home-credit-default-risk_필사.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Home Credit Default Risk 필사\n첫 번째 노트북: https://www.kaggle.com/artgor/eda-basic-fe-and-lgb  \n\nThis kernel will contain EDA, visualization, feature engineering and some modeling.","metadata":{}},{"cell_type":"markdown","source":"- **대회 목적**: 신청자가 대출금을 상환할 능력이 되는지 예측하기\n- **대회 설명**\n\n    Home Credit은 비은행 인구에 대한 금융 포함 범위를 넓히기 위해 노력한다. 해당 고객이 긍정적인 대출 경험을 겪도록 하기 위해 홈 크레딧은 통신 및 거래 정보를 포함한 다양한 데이터를 사용하여 고객의 상환 능력을 예측한다.\n\n    상환 능력이 있는 고객이 거절당하지 않도록 보장하고 그들의 고객이 성공할 수 있도록 원금, 만기, 상환 달력으로 대출을 받을 수 있도록 해줄 것이다.\n\n- **평가 방법:** ROC curve between the predicted probability and obs target","metadata":{}},{"cell_type":"markdown","source":"**Data Description**\n\n- application_{train|test}.csv\n\n    Main table. train/test를 위해 두 개의 파일로 분할.\n\n    모든 애플리케이션에 대한 정적 데이터. 한 행은 대출 하나를 나타냄\n\n- bureau.csv\n\n    신용국에 신고된 다른 금융기관에서 제공한 모든 고객의 이전 크레딧(샘플에 대출이 있는 고객의 경우)\n    샘플에 있는 모든 대출 건에 대해, 신청일 전에 고객이 크레딧뷰로에 가지고 있던 신용거래 한도만큼 행 존재\n\n- bureau_balance.csv\n\n    크레딧뷰로의 이전 크레딧 월별 잔액. 보고된 모든 이전 크레딧의 월별 기록 행. \n\n    즉, 표에는 (샘플의 #대여 * 이전 크레딧의 상대적인 월 수 * 이전 크레딧에 대해 관찰 가능한 기록이 있는 월 수) 행이 존재\n\n- POS_CASH_balance.csv\n\n    신청자가 홈 크레딧을 통해 가지고 있던 이전 POS(판매 시점) 및 현금 대출의 월별 잔액 스냅샷.\n    샘플의 대출과 관련된 홈 크레딧(소비자 신용 및 현금 대출)의 이전 신용 기록의 각 월마다 한 행이 있음. \n\n    즉, 표에는 (#샘플의 대출 * 상대적인 이전 신용 번호 * 이전 신용에 대해 관측할 수 있는 일부 기록이 있는 월 수) 행이 존재\n\n- credit_card_balance.csv\n\n    신청자가 홈 크레딧을 통해 가지고 있는 이전 신용카드의 월별 잔액 스냅샷.\n    샘플의 대출과 관련된 홈 크레딧(소비자 신용 및 현금 대출)의 이전 신용에 대한 각 월별 한 행이 있습니다. \n\n    즉, 표에는 (#샘플의 대출 * 이전 신용 카드 번호 * 이전 신용 카드에 대해 관측할 수 있는 일부 기록이 있는 월 수) 행이 존재\n\n- previous_application.csv\n\n    대출이 있는 고객의 홈 크레딧 대출에 대한 이전의 모든 신청서.\n    데이터 샘플에는 대출과 관련된 이전 애플리케이션마다 한 행이 존재\n\n- installments_payments.csv (할부)\n\n    당사의 샘플에 있는 대출과 관련하여 홈 크레딧에서 이전에 지출된 신용에 대한 상환 이력.\n    결제가 이루어질 때마다 한 행이 존재.\n    한 행은 1회의 할부금 또는 1회의 할부금 또는 샘플의 대출과 관련된 이전의 홈 크레딧 1회의 할부와 같음\n\n- HomeCredit_columns_description.csv\n\n    데이터 파일의 칼럼에 대한 설명","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-07-02T07:06:09.453219Z","iopub.execute_input":"2021-07-02T07:06:09.453795Z","iopub.status.idle":"2021-07-02T07:06:09.467686Z","shell.execute_reply.started":"2021-07-02T07:06:09.453709Z","shell.execute_reply":"2021-07-02T07:06:09.466585Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/home-credit-default-risk/sample_submission.csv\n/kaggle/input/home-credit-default-risk/bureau_balance.csv\n/kaggle/input/home-credit-default-risk/POS_CASH_balance.csv\n/kaggle/input/home-credit-default-risk/application_train.csv\n/kaggle/input/home-credit-default-risk/HomeCredit_columns_description.csv\n/kaggle/input/home-credit-default-risk/application_test.csv\n/kaggle/input/home-credit-default-risk/previous_application.csv\n/kaggle/input/home-credit-default-risk/credit_card_balance.csv\n/kaggle/input/home-credit-default-risk/installments_payments.csv\n/kaggle/input/home-credit-default-risk/bureau.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nimport datetime\nimport lightgbm as lgb\nfrom scipy import stats\nfrom scipy.sparse import hstack, csr_matrix\nfrom sklearn.model_selection import train_test_split, StratifiedKFold\nfrom wordcloud import WordCloud\nfrom collections import Counter\nfrom nltk.corpus import stopwords\nfrom nltk.util import ngrams\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import LabelEncoder\nle = LabelEncoder()\nimport os\nimport xgboost as xgb\nimport lightgbm as lgb\n\nimport os\nimport matplotlib.pyplot as plt\n%matplotlib inline\npd.set_option('max_columns', 150)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:06:09.469231Z","iopub.execute_input":"2021-07-02T07:06:09.469548Z","iopub.status.idle":"2021-07-02T07:06:13.014573Z","shell.execute_reply.started":"2021-07-02T07:06:09.469517Z","shell.execute_reply":"2021-07-02T07:06:13.013743Z"},"trusted":true},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style type='text/css'>\n.datatable table.frame { margin-bottom: 0; }\n.datatable table.frame thead { border-bottom: none; }\n.datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n.datatable .bool    { background: #DDDD99; }\n.datatable .object  { background: #565656; }\n.datatable .int     { background: #5D9E5D; }\n.datatable .float   { background: #4040CC; }\n.datatable .str     { background: #CC4040; }\n.datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n.datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n.datatable th:nth-child(2) { padding-left: 12px; }\n.datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n.datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n.datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n.datatable .footer { font-size: 9px; }\n.datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n</style>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"There are several files with data, lets go through them step by step.","metadata":{}},{"cell_type":"code","source":"folder = '/kaggle/input/home-credit-default-risk/'\napplication_train = pd.read_csv(os.path.join(folder, 'application_train.csv'))\napplication_test = pd.read_csv(os.path.join(folder, 'application_test.csv'))\nbureau = pd.read_csv(os.path.join(folder, 'bureau.csv'))\nbureau_balance = pd.read_csv(os.path.join(folder, 'bureau_balance.csv'))\nPOS_CASH_balance = pd.read_csv(os.path.join(folder, 'POS_CASH_balance.csv'))\ncredit_card_balance = pd.read_csv(os.path.join(folder, 'credit_card_balance.csv'))\nprevious_application = pd.read_csv(os.path.join(folder, 'previous_application.csv'))\ninstallments_payments = pd.read_csv(os.path.join(folder, 'installments_payments.csv'))\nsample_submission = pd.read_csv(os.path.join(folder, 'sample_submission.csv'))","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:06:13.016143Z","iopub.execute_input":"2021-07-02T07:06:13.016627Z","iopub.status.idle":"2021-07-02T07:07:39.951140Z","shell.execute_reply.started":"2021-07-02T07:06:13.016585Z","shell.execute_reply":"2021-07-02T07:07:39.949324Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"## Data Exploration\n\n**application_train and application_test**  \nThese are main files with data and technically we can use only them to make predictions. Obviously using additional data is necessary to improve score.","metadata":{}},{"cell_type":"code","source":"application_train.head()","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:39.953816Z","iopub.execute_input":"2021-07-02T07:07:39.954171Z","iopub.status.idle":"2021-07-02T07:07:40.108442Z","shell.execute_reply.started":"2021-07-02T07:07:39.954131Z","shell.execute_reply":"2021-07-02T07:07:40.107710Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n0      100002       1         Cash loans           M            N   \n1      100003       0         Cash loans           F            N   \n2      100004       0    Revolving loans           M            Y   \n3      100006       0         Cash loans           F            N   \n4      100007       0         Cash loans           M            N   \n\n  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n0               Y             0          202500.0    406597.5      24700.5   \n1               N             0          270000.0   1293502.5      35698.5   \n2               Y             0           67500.0    135000.0       6750.0   \n3               Y             0          135000.0    312682.5      29686.5   \n4               Y             0          121500.0    513000.0      21865.5   \n\n   AMT_GOODS_PRICE NAME_TYPE_SUITE NAME_INCOME_TYPE  \\\n0         351000.0   Unaccompanied          Working   \n1        1129500.0          Family    State servant   \n2         135000.0   Unaccompanied          Working   \n3         297000.0   Unaccompanied          Working   \n4         513000.0   Unaccompanied          Working   \n\n             NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  \\\n0  Secondary / secondary special  Single / not married  House / apartment   \n1               Higher education               Married  House / apartment   \n2  Secondary / secondary special  Single / not married  House / apartment   \n3  Secondary / secondary special        Civil marriage  House / apartment   \n4  Secondary / secondary special  Single / not married  House / apartment   \n\n   REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  \\\n0                    0.018801       -9461           -637            -3648.0   \n1                    