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 Target Variable
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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df.shape
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# Create a crosstab of default and grade
pd.crosstab(df.default, df.grade)
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# Create a crosstab of default and grade - percentage by default type
pd.crosstab(df.default, df.grade, normalize="index")
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# Create a crosstab of default and grade - percentage by all type
pd.crosstab(df.default, df.grade, normalize="all")
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plt.style.use('seaborn')
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# Create a crosstab of default and grade - percentage by default type
sns.countplot(x="grade", data=df, hue="default", order=['A','B','C','D','E', 'F', 'G'])
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sns.stripplot(x="default", y="age", data=df, jitter=True)
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sns.stripplot(x="default", y="income", data=df, jitter=True)
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# Create the transformed income variable
df['income_log'] = np.log(df.income)
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sns.stripplot(x="default", y="income_log", data=df, jitter=True ,alpha=0.5)
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#Plot age, years and default
plt.scatter(df.years, df.age, c=df.default, alpha=0.6, cmap=plt.cm.viridis)
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df.plot(kind="scatter", x='age', y='income_log', c=df.default, cmap=plt.cm.viridis)
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sns.stripplot(x="grade", y="age", data=df, hue="default" , jitter=True)
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plt.scatter(x=df.amount, y=df.interest, c=df.default, alpha = 0.5, cmap="viridis")
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