Exercise

# Minimizing a loss function

In this exercise you'll implement linear regression "from scratch" using `scipy.optimize.minimize`

.

We'll train a model on the Boston housing price data set, which is already loaded into the variables `X`

and `y`

. For simplicity, we won't include an intercept in our regression model.

Instructions

**100 XP**

- Fill in the loss function for least squares linear regression.
- Print out the coefficients from fitting sklearn's
`LinearRegression`

.