0.003541      -16765          -1188            -1186.0   \n2                    0.010032      -19046           -225            -4260.0   \n3                    0.008019      -19005          -3039            -9833.0   \n4                    0.028663      -19932          -3038            -4311.0   \n\n   DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  \\\n0            -2120          NaN           1               1                0   \n1             -291          NaN           1               1                0   \n2            -2531         26.0           1               1                1   \n3            -2437          NaN           1               1                0   \n4            -3458          NaN           1               1                0   \n\n   FLAG_CONT_MOBILE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  \\\n0                 1           1           0        Laborers              1.0   \n1                 1           1           0      Core staff              2.0   \n2                 1           1           0        Laborers              1.0   \n3                 1           0           0        Laborers              2.0   \n4                 1           0           0      Core staff              1.0   \n\n   REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  \\\n0                     2                            2   \n1                     1                            1   \n2                     2                            2   \n3                     2                            2   \n4                     2                            2   \n\n  WEEKDAY_APPR_PROCESS_START  HOUR_APPR_PROCESS_START  \\\n0                  WEDNESDAY                       10   \n1                     MONDAY                       11   \n2                     MONDAY                        9   \n3                  WEDNESDAY                       17   \n4                   THURSDAY                       11   \n\n   REG_REGION_NOT_LIVE_REGION  REG_REGION_NOT_WORK_REGION  \\\n0                           0                           0   \n1                           0                           0   \n2                           0                           0   \n3                           0                           0   \n4                           0                           0   \n\n   LIVE_REGION_NOT_WORK_REGION  REG_CITY_NOT_LIVE_CITY  \\\n0                            0                       0   \n1                            0                       0   \n2                            0                       0   \n3                            0                       0   \n4                            0                       0   \n\n   REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY       ORGANIZATION_TYPE  \\\n0                       0                        0  Business Entity Type 3   \n1                       0                        0                  School   \n2                       0                        0              Government   \n3                       0                        0  Business Entity Type 3   \n4                       1                        1                Religion   \n\n   EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  APARTMENTS_AVG  BASEMENTAREA_AVG  \\\n0      0.083037      0.262949      0.139376          0.0247            0.0369   \n1      0.311267      0.622246           NaN          0.0959            0.0529   \n2           NaN      0.555912      0.729567             NaN               NaN   \n3           NaN      0.650442           NaN             NaN               NaN   \n4           NaN      0.322738           NaN             NaN               NaN   \n\n   YEARS_BEGINEXPLUATATION_AVG  YEARS_BUILD_AVG  COMMONAREA_AVG  \\\n0                       0.9722           0.6192          0.0143   \n1                       0.9851           0.7960          0.0605   \n2                          NaN              NaN             NaN   \n3                          NaN              NaN             NaN   \n4                          NaN              NaN             NaN   \n\n   ELEVATORS_AVG  ENTRANCES_AVG  FLOORSMAX_AVG  FLOORSMIN_AVG  LANDAREA_AVG  \\\n0           0.00         0.0690         0.0833         0.1250        0.0369   \n1           0.08         0.0345         0.2917         0.3333        0.0130   \n2            NaN            NaN            NaN            NaN           NaN   \n3            NaN            NaN            NaN            NaN           NaN   \n4            NaN            NaN            NaN            NaN           NaN   \n\n   LIVINGAPARTMENTS_AVG  LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  \\\n0                0.0202          0.0190                   0.0000   \n1                0.0773          0.0549                   0.0039   \n2                   NaN             NaN                      NaN   \n3                   NaN             NaN                      NaN   \n4                   NaN             NaN                      NaN   \n\n   NONLIVINGAREA_AVG  APARTMENTS_MODE  BASEMENTAREA_MODE  \\\n0             0.0000           0.0252             0.0383   \n1             0.0098           0.0924             0.0538   \n2                NaN              NaN                NaN   \n3                NaN              NaN                NaN   \n4                NaN              NaN                NaN   \n\n   YEARS_BEGINEXPLUATATION_MODE  YEARS_BUILD_MODE  COMMONAREA_MODE  \\\n0                        0.9722            0.6341           0.0144   \n1                        0.9851            0.8040           0.0497   \n2                           NaN               NaN              NaN   \n3                           NaN               NaN              NaN   \n4                           NaN               NaN              NaN   \n\n   ELEVATORS_MODE  ENTRANCES_MODE  FLOORSMAX_MODE  FLOORSMIN_MODE  \\\n0          0.0000          0.0690          0.0833          0.1250   \n1          0.0806          0.0345          0.2917          0.3333   \n2             NaN             NaN             NaN             NaN   \n3             NaN             NaN             NaN             NaN   \n4             NaN             NaN             NaN             NaN   \n\n   LANDAREA_MODE  LIVINGAPARTMENTS_MODE  LIVINGAREA_MODE  \\\n0         0.0377                  0.022           0.0198   \n1         0.0128                  0.079           0.0554   \n2            NaN                    NaN              NaN   \n3            NaN                    NaN              NaN   \n4            NaN                    NaN              NaN   \n\n   NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  APARTMENTS_MEDI  \\\n0                       0.0                 0.0           0.0250   \n1                       0.0                 0.0           0.0968   \n2                       NaN                 NaN              NaN   \n3                       NaN                 NaN              NaN   \n4                       NaN                 NaN              NaN   \n\n   BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  YEARS_BUILD_MEDI  \\\n0             0.0369                        0.9722            0.6243   \n1             0.0529                        0.9851            0.7987   \n2                NaN                           NaN               NaN   \n3                NaN                           NaN               NaN   \n4                NaN                           NaN               NaN   \n\n   COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  FLOORSMAX_MEDI  \\\n0           0.0144            0.00          0.0690          0.0833   \n1           0.0608            0.08          0.0345          0.2917   \n2              NaN             NaN             NaN             NaN   \n3              NaN             NaN             NaN             NaN   \n4              NaN             NaN             NaN             NaN   \n\n   FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  LIVINGAREA_MEDI  \\\n0          0.1250         0.0375                 0.0205           0.0193   \n1          0.3333         0.0132                 0.0787           0.0558   \n2             NaN            NaN                    NaN              NaN   \n3             NaN            NaN                    NaN              NaN   \n4             NaN            NaN                    NaN              NaN   \n\n   NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI FONDKAPREMONT_MODE  \\\n0                    0.0000                0.00   reg oper account   \n1                    0.0039                0.01   reg oper account   \n2                       NaN                 NaN                NaN   \n3                       NaN                 NaN                NaN   \n4                       NaN                 NaN                NaN   \n\n   HOUSETYPE_MODE  TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  \\\n0  block of flats          0.0149       Stone, brick                  No   \n1  block of flats          0.0714              Block                  No   \n2             NaN             NaN                NaN                 NaN   \n3             NaN             NaN                NaN                 NaN   \n4             NaN             NaN                NaN                 NaN   \n\n   OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  \\\n0                       2.0                       2.0   \n1                       1.0                       0.0   \n2                       0.0                       0.0   \n3                       2.0                       0.0   \n4                       0.0                       0.0   \n\n   OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  \\\n0                       2.0                       2.0                 -1134.0   \n1                       1.0                       0.0                  -828.0   \n2                       0.0                       0.0                  -815.0   \n3                       2.0                       0.0                  -617.0   \n4                       0.0                       0.0                 -1106.0   \n\n   FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  \\\n0                0                1                0                0   \n1                0                1                0                0   \n2                0                0                0                0   \n3                0                1                0                0   \n4                0                0                0                0   \n\n   FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  \\\n0                0                0                0                0   \n1                0                0                0                0   \n2                0                0                0                0   \n3                0                0                0                0   \n4                0                0                1                0   \n\n   FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n0                         0.0                        0.0   \n1                         0.0                        0.0   \n2                         0.0                        0.0   \n3                         NaN                        NaN   \n4                         0.0                        0.0   \n\n   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n0                         0.0                        0.0   \n1                         0.0                        0.0   \n2                         0.0                        0.0   \n3                         NaN                        NaN   \n4                         0.0                        0.0   \n\n   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n0                        0.0                         1.0  \n1                        0.0                         0.0  \n2                        0.0                         0.0  \n3                        NaN                         NaN  \n4                        0.0                         0.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SK_ID_CURR</th>\n      <th>TARGET</th>\n      <th>NAME_CONTRACT_TYPE</th>\n      <th>CODE_GENDER</th>\n      <th>FLAG_OWN_CAR</th>\n      <th>FLAG_OWN_REALTY</th>\n      <th>CNT_CHILDREN</th>\n      <th>AMT_INCOME_TOTAL</th>\n      <th>AMT_CREDIT</th>\n      <th>AMT_ANNUITY</th>\n      <th>AMT_GOODS_PRICE</th>\n      <th>NAME_TYPE_SUITE</th>\n      <th>NAME_INCOME_TYPE</th>\n      <th>NAME_EDUCATION_TYPE</th>\n      <th>NAME_FAMILY_STATUS</th>\n      <th>NAME_HOUSING_TYPE</th>\n      <th>REGION_POPULATION_RELATIVE</th>\n      <th>DAYS_BIRTH</th>\n      <th>DAYS_EMPLOYED</th>\n      <th>DAYS_REGISTRATION</th>\n      <th>DAYS_ID_PUBLISH</th>\n      <th>OWN_CAR_AGE</th>\n      <th>FLAG_MOBIL</th>\n      <th>FLAG_EMP_PHONE</th>\n      <th>FLAG_WORK_PHONE</th>\n      <th>FLAG_CONT_MOBILE</th>\n      <th>FLAG_PHONE</th>\n      <th>FLAG_EMAIL</th>\n      <th>OCCUPATION_TYPE</th>\n      <th>CNT_FAM_MEMBERS</th>\n      <th>REGION_RATING_CLIENT</th>\n      <th>REGION_RATING_CLIENT_W_CITY</th>\n      <th>WEEKDAY_APPR_PROCESS_START</th>\n      <th>HOUR_APPR_PROCESS_START</th>\n      <th>REG_REGION_NOT_LIVE_REGION</th>\n      <th>REG_REGION_NOT_WORK_REGION</th>\n      <th>LIVE_REGION_NOT_WORK_REGION</th>\n      <th>REG_CITY_NOT_LIVE_CITY</th>\n      <th>REG_CITY_NOT_WORK_CITY</th>\n      <th>LIVE_CITY_NOT_WORK_CITY</th>\n      <th>ORGANIZATION_TYPE</th>\n      <th>EXT_SOURCE_1</th>\n      <th>EXT_SOURCE_2</th>\n      <th>EXT_SOURCE_3</th>\n      <th>APARTMENTS_AVG</th>\n      <th>BASEMENTAREA_AVG</th>\n      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n      <th>YEARS_BUILD_AVG</th>\n      <th>COMMONAREA_AVG</th>\n      <th>ELEVATORS_AVG</th>\n      <th>ENTRANCES_AVG</th>\n      <th>FLOORSMAX_AVG</th>\n      <th>FLOORSMIN_AVG</th>\n      <th>LANDAREA_AVG</th>\n      <th>LIVINGAPARTMENTS_AVG</th>\n      <th>LIVINGAREA_AVG</th>\n      <th>NONLIVINGAPARTMENTS_AVG</th>\n      <th>NONLIVINGAREA_AVG</th>\n      <th>APARTMENTS_MODE</th>\n      <th>BASEMENTAREA_MODE</th>\n      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n      <th>YEARS_BUILD_MODE</th>\n      <th>COMMONAREA_MODE</th>\n      <th>ELEVATORS_MODE</th>\n      <th>ENTRANCES_MODE</th>\n      <th>FLOORSMAX_MODE</th>\n      <th>FLOORSMIN_MODE</th>\n      <th>LANDAREA_MODE</th>\n      <th>LIVINGAPARTMENTS_MODE</th>\n      <th>LIVINGAREA_MODE</th>\n      <th>NONLIVINGAPARTMENTS_MODE</th>\n      <th>NONLIVINGAREA_MODE</th>\n      <th>APARTMENTS_MEDI</th>\n      <th>BASEMENTAREA_MEDI</th>\n      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n      <th>YEARS_BUILD_MEDI</th>\n      <th>COMMONAREA_MEDI</th>\n      <th>ELEVATORS_MEDI</th>\n      <th>ENTRANCES_MEDI</th>\n      <th>FLOORSMAX_MEDI</th>\n      <th>FLOORSMIN_MEDI</th>\n      <th>LANDAREA_MEDI</th>\n      <th>LIVINGAPARTMENTS_MEDI</th>\n      <th>LIVINGAREA_MEDI</th>\n      <th>NONLIVINGAPARTMENTS_MEDI</th>\n      <th>NONLIVINGAREA_MEDI</th>\n      <th>FONDKAPREMONT_MODE</th>\n      <th>HOUSETYPE_MODE</th>\n      <th>TOTALAREA_MODE</th>\n      <th>WALLSMATERIAL_MODE</th>\n      <th>EMERGENCYSTATE_MODE</th>\n      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n      <th>DAYS_LAST_PHONE_CHANGE</th>\n      <th>FLAG_DOCUMENT_2</th>\n      <th>FLAG_DOCUMENT_3</th>\n      <th>FLAG_DOCUMENT_4</th>\n      <th>FLAG_DOCUMENT_5</th>\n      <th>FLAG_DOCUMENT_6</th>\n      <th>FLAG_DOCUMENT_7</th>\n      <th>FLAG_DOCUMENT_8</th>\n      <th>FLAG_DOCUMENT_9</th>\n      <th>FLAG_DOCUMENT_10</th>\n      <th>FLAG_DOCUMENT_11</th>\n      <th>FLAG_DOCUMENT_12</th>\n      <th>FLAG_DOCUMENT_13</th>\n      <th>FLAG_DOCUMENT_14</th>\n      <th>FLAG_DOCUMENT_15</th>\n      <th>FLAG_DOCUMENT_16</th>\n      <th>FLAG_DOCUMENT_17</th>\n      <th>FLAG_DOCUMENT_18</th>\n      <th>FLAG_DOCUMENT_19</th>\n      <th>FLAG_DOCUMENT_20</th>\n      <th>FLAG_DOCUMENT_21</th>\n      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100002</td>\n      <td>1</td>\n      <td>Cash loans</td>\n      <td>M</td>\n      <td>N</td>\n      <td>Y</td>\n      <td>0</td>\n      <td>202500.0</td>\n      <td>406597.5</td>\n      <td>24700.5</td>\n      <td>351000.0</td>\n      <td>Unaccompanied</td>\n      <td>Working</td>\n      <td>Secondary / secondary special</td>\n      <td>Single / not married</td>\n      <td>House / apartment</td>\n      <td>0.018801</td>\n      <td>-9461</td>\n      <td>-637</td>\n      <td>-3648.0</td>\n      <td>-2120</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Laborers</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>WEDNESDAY</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Business Entity Type 3</td>\n      <td>0.083037</td>\n      <td>0.262949</td>\n      <td>0.139376</td>\n      <td>0.0247</td>\n      <td>0.0369</td>\n      <td>0.9722</td>\n      <td>0.6192</td>\n      <td>0.0143</td>\n      <td>0.00</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0369</td>\n      <td>0.0202</td>\n      <td>0.0190</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0252</td>\n      <td>0.0383</td>\n      <td>0.9722</td>\n      <td>0.6341</td>\n      <td>0.0144</td>\n      <td>0.0000</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0377</td>\n      <td>0.022</td>\n      <td>0.0198</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0250</td>\n      <td>0.0369</td>\n      <td>0.9722</td>\n      <td>0.6243</td>\n      <td>0.0144</td>\n      <td>0.00</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0375</td>\n      <td>0.0205</td>\n      <td>0.0193</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>reg oper account</td>\n      <td>block of flats</td>\n      <td>0.0149</td>\n      <td>Stone, brick</td>\n      <td>No</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>-1134.