Created by SmirkyGraphs. Code: GitHub. Source: Kaggle.

Introduction

Index

1) Introduction

2) Data Exploration

3) Predictions

Importing Libraries

In [4]:
# For Data
import pandas as pd
import numpy as np

# For Visualizations
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline

Creating Functions

In [5]:
def missingdata(frame):
    frames = []
    
    count = frame.isnull().sum()
    frames.append(count)
    
    percent = frame.isnull().sum()/frame.shape[0]
    frames.append(percent)
    
    output = pd.concat([count, percent], axis=1, keys=['Count', 'Percent'])
    output = output[output['Percent'] > 0.01].sort_values('Percent', ascending=False)
    
    if output.empty == True:
        print("No missing data > 1%")
    else:
        return output
In [6]:
def graph_objects(frame, hue=None):
    
    if hue is not None:
        hue = hue
        
    df = frame.dtypes
    df.index.name = 'columns'
    df = pd.DataFrame(df, columns=['dtype'])
    df = df.reset_index()
    df = df[df['dtype'] == 'object']
    list_objects = df['columns'].tolist()
    
    for obj in list_objects:
        plt.figure(figsize=(11,5))
        plot = sns.countplot(obj, data=frame, hue=hue)
        plt.xticks(rotation=90)

Data Exploration

Application Train

This is the main table, broken into two files for Train (with TARGET) and Test (without TARGET).

Static data for all applications. One row represents one loan in our data sample.

