from mywebstyle import plot_style
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import kaggle
plot_style('#f4f4f4')
loandata = pd.read_csv('lending_club_loan_two.csv')
first_13_cols = loandata.iloc[:, :13]
first_13_cols.head()

Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /Users/macpc/.kaggle/kaggle.json'

	loan_amnt	term	int_rate	installment	grade	sub_grade	emp_title	emp_length	home_ownership	annual_inc	verification_status	issue_d	loan_status
0	10000	36 months	11.44	329.48	B	B4	Marketing	10+ years	RENT	117000.0	Not Verified	Jan-15	Fully Paid
1	8000	36 months	11.99	265.68	B	B5	Credit analyst	4 years	MORTGAGE	65000.0	Not Verified	Jan-15	Fully Paid
2	15600	36 months	10.49	506.97	B	B3	Statistician	< 1 year	RENT	43057.0	Source Verified	Jan-15	Fully Paid
3	7200	36 months	6.49	220.65	A	A2	Client Advocate	6 years	RENT	54000.0	Not Verified	Nov-14	Fully Paid
4	24375	60 months	17.27	609.33	C	C5	Destiny Management Inc.	9 years	MORTGAGE	55000.0	Verified	Apr-13	Charged Off

second_13_cols = loandata.iloc[:, 13:]
second_13_cols.head()

	purpose	title	dti	earliest_cr_line	open_acc	pub_rec	revol_bal	revol_util	total_acc	initial_list_status	application_type	mort_acc	pub_rec_bankruptcies	address
0	vacation	Vacation	26.24	Jun-90	16	0	36369	41.8	25	w	INDIVIDUAL	0.0	0.0	0174 Michelle Gateway\r\nMendozaberg, OK 22690
1	debt_consolidation	Debt consolidation	22.05	Jul-04	17	0	20131	53.3	27	f	INDIVIDUAL	3.0	0.0	1076 Carney Fort Apt. 347\r\nLoganmouth, SD 05113
2	credit_card	Credit card refinancing	12.79	Aug-07	13	0	11987	92.2	26	f	INDIVIDUAL	0.0	0.0	87025 Mark Dale Apt. 269\r\nNew Sabrina, WV 05113
3	credit_card	Credit card refinancing	2.60	Sep-06	6	0	5472	21.5	13	f	INDIVIDUAL	0.0	0.0	823 Reid Ford\r\nDelacruzside, MA 00813
4	credit_card	Credit Card Refinance	33.95	Mar-99	13	0	24584	69.8	43	f	INDIVIDUAL	1.0	0.0	679 Luna Roads\r\nGreggshire, VA 11650

loandata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 395900 entries, 0 to 395899
Data columns (total 27 columns):
 #   Column                Non-Null Count   Dtype  
---  ------                --------------   -----  
 0   loan_amnt             395900 non-null  int64  
 1   term                  395900 non-null  object 
 2   int_rate              395900 non-null  float64
 3   installment           395900 non-null  float64
 4   grade                 395900 non-null  object 
 5   sub_grade             395900 non-null  object 
 6   emp_title             372982 non-null  object 
 7   emp_length            377608 non-null  object 
 8   home_ownership        395900 non-null  object 
 9   annual_inc            395900 non-null  float64
 10  verification_status   395900 non-null  object 
 11  issue_d               395900 non-null  object 
 12  loan_status           395900 non-null  object 
 13  purpose               395900 non-null  object 
 14  title                 394145 non-null  object 
 15  dti                   395900 non-null  float64
 16  earliest_cr_line      395900 non-null  object 
 17  open_acc              395900 non-null  int64  
 18  pub_rec               395900 non-null  int64  
 19  revol_bal             395900 non-null  int64  
 20  revol_util            395624 non-null  float64
 21  total_acc             395900 non-null  int64  
 22  initial_list_status   395900 non-null  object 
 23  application_type      395900 non-null  object 
 24  mort_acc              358117 non-null  float64
 25  pub_rec_bankruptcies  395365 non-null  float64
 26  address               395900 non-null  object 
dtypes: float64(7), int64(5), object(15)
memory usage: 81.6+ MB

sns.heatmap(loandata.isnull(), yticklabels=False, cbar=False, cmap='viridis')

