Activity 10.01 – Normalizing and smoothing data

Suppose you are an analyst in a financial advisory firm. Your manager has given three stock symbols to you and requested your input on how they may be correlated with their price behavior. You are provided a stocks.csv data file, which contains the symbols, closing prices, trading volumes, and a sentiment indicator (some view of the quality of the stocks, but you are not told the exact definition). Your initial goal here is to determine whether all three stocks show similar market characteristics or not, and if any or all of them do, make an initial visualization using smoothing. The long-term goal is to try to build some predictive models, so you will split the data into train and test sets. As it is time series, it's important to split on time, not randomly. For this activity, all you will need is the pandas library, a scaling module from sklearn, and matplotlib. Load them in the first cell of the notebook:

ximport pandas...