Predicting using a regression model
Now that you've fit a model with the California housing data, lets see what predictions it generates on some new data. You can investigate the underlying relationship that the model has found between inputs and outputs by feeding in a range of numbers as inputs and seeing what the model predicts for each input.
A 1-D array new_inputs
consisting of 100 "new" values for "MedHouseVal"
(median house value) is available in your workspace along with the model
you fit in the previous exercise.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Exercise instructions
- Review
new_inputs
in the shell. - Reshape
new_inputs
appropriately to generate predictions. - Run the given code to visualize the predictions.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Generate predictions with the model using those inputs
predictions = ____
# Visualize the inputs and predicted values
plt.scatter(new_inputs, predictions, color='r', s=3)
plt.xlabel('inputs')
plt.ylabel('predictions')
plt.show()