loandata.describe().T

	count	mean	std	min	25%	50%	75%	max
loan_amnt	395900.0	14114.249305	8357.637338	500.00	8000.00	12000.00	20000.00	40000.00
int_rate	395900.0	13.639385	4.472112	5.32	10.49	13.33	16.49	30.99
installment	395900.0	431.859947	250.733444	16.08	250.33	375.43	567.30	1533.81
annual_inc	395900.0	74206.819251	61645.032777	0.00	45000.00	64000.00	90000.00	8706582.00
dti	395900.0	17.379187	18.021550	0.00	11.28	16.91	22.98	9999.00
open_acc	395900.0	11.311081	5.137591	0.00	8.00	10.00	14.00	90.00
pub_rec	395900.0	0.178204	0.530716	0.00	0.00	0.00	0.00	86.00
revol_bal	395900.0	15844.331435	20589.846553	0.00	6026.00	11181.00	19620.00	1743266.00
revol_util	395624.0	53.793449	24.452575	0.00	35.80	54.80	72.90	892.30
total_acc	395900.0	25.414622	11.887279	2.00	17.00	24.00	32.00	151.00
mort_acc	358117.0	1.814091	2.148006	0.00	0.00	1.00	3.00	34.00
pub_rec_bankruptcies	395365.0	0.121647	0.356176	0.00	0.00	0.00	0.00	8.00

sns.countplot(x='loan_status', data=loandata)
fp = np.round(
    len(loandata[loandata['loan_status'] == 'Fully Paid'])/len(loandata)*100, 2
)

co = np.round(
    len(loandata[loandata['loan_status'] == 'Charged Off']) /
    len(loandata)*100, 2
)

fig = plt.figure(figsize=(9, 4))
ax1 = fig.add_subplot(121)
loandata[loandata['loan_status'] == 'Charged Off']['loan_amnt'].hist(
    alpha=0.5, color='red', bins=30, ax=ax1,
    label='Charged Off'
)
loandata[loandata['loan_status'] == 'Fully Paid']['loan_amnt'].hist(
    alpha=0.5, color='blue', bins=30, ax=ax1,
    label='Fully Paid'
)
ax1.set_title('Loan Amount Distribution')
ax1.set_xlabel('Loan Amount')
ax1.legend()

ax2 = fig.add_subplot(122)
loandata[loandata['loan_status'] == 'Charged Off']['installment'].hist(
    alpha=0.5, color='red', bins=30, ax=ax2,
    label='Charged Off'
)
loandata[loandata['loan_status'] == 'Fully Paid']['installment'].hist(
    alpha=0.5, color='blue', bins=30, ax=ax2,
    label='Fully Paid'
)
ax2.set_title('Installment Distribution')
ax2.set_xlabel('Installment')
ax2.legend()

fig = plt.figure(figsize=(8.8, 4))
ax1 = fig.add_subplot(121)
sns.boxplot(
    x='loan_status', y='loan_amnt', hue='loan_status',
    data=loandata, ax=ax1, palette='winter'
)
ax1.set_title('Loan Amount Boxplot')

ax2 = fig.add_subplot(122)
sns.boxplot(
    x='loan_status', y='installment', hue='loan_status',
    data=loandata, ax=ax2, palette='winter'
)
ax2.set_title('Installment Boxplot')

Text(0.5, 1.0, 'Installment Boxplot')

fig = plt.figure(figsize=(8.8, 4))
ax1 = fig.add_subplot(121)
sns.countplot(
    x='loan_status',
    hue='term', data=loandata,
    palette='RdBu_r', ax=ax1
)
ax2 = fig.add_subplot(122)
sns.countplot(
    x='loan_status',
    hue='grade', data=loandata,
    palette='winter', ax=ax2
)

fig, ax = plt.subplots(figsize=(10, 5))
sns.histplot(x='sub_grade', hue='loan_status', data=loandata, ax=ax)

loandata['emp_title'] = loandata['emp_title'].str.lower()
loandata.emp_title.value_counts()[:25]

emp_title
manager                     5635
teacher                     5426
registered nurse            2626
supervisor                  2589
sales                       2381
driver                      2306
owner                       2200
rn                          2072
project manager             1776
office manager              1638
general manager             1460
truck driver                1288
director                    1192
engineer                    1187
police officer              1041
vice president               961
sales manager                961
operations manager           960
store manager                941
president                    877
administrative assistant     865
accountant                   845
account manager              845
technician                   839
mechanic                     753
Name: count, dtype: int64

pd.set_option('future.no_silent_downcasting', True)
loandata['emp_length'] = loandata['emp_length'].replace({
    '< 1 year': 0,
    '1 year': 1,
    '2 years': 2,
    '3 years': 3,
    '4 years': 4,
    '5 years': 5,
    '6 years': 6,
    '7 years': 7,
    '8 years': 8,
    '9 years': 9,
    '10+ years': 10
}
).infer_objects(copy=False)

