You cleaned that data before making a Decision Tree

Wow, good thing you decided to clean out all that. You found some real problematic artifacts in there. Some doofus put the timestamps for the panda birthdays in excel and you began optimizing on one hundred year old pandas before you found the mistake! When you made all that data squeaky clean you came up with one interesting looking decision tree to match up all these less than horny pandas during their peak ovulation cycle. Creating some arbitrary structures on what certain pandas found attractive according to appearance, social orders, personalities, and ‘That Spark’ your tree decision structure certainly looked like a thing. You weren’t sure why the women with big furry ears were attracted to the males with that extra wiggle in there step, but hey! Who are you to judge. The tree is made, what do ya do?

Evaluate it with a testing dataset, maybe they’re just asexual or something

Good enough…That’s a good expected value of panda cubs

Try Using Naive Bayes

Try using Regression

Try using a Neural Network