Basic Info

In [7]:
application_train = pd.read_csv('./input/application_train.csv')
In [8]:
application_train.info(verbose=True, null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 122 columns):
SK_ID_CURR                      307511 non-null int64
TARGET                          307511 non-null int64
NAME_CONTRACT_TYPE              307511 non-null object
CODE_GENDER                     307511 non-null object
FLAG_OWN_CAR                    307511 non-null object
FLAG_OWN_REALTY                 307511 non-null object
CNT_CHILDREN                    307511 non-null int64
AMT_INCOME_TOTAL                307511 non-null float64
AMT_CREDIT                      307511 non-null float64
AMT_ANNUITY                     307499 non-null float64
AMT_GOODS_PRICE                 307233 non-null float64
NAME_TYPE_SUITE                 306219 non-null object
NAME_INCOME_TYPE                307511 non-null object
NAME_EDUCATION_TYPE             307511 non-null object
NAME_FAMILY_STATUS              307511 non-null object
NAME_HOUSING_TYPE               307511 non-null object
REGION_POPULATION_RELATIVE      307511 non-null float64
DAYS_BIRTH                      307511 non-null int64
DAYS_EMPLOYED                   307511 non-null int64
DAYS_REGISTRATION               307511 non-null float64
DAYS_ID_PUBLISH                 307511 non-null int64
OWN_CAR_AGE                     104582 non-null float64
FLAG_MOBIL                      307511 non-null int64
FLAG_EMP_PHONE                  307511 non-null int64
FLAG_WORK_PHONE                 307511 non-null int64
FLAG_CONT_MOBILE                307511 non-null int64
FLAG_PHONE                      307511 non-null int64
FLAG_EMAIL                      307511 non-null int64
OCCUPATION_TYPE                 211120 non-null object
CNT_FAM_MEMBERS                 307509 non-null float64
REGION_RATING_CLIENT            307511 non-null int64
REGION_RATING_CLIENT_W_CITY     307511 non-null int64
WEEKDAY_APPR_PROCESS_START      307511 non-null object
HOUR_APPR_PROCESS_START         307511 non-null int64
REG_REGION_NOT_LIVE_REGION      307511 non-null int64
REG_REGION_NOT_WORK_REGION      307511 non-null int64
LIVE_REGION_NOT_WORK_REGION     307511 non-null int64
REG_CITY_NOT_LIVE_CITY          307511 non-null int64
REG_CITY_NOT_WORK_CITY          307511 non-null int64
LIVE_CITY_NOT_WORK_CITY         307511 non-null int64
ORGANIZATION_TYPE               307511 non-null object
EXT_SOURCE_1                    134133 non-null float64
EXT_SOURCE_2                    306851 non-null float64
EXT_SOURCE_3                    246546 non-null float64
APARTMENTS_AVG                  151450 non-null float64
BASEMENTAREA_AVG                127568 non-null float64
YEARS_BEGINEXPLUATATION_AVG     157504 non-null float64
YEARS_BUILD_AVG                 103023 non-null float64
COMMONAREA_AVG                  92646 non-null float64
ELEVATORS_AVG                   143620 non-null float64
ENTRANCES_AVG                   152683 non-null float64
FLOORSMAX_AVG                   154491 non-null float64
FLOORSMIN_AVG                   98869 non-null float64
LANDAREA_AVG                    124921 non-null float64
LIVINGAPARTMENTS_AVG            97312 non-null float64
LIVINGAREA_AVG                  153161 non-null float64
NONLIVINGAPARTMENTS_AVG         93997 non-null float64
NONLIVINGAREA_AVG               137829 non-null float64
APARTMENTS_MODE                 151450 non-null float64
BASEMENTAREA_MODE               127568 non-null float64
YEARS_BEGINEXPLUATATION_MODE    157504 non-null float64
YEARS_BUILD_MODE                103023 non-null float64
COMMONAREA_MODE                 92646 non-null float64
ELEVATORS_MODE                  143620 non-null float64
ENTRANCES_MODE                  152683 non-null float64
FLOORSMAX_MODE                  154491 non-null float64
FLOORSMIN_MODE                  98869 non-null float64
LANDAREA_MODE                   124921 non-null float64
LIVINGAPARTMENTS_MODE           97312 non-null float64
LIVINGAREA_MODE                 153161 non-null float64
NONLIVINGAPARTMENTS_MODE        93997 non-null float64
NONLIVINGAREA_MODE              137829 non-null float64
APARTMENTS_MEDI                 151450 non-null float64
BASEMENTAREA_MEDI               127568 non-null float64
YEARS_BEGINEXPLUATATION_MEDI    157504 non-null float64
YEARS_BUILD_MEDI                103023 non-null float64
COMMONAREA_MEDI                 92646 non-null float64
ELEVATORS_MEDI                  143620 non-null float64
ENTRANCES_MEDI                  152683 non-null float64
FLOORSMAX_MEDI                  154491 non-null float64
FLOORSMIN_MEDI                  98869 non-null float64
LANDAREA_MEDI                   124921 non-null float64
LIVINGAPARTMENTS_MEDI           97312 non-null float64
LIVINGAREA_MEDI                 153161 non-null float64
NONLIVINGAPARTMENTS_MEDI        93997 non-null float64
NONLIVINGAREA_MEDI              137829 non-null float64
FONDKAPREMONT_MODE              97216 non-null object
HOUSETYPE_MODE                  153214 non-null object
TOTALAREA_MODE                  159080 non-null float64
WALLSMATERIAL_MODE              151170 non-null object
EMERGENCYSTATE_MODE             161756 non-null object
OBS_30_CNT_SOCIAL_CIRCLE        306490 non-null float64
DEF_30_CNT_SOCIAL_CIRCLE        306490 non-null float64
OBS_60_CNT_SOCIAL_CIRCLE        306490 non-null float64
DEF_60_CNT_SOCIAL_CIRCLE        306490 non-null float64
DAYS_LAST_PHONE_CHANGE          307510 non-null float64
FLAG_DOCUMENT_2                 307511 non-null int64
FLAG_DOCUMENT_3                 307511 non-null int64
FLAG_DOCUMENT_4                 307511 non-null int64
FLAG_DOCUMENT_5                 307511 non-null int64
FLAG_DOCUMENT_6                 307511 non-null int64
FLAG_DOCUMENT_7                 307511 non-null int64
FLAG_DOCUMENT_8                 307511 non-null int64
FLAG_DOCUMENT_9                 307511 non-null int64
FLAG_DOCUMENT_10                307511 non-null int64
FLAG_DOCUMENT_11                307511 non-null int64
FLAG_DOCUMENT_12                307511 non-null int64
FLAG_DOCUMENT_13                307511 non-null int64
FLAG_DOCUMENT_14                307511 non-null int64
FLAG_DOCUMENT_15                307511 non-null int64
FLAG_DOCUMENT_16                307511 non-null int64
FLAG_DOCUMENT_17                307511 non-null int64
FLAG_DOCUMENT_18                307511 non-null int64
FLAG_DOCUMENT_19                307511 non-null int64
FLAG_DOCUMENT_20                307511 non-null int64
FLAG_DOCUMENT_21                307511 non-null int64
AMT_REQ_CREDIT_BUREAU_HOUR      265992 non-null float64
AMT_REQ_CREDIT_BUREAU_DAY       265992 non-null float64
AMT_REQ_CREDIT_BUREAU_WEEK      265992 non-null float64
AMT_REQ_CREDIT_BUREAU_MON       265992 non-null float64
AMT_REQ_CREDIT_BUREAU_QRT       265992 non-null float64
AMT_REQ_CREDIT_BUREAU_YEAR      265992 non-null float64
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
In [9]:
application_train.head()
Out[9]:
SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY ... FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR
0 100002 1 Cash loans M N Y 0 202500.0 406597.5 24700.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
1 100003 0 Cash loans F N N 0 270000.0 1293502.5 35698.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
2 100004 0 Revolving loans M Y Y 0 67500.0 135000.0 6750.0 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
3 100006 0 Cash loans F N Y 0 135000.0 312682.5 29686.5 ... 0 0 0 0 NaN NaN NaN NaN NaN NaN
4 100007 0 Cash loans M N Y 0 121500.0 513000.0 21865.5 ... 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 122 columns

