Fitting a simple regression model

Now we'll look at a larger number of companies. Recall that we have historical price values for many companies. Let's use data from several companies to predict the value of a test company. You'll attempt to predict the value of the Apple stock price using the values of NVidia, Ebay, and Yahoo. Each of these is stored as a column in the all_prices DataFrame. Below is a mapping from company name to column name:

ebay: "EBAY"
nvidia: "NVDA"
yahoo: "YHOO"
apple: "AAPL"

We'll use these columns to define the input/output arrays in our model.

This exercise is part of the course

Machine Learning for Time Series Data in Python

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Exercise instructions

Create the X and y arrays by using the column names provided.
The input values should be from the companies "ebay", "nvidia", and "yahoo"
The output values should be from the company "apple"
Use the data to train and score the model with cross-validation.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score

# Use stock symbols to extract training data
X = all_prices[[____]]
y = all_prices[[____]]

# Fit and score the model with cross-validation
scores = cross_val_score(Ridge(), ____, ____, cv=3)
print(scores)

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