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100003</td>\n      <td>0</td>\n      <td>Cash loans</td>\n      <td>F</td>\n      <td>N</td>\n      <td>N</td>\n      <td>0</td>\n      <td>270000.0</td>\n      <td>1293502.5</td>\n      <td>35698.5</td>\n      <td>1129500.0</td>\n      <td>Family</td>\n      <td>State servant</td>\n      <td>Higher education</td>\n      <td>Married</td>\n      <td>House / apartment</td>\n      <td>0.003541</td>\n      <td>-16765</td>\n      <td>-1188</td>\n      <td>-1186.0</td>\n      <td>-291</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Core staff</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>MONDAY</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>School</td>\n      <td>0.311267</td>\n      <td>0.622246</td>\n      <td>NaN</td>\n      <td>0.0959</td>\n      <td>0.0529</td>\n      <td>0.9851</td>\n      <td>0.7960</td>\n      <td>0.0605</td>\n      <td>0.08</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0130</td>\n      <td>0.0773</td>\n      <td>0.0549</td>\n      <td>0.0039</td>\n      <td>0.0098</td>\n      <td>0.0924</td>\n      <td>0.0538</td>\n      <td>0.9851</td>\n      <td>0.8040</td>\n      <td>0.0497</td>\n      <td>0.0806</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0128</td>\n      <td>0.079</td>\n      <td>0.0554</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0968</td>\n      <td>0.0529</td>\n      <td>0.9851</td>\n      <td>0.7987</td>\n      <td>0.0608</td>\n      <td>0.08</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0132</td>\n      <td>0.0787</td>\n      <td>0.0558</td>\n      <td>0.0039</td>\n      <td>0.01</td>\n      <td>reg oper account</td>\n      <td>block of flats</td>\n      <td>0.0714</td>\n      <td>Block</td>\n      <td>No</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>-828.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100004</td>\n      <td>0</td>\n      <td>Revolving loans</td>\n      <td>M</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>0</td>\n      <td>67500.0</td>\n      <td>135000.0</td>\n      <td>6750.0</td>\n      <td>135000.0</td>\n      <td>Unaccompanied</td>\n      <td>Working</td>\n      <td>Secondary / secondary special</td>\n      <td>Single / not married</td>\n      <td>House / apartment</td>\n      <td>0.010032</td>\n      <td>-19046</td>\n      <td>-225</td>\n      <td>-4260.0</td>\n      <td>-2531</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Laborers</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>MONDAY</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Government</td>\n      <td>NaN</td>\n      <td>0.555912</td>\n      <td>0.729567</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>-815.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100006</td>\n      <td>0</td>\n      <td>Cash loans</td>\n      <td>F</td>\n      <td>N</td>\n      <td>Y</td>\n      <td>0</td>\n      <td>135000.0</td>\n      <td>312682.5</td>\n      <td>29686.5</td>\n      <td>297000.0</td>\n      <td>Unaccompanied</td>\n      <td>Working</td>\n      <td>Secondary / secondary special</td>\n      <td>Civil marriage</td>\n      <td>House / apartment</td>\n      <td>0.008019</td>\n      <td>-19005</td>\n      <td>-3039</td>\n      <td>-9833.0</td>\n      <td>-2437</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Laborers</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>WEDNESDAY</td>\n      <td>17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Business Entity Type 3</td>\n      <td>NaN</td>\n      <td>0.650442</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>-617.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100007</td>\n      <td>0</td>\n      <td>Cash loans</td>\n      <td>M</td>\n      <td>N</td>\n      <td>Y</td>\n      <td>0</td>\n      <td>121500.0</td>\n      <td>513000.0</td>\n      <td>21865.5</td>\n      <td>513000.0</td>\n      <td>Unaccompanied</td>\n      <td>Working</td>\n      <td>Secondary / secondary special</td>\n      <td>Single / not married</td>\n      <td>House / apartment</td>\n      <td>0.028663</td>\n      <td>-19932</td>\n      <td>-3038</td>\n      <td>-4311.0</td>\n      <td>-3458</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Core staff</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>THURSDAY</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Religion</td>\n      <td>NaN</td>\n      <td>0.322738</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>-1106.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We have 122 col in just main file! Let's take a look on some of them.","metadata":{}},{"cell_type":"markdown","source":"### Categorical features\n**target**  \n1 - 지급에 어려움이 있는 고객: 저희 샘플에서 대출의 첫 Y 할부 중 적어도 하나에 대해 X일 이상 연체된 고객  \n0 - other cases\n\n`value_counts(normalize=True)`: 비율 확인(sum으로 나눠준 결과)","metadata":{}},{"cell_type":"code","source":"application_train.TARGET.value_counts(normalize=True)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:40.109730Z","iopub.execute_input":"2021-07-02T07:07:40.110024Z","iopub.status.idle":"2021-07-02T07:07:40.134403Z","shell.execute_reply.started":"2021-07-02T07:07:40.109986Z","shell.execute_reply":"2021-07-02T07:07:40.133319Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"0    0.919271\n1    0.080729\nName: TARGET, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"We have disbalanced target, though disbalnce isn't really serious.","metadata":{}},{"cell_type":"markdown","source":"**NAME_CONTRACT_TYPE**  \nIdentification if loan is cash or revolving(대출이 현금인지 revolving인지 식별)  \n`pd.crosstab()`: 교차표 만들기","metadata":{}},{"cell_type":"code","source":"pd.crosstab(application_train.TARGET, application_train.NAME_CONTRACT_TYPE, dropna=False, normalize='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:40.136123Z","iopub.execute_input":"2021-07-02T07:07:40.136766Z","iopub.status.idle":"2021-07-02T07:07:40.254782Z","shell.execute_reply.started":"2021-07-02T07:07:40.136719Z","shell.execute_reply":"2021-07-02T07:07:40.253765Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"NAME_CONTRACT_TYPE  Cash loans  Revolving loans\nTARGET                                         \n0                     0.829274         0.089997\n1                     0.075513         0.005216","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>NAME_CONTRACT_TYPE</th>\n      <th>Cash loans</th>\n      <th>Revolving loans</th>\n    </tr>\n    <tr>\n      <th>TARGET</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.829274</td>\n      <td>0.089997</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.075513</td>\n      <td>0.005216</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that there are two types of contract - cash loans and revolving loans. Most of the loands are cash loans which are defaulted.","metadata":{}},{"cell_type":"markdown","source":"**CODE_GENDER**","metadata":{}},{"cell_type":"code","source":"pd.crosstab(application_train.TARGET, application_train.CODE_GENDER, dropna = False)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:40.257498Z","iopub.execute_input":"2021-07-02T07:07:40.257869Z","iopub.status.idle":"2021-07-02T07:07:40.334994Z","shell.execute_reply.started":"2021-07-02T07:07:40.257838Z","shell.execute_reply":"2021-07-02T07:07:40.333874Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"CODE_GENDER       F      M  XNA\nTARGET                         \n0            188278  94404    4\n1             14170  10655    0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>CODE_GENDER</th>\n      <th>F</th>\n      <th>M</th>\n      <th>XNA</th>\n    </tr>\n    <tr>\n      <th>TARGET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>188278</td>\n      <td>94404</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>14170</td>\n      <td>10655</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that women take more loans and higher percentage of them repays the loans. And there are 4 people with unidentified gender, who repayed their loans :)","metadata":{}},{"cell_type":"markdown","source":"**FLG_OWN_CAR and FLAG_OWN_REALTY**  \nflag_own_realty: flat이나 house 보유","metadata":{}},{"cell_type":"code","source":"print('There are {0} people with realty. {1}% of them repay loans.'