loandata['emp_length_group'] = pd.cut(
    loandata['emp_length'],
    bins=[-1, 2, 7, 10],  # Bins: <3 years, 3-7 years, > 7 years
    labels=['Short-term', 'Mid-term', 'Long-term']
)

sns.countplot(
    x='emp_length_group',
    hue='loan_status',
    data=loandata,
    palette='winter',
    stat='count'
)

fig = plt.figure(figsize=(8, 10))
ax1 = fig.add_subplot(211)
annual_income_threshod = loandata['annual_inc'].quantile(0.95)
filtered_income = loandata[loandata['annual_inc'] <= annual_income_threshod]
sns.boxplot(
    x='loan_status', y='annual_inc',
    hue='loan_status', palette='winter',
    data=filtered_income, ax=ax1
)
ax1.set_title('Income Distribution')
ax1.set_xlabel('Loan Status')
ax1.set_ylabel('Annual Income')

ax2 = fig.add_subplot(212)
sns.countplot(
    x='home_ownership', hue='loan_status',
    data=loandata, ax=ax2
)

loandata['issue_d'] = pd.to_datetime(
    loandata['issue_d'], format='%b-%y'
)
loandata = loandata.sort_values('issue_d')
loan_status_trend = loandata.groupby(
    ['issue_d', 'loan_status']).size().unstack()

fig = plt.figure(figsize=(8, 10))
ax1 = fig.add_subplot(211)
loan_status_trend.plot(
    kind='line', marker='o', ax=ax1
)
ax1.set_title('Loan Status Over Time by Issue Date')
ax1.set_xlabel('Issue Date(mm-yyyy)')
ax1.set_ylabel('Number of Loans')
ax1.legend(title='Loan Status')

ax2 = fig.add_subplot(212)
sns.countplot(
    x='verification_status', hue='loan_status',
    data=loandata, palette='winter', ax=ax2
)

loandata['purpose'].value_counts()

purpose
debt_consolidation    234420
credit_card            82998
home_improvement       24024
other                  21177
major_purchase          8788
small_business          5701
car                     4696
medical                 4194
moving                  2853
vacation                2452
house                   2201
wedding                 1811
renewable_energy         328
educational              257
Name: count, dtype: int64

fig = plt.figure(figsize=(9, 4))
ax1 = fig.add_subplot(121)
sns.countplot(
    y='purpose', hue='loan_status',
    data=loandata, palette='coolwarm'
)
ax1.set_title('Loan Purpose')

ax2 = fig.add_subplot(122)
dti_threshold = loandata['dti'].quantile(0.95)
filtereddata = loandata[loandata['dti'] <= dti_threshold]

sns.boxplot(
    x='loan_status', y='dti',
    hue='loan_status', data=filtereddata,
    palette='coolwarm', ax=ax2
)
ax2.set_title('Debt-to-Income Ratio on Loan Status')

Text(0.5, 1.0, 'Debt-to-Income Ratio on Loan Status')

Insights: From the purpose column, we see that most of the loans that were charged off were used to make debt consolidation. Therefore, debt consolidation may have been a significant factor when a loan is charged off. Another insight we obtain from the debt-to-income ratio is that the charged off loans have higher dti ratio

fig = plt.figure(figsize=(9, 4))
ax1 = fig.add_subplot(121)
filtered_open_account_threshold = loandata['open_acc'].quantile(0.98)
filtered_open_account = loandata[loandata['open_acc']
                                 <= filtered_open_account_threshold]
filtered_open_account[filtered_open_account['loan_status'] == 'Fully Paid']['open_acc'].hist(
    alpha=0.5, color='green', bins=30, label='Fully Paid', ax=ax1
)
filtered_open_account[filtered_open_account['loan_status'] == 'Charged Off']['open_acc'].hist(
    alpha=0.5, color='red', bins=30, label='Charged Off', ax=ax1
)
ax1.set_xlabel('Number of Credit Acc.')
ax1.legend()
ax1.set_title('Number of credit accounts and Loan Status')

loandata['pub_rec_group'] = pd.cut(
    loandata['pub_rec'], bins=[-1, 0, 1, 3, loandata['pub_rec'].max()],
    labels=['0', '1', '2-3', '4+']
)
loan_status_by_pub_rec = loandata.groupby(
    ['pub_rec_group', 'loan_status'], observed=False
).size().unstack()

ax2 = fig.add_subplot(122)
loan_status_by_pub_rec.plot(
    kind='bar', stacked=False, edgecolor='black',
    color=['#1f77b4', '#ff7f0e'], ax=ax2
)
ax2.set_title('Public Records and Loan Status')
ax2.set_xlabel('Public Records')

