Here are what the columns represent:
- credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.
- purpose: The purpose of the loan (takes values “credit_card”, “debt_consolidation”, “educational”, “major_purchase”, “small_business”, and “all_other”).
- int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.
- installment: The monthly installments owed by the borrower if the loan is funded.
- log.annual.inc: The natural log of the self-reported annual income of the borrower.
- dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).
- fico: The FICO credit score of the borrower.
- days.with.cr.line: The number of days the borrower has had a credit line.
- revol.bal: The borrower’s revolving balance (amount unpaid at the end of the credit card billing cycle).
- revol.util: The borrower’s revolving line utilization rate (the amount of the credit line used relative to total credit available).
- inq.last.6mths: The borrower’s number of inquiries by creditors in the last 6 months.
- delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.
- pub.rec: The borrower’s number of derogatory public records (bankruptcy filings, tax liens, or judgments).
Import the usual libraries for pandas and plotting. You can import sklearn later on.
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings("ignore") %matplotlib inline
** Use pandas to read loan_data.csv as a dataframe called loans.**
loans = pd.read_csv("loan_data.csv")
** Check out the info(), head(), and describe() methods on loans.**
Let’s do some data visualization! We’ll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don’t worry about the colors matching, just worry about getting the main idea of the plot.
** Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**
Note: This is pretty tricky, feel free to reference the solutions. You’ll probably need one line of code for each histogram, I also recommend just using pandas built in .hist()
plt.figure(figsize=(10,6)) loans[loans["credit.policy"]==1]["fico"].hist(bins=35,color="blue",label="Credit Policy = 1",alpha=0.6) loans[loans["credit.policy"]==0]["fico"].hist(bins=35,color="red",label="Credit Policy = 0",alpha=0.6) plt.legend() plt.xlabel("FICO")
** Create a similar figure, except this time select by the not.fully.paid column.**
plt.figure(figsize=(10,6)) loans[loans["not.fully.paid"]==1]["fico"].hist(bins=35,color="blue",label="not.fully.paid = 1",alpha=0.6) loans[loans["not.fully.paid"]==0]["fico"].hist(bins=35,color="red",label="not.fully.paid = 0",alpha=0.6) plt.legend() plt.xlabel("FICO")
** Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. **
** Let’s see the trend between FICO score and interest rate. Recreate the following jointplot.**
** Create the following lmplots to see if the trend differed between not.fully.paid and credit.policy. Check the documentation for lmplot() if you can’t figure out how to separate it into columns.**
Let’s get ready to set up our data for our Random Forest Classification Model!
Check loans.info() again.
Notice that the purpose column as categorical
That means we need to transform them using dummy variables so sklearn will be able to understand them. Let’s do this in one clean step using pd.get_dummies.
Let’s show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.
Create a list of 1 element containing the string ‘purpose’. Call this list cat_feats.
cat_feats = ["purpose"]
Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data.
final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)
Now its time to split our data into a training set and a testing set!
** Use sklearn to split your data into a training set and a testing set as we’ve done in the past.**
from sklearn.model_selection import train_test_split
X = final_data.drop("not.fully.paid",axis=1) y = final_data["not.fully.paid"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
Let’s start by training a single decision tree first!
** Import DecisionTreeClassifier**
from sklearn.tree import DecisionTreeClassifier
Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.
dtree = DecisionTreeClassifier()
Create predictions from the test set and create a classification report and a confusion matrix.
prediction = dtree.predict(X_test)
from sklearn.metrics import confusion_matrix,classification_report
print(confusion_matrix(y_test,prediction)) print("\n") print(classification_report(y_test,prediction))
Now its time to train our model!
Create an instance of the RandomForestClassifier class and fit it to our training data from the previous step.
from sklearn.ensemble import RandomForestClassifier
rtree = RandomForestClassifier(n_estimators=300)
Let’s predict off the y_test values and evaluate our model.
** Predict the class of not.fully.paid for the X_test data.**
prediction = rtree.predict(X_test)
Now create a classification report from the results. Do you get anything strange or some sort of warning?
Show the Confusion Matrix for the predictions.
What performed better the random forest or the decision tree?