We have seen how to implement a linear regression algorithm in Python, and how to use the linear regression model from scikit-learn. We learned:

- We can measure how well a line fits by measuring
*loss*. - The goal of linear regression is to minimize loss.
- To find the line of best fit, we try to find the
`b`

value (intercept) and the`m`

value (slope) that minimize loss. *Convergence*refers to when the parameters stop changing with each iteration.*Learning rate*refers to how much the parameters are changed on each iteration.- We can use Scikit-learn’s
`LinearRegression()`

model to perform linear regression on a set of points.

These are important tools to have in your toolkit as you continue your exploration of data science.

### Instructions

Find another dataset, maybe in scikit-learn’s example datasets. Or on Kaggle, a great resource for tons of interesting data.

Try to perform linear regression on your own! If you find any cool linear correlations, make sure to share them!

As a starter, we’ve loaded in the Boston housing dataset. We made the `X`

values the nitrogen oxides concentration (parts per 10 million), and the `y`

values the housing prices. See if you can perform regression on these houses!