To lend funds or not to lend is the question?
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To lend funds or not to lend is the question?
The following analysis is based on a publicly available dataset hosted at Kaggle. The full code is located on my github
EXPLORATORY DATA ANALYSIS
 The dataset is a single csv file. It has a shape of 42,542 observations in 144 variables.
 The response or dependent variable is “loan_status” and is categorical in nature.
 Off the 144 variables, majority of them (~110) are continuous in nature and rest are categorical data types.
 All 144 variables have missing values. – Variables with 80% missing data were removed. The dataset size reduced to 54 variables.
 Correlation treatment helped reduce dataset size to 45 variables. Turns out, independent variables such as
funded amount, funded amount inv, installment, total payment, total payment inv, total rec prncp, total rec int, collection recovery fee and pub rec bankruptcies
are strongly correlated (>=80%) with the dependent variable.  By this stage, the dataset shape is 42,542 observations in 45 variables (25 continuous, 3 datetime, and 17 categorical).
 The dependent variable has 4 factor levels. I recoded the 4 factor levels to 2 as asked by the assignment.
 34116 observations for loans that were fully paid
 8426 observations for loans that were charged off
 The dependent variable was label encoded to make it suitable for model building. As earlier stated, it’s now a binary categorical variable with two levels. Label 1 refers to Fully Paid and Label 0 refers to Charged Off.

It should be noted, the dependent variable is imbalanced in nature. This means, data balancing method need to be applied for building a robust model.
import pandas as pd pd.options.mode.chained_assignment = None import numpy as np import seaborn as sns import matplotlib.pyplot as plt from scipy.stats import ttest_1samp from sklearn import preprocessing from imblearn.over_sampling import SMOTE from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report
df = pd.read_csv(“../data/LoanStats3a.csv”, skiprows=1, low_memory=False) print(“\ndata shape: “, df.shape) # (42538, 144) df[‘loan_status’]=df[‘loan_status’].replace({‘Does not meet the credit policy. Status:Fully Paid’:’Fully Paid’, ‘Does not meet the credit policy. Status:Charged Off’:’Charged Off’} ) print(df[‘loan_status’].value_counts()) def missing_data_stats(df): mis_val = df.isnull().sum() mis_val_percent = 100 * df.isnull().sum() / len(df) mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1) mis_val_table_ren_columns = mis_val_table.rename( columns = {0 : ‘Missing Values’, 1 : ‘% of Total Values’}) mis_val_table_ren_columns = mis_val_table_ren_columns[ mis_val_table_ren_columns.iloc[:,1] != 0].sort_values( ‘% of Total Values’, ascending=False).round(1) print (“The dataframe has “ + str(df.shape[1]) + “ columns.\n”
“There are “ + str(mis_val_table_ren_columns.shape[0]) + “ columns with missing values.”) return mis_val_table_ren_columnsmissing_data_stats(df)
df1 = df[df.columns[df.isnull().mean()<=0.80]] print(“df1 shape: “,df1.shape) # (42542, 54) cols = df1.columns.values df1[cols]=df1[cols].fillna(df1.mode().iloc[0]) missing_data_stats(df1)
Initial plots
 A histogram comparing the annual income of applicants from the states of West Virginia (WV) and New Mexico (NM). Is there any relationship here?
Fig1: Average annual income of applicants from WV and NM
 The top Top 3 states with highest number of loan defaults are
California (CA)
,New York (NY)
andTexas (TX)
.
Fig2: Top 3 states with highest loan defaults
DATA SAMPLING
 To build a classifier model, I took following steps,
 Data shape at this stage was
(42542, 45)
.  Took a 0.05% random sample of the dataset for further analysis.
 Data shape of sample size was
(2127, 45)
.  The reason I took a sample of the original dataset was the presence of several categorical variables with factor levels greater than 5. Label encoding such categorical variables yielded meaningless information in model building and onehot encoding blew up the dataset size to more than 3GB!
 Did label encoding for categorical variables with factor levels less than or equal to 2
(term, pymnt_plan, initial_list_status, application_type, hardship_flag, debt_settlement_flag, target)
.  Did onehot encoding for rest of categorical variables with factor levels greater than 2. Dataset shape becomes
(2127, 6965)
 Data shape at this stage was
MODEL BUILDING

Null Hypothesis: From Fig1, its apparent there is no relationship between the average annual income of applicants from WV and NM. To verify this claim further, a significance test is conducted.
annual_inc_mean = np.mean(df_f[‘annual_inc’]) tset, pval = ttest_1samp(df_f[‘annual_inc’], annual_inc_mean) print(“pvalues: “,pval) if (pval < 0.05):# pvalue is 0.05 or 5% print(“ we are rejecting null hypothesis”) else: print(“we are accepting null hypothesis”)
 Used label encoded data.
 Performed a stratified random sampling to split the dataset into 80% train and 20% test parts (in code, see lines 124 to line 154).
 Chose logistic regression algorithm
 Building a classification model on imbalanced dependent variable
 F1 score for loan status with value Charged Off (0) is 90%
 F1 score for loan status with value Fully Paid (1) is 98%
 Applied synthetic minority over sampling (SMOTE) method for data balancing
 F1 score for loan status with value Charged Off (0) is 99%
 F1 score for loan status with value Fully Paid (1) is 100%
Model Summary statistcis as follws below,
Imbalanced data classification
precision recall f1score support 0 1.00 0.74 0.85 68 1 0.95 1.00 0.98 358 accuracy 0.96 426 macro avg 0.98 0.87 0.91 426 weighted avg 0.96 0.96 0.96 426 Resampled data shape: (2856, 6975) Balanced target 0 1428 1 1428 Name: target, dtype: int64
Balanced data using SMOTE
precision recall f1score support 0 0.98 0.90 0.94 68 1 0.98 1.00 0.99 358 accuracy 0.98 426 macro avg 0.98 0.95 0.96 426 weighted avg 0.98 0.98 0.98 426
End notes
To develop a strategy for risk averse customers, the following points may be considered;
 We should target semiurban or rural locations. Reason, such areas are replete with middleeconomic class and/or lower economic class groups of people. In such sections of society, the penetration of information on Peer to Peer (P2P) lending is low. Our priority should be to educate such masses of people on the benefits and pitfalls of P2P lending as compared to other lending methods.
 Next, such customers can be educated about the Mutual Fund (MF) investment options, in particular the debt MF growth option. This strategy may help to maintain low default rates because the debt MF expense ratio charged by MF companies are comparatively less as compared to equity MF expense ratios.
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