We have constructed a way to find the “best” `b`

and `m`

values using gradient descent! Let’s try this on the set of baseball players’ heights and weights that we saw at the beginning of the lesson.

### Instructions

**1.**

Run the code in **script.py**.

This is a scatterplot of weight vs height.

**2.**

We have imported your `gradient_descent()`

function. Call it with parameters:

`X`

`y`

`num_iterations`

of`1000`

`learning_rate`

of`0.0001`

Store the result in variables called `b`

and `m`

.

**3.**

Create a list called `y_predictions`

. Set it to be every element of `X`

multiplied by `m`

and added to `b`

.

The easiest way to do this would be a list comprehension:

new_y = [element*slope + intercept for element in y]

**4.**

Plot `X`

vs `y_predictions`

on the same plot as the scatterplot.

Does the line look right?