Welcome back.
The feature correlation heat map is very important.
Once you understand the correlation between the features, you can start pre-processing your data.
So here we need to prepare our data for neural network.
Let's start with x equals to dataset dot drop mpg and y equals to dataset and get the mpg.
Why we dropped the mpg.
Why we removed the mpg column from our data set.
Why we removed this column from the data set?
Because simply, we want to build a model that predicts MPG based on other features.
We therefore need to separate features x the input variables used to make predictions, and target y,
the output variable we want to predict.
So we're going to predict the MPG and based on the x features all columns except MPG.
This includes cylinders, displacement, horsepower, weight, and etc. and target would be only the
mpg column.
Let's print the shape of our data set.
So here we have x dot shape and y dot shape run.
Here we go.
Features shape 392.
With seven we removed one column and the target shape is 392.
So we get the features shape 392 with seven columns and target shape 392.
Your target vector Y has 392 values, one for each data set.