing, cost function formulation, and optimization using gradient descent.
- Implementation of a cost function for linear regression.
- Use of gradient descent to optimize model parameters.
- Data reading, processing, and handling using NumPy.
- Visualization of data and the regression line to evaluate model performance.
- Basic understanding of linear regression.
- Application of numerical optimization techniques.
- Data handling and manipulation using Python and NumPy.
This project is part of the curriculum at 42 School, designed to introduce students to key concepts in machine learning.