Missing Values

In [10]:
missingdata(application_train)
Out[10]:
Count Percent
COMMONAREA_MEDI 214865 0.698723
COMMONAREA_AVG 214865 0.698723
COMMONAREA_MODE 214865 0.698723
NONLIVINGAPARTMENTS_AVG 213514 0.694330
NONLIVINGAPARTMENTS_MEDI 213514 0.694330
NONLIVINGAPARTMENTS_MODE 213514 0.694330
FONDKAPREMONT_MODE 210295 0.683862
LIVINGAPARTMENTS_MODE 210199 0.683550
LIVINGAPARTMENTS_MEDI 210199 0.683550
LIVINGAPARTMENTS_AVG 210199 0.683550
FLOORSMIN_MEDI 208642 0.678486
FLOORSMIN_MODE 208642 0.678486
FLOORSMIN_AVG 208642 0.678486
YEARS_BUILD_AVG 204488 0.664978
YEARS_BUILD_MEDI 204488 0.664978
YEARS_BUILD_MODE 204488 0.664978
OWN_CAR_AGE 202929 0.659908
LANDAREA_AVG 182590 0.593767
LANDAREA_MEDI 182590 0.593767
LANDAREA_MODE 182590 0.593767
BASEMENTAREA_MODE 179943 0.585160
BASEMENTAREA_AVG 179943 0.585160
BASEMENTAREA_MEDI 179943 0.585160
EXT_SOURCE_1 173378 0.563811
NONLIVINGAREA_AVG 169682 0.551792
NONLIVINGAREA_MODE 169682 0.551792
NONLIVINGAREA_MEDI 169682 0.551792
ELEVATORS_AVG 163891 0.532960
ELEVATORS_MEDI 163891 0.532960
ELEVATORS_MODE 163891 0.532960
WALLSMATERIAL_MODE 156341 0.508408
APARTMENTS_MEDI 156061 0.507497
APARTMENTS_AVG 156061 0.507497
APARTMENTS_MODE 156061 0.507497
ENTRANCES_MODE 154828 0.503488
ENTRANCES_AVG 154828 0.503488
ENTRANCES_MEDI 154828 0.503488
LIVINGAREA_MODE 154350 0.501933
LIVINGAREA_AVG 154350 0.501933
LIVINGAREA_MEDI 154350 0.501933
HOUSETYPE_MODE 154297 0.501761
FLOORSMAX_MEDI 153020 0.497608
FLOORSMAX_MODE 153020 0.497608
FLOORSMAX_AVG 153020 0.497608
YEARS_BEGINEXPLUATATION_MEDI 150007 0.487810
YEARS_BEGINEXPLUATATION_AVG 150007 0.487810
YEARS_BEGINEXPLUATATION_MODE 150007 0.487810
TOTALAREA_MODE 148431 0.482685
EMERGENCYSTATE_MODE 145755 0.473983
OCCUPATION_TYPE 96391 0.313455
EXT_SOURCE_3 60965 0.198253
AMT_REQ_CREDIT_BUREAU_HOUR 41519 0.135016
AMT_REQ_CREDIT_BUREAU_DAY 41519 0.135016
AMT_REQ_CREDIT_BUREAU_WEEK 41519 0.135016
AMT_REQ_CREDIT_BUREAU_MON 41519 0.135016
AMT_REQ_CREDIT_BUREAU_QRT 41519 0.135016
AMT_REQ_CREDIT_BUREAU_YEAR 41519 0.135016