.format(application_train[application_train.FLAG_OWN_REALTY == 'Y'].shape[0],\n                                                                    np.round(application_train[application_train.FLAG_OWN_REALTY =='Y'].TARGET.\n                                                                            value_counts(normalize=True).values[1], 3) * 100))\n\nprint('There are {0} people with cars. {1}% of them repay loans.'.format(application_train[application_train.FLAG_OWN_CAR == 'Y'].shape[0], \n                                                                    np.round(application_train[application_train.FLAG_OWN_CAR == 'Y'].TARGET.\n                                                                             value_counts(normalize=True).values[1], 4) * 100))\n\nprint('Average age of the car is {:.2f} years.'.format(application_train.groupby(['FLAG_OWN_CAR'])['OWN_CAR_AGE'].mean().values[1]))","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:40.337225Z","iopub.execute_input":"2021-07-02T07:07:40.337741Z","iopub.status.idle":"2021-07-02T07:07:40.943679Z","shell.execute_reply.started":"2021-07-02T07:07:40.337701Z","shell.execute_reply":"2021-07-02T07:07:40.942417Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"There are 213312 people with realty. 8.0% of them repay loans.\nThere are 104587 people with cars. 7.24% of them repay loans.\nAverage age of the car is 12.06 years.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**CNT_CHILDEREN and NAME_FAMILY_STATUS**","metadata":{}},{"cell_type":"code","source":"pd.crosstab(application_train.CNT_CHILDREN, application_train.NAME_FAMILY_STATUS, dropna = False)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:40.945188Z","iopub.execute_input":"2021-07-02T07:07:40.945605Z","iopub.status.idle":"2021-07-02T07:07:41.024709Z","shell.execute_reply.started":"2021-07-02T07:07:40.945557Z","shell.execute_reply":"2021-07-02T07:07:41.023499Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"NAME_FAMILY_STATUS  Civil marriage  Married  Separated  Single / not married  \\\nCNT_CHILDREN                                                                   \n0                            20947   126575      14132                 38810   \n1                             6588    43696       4389                  5578   \n2                             1936    22496       1111                   958   \n3                              262     3190        122                    85   \n4                               33      370         12                     9   \n5                                5       74          2                     1   \n6                                2       17          1                     0   \n7                                1        5          0                     0   \n8                                0        2          0                     0   \n9                                1        0          0                     1   \n10                               0        2          0                     0   \n11                               0        1          0                     0   \n12                               0        2          0                     0   \n14                               0        2          1                     0   \n19                               0        0          0                     2   \n\nNAME_FAMILY_STATUS  Unknown  Widow  \nCNT_CHILDREN                        \n0                         2  14905  \n1                         0    868  \n2                         0    248  \n3                         0     58  \n4                         0      5  \n5                         0      2  \n6                         0      1  \n7                         0      1  \n8                         0      0  \n9                         0      0  \n10                        0      0  \n11                        0      0  \n12                        0      0  \n14                        0      0  \n19                        0      0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>NAME_FAMILY_STATUS</th>\n      <th>Civil marriage</th>\n      <th>Married</th>\n      <th>Separated</th>\n      <th>Single / not married</th>\n      <th>Unknown</th>\n      <th>Widow</th>\n    </tr>\n    <tr>\n      <th>CNT_CHILDREN</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20947</td>\n      <td>126575</td>\n      <td>14132</td>\n      <td>38810</td>\n      <td>2</td>\n      <td>14905</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6588</td>\n      <td>43696</td>\n      <td>4389</td>\n      <td>5578</td>\n      <td>0</td>\n      <td>868</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1936</td>\n      <td>22496</td>\n      <td>1111</td>\n      <td>958</td>\n      <td>0</td>\n      <td>248</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>262</td>\n      <td>3190</td>\n      <td>122</td>\n      <td>85</td>\n      <td>0</td>\n      <td>58</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>370</td>\n      <td>12</td>\n      <td>9</td>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>74</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2</td>\n      <td>17</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that most of the people are married and have zero children. In face we can divide people into two group based on their family status - living together with their partner of single.  \n\n**CNT_FAM_MAMBERS**: How many family members does client have","metadata":{}},{"cell_type":"code","source":"pd.crosstab(application_train.CNT_CHILDREN, application_train.CNT_FAM_MEMBERS, dropna=False)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.026532Z","iopub.execute_input":"2021-07-02T07:07:41.027041Z","iopub.status.idle":"2021-07-02T07:07:41.095391Z","shell.execute_reply.started":"2021-07-02T07:07:41.026991Z","shell.execute_reply":"2021-07-02T07:07:41.094321Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"CNT_FAM_MEMBERS   1.0     2.0    3.0    4.0   5.0   6.0   7.0   8.0   9.0   \\\nCNT_CHILDREN                                                                 \n0                67847  147522      0      0     0     0     0     0     0   \n1                    0   10835  50284      0     0     0     0     0     0   \n2                    0       0   2317  24432     0     0     0     0     0   \n3                    0       0      0    265  3452     0     0     0     0   \n4                    0       0      0      0    26   403     0     0     0   \n5                    0       0      0      0     0     5    79     0     0   \n6                    0       0      0      0     0     0     2    19     0   \n7                    0       0      0      0     0     0     0     1     6   \n8                    0       0      0      0     0     0     0     0     0   \n9                    0       0      0      0     0     0     0     0     0   \n10                   0       0      0      0     0     0     0     0     0   \n11                   0       0      0      0     0     0     0     0     0   \n12                   0       0      0      0     0     0     0     0     0   \n14                   0       0      0      0     0     0     0     0     0   \n19                   0       0      0      0     0     0     0     0     0   \n\nCNT_FAM_MEMBERS  10.0  11.0  12.0  13.0  14.0  15.0  16.0  20.0  \nCNT_CHILDREN                                                     \n0                   0     0     0     0     0     0     0     0  \n1                   0     0     0     0     0     0     0     0  \n2                   0     0     0     0     0     0     0     0  \n3                   0     0     0     0     0     0     0     0  \n4                   0     0     0     0     0     0     0     0  \n5                   0     0     0     0     0     0     0     0  \n6                   0     0     0     0     0     0     0     0  \n7                   0     0     0     0     0     0     0     0  \n8                   2     0     0     0     0     0     0     0  \n9                   1     1     0     0     0     0     0     0  \n10                  0     0     2     0     0     0     0     0  \n11                  0     0     0     1     0     0     0     0  \n12                  0     0     0     0     2     0     0     0  \n14                  0     0     0     0     0     1     2     0  \n19                  0     0     0     0     0     0     0     2  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>CNT_FAM_MEMBERS</th>\n      <th>1.0</th>\n      <th>2.0</th>\n      <th>3.0</th>\n      <th>4.0</th>\n      <th>5.0</th>\n      <th>6.0</th>\n      <th>7.0</th>\n      <th>8.0</th>\n      <th>9.0</th>\n      <th>10.0</th>\n      <th>11.0</th>\n      <th>12.0</th>\n      <th>13.0</th>\n      <th>14.0</th>\n      <th>15.0</th>\n      <th>16.0</th>\n      <th>20.0</th>\n    </tr>\n    <tr>\n      <th>CNT_CHILDREN</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>67847</td>\n      <td>147522</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>10835</td>\n      <td>50284</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2317</td>\n      <td>24432</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>265</td>\n      <td>3452</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26</td>\n      <td>403</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>79</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>19</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"It isn't surprising that there are a lot of families consisting of two or one adults. Also ther are families with two adults and 1-2 children.","metadata":{}},{"cell_type":"markdown","source":"**NAME_TYPE_SUITE**  \nThis featuer shows who was accompanying client when he was applying for the loan. 대출을 신청할 때 동행한 사람","metadata":{}},{"cell_type":"code","source":"application_train['NAME_TYPE_SUITE'].value_counts(dropna=False)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.096818Z","iopub.execute_input":"2021-07-02T07:07:41.097116Z","iopub.status.idle":"2021-07-02T07:07:41.183500Z","shell.execute_reply.started":"2021-07-02T07:07:41.097089Z","shell.execute_reply":"2021-07-02T07:07:41.182032Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Unaccompanied      248526\nFamily              40149\nSpouse, partner     11370\nChildren             3267\nOther_B              1770\nNaN                  1292\nOther_A               866\nGroup of people       271\nName: NAME_TYPE_SUITE, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"pd.crosstab(application_train.NAME_TYPE_SUITE, application_train.NAME_FAMILY_STATUS, dropna=False)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.185182Z","iopub.execute_input":"2021-07-02T07:07:41.185515Z","iopub.status.idle":"2021-07-02T07:07:41.310113Z","shell.execute_reply.started":"2021-07-02T07:07:41.185481Z","shell.execute_reply":"2021-07-02T07:07:41.309320Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"NAME_FAMILY_STATUS  Civil marriage  Married  Separated  Single / not married  \\\nNAME_TYPE_SUITE                                                                \nChildren                       253     1940        318                   378   \nFamily                        3702    28617       1747                  4150   \nGroup of people                 29      165         20                    44   \nOther_A                        147      340         70                   268   \nOther_B                        171      854        132                   486   \nSpouse, partner               1247     9685         74                   345   \nUnaccompanied                24112   154047      17334                 39529   \n\nNAME_FAMILY_STATUS  Unknown  Widow  \nNAME_TYPE_SUITE                     \nChildren                  0    378  \nFamily                    0   1933  \nGroup of people           0     13  \nOther_A                   0     41  \nOther_B                   0    127  \nSpouse, partner           0     19  \nUnaccompanied             0  13504  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>NAME_FAMILY_STATUS</th>\n      <th>Civil marriage</th>\n      <th>Married</th>\n      <th>Separated</th>\n      <th>Single / not married</th>\n      <th>Unknown</th>\n      <th>Widow</th>\n    </tr>\n    <tr>\n      <th>NAME_TYPE_SUITE</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Children</th>\n      <td>253</td>\n      <td>1940</td>\n      <td>318</td>\n      <td>378</td>\n      <td>0</td>\n      <td>378</td>\n    </tr>\n    <tr>\n      <th>Family</th>\n      <td>3702</td>\n      <td>28617</td>\n      <td>1747</td>\n      <td>4150</td>\n      <td>0</td>\n      <td>1933</td>\n    </tr>\n    <tr>\n      <th>Group of people</th>\n      <td>29</td>\n      <td>165</td>\n      <td>20</td>\n      <td>44</td>\n      <td>0</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>Other_A</th>\n      <td>147</td>\n      <td>340</td>\n      <td>70</td>\n      <td>268</td>\n      <td>0</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>Other_B</th>\n      <td>171</td>\n      <td>854</td>\n      <td>132</td>\n      <td>486</td>\n      <td>0</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>Spouse, partner</th>\n      <td>1247</td>\n      <td>9685</td>\n      <td>74</td>\n      <td>345</td>\n      <td>0</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>Unaccompanied</th>\n      <td>24112</td>\n      <td>154047</td>\n      <td>17334</td>\n      <td>39529</td>\n      <td>0</td>\n      <td>13504</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"It is intersting to see that these two variables sometimes contradict each other. For example, seperated, single or widowed applicants were sometimes accompanied by their partner. I suppose this means unofficial relationships? Also sometimes children accompanied the applicant. Maybe these were adult childred?","metadata":{}},{"cell_type":"markdown","source":"**NAME_INCOME_TYPE**  \nClients income type (businessman, working, maternity leave,?","metadata":{}},{"cell_type":"code","source":"application_train.groupby(['NAME_INCOME_TYPE']).agg({'AMT_INCOME_TOTAL': ['mean', 'median', 'count']})","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.311265Z","iopub.execute_input":"2021-07-02T07:07:41.311654Z","iopub.status.idle":"2021-07-02T07:07:41.380447Z","shell.execute_reply.started":"2021-07-02T07:07:41.311625Z","shell.execute_reply":"2021-07-02T07:07:41.379284Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"                     AMT_INCOME_TOTAL                  \n                                 mean    median   count\nNAME_INCOME_TYPE                                       \nBusinessman             652500.000000  495000.0      10\nCommercial associate    202955.327289  180000.0   71617\nMaternity leave         140400.000000   90000.0       5\nPensioner               136401.292273  117000.0   55362\nState servant           179737.969506  157500.0   21703\nStudent                 170500.000000  157500.0      18\nUnemployed              110536.363636   78750.0      22\nWorking                 163169.889223  135000.0  158774","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">AMT_INCOME_TOTAL</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n      <th>count</th>\n    </tr>\n    <tr>\n      <th>NAME_INCOME_TYPE</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Businessman</th>\n      <td>652500.000000</td>\n      <td>495000.0</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>Commercial associate</th>\n      <td>202955.327289</td>\n      <td>180000.0</td>\n      <td>71617</td>\n    </tr>\n    <tr>\n      <th>Maternity leave</th>\n      <td>140400.000000</td>\n      <td>90000.0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>136401.292273</td>\n      <td>117000.0</td>\n      <td>55362</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>179737.969506</td>\n      <td>157500.0</td>\n      <td>21703</td>\n    </tr>\n    <tr>\n      <th>Student</th>\n      <td>170500.000000</td>\n      <td>157500.0</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>Unemployed</th>\n      <td>110536.363636</td>\n      <td>78750.0</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>163169.889223</td>\n      <td>135000.0</td>\n      <td>158774</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Maternity leave: 출산휴가","metadata":{}},{"cell_type":"code","source":"application_train[application_train['NAME_INCOME_TYPE'] == 'Maternity leave']['CODE_GENDER'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.381735Z","iopub.execute_input":"2021-07-02T07:07:41.382039Z","iopub.status.idle":"2021-07-02T07:07:41.435764Z","shell.execute_reply.started":"2021-07-02T07:07:41.382007Z","shell.execute_reply":"2021-07-02T07:07:41.434661Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"F    4\nM    1\nName: CODE_GENDER, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that there are 4 categories with little amount of people in them: several high-income businessmen, 4 women and 1 man on maternity leave, and some unemployed/students. It is quite interesting that unemployed/students have quite a high income. And of course, most of people work.","metadata":{}},{"cell_type":"code","source":"# occupation_type: 직업\ns = pd.crosstab(application_train.NAME_INCOME_TYPE, application_train.OCCUPATION_TYPE, dropna = False).style.background_gradient(cmap='viridis', \n                                                                                                        low = .5, high = 0).highlight_null('red')\ns","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.438711Z","iopub.execute_input":"2021-07-02T07:07:41.439036Z","iopub.status.idle":"2021-07-02T07:07:41.660747Z","shell.execute_reply.started":"2021-07-02T07:07:41.438989Z","shell.execute_reply":"2021-07-02T07:07:41.659982Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7f29b55221d0>","text/html":"<style  type=\"text/css\" 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#f1f1f1;\n        }#T_fcfef_row4_col14{\n            background-color:  #30698e;\n            color:  #f1f1f1;\n        }#T_fcfef_row4_col15{\n            background-color:  #20a486;\n            color:  #000000;\n        }#T_fcfef_row4_col17{\n            background-color:  #2c718e;\n            color:  #f1f1f1;\n        }#T_fcfef_row6_col0,#T_fcfef_row6_col1,#T_fcfef_row6_col2,#T_fcfef_row6_col3,#T_fcfef_row6_col4,#T_fcfef_row6_col5,#T_fcfef_row6_col6,#T_fcfef_row6_col7,#T_fcfef_row6_col8,#T_fcfef_row6_col9,#T_fcfef_row6_col10,#T_fcfef_row6_col11,#T_fcfef_row6_col12,#T_fcfef_row6_col13,#T_fcfef_row6_col14,#T_fcfef_row6_col15,#T_fcfef_row6_col16,#T_fcfef_row6_col17{\n            background-color:  #fde725;\n            color:  #000000;\n        }</style><table id=\"T_fcfef_\" ><thead>    <tr>        <th class=\"index_name level0\" >OCCUPATION_TYPE</th>        <th class=\"col_heading level0 col0\" >Accountants</th>        <th class=\"col_heading level0 col1\" >Cleaning staff</th>        <th class=\"col_heading level0 col2\" >Cooking staff</th>        <th class=\"col_heading level0 col3\" >Core staff</th>        <th class=\"col_heading level0 col4\" >Drivers</th>        <th class=\"col_heading level0 col5\" >HR staff</th>        <th class=\"col_heading level0 col6\" >High skill tech staff</th>        <th class=\"col_heading level0 col7\" >IT staff</th>        <th class=\"col_heading level0 col8\" >Laborers</th>        <th class=\"col_heading level0 col9\" >Low-skill Laborers</th>        <th class=\"col_heading level0 col10\" >Managers</th>        <th class=\"col_heading level0 col11\" >Medicine staff</th>        <th class=\"col_heading level0 col12\" >Private service staff</th>        <th class=\"col_heading level0 col13\" >Realty agents</th>        <th class=\"col_heading level0 col14\" >Sales staff</th>        <th class=\"col_heading level0 col15\" >Secretaries</th>        <th class=\"col_heading level0 col16\" >Security staff</th>        <th class=\"col_heading level0 col17\" >Waiters/barmen staff</th>    </tr>    <tr>        <th class=\"index_name level0\" >NAME_INCOME_TYPE</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_fcfef_level0_row0\" class=\"row_heading level0 row0\" >Businessman</th>\n                        <td id=\"T_fcfef_row0_col0\" class=\"data row0 col0\" >0</td>\n                        <td id=\"T_fcfef_row0_col1\" class=\"data row0 col1\" >0</td>\n                        <td id=\"T_fcfef_row0_col2\" class=\"data row0 col2\" >0</td>\n                        <td id=\"T_fcfef_row0_col3\" class=\"data row0 col3\" >1</td>\n                        <td id=\"T_fcfef_row0_col4\" class=\"data row0 col4\" >0</td>\n                        <td id=\"T_fcfef_row0_col5\" class=\"data row0 col5\" >0</td>\n                        <td id=\"T_fcfef_row0_col6\" class=\"data row0 col6\" >0</td>\n                        <td id=\"T_fcfef_row0_col7\" class=\"data row0 col7\" >0</td>\n                        <td id=\"T_fcfef_row0_col8\" class=\"data row0 col8\" >0</td>\n                        <td id=\"T_fcfef_row0_col9\" class=\"data row0 col9\" >0</td>\n                        <td id=\"T_fcfef_row0_col10\" class=\"data row0 col10\" >7</td>\n                        <td id=\"T_fcfef_row0_col11\" class=\"data row0 col11\" >0</td>\n                        <td id=\"T_fcfef_row0_col12\" class=\"data row0 col12\" >0</td>\n                        <td id=\"T_fcfef_row0_col13\" class=\"data row0 col13\" >0</td>\n                        <td id=\"T_fcfef_row0_col14\" class=\"data row0 col14\" >0</td>\n                        <td id=\"T_fcfef_row0_col15\" class=\"data row0 col15\" >0</td>\n                        <td id=\"T_fcfef_row0_col16\" class=\"data row0 col16\" >0</td>\n                        <td id=\"T_fcfef_row0_col17\" class=\"data row0 col17\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fcfef_level0_row1\" class=\"row_heading level0 row1\" >Commercial associate</th>\n                        <td id=\"T_fcfef_row1_col0\" class=\"data row1 col0\" >3970</td>\n                        <td id=\"T_fcfef_row1_col1\" class=\"data row1 col1\" >892</td>\n                        <td id=\"T_fcfef_row1_col2\" class=\"data row1 col2\" >1305</td>\n                        <td id=\"T_fcfef_row1_col3\" class=\"data row1 col3\" >6528</td>\n                        <td id=\"T_fcfef_row1_col4\" class=\"data row1 col4\" >4916</td>\n                        <td id=\"T_fcfef_row1_col5\" class=\"data row1 col5\" >220</td>\n                        <td id=\"T_fcfef_row1_col6\" class=\"data row1 col6\" >3495</td>\n                        <td id=\"T_fcfef_row1_col7\" class=\"data row1 col7\" >204</td>\n                        <td id=\"T_fcfef_row1_col8\" class=\"data row1 col8\" >12763</td>\n                        <td id=\"T_fcfef_row1_col9\" class=\"data row1 col9\" >370</td>\n                        <td id=\"T_fcfef_row1_col10\" class=\"data row1 col10\" >8916</td>\n                        <td id=\"T_fcfef_row1_col11\" class=\"data row1 col11\" >1036</td>\n                        <td id=\"T_fcfef_row1_col12\" class=\"data row1 col12\" >989</td>\n                        <td id=\"T_fcfef_row1_col13\" class=\"data row1 col13\" >324</td>\n                        <td id=\"T_fcfef_row1_col14\" class=\"data row1 col14\" >11104</td>\n                        <td id=\"T_fcfef_row1_col15\" class=\"data row1 col15\" >343</td>\n                        <td id=\"T_fcfef_row1_col16\" class=\"data row1 col16\" >1559</td>\n                        <td id=\"T_fcfef_row1_col17\" class=\"data row1 col17\" >386</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fcfef_level0_row2\" class=\"row_heading level0 row2\" >Maternity leave</th>\n                        <td id=\"T_fcfef_row2_col0\" class=\"data row2 col0\" >1</td>\n                        <td id=\"T_fcfef_row2_col1\" class=\"data row2 col1\" >0</td>\n                        <td id=\"T_fcfef_row2_col2\" class=\"data row2 col2\" >0</td>\n                        <td id=\"T_fcfef_row2_col3\" class=\"data row2 col3\" >2</td>\n                        <td id=\"T_fcfef_row2_col4\" class=\"data row2 col4\" >0</td>\n                        <td id=\"T_fcfef_row2_col5\" class=\"data row2 col5\" >0</td>\n                        <td id=\"T_fcfef_row2_col6\" class=\"data row2 col6\" >0</td>\n                        <td id=\"T_fcfef_row2_col7\" class=\"data row2 col7\" >0</td>\n                        <td id=\"T_fcfef_row2_col8\" class=\"data row2 col8\" >1</td>\n                        <td id=\"T_fcfef_row2_col9\" class=\"data row2 col9\" >0</td>\n                        <td