Text(0.5, 0, 'Public Records')

sns.boxplot(
    y='open_acc', x='loan_status',
    hue='loan_status',
    data=filtered_open_account
)

Insights: Number of credit account seems normally distributed among both groups except for some outliers. However, the mean number of credit accounts are slightly higher for the charged off category than the fully paid category. So, higher credit account has some sort of relation with loan being charged off. Also, people who doesn’t have any public record seems to have higher chance of loan status being charged off

revol_bal_threshold = loandata['revol_bal'].quantile(0.95)
filtered_revol_bal = loandata[loandata['revol_bal'] <= revol_bal_threshold]
sns.boxplot(
    x='loan_status', y='revol_bal',
    data=filtered_revol_bal, hue='loan_status',
    palette='Set2'
)

revol_util_threshold = loandata['revol_util'].quantile(0.95)
filtered_revol_util = loandata[loandata['revol_util'] <= revol_util_threshold]
sns.boxplot(
    x='loan_status', y='revol_util',
    hue='loan_status', data=filtered_revol_util,
    palette='Set1'
)

fig = plt.figure(figsize=(9, 4))
ax1 = fig.add_subplot(121)
revol_bal_threshold = loandata['revol_bal'].quantile(0.95)
filtered_revol_bal = loandata[loandata['revol_bal'] <= revol_bal_threshold]
sns.boxplot(
    x='loan_status', y='revol_bal',
    data=filtered_revol_bal, hue='loan_status',
    palette='Set2', ax=ax1
)
ax1.set_xlabel('Loan Status')
ax1.set_ylabel('Revolving Balance')
ax1.set_title('Revolving Balance vs Loan Status')

ax2 = fig.add_subplot(122)
revol_util_threshold = loandata['revol_util'].quantile(0.95)
filtered_revol_util = loandata[loandata['revol_util'] <= revol_util_threshold]
sns.boxplot(
    x='loan_status', y='revol_util',
    hue='loan_status', data=filtered_revol_util,
    palette='Set1', ax=ax2
)
ax2.set_xlabel('Loan Status')
ax2.set_ylabel('Revolving Utilization')
ax2.set_title('Revolving Utilization vs Loan Status')

Text(0.5, 1.0, 'Revolving Utilization vs Loan Status')

loandata['total_acc'].value_counts()

total_acc
21     14274
22     14255
20     14220
23     13915
24     13874
       ...  
151        1
104        1
135        1
108        1
115        1
Name: count, Length: 118, dtype: int64

fig = plt.figure(figsize=(9, 4))
ax1 = fig.add_subplot(121)
filtered_total_account_threshold = loandata['total_acc'].quantile(0.95)
filtered_total_account = loandata[loandata['total_acc']
                                  <= filtered_total_account_threshold]
filtered_total_account[filtered_total_account['loan_status'] == 'Fully Paid']['total_acc'].hist(
    alpha=0.5, color='green', bins=30, label='Fully Paid', ax=ax1
)
filtered_total_account[filtered_total_account['loan_status'] == 'Charged Off']['total_acc'].hist(
    alpha=0.5, color='red', bins=30, label='Charged Off', ax=ax1
)
ax1.set_xlabel('Number of Total Credit Acc.')
ax1.legend()
ax1.set_title('Number of Total credit accounts and Loan Status')


ax2 = fig.add_subplot(122)
sns.countplot(
    x='initial_list_status', hue='loan_status',
    data=loandata, palette='winter'
)
ax2.set_title('Initial Status and Loan Status')
ax2.set_xlabel('Initial Status (Funded or Withdrawn)')

Text(0.5, 0, 'Initial Status (Funded or Withdrawn)')

sns.boxplot(
    y='total_acc', x='loan_status',
    hue='loan_status',
    data=filtered_total_account
)

loandata['application_type'].value_counts()

application_type
INDIVIDUAL    395189
JOINT            425
DIRECT_PAY       286
Name: count, dtype: int64

loandata['mort_acc'].value_counts()

mort_acc
0.0     139727
1.0      60392
2.0      49931
3.0      38040
4.0      27880
5.0      18188
6.0      11067
7.0       6050
8.0       3121
9.0       1655
10.0       865
11.0       479
12.0       264
13.0       146
14.0       107
15.0        61
16.0        37
17.0        22
18.0        18
19.0        15
20.0        13
24.0        10
22.0         7
25.0         4
21.0         4
27.0         3
23.0         2
31.0         2
32.0         2
26.0         2
34.0         1
30.0         1
28.0         1
Name: count, dtype: int64

mort_acc_summary = loandata.groupby('loan_status')['mort_acc'].describe()
print(mort_acc_summary)

                count      mean       std  min  25%  50%  75%   max
loan_status                                                        
Charged Off   72103.0  1.501214  1.974335  0.0  0.0  1.0  2.0  23.0
Fully Paid   286014.0  1.892967  2.182550  0.0  0.0  1.0  3.0  34.0

