Loan Data

This dataset consists of more than 9,500 loans with information on the loan structure, the borrower, and whether the loan was pain back in full. This data was extracted from LendingClub.com, which is a company that connects borrowers with investors.

Not sure where to begin? Scroll to the bottom to find challenges!

import pandas as pd
loan_data = pd.read_csv("loan_data.csv")
print(loan_data.shape)
loan_data.head(100)

Data dictionary

	Variable	Explanation
0	credit_policy	1 if the customer meets the credit underwriting criteria; 0 otherwise.
1	purpose	The purpose of the loan.
2	int_rate	The interest rate of the loan (more risky borrowers are assigned higher interest rates).
3	installment	The monthly installments owed by the borrower if the loan is funded.
4	log_annual_inc	The natural log of the self-reported annual income of the borrower.
5	dti	The debt-to-income ratio of the borrower (amount of debt divided by annual income).
6	fico	The FICO credit score of the borrower.
7	days_with_cr_line	The number of days the borrower has had a credit line.
8	revol_bal	The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).
9	revol_util	The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).
10	inq_last_6mths	The borrower's number of inquiries by creditors in the last 6 months.
11	delinq_2yrs	The number of times the borrower had been 30+ days past due on a payment in the past 2 years.
12	pub_rec	The borrower's number of derogatory public records.
13	not_fully_paid	1 if the loan is not fully paid; 0 otherwise.

Source of dataset.

Don't know where to start?

Challenges are brief tasks designed to help you practice specific skills:

🗺️ Explore: Generate a correlation matrix between the numeric columns. What columns are positively and negatively correlated with each other? Does it change if you segment it by the purpose of the loan?
📊 Visualize: Plot histograms for every numeric column with a color element to segment the bars by not_fully_paid.
🔎 Analyze: Do loans with the same purpose have similar qualities not shared by loans with differing purposes? You can consider only fully paid loans.

Scenarios are broader questions to help you develop an end-to-end project for your portfolio:

You recently got a job as a machine learning scientist at a startup that wants to automate loan approvals. As your first project, your manager would like you to build a classifier to predict whether a loan will be paid back based on this data. There are two things to note. First, there is class imbalance; there are fewer examples of loans not fully paid. Second, it's more important to accurately predict whether a loan will not be paid back rather than if a loan is paid back. Your manager will want to know how you accounted for this in training and evaluation your model.

You will need to prepare a report that is accessible to a broad audience. It will need to outline your motivation, analysis steps, findings, and conclusions.

loan_data.info()

loan_data.describe()

unique_items = loan_data['purpose'].unique()
unique_items

import matplotlib.pyplot as plt

loan_data.hist(bins=30, figsize=(20, 15))
plt.suptitle('Feature Distributions')
plt.show()

import pandas as pd
import plotly.graph_objects as go


# Calculate counts of credit.policy values
policy_counts = loan_data['credit.policy'].value_counts()

# Step 2: Create the doughnut chart using Plotly
labels = ['Meets Credit Policy', 'Does Not Meet Credit Policy']
values = policy_counts.values

fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=0.7)])

fig.update_layout(title='Distribution of Credit Policy',title_x = 0.5,
                  annotations=[dict(text=f'Total Loans: {loan_data.shape[0]}', x=0.5, y=0.5, font_size=20, showarrow=False)])

fig.show()

import plotly.express as px

# Step 1: Prepare Data
nested_data = loan_data.groupby(['credit.policy', 'not.fully.paid']).size().reset_index(name='count')

# Map credit.policy and not.fully.paid values to custom labels
credit_policy_labels = {1: 'Meets Credit Policy', 0: 'Does Not Meet Credit Policy'}
not_fully_paid_labels = {1: 'Not Fully Paid', 0: 'Fully Paid'}
nested_data['credit.policy'] = nested_data['credit.policy'].map(credit_policy_labels)
nested_data['not.fully.paid'] = nested_data['not.fully.paid'].map(not_fully_paid_labels)

