Hello everyone and welcome to the decision trees and ran them for this project overview lecture and
this lecture we're going to give you some background about the project and go over the notebook for
the project.
Let's go ahead and get started by jumping to the project notebook.
OK here I am at the project notebook for this project we're going to be exploring some real world data
that was publicly available from Lending Club dot com if you ever heard of it Lending Club connects
people who need money known as borrowers with people who have money known as investors.
And hopefully as an investor you're going to want to invest in people who show a profile of having a
high probability of paying you back.
And we're going to try to create a model to help predict this.
Now a quick side note Lending Club as a company had a very interesting year in 2016.
There was some scandal and a fraud involved there.
We're going to do is check out some of data.
But keep that context in mind and you should know that this data is from before the company even went
public.
It's from the year 2007 to 2010.
So much earlier before these sort of scandals came to light.
But it should be unrelated to other data we're looking at here.
Just wanted to add in that side note and there's a link to the Wikipedia page if you're interested in
finding out more about that.
But all that stuff shouldn't really affect the data we're looking at here.
What I want you to do is you can either download the data from this link or just use the CAC file it's
already provided.
I would recommend you use the C S V file provided with the download and the notebooks because it's already
been cleaned of no values for you.
There are a lot of columns here but they basically represent the borrowers needs.
Andy Barr's reasons and some of their features when they borrowed a specific line of credit from an
investor you can figure out things such as the FICO credit score of the bar and the number of days the
borrower had that credit line.
And so basically all these rows represents an actual loan that Lending Club gave them.
And you'll notice that there's a line here.
You know if you scroll down and start checking out info on the data frame there is one column called
not fully paid.
This is the column we're going to try to predict.
We want to predict whether or not the loan was fully paid back or not.
Then what you're going to do is just do some exploratory data analysis creating some histograms based
off the various features and then you're also going to start setting up the data will need to deal categorical
features so there will be some data cleaning.
In this project.
And then finally you'll use train to split to split up your data into training and test set and you'll
train both a decision tree model and a random force model and compare it to.
All right I hope you enjoy this project and you're going to be dealing with a real world data set.
You'll see how to apply your python knowledge and machine learning with sikat learn knowledge to use
something in the real world.
OK thanks everyone and I'll see you at the next lecture where we begin to go over the solutions for
this project.