This Python code integrates numpy and matplotlib to explore the concept of cost function in linear regression, specifically using an interactive visualization setup that incorporates custom utility ...

The aim is to create a linear regression model from first principles and only using the in-built packages in Python 3.7. A linear regression equation can be defined as: h(xi) = b0 + b1 * xi. where: ...

Regularization is a technique that adds a penalty term to the cost function of a linear regression model, which reduces the magnitude of the coefficients and prevents them from becoming too large ...

To minimize the error, we need to minimize the Linear Regression Cost Function. Lesser the cost function, better the learning, more accurate will be the predictions. Learn With Jay.

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.