Before beginning construction of your algorithm, you first need to familiarize with the data that you're going to use to train and test your algorithm. Open your Jupyter Notebook and import the data file liquidity data dot csv along with NumPy, Pandas, Pyplot from Matplotlib and Seaborn. Then after importing that data display the first three rows of your dataframe, you'll notice that the tickers have been removed from your dataset. This is intentional. The only information included is the data you want your algorithm to use to find relationships and make predictions.

As usual, we're going to start by importing our external packages, NumPy, Pandas, Pyplot from Matplotlib and Seaborn. Then we're going to create the new data, which I've called here, liquidity data using the pandas read csv function. I'm pulling in that liquidity data csv file, and then I'm going to display the first three rows of that new dataframe. When you execute that code cell, it should look like this.