Visualizations

In [11]:
sns.countplot('TARGET', data=application_train)
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x358223c780>
In [12]:
graph_objects(application_train, hue='TARGET')
In [13]:
sns.countplot('CNT_CHILDREN', data=application_train, hue='TARGET')
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584901860>
In [14]:
sns.countplot('FLAG_EMP_PHONE', data=application_train, hue='TARGET')
Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x35820e12b0>
In [15]:
sns.countplot('FLAG_PHONE', data=application_train, hue='TARGET')
Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584573128>
In [16]:
sns.countplot('FLAG_EMAIL', data=application_train, hue='TARGET')
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x35845c9710>
In [17]:
sns.countplot('REGION_RATING_CLIENT', data=application_train, hue='TARGET')
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x35845c14e0>
In [18]:
sns.countplot('HOUR_APPR_PROCESS_START', data=application_train, hue='TARGET')
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584678630>
In [19]:
sns.distplot(application_train["DAYS_ID_PUBLISH"])
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x35846db390>
In [20]:
days_employed = application_train[application_train['DAYS_EMPLOYED'] < 365243]
sns.distplot(days_employed["DAYS_EMPLOYED"])
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x35847ddc18>
In [21]:
sns.distplot(application_train["DAYS_REGISTRATION"])
Out[21]:
<matplotlib.axes._subplots.AxesSubplot at 0x358498c160>
In [22]:
sns.distplot(application_train.OWN_CAR_AGE.dropna())
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584a2a940>
In [23]:
sns.distplot(application_train.CNT_FAM_MEMBERS.dropna())
Out[23]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584b09c18>
In [24]:
sns.distplot(application_train.AMT_GOODS_PRICE.dropna())
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584536908>
In [25]:
sns.distplot(application_train.AMT_ANNUITY.dropna())
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584547518>

Bureau Balance

Monthly balances of previous credits in Credit Bureau.

This table has one row for each month of history of every previous credit reported to Credit Bureau i.e the table has

(#loans in sample # of relative previous credits # of months where we have some history observable for the previous credits) rows.

Basic Info

In [26]:
credit = sns.FacetGrid(application_train, hue="TARGET", height=6, aspect=2)
credit = credit.map(sns.distplot, "AMT_CREDIT")

Monthly balances of previous credits in Credit Bureau.

This table has one row for each month of history of every previous credit reported to Credit Bureau – i.e the table has

(#loans in sample # of relative previous credits # of months where we have some history observable for the previous credits) rows.

In [27]:
bureau_balance = pd.read_csv('./input/bureau_balance.csv')
In [28]:
bureau_balance.info(null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
SK_ID_BUREAU      27299925 non-null int64
MONTHS_BALANCE    27299925 non-null int64
STATUS            27299925 non-null object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB
In [29]:
bureau_balance.head()
Out[29]:
SK_ID_BUREAU MONTHS_BALANCE STATUS
0 5715448 0 C
1 5715448 -1 C
2 5715448 -2 C
3 5715448 -3 C
4 5715448 -4 C

Missing Values

In [30]:
missingdata(bureau_balance)

No missing data > 1%

Visualizations

In [31]:
graph_objects(bureau_balance)

Bureau

All client's previous credits provided by other financial institutions that were reported to Credit Bureau (for clients who have a loan in our sample).

For every loan in our sample, there are as many rows as number of credits the client had in Credit Bureau before the application date.