# Bin the mort_acc column into categories
loandata['mort_acc_group'] = pd.cut(
    loandata['mort_acc'], bins=[-1, 0, 2, 5, 10, loandata['mort_acc'].max()],
    labels=['0', '1-2', '3-5', '6-10', '10+']
)

# Plot the grouped bar chart
mort_acc_counts = loandata.groupby(
    ['mort_acc_group', 'loan_status'], observed=False
).size().unstack()

mort_acc_counts.plot(kind='bar', stacked=False,
                     figsize=(10, 6), colormap='viridis')
plt.title('Loan Status by Number of Mortgage Accounts')
plt.xlabel('Number of Mortgage Accounts (Grouped)')
plt.ylabel('Number of Loans')
plt.xticks(rotation=0)
plt.legend(title='Loan Status')
plt.show()

loandata['pub_rec_bankruptcies'].value_counts()

pub_rec_bankruptcies
0.0    350265
1.0     42776
2.0      1846
3.0       351
4.0        82
5.0        32
6.0         7
7.0         4
8.0         2
Name: count, dtype: int64

loandata['pub_rec_bankruptcies_group'] = pd.cut(
    loandata['pub_rec_bankruptcies'], bins=[-1, 0,
                                            loandata['pub_rec_bankruptcies'].max()],
    labels=['0', '1 or More']
)

# Plot the grouped bar chart
pub_rec_bankruptcies_counts = loandata.groupby(
    ['pub_rec_bankruptcies_group', 'loan_status'], observed=False
).size().unstack()

pub_rec_bankruptcies_counts.plot(
    kind='bar', stacked=False, figsize=(10, 6), colormap='viridis')
plt.title('Loan Status by Number of Public Record of Bankruptcies')
plt.xlabel('Public Record of Bankruptcies (Grouped)')
plt.ylabel('Number of Loans')
plt.xticks(rotation=0)
plt.legend(title='Loan Status')
plt.show()

fig = plt.figure(figsize=(9, 4))

ax1 = fig.add_subplot(121)

loandata['mort_acc_group'] = pd.cut(
    loandata['mort_acc'], bins=[-1, 0, 2, 5, 10, loandata['mort_acc'].max()],
    labels=['0', '1-2', '3-5', '6-10', '10+']
)

mort_acc_counts = loandata.groupby(
    ['mort_acc_group', 'loan_status'], observed=False
).size().unstack()

mort_acc_counts.plot(
    kind='bar', stacked=False,
    colormap='viridis', ax=ax1
)
ax1.set_title('Loan Status by the # of Mort. Acc')
ax1.set_xlabel('Number of Mortgage Accounts (Grouped)')
ax1.set_ylabel('Number of Loans')
ax1.set_xticklabels(ax1.get_xticklabels(), rotation=0)
ax1.legend(title='Loan Status')

ax2 = fig.add_subplot(122)

loandata['pub_rec_bankruptcies_group'] = pd.cut(
    loandata['pub_rec_bankruptcies'], bins=[-1, 0,
                                            loandata['pub_rec_bankruptcies'].max()],
    labels=['0', '1 or More']
)

# Plot the grouped bar chart
pub_rec_bankruptcies_counts = loandata.groupby(
    ['pub_rec_bankruptcies_group', 'loan_status'], observed=False
).size().unstack()

pub_rec_bankruptcies_counts.plot(
    kind='bar', stacked=False,
    colormap='coolwarm', ax=ax2
)
ax2.set_title('Loan Status by the # of Pub Rec of Bankruptcies')
ax2.set_xlabel('Public Record of Bankruptcies (Grouped)')
ax2.set_ylabel('Number of Loans')
ax2.set_xticklabels(ax2.get_xticklabels(), rotation=0)
ax2.legend(title='Loan Status')
plt.show()

loandata['application_type'].value_counts()

application_type
INDIVIDUAL    395189
JOINT            425
DIRECT_PAY       286
Name: count, dtype: int64

sns.countplot(
    x='loan_status', hue= 'application_type', 
    data=loandata, palette='winter'
)

numeric_loandata = loandata.select_dtypes(include=['float64','int64'])
plt.figure(figsize=(10,8))
sns.heatmap(numeric_loandata.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.show()