You are provided with the following data: loan_data.csv
This is the historical data that the bank has provided. It has the following columns
Application Attributes:
years: Number of years the applicant has been employed ownership: Whether the applicant owns a house or not income: Annual income of the applicant age: Age of the applicant Behavioural Attributes:
grade: Credit grade of the applicantOutcome Variable:
amount : Amount of Loan provided to the applicant interest: Interest rate charged for the applicant default : Whether the applicant has defaulted or not Let us build some intuition around the Loan Data
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#Load the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
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#Default Variables
%matplotlib inline
plt.rcParams['figure.figsize'] = (16,9)
plt.rcParams['font.size'] = 18
plt.style.use('fivethirtyeight')
pd.set_option('display.float_format', lambda x: '%.2f' % x)
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#Load the dataset
df = pd.read_csv("data/loan_data_clean.csv")
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df.head()
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# Create a crosstab of default and grade
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# Create a crosstab of default and grade - percentage by default type
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# Create a crosstab of default and grade - percentage by all type
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# Create a crosstab of default and grade - percentage by default type
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# Create the transformed income variable
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#Plot age, years and default
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