Basic Info

In [32]:
bureau = pd.read_csv('./input/bureau.csv')
In [33]:
bureau.info(verbose=True, null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
SK_ID_CURR                1716428 non-null int64
SK_ID_BUREAU              1716428 non-null int64
CREDIT_ACTIVE             1716428 non-null object
CREDIT_CURRENCY           1716428 non-null object
DAYS_CREDIT               1716428 non-null int64
CREDIT_DAY_OVERDUE        1716428 non-null int64
DAYS_CREDIT_ENDDATE       1610875 non-null float64
DAYS_ENDDATE_FACT         1082775 non-null float64
AMT_CREDIT_MAX_OVERDUE    591940 non-null float64
CNT_CREDIT_PROLONG        1716428 non-null int64
AMT_CREDIT_SUM            1716415 non-null float64
AMT_CREDIT_SUM_DEBT       1458759 non-null float64
AMT_CREDIT_SUM_LIMIT      1124648 non-null float64
AMT_CREDIT_SUM_OVERDUE    1716428 non-null float64
CREDIT_TYPE               1716428 non-null object
DAYS_CREDIT_UPDATE        1716428 non-null int64
AMT_ANNUITY               489637 non-null float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
In [34]:
bureau.head()
Out[34]:
SK_ID_CURR SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY DAYS_CREDIT CREDIT_DAY_OVERDUE DAYS_CREDIT_ENDDATE DAYS_ENDDATE_FACT AMT_CREDIT_MAX_OVERDUE CNT_CREDIT_PROLONG AMT_CREDIT_SUM AMT_CREDIT_SUM_DEBT AMT_CREDIT_SUM_LIMIT AMT_CREDIT_SUM_OVERDUE CREDIT_TYPE DAYS_CREDIT_UPDATE AMT_ANNUITY
0 215354 5714462 Closed currency 1 -497 0 -153.0 -153.0 NaN 0 91323.0 0.0 NaN 0.0 Consumer credit -131 NaN
1 215354 5714463 Active currency 1 -208 0 1075.0 NaN NaN 0 225000.0 171342.0 NaN 0.0 Credit card -20 NaN
2 215354 5714464 Active currency 1 -203 0 528.0 NaN NaN 0 464323.5 NaN NaN 0.0 Consumer credit -16 NaN
3 215354 5714465 Active currency 1 -203 0 NaN NaN NaN 0 90000.0 NaN NaN 0.0 Credit card -16 NaN
4 215354 5714466 Active currency 1 -629 0 1197.0 NaN 77674.5 0 2700000.0 NaN NaN 0.0 Consumer credit -21 NaN

Missing Values

In [35]:
missingdata(bureau)
Out[35]:
Count Percent
AMT_ANNUITY 1226791 0.714735
AMT_CREDIT_MAX_OVERDUE 1124488 0.655133
DAYS_ENDDATE_FACT 633653 0.369170
AMT_CREDIT_SUM_LIMIT 591780 0.344774
AMT_CREDIT_SUM_DEBT 257669 0.150119
DAYS_CREDIT_ENDDATE 105553 0.061496

Visualizations

In [36]:
graph_objects(bureau)
In [37]:
sns.distplot(bureau.DAYS_CREDIT.dropna())
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x3582b155c0>
In [38]:
sns.distplot(bureau.DAYS_CREDIT_UPDATE.dropna())
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x3582153dd8>

Credit Card Balance

Monthly balance snapshots of previous credit cards that the applicant has with Home Credit.

This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample

(#loans in sample # of relative previous credit cards # of months where we have some history observable for the previous credit card) rows.

