Forecasting Apple stock price data

We have now covered everything we need to know about time series for the TensorFlow Developer Certificate exam. Let us round off this chapter and the book with a real-world use case on time series. For this exercise, we will be working with a real-world dataset (Apple closing day stock price). Let’s see how we can do this next. The Jupyter notebook for this exercise can be found here: https://github.com/PacktPublishing/TensorFlow-Developer-Certificate-Guide. Let’s begin:

1. We start by importing the required libraries:

   import numpy as np

   import matplotlib.pyplot as plt

   import tensorflow as tf

   from tensorflow import keras

   import yfinance as yf

   Here, we are using a new library called yfinance. This lets us access the Apple stock data for our case study.

Note

You may want to run pip install yfinance to get it working if the import fails.

2. Create a DataFrame:

   df_apple = yf.Ticker(tickerSymbol)

   df_apple = df_apple.history(period...