# Step 2: Create Nested Donut Chart using Plotly
fig = px.sunburst(nested_data, 
                  path=['credit.policy', 'not.fully.paid'], 
                  values='count',
                  color='not.fully.paid',
                  color_discrete_map={'Fully Paid': 'lightblue', 'Not Fully Paid': 'darkblue'},
                  title='Credit Policy and Not Fully Paid Distribution')

fig.update_traces(textinfo='label+percent entry')

fig.show()

import pandas as pd
import plotly.express as px

purpose_counts = loan_data['purpose'].value_counts()

# Step 2: Create the bar chart using Plotly with different colors for each bar
fig = px.bar(purpose_counts, x=purpose_counts.index, y=purpose_counts.values, 
             labels={'x': 'Purpose', 'y': 'Count'}, 
             title='Distribution of Loans by Purpose',
             color=purpose_counts.index,  # Color based on purpose
             color_discrete_sequence=px.colors.qualitative.Plotly)  

fig.show()

# Step 1: Prepare Data
purpose_credit_counts = loan_data.groupby(['purpose', 'credit.policy']).size().reset_index(name='count')

# Map credit.policy values to custom labels
credit_policy_labels = {1: 'Meets Credit Policy', 0: 'Does Not Meet Credit Policy'}
purpose_credit_counts['credit.policy'] = purpose_credit_counts['credit.policy'].map(credit_policy_labels)

# Step 2: Create Sunburst Chart using Plotly
fig = px.sunburst(purpose_credit_counts, 
                  path=['purpose', 'credit.policy'], 
                  values='count',
                  color='credit.policy',
                  color_discrete_map={'Meets Credit Policy': 'lightblue', 'Does Not Meet Credit Policy': 'darkblue'},
                  title='Loan Purpose and Credit Policy Distribution')

fig.update_traces(textinfo='label+percent entry')

fig.show()

import plotly.express as px

# Step 1: Prepare Data
purpose_credit_counts = loan_data.groupby(['purpose', 'credit.policy']).size().reset_index(name='count')

# Map credit.policy values to custom labels
credit_policy_labels = {1: 'Meets Credit Policy', 0: 'Does Not Meet Credit Policy'}
purpose_credit_counts['credit.policy'] = purpose_credit_counts['credit.policy'].map(credit_policy_labels)

# Normalize the counts to get percentages
total_counts = purpose_credit_counts.groupby('purpose')['count'].transform('sum')
purpose_credit_counts['percentage'] = purpose_credit_counts['count'] / total_counts * 100

# Step 2: Create 100% Stacked Bar Chart using Plotly
fig = px.bar(purpose_credit_counts, 
             x='purpose', 
             y='percentage', 
             color='credit.policy', 
             title='Loan Purpose and Credit Policy Distribution (100% Stacked)',
             labels={'percentage': 'Percentage', 'purpose': 'Purpose', 'credit.policy': 'Credit Policy'},
             color_discrete_map={'Meets Credit Policy': 'lightblue', 'Does Not Meet Credit Policy': 'darkblue'})

fig.update_layout(barmode='stack')

# Add percentage labels to the bars
fig.update_traces(texttemplate='%{y:.2f}%', textposition='inside')

fig.show()

import plotly.express as px

# Step 1: Prepare Data
sunburst_data = loan_data.groupby(['not.fully.paid', 'purpose']).size().reset_index(name='count')

# Map not.fully.paid values to custom labels
not_fully_paid_labels = {1: 'Not Fully Paid', 0: 'Fully Paid'}
sunburst_data['not.fully.paid'] = sunburst_data['not.fully.paid'].map(not_fully_paid_labels)

# Step 2: Create Sunburst Chart using Plotly
fig = px.sunburst(sunburst_data, 
                  path=['not.fully.paid', 'purpose'], 
                  values='count',
                  color='not.fully.paid',
                  color_discrete_map={'Fully Paid': 'lightblue', 'Not Fully Paid': 'darkblue'},
                  title='Not Fully Paid Status and Loan Purpose Distribution')

fig.update_traces(textinfo='label+percent entry')

fig.show()

import seaborn as sns
import matplotlib.pyplot as plt

# Correlation matrix
correlation_matrix = loan_data.corr()

# Plotting the correlation matrix
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Matrix')
plt.show()

‌
‌
‌

Loan Data

.mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Loan Data

Data dictionary

Don't know where to start?

Loan Data