Basic Info

In [39]:
credit_card_balance = pd.read_csv('./input/credit_card_balance.csv')
In [40]:
credit_card_balance.info(verbose=True, null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
SK_ID_PREV                    3840312 non-null int64
SK_ID_CURR                    3840312 non-null int64
MONTHS_BALANCE                3840312 non-null int64
AMT_BALANCE                   3840312 non-null float64
AMT_CREDIT_LIMIT_ACTUAL       3840312 non-null int64
AMT_DRAWINGS_ATM_CURRENT      3090496 non-null float64
AMT_DRAWINGS_CURRENT          3840312 non-null float64
AMT_DRAWINGS_OTHER_CURRENT    3090496 non-null float64
AMT_DRAWINGS_POS_CURRENT      3090496 non-null float64
AMT_INST_MIN_REGULARITY       3535076 non-null float64
AMT_PAYMENT_CURRENT           3072324 non-null float64
AMT_PAYMENT_TOTAL_CURRENT     3840312 non-null float64
AMT_RECEIVABLE_PRINCIPAL      3840312 non-null float64
AMT_RECIVABLE                 3840312 non-null float64
AMT_TOTAL_RECEIVABLE          3840312 non-null float64
CNT_DRAWINGS_ATM_CURRENT      3090496 non-null float64
CNT_DRAWINGS_CURRENT          3840312 non-null int64
CNT_DRAWINGS_OTHER_CURRENT    3090496 non-null float64
CNT_DRAWINGS_POS_CURRENT      3090496 non-null float64
CNT_INSTALMENT_MATURE_CUM     3535076 non-null float64
NAME_CONTRACT_STATUS          3840312 non-null object
SK_DPD                        3840312 non-null int64
SK_DPD_DEF                    3840312 non-null int64
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB
In [41]:
credit_card_balance.head()
Out[41]:
SK_ID_PREV SK_ID_CURR MONTHS_BALANCE AMT_BALANCE AMT_CREDIT_LIMIT_ACTUAL AMT_DRAWINGS_ATM_CURRENT AMT_DRAWINGS_CURRENT AMT_DRAWINGS_OTHER_CURRENT AMT_DRAWINGS_POS_CURRENT AMT_INST_MIN_REGULARITY ... AMT_RECIVABLE AMT_TOTAL_RECEIVABLE CNT_DRAWINGS_ATM_CURRENT CNT_DRAWINGS_CURRENT CNT_DRAWINGS_OTHER_CURRENT CNT_DRAWINGS_POS_CURRENT CNT_INSTALMENT_MATURE_CUM NAME_CONTRACT_STATUS SK_DPD SK_DPD_DEF
0 2562384 378907 -6 56.970 135000 0.0 877.5 0.0 877.5 1700.325 ... 0.000 0.000 0.0 1 0.0 1.0 35.0 Active 0 0
1 2582071 363914 -1 63975.555 45000 2250.0 2250.0 0.0 0.0 2250.000 ... 64875.555 64875.555 1.0 1 0.0 0.0 69.0 Active 0 0
2 1740877 371185 -7 31815.225 450000 0.0 0.0 0.0 0.0 2250.000 ... 31460.085 31460.085 0.0 0 0.0 0.0 30.0 Active 0 0
3 1389973 337855 -4 236572.110 225000 2250.0 2250.0 0.0 0.0 11795.760 ... 233048.970 233048.970 1.0 1 0.0 0.0 10.0 Active 0 0
4 1891521 126868 -1 453919.455 450000 0.0 11547.0 0.0 11547.0 22924.890 ... 453919.455 453919.455 0.0 1 0.0 1.0 101.0 Active 0 0

5 rows × 23 columns

Missing Values

In [42]:
missingdata(credit_card_balance)
Out[42]:
Count Percent
AMT_PAYMENT_CURRENT 767988 0.199981
AMT_DRAWINGS_ATM_CURRENT 749816 0.195249
AMT_DRAWINGS_OTHER_CURRENT 749816 0.195249
AMT_DRAWINGS_POS_CURRENT 749816 0.195249
CNT_DRAWINGS_ATM_CURRENT 749816 0.195249
CNT_DRAWINGS_OTHER_CURRENT 749816 0.195249
CNT_DRAWINGS_POS_CURRENT 749816 0.195249
AMT_INST_MIN_REGULARITY 305236 0.079482
CNT_INSTALMENT_MATURE_CUM 305236 0.079482

Visualizations

In [43]:
graph_objects(credit_card_balance)
In [44]:
sns.distplot(credit_card_balance.MONTHS_BALANCE.dropna())
Out[44]:
<matplotlib.axes._subplots.AxesSubplot at 0x3582ac3d68>

Installments Payments

Repayment history for the previously disbursed credits in Home Credit related to the loans in our sample.

There is a) one row for every payment that was made plus b) one row each for missed payment.

One row is equivalent to one payment of one installment OR one installment corresponding to one payment of one previous Home Credit credit related to loans in our sample.

Basic Info

In [45]:
installments_payments = pd.read_csv('./input/installments_payments.csv')
In [46]:
installments_payments.info(verbose=True, null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
SK_ID_PREV                13605401 non-null int64
SK_ID_CURR                13605401 non-null int64
NUM_INSTALMENT_VERSION    13605401 non-null float64
NUM_INSTALMENT_NUMBER     13605401 non-null int64
DAYS_INSTALMENT           13605401 non-null float64
DAYS_ENTRY_PAYMENT        13602496 non-null float64
AMT_INSTALMENT            13605401 non-null float64
AMT_PAYMENT               13602496 non-null float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
In [47]:
installments_payments.head()
Out[47]:
SK_ID_PREV SK_ID_CURR NUM_INSTALMENT_VERSION NUM_INSTALMENT_NUMBER DAYS_INSTALMENT DAYS_ENTRY_PAYMENT AMT_INSTALMENT AMT_PAYMENT
0 1054186 161674 1.0 6 -1180.0 -1187.0 6948.360 6948.360
1 1330831 151639 0.0 34 -2156.0 -2156.0 1716.525 1716.525
2 2085231 193053 2.0 1 -63.0 -63.0 25425.000 25425.000
3 2452527 199697 1.0 3 -2418.0 -2426.0 24350.130 24350.130
4 2714724 167756 1.0 2 -1383.0 -1366.0 2165.040 2160.585

