Check the correlations

Before we fit our first machine learning model, let's look at the correlations between features and targets. Ideally we want large (near 1 or -1) correlations between features and targets. Examining correlations can help us tweak features to maximize correlation (for example, altering the timeperiod argument in the talib functions). It can also help us remove features that aren't correlated to the target.

To easily plot a correlation matrix, we can use seaborn's heatmap() function. This takes a correlation matrix as the first argument, and has many other options. Check out the annot option -- this will help us turn on annotations.

Plot a heatmap of the correlation matrix (corr) we calculated in the last exercise (seaborn has been imported as sns for you).
Turn annotations on using the sns.heatmap() option annot=True.
Show the plot with plt.show(), and clear the plot area with plt.clf() to prepare for our second plot.
Create a scatter plot of the most correlated feature/variable with the target (5d_close_future_pct) from the lng_df DataFrame.

Take Hint (-30 XP)

Exercise

Check the correlations

Instructions