macro average and weighted average meaning in classification_report
Your data set is unbalanced since 28432 out of 28481 examples belong to class 0 (that is 99.8%). Therefore, your predictor almost always predicts any given sample as belonging to class 0 and thereby achieves very high scores like precision and recall for class 0 and very low scores for class 1.

In the case of weighted average the performance metrics are weighted accordingly: $$score_{weighted\text{-}avg} = 0.998 \cdot score_{class\text{ }0} + 0.002 \cdot score_{class\text{ }1}$$ Which turns out to be 1 due the class imbalances. 

However, macro avg is not weighted and therefore $$score_{macro\text{-}avg} = 0.5 \cdot score_{class\text{ }0} + 0.5 \cdot score_{class\text{ }1}$$

Since your model just guesses to almost always predict class 0 these scores turn out to be poor. 

Going forward I suggest start reading about unbalanced classification problems. There are many approaches how to tackle this. One important question here is whether false predictions for the two classes lead to different cost or not (which, for example, typically is the case in medical applications, spam filters or financial transactions). Because if they do not then a predictor always guessing the majority class could even make sense. But that strongly depends on the area and way of application.