Missing Values

In [48]:
missingdata(installments_payments)

No missing data > 1%

Visualizations

In [49]:
sns.distplot(installments_payments.DAYS_INSTALMENT.dropna())
Out[49]:
<matplotlib.axes._subplots.AxesSubplot at 0x3583977390>
In [50]:
sns.distplot(installments_payments.DAYS_ENTRY_PAYMENT.dropna())
Out[50]:
<matplotlib.axes._subplots.AxesSubplot at 0x3582063b38>

POS CASH Balance

Monthly balance snapshots of previous POS (point of sales) and cash loans that the applicant had with Home Credit.

This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample

(#loans in sample # of relative previous credits # of months in which we have some history observable for the previous credits) rows.

Basic Info

In [51]:
POS_CASH_balance = pd.read_csv('./input/POS_CASH_balance.csv')
In [52]:
POS_CASH_balance.info(verbose=True, null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
SK_ID_PREV               10001358 non-null int64
SK_ID_CURR               10001358 non-null int64
MONTHS_BALANCE           10001358 non-null int64
CNT_INSTALMENT           9975287 non-null float64
CNT_INSTALMENT_FUTURE    9975271 non-null float64
NAME_CONTRACT_STATUS     10001358 non-null object
SK_DPD                   10001358 non-null int64
SK_DPD_DEF               10001358 non-null int64
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB
In [53]:
POS_CASH_balance.head()
Out[53]:
SK_ID_PREV SK_ID_CURR MONTHS_BALANCE CNT_INSTALMENT CNT_INSTALMENT_FUTURE NAME_CONTRACT_STATUS SK_DPD SK_DPD_DEF
0 1803195 182943 -31 48.0 45.0 Active 0 0
1 1715348 367990 -33 36.0 35.0 Active 0 0
2 1784872 397406 -32 12.0 9.0 Active 0 0
3 1903291 269225 -35 48.0 42.0 Active 0 0
4 2341044 334279 -35 36.0 35.0 Active 0 0

Missing Values

In [54]:
missingdata(POS_CASH_balance)

No missing data > 1%

Visualizations

In [55]:
graph_objects(POS_CASH_balance)
In [56]:
sns.distplot(POS_CASH_balance.MONTHS_BALANCE)
Out[56]:
<matplotlib.axes._subplots.AxesSubplot at 0x358388df28>
In [57]:
sns.distplot(POS_CASH_balance.CNT_INSTALMENT.dropna())
Out[57]:
<matplotlib.axes._subplots.AxesSubplot at 0x3583a2b0b8>
In [58]:
sns.distplot(POS_CASH_balance.CNT_INSTALMENT_FUTURE.dropna())
Out[58]:
<matplotlib.axes._subplots.AxesSubplot at 0x3583a98c88>

Previous Application

All previous applications for Home Credit loans of clients who have loans in our sample.

There is one row for each previous application related to loans in our data sample.

