LENDING CLUB BACKGROUND Lending Club is a peer-to-peer (P2P) lending platform, where borrowers submit their loan applications and individual lenders select the applications that they want to fund. Borrowers receive the full amount of the issued loan minus the origination fee, which is paid to the company. Investors purchase notes backed by the personal loans and pay Lending Club a service fee.
P2P lending brings down the cost of personal loans compared to traditional financing by connecting the borrowers and investors directly. However, there is always a risk of investing in a bad loan. In fact, the default rate for P2P loans are much higher than that of traditional loans. Therefore, the lending industry is highly interested in providing the investors with comprehensive risk assessment of the loan applications. The company shares data about all loan applications made through its platform.
In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_graphviz, export_text
from IPython.display import Image
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score
from sklearn.metrics import roc_curve, auc, average_precision_score
In [2]:
train = pd.read_excel('lendingclub_traindata.xlsx')
# 1 = good, 0 = default
print(train.head())
In [3]:
# remove target column to create feature only dataset
X = train.drop('loan_status',axis=1)
# store target column
y = train['loan_status']
print(X.shape, y.shape)
In [4]:
X.columns = ['Owns Home','Income','dti','FICO']
In [5]:
clf = DecisionTreeClassifier(criterion='entropy',max_depth=4,min_samples_split=1000,min_samples_leaf=200,random_state=0)
clf = clf.fit(X,y)
fig, ax = plt.subplots(figsize=(40, 30))
plot_tree(clf, filled=True, feature_names=X.columns, proportion=False)
plt.show()
In [6]:
r = export_text(clf,feature_names=['Owns Home','Income','dti','FICO'])
In [7]:
print(r)
In [8]:
test = pd.read_excel('lendingclub_testdata.xlsx')
# 1 = good, 0 = default
print(test.head())
In [9]:
# remove target column to create feature only dataset
X_test = test.drop('loan_status',axis=1)
# store target column
y_test = test['loan_status']
y_pred = clf.predict(X_test)
Note that for the default prediciton (i.e., with threshold=0.5), it totally missed all the bad loans.¶
In [10]:
n_test = len(y_test)
cm = (confusion_matrix(y_test,y_pred,labels=[1, 0], sample_weight=None)/n_test)*100
plt.figure(figsize=(6, 4)) # format the plot size
ax = plt.subplot()
sns.heatmap(cm, annot=True, ax = ax, fmt='.4g', cmap="Blues")
ax.set_xlabel('\nPredicted labels'); ax.set_ylabel('True labels\n')
ax.xaxis.tick_top()
ax.yaxis.set_ticklabels(['Good','Defaulted'],verticalalignment='center')
ax.xaxis.set_ticklabels(['Good','Defaulted'])
plt.show()
In [11]:
THRESHOLD = [.5, .75, .80, .85]
results = pd.DataFrame(columns=["THRESHOLD", "accuracy", "recall", "tnr", "fpr", "precision", "f1_score"]) # df to store results
results['THRESHOLD'] = THRESHOLD # threshold column
n_test = len(y_test)
Q = clf.predict_proba(X_test)[:,1]
j = 0
for i in THRESHOLD: # iterate over each threshold
# fit data to model
preds = np.where(Q>i, 1, 0) # if prob > threshold, predict 1
cm = (confusion_matrix(y_test, preds,labels=[1, 0], sample_weight=None)/n_test)*100
# confusion matrix (in percentage)
print('Confusion matrix for threshold =',i)
print(cm)
print(' ')
TP = cm[0][0] # True Positives
FN = cm[0][1] # False Positives
FP = cm[1][0] # True Negatives
TN = cm[1][1] # False Negatives
results.iloc[j,1] = accuracy_score(y_test, preds)
results.iloc[j,2] = recall_score(y_test, preds)
results.iloc[j,3] = TN/(FP+TN) # True negative rate
results.iloc[j,4] = FP/(FP+TN) # False positive rate
results.iloc[j,5] = precision_score(y_test, preds)
results.iloc[j,6] = f1_score(y_test, preds)
j += 1
print('ALL METRICS')
print(results.T.to_string(header=False))
In [12]:
# Compute the ROC curve and AUC
fpr, tpr, _ = roc_curve(y_test, Q)
roc_auc = auc(fpr,tpr)
In [13]:
plt.figure(figsize=(8,6)) # format the plot size
lw = 1.5
plt.plot(fpr, tpr, color='darkorange', marker='.',
lw=lw, label='Decision Tree (AUC = %0.4f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--',
label='Random Prediction (AUC = 0.5)' )
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic curve')
plt.legend(loc="lower right")
plt.show()
