Identifying safe loans with decision trees
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The LendingClub is a peer-to-peer leading company that directly connects borrowers and potential lenders/investors.
In this project, you will build a classification model to predict whether or not a loan provided by LendingClub
is likely to default.
Here is the link to LendingClub
https://www.lendingclub.com/

You will be using data from LendingClub. Slightly cleaned data are available from
file lending-club-data.csv.zip

Start with exploring how the data look like.
We can see that over half of the loan grades are assigned values B or C.
Each loan is assigned one of these grades, along with a more finely discretized feature
called sub_grade (feel free to explore that feature column as well!).
These values depend on the loan application and credit report, and determine the
interest rate of the loan.
More information can be found at
https://www.lendingclub.com/public/rates-and-fees.action

Use decision tree to build a classifier.

Explore label predictions

Explore probability predictions

Evaluate accuracy of the decision tree model.