Understanding your data

You will soon start building models in Keras to predict wages based on various professional and demographic factors. Before you start building a model, it's good to understand your data by performing some exploratory analysis.

The data is pre-loaded into a pandas DataFrame called df. Use the .head() and .describe() methods in the IPython Shell for a quick overview of the DataFrame.

The target variable you'll be predicting is wage_per_hour. Some of the predictor variables are binary indicators, where a value of 1 represents True, and 0 represents False.

Of the 9 predictor variables in the DataFrame, how many are binary indicators? The min and max values as shown by .describe() will be informative here. How many binary indicator predictors are there?