Basic Info

In [59]:
previous_application = pd.read_csv('./input/previous_application.csv')
In [60]:
previous_application.info(verbose=True, null_counts=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
SK_ID_PREV                     1670214 non-null int64
SK_ID_CURR                     1670214 non-null int64
NAME_CONTRACT_TYPE             1670214 non-null object
AMT_ANNUITY                    1297979 non-null float64
AMT_APPLICATION                1670214 non-null float64
AMT_CREDIT                     1670213 non-null float64
AMT_DOWN_PAYMENT               774370 non-null float64
AMT_GOODS_PRICE                1284699 non-null float64
WEEKDAY_APPR_PROCESS_START     1670214 non-null object
HOUR_APPR_PROCESS_START        1670214 non-null int64
FLAG_LAST_APPL_PER_CONTRACT    1670214 non-null object
NFLAG_LAST_APPL_IN_DAY         1670214 non-null int64
RATE_DOWN_PAYMENT              774370 non-null float64
RATE_INTEREST_PRIMARY          5951 non-null float64
RATE_INTEREST_PRIVILEGED       5951 non-null float64
NAME_CASH_LOAN_PURPOSE         1670214 non-null object
NAME_CONTRACT_STATUS           1670214 non-null object
DAYS_DECISION                  1670214 non-null int64
NAME_PAYMENT_TYPE              1670214 non-null object
CODE_REJECT_REASON             1670214 non-null object
NAME_TYPE_SUITE                849809 non-null object
NAME_CLIENT_TYPE               1670214 non-null object
NAME_GOODS_CATEGORY            1670214 non-null object
NAME_PORTFOLIO                 1670214 non-null object
NAME_PRODUCT_TYPE              1670214 non-null object
CHANNEL_TYPE                   1670214 non-null object
SELLERPLACE_AREA               1670214 non-null int64
NAME_SELLER_INDUSTRY           1670214 non-null object
CNT_PAYMENT                    1297984 non-null float64
NAME_YIELD_GROUP               1670214 non-null object
PRODUCT_COMBINATION            1669868 non-null object
DAYS_FIRST_DRAWING             997149 non-null float64
DAYS_FIRST_DUE                 997149 non-null float64
DAYS_LAST_DUE_1ST_VERSION      997149 non-null float64
DAYS_LAST_DUE                  997149 non-null float64
DAYS_TERMINATION               997149 non-null float64
NFLAG_INSURED_ON_APPROVAL      997149 non-null float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
In [61]:
previous_application.head()
Out[61]:
SK_ID_PREV SK_ID_CURR NAME_CONTRACT_TYPE AMT_ANNUITY AMT_APPLICATION AMT_CREDIT AMT_DOWN_PAYMENT AMT_GOODS_PRICE WEEKDAY_APPR_PROCESS_START HOUR_APPR_PROCESS_START ... NAME_SELLER_INDUSTRY CNT_PAYMENT NAME_YIELD_GROUP PRODUCT_COMBINATION DAYS_FIRST_DRAWING DAYS_FIRST_DUE DAYS_LAST_DUE_1ST_VERSION DAYS_LAST_DUE DAYS_TERMINATION NFLAG_INSURED_ON_APPROVAL
0 2030495 271877 Consumer loans 1730.430 17145.0 17145.0 0.0 17145.0 SATURDAY 15 ... Connectivity 12.0 middle POS mobile with interest 365243.0 -42.0 300.0 -42.0 -37.0 0.0
1 2802425 108129 Cash loans 25188.615 607500.0 679671.0 NaN 607500.0 THURSDAY 11 ... XNA 36.0 low_action Cash X-Sell: low 365243.0 -134.0 916.0 365243.0 365243.0 1.0
2 2523466 122040 Cash loans 15060.735 112500.0 136444.5 NaN 112500.0 TUESDAY 11 ... XNA 12.0 high Cash X-Sell: high 365243.0 -271.0 59.0 365243.0 365243.0 1.0
3 2819243 176158 Cash loans 47041.335 450000.0 470790.0 NaN 450000.0 MONDAY 7 ... XNA 12.0 middle Cash X-Sell: middle 365243.0 -482.0 -152.0 -182.0 -177.0 1.0
4 1784265 202054 Cash loans 31924.395 337500.0 404055.0 NaN 337500.0 THURSDAY 9 ... XNA 24.0 high Cash Street: high NaN NaN NaN NaN NaN NaN

5 rows × 37 columns

Missing Values

In [62]:
missingdata(previous_application)
Out[62]:
Count Percent
RATE_INTEREST_PRIMARY 1664263 0.996437
RATE_INTEREST_PRIVILEGED 1664263 0.996437
AMT_DOWN_PAYMENT 895844 0.536365
RATE_DOWN_PAYMENT 895844 0.536365
NAME_TYPE_SUITE 820405 0.491198
DAYS_FIRST_DRAWING 673065 0.402981
DAYS_FIRST_DUE 673065 0.402981
DAYS_LAST_DUE_1ST_VERSION 673065 0.402981
DAYS_LAST_DUE 673065 0.402981
DAYS_TERMINATION 673065 0.402981
NFLAG_INSURED_ON_APPROVAL 673065 0.402981
AMT_GOODS_PRICE 385515 0.230818
AMT_ANNUITY 372235 0.222867
CNT_PAYMENT 372230 0.222864

Visualizations

In [63]:
graph_objects(previous_application)
In [64]:
sns.distplot(previous_application.AMT_ANNUITY.dropna())
Out[64]:
<matplotlib.axes._subplots.AxesSubplot at 0x3584401358>
In [65]:
sns.distplot(previous_application.AMT_APPLICATION.dropna())
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x35a9c7b518>