id=\"T_fcfef_row2_col10\" class=\"data row2 col10\" >0</td>\n                        <td id=\"T_fcfef_row2_col11\" class=\"data row2 col11\" >0</td>\n                        <td id=\"T_fcfef_row2_col12\" class=\"data row2 col12\" >0</td>\n                        <td id=\"T_fcfef_row2_col13\" class=\"data row2 col13\" >0</td>\n                        <td id=\"T_fcfef_row2_col14\" class=\"data row2 col14\" >0</td>\n                        <td id=\"T_fcfef_row2_col15\" class=\"data row2 col15\" >0</td>\n                        <td id=\"T_fcfef_row2_col16\" class=\"data row2 col16\" >0</td>\n                        <td id=\"T_fcfef_row2_col17\" class=\"data row2 col17\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fcfef_level0_row3\" class=\"row_heading level0 row3\" >Pensioner</th>\n                        <td id=\"T_fcfef_row3_col0\" class=\"data row3 col0\" >0</td>\n                        <td id=\"T_fcfef_row3_col1\" class=\"data row3 col1\" >2</td>\n                        <td id=\"T_fcfef_row3_col2\" class=\"data row3 col2\" >0</td>\n                        <td id=\"T_fcfef_row3_col3\" class=\"data row3 col3\" >0</td>\n                        <td id=\"T_fcfef_row3_col4\" class=\"data row3 col4\" >0</td>\n                        <td id=\"T_fcfef_row3_col5\" class=\"data row3 col5\" >0</td>\n                        <td id=\"T_fcfef_row3_col6\" class=\"data row3 col6\" >0</td>\n                        <td id=\"T_fcfef_row3_col7\" class=\"data row3 col7\" >0</td>\n                        <td id=\"T_fcfef_row3_col8\" class=\"data row3 col8\" >1</td>\n                        <td id=\"T_fcfef_row3_col9\" class=\"data row3 col9\" >0</td>\n                        <td id=\"T_fcfef_row3_col10\" class=\"data row3 col10\" >0</td>\n                        <td id=\"T_fcfef_row3_col11\" class=\"data row3 col11\" >1</td>\n                        <td id=\"T_fcfef_row3_col12\" class=\"data row3 col12\" >0</td>\n                        <td id=\"T_fcfef_row3_col13\" class=\"data row3 col13\" >0</td>\n                        <td id=\"T_fcfef_row3_col14\" class=\"data row3 col14\" >1</td>\n                        <td id=\"T_fcfef_row3_col15\" class=\"data row3 col15\" >0</td>\n                        <td id=\"T_fcfef_row3_col16\" class=\"data row3 col16\" >0</td>\n                        <td id=\"T_fcfef_row3_col17\" class=\"data row3 col17\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fcfef_level0_row4\" class=\"row_heading level0 row4\" >State servant</th>\n                        <td id=\"T_fcfef_row4_col0\" class=\"data row4 col0\" >729</td>\n                        <td id=\"T_fcfef_row4_col1\" class=\"data row4 col1\" >261</td>\n                        <td id=\"T_fcfef_row4_col2\" class=\"data row4 col2\" >453</td>\n                        <td id=\"T_fcfef_row4_col3\" class=\"data row4 col3\" >6956</td>\n                        <td id=\"T_fcfef_row4_col4\" class=\"data row4 col4\" >815</td>\n                        <td id=\"T_fcfef_row4_col5\" class=\"data row4 col5\" >64</td>\n                        <td id=\"T_fcfef_row4_col6\" class=\"data row4 col6\" >1033</td>\n                        <td id=\"T_fcfef_row4_col7\" class=\"data row4 col7\" >55</td>\n                        <td id=\"T_fcfef_row4_col8\" class=\"data row4 col8\" >1868</td>\n                        <td id=\"T_fcfef_row4_col9\" class=\"data row4 col9\" >25</td>\n                        <td id=\"T_fcfef_row4_col10\" class=\"data row4 col10\" >2142</td>\n                        <td id=\"T_fcfef_row4_col11\" class=\"data row4 col11\" >2585</td>\n                        <td id=\"T_fcfef_row4_col12\" class=\"data row4 col12\" >38</td>\n                        <td id=\"T_fcfef_row4_col13\" class=\"data row4 col13\" >9</td>\n                        <td id=\"T_fcfef_row4_col14\" class=\"data row4 col14\" >186</td>\n                        <td id=\"T_fcfef_row4_col15\" class=\"data row4 col15\" >264</td>\n                        <td id=\"T_fcfef_row4_col16\" class=\"data row4 col16\" >380</td>\n                        <td id=\"T_fcfef_row4_col17\" class=\"data row4 col17\" >53</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fcfef_level0_row5\" class=\"row_heading level0 row5\" >Student</th>\n                        <td id=\"T_fcfef_row5_col0\" class=\"data row5 col0\" >1</td>\n                        <td id=\"T_fcfef_row5_col1\" class=\"data row5 col1\" >0</td>\n                        <td id=\"T_fcfef_row5_col2\" class=\"data row5 col2\" >0</td>\n                        <td id=\"T_fcfef_row5_col3\" class=\"data row5 col3\" >3</td>\n                        <td id=\"T_fcfef_row5_col4\" class=\"data row5 col4\" >1</td>\n                        <td id=\"T_fcfef_row5_col5\" class=\"data row5 col5\" >0</td>\n                        <td id=\"T_fcfef_row5_col6\" class=\"data row5 col6\" >0</td>\n                        <td id=\"T_fcfef_row5_col7\" class=\"data row5 col7\" >0</td>\n                        <td id=\"T_fcfef_row5_col8\" class=\"data row5 col8\" >5</td>\n                        <td id=\"T_fcfef_row5_col9\" class=\"data row5 col9\" >2</td>\n                        <td id=\"T_fcfef_row5_col10\" class=\"data row5 col10\" >0</td>\n                        <td id=\"T_fcfef_row5_col11\" class=\"data row5 col11\" >0</td>\n                        <td id=\"T_fcfef_row5_col12\" class=\"data row5 col12\" >0</td>\n                        <td id=\"T_fcfef_row5_col13\" class=\"data row5 col13\" >0</td>\n                        <td id=\"T_fcfef_row5_col14\" class=\"data row5 col14\" >0</td>\n                        <td id=\"T_fcfef_row5_col15\" class=\"data row5 col15\" >0</td>\n                        <td id=\"T_fcfef_row5_col16\" class=\"data row5 col16\" >0</td>\n                        <td id=\"T_fcfef_row5_col17\" class=\"data row5 col17\" >1</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fcfef_level0_row6\" class=\"row_heading level0 row6\" >Working</th>\n                        <td id=\"T_fcfef_row6_col0\" class=\"data row6 col0\" >5112</td>\n                        <td id=\"T_fcfef_row6_col1\" class=\"data row6 col1\" >3498</td>\n                        <td id=\"T_fcfef_row6_col2\" class=\"data row6 col2\" >4188</td>\n                        <td id=\"T_fcfef_row6_col3\" class=\"data row6 col3\" >14080</td>\n                        <td id=\"T_fcfef_row6_col4\" class=\"data row6 col4\" >12871</td>\n                        <td id=\"T_fcfef_row6_col5\" class=\"data row6 col5\" >279</td>\n                        <td id=\"T_fcfef_row6_col6\" class=\"data row6 col6\" >6852</td>\n                        <td id=\"T_fcfef_row6_col7\" class=\"data row6 col7\" >267</td>\n                        <td id=\"T_fcfef_row6_col8\" class=\"data row6 col8\" >40548</td>\n                        <td id=\"T_fcfef_row6_col9\" class=\"data row6 col9\" >1696</td>\n                        <td id=\"T_fcfef_row6_col10\" class=\"data row6 col10\" >10306</td>\n                        <td id=\"T_fcfef_row6_col11\" class=\"data row6 col11\" >4915</td>\n                        <td id=\"T_fcfef_row6_col12\" class=\"data row6 col12\" >1625</td>\n                        <td id=\"T_fcfef_row6_col13\" class=\"data row6 col13\" >418</td>\n                        <td id=\"T_fcfef_row6_col14\" class=\"data row6 col14\" >20811</td>\n                        <td id=\"T_fcfef_row6_col15\" class=\"data row6 col15\" >698</td>\n                        <td id=\"T_fcfef_row6_col16\" class=\"data row6 col16\" >4782</td>\n                        <td id=\"T_fcfef_row6_col17\" class=\"data row6 col17\" >908</td>\n            </tr>\n    </tbody></table>"},"metadata":{}}]},{"cell_type":"markdown","source":"**AMT_GOODS_PRICE**  \nFor consumer loans it is the price of the goods for which the loan is given. (상품 가격)","metadata":{}},{"cell_type":"code","source":"print('{0} zero values.'.format(application_train[application_train['AMT_GOODS_PRICE'].isnull()].shape[0]))","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.661708Z","iopub.execute_input":"2021-07-02T07:07:41.662082Z","iopub.status.idle":"2021-07-02T07:07:41.670458Z","shell.execute_reply.started":"2021-07-02T07:07:41.662054Z","shell.execute_reply":"2021-07-02T07:07:41.668882Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"278 zero values.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"So this means that only 278 loans have some other type. Let's go deeper.","metadata":{}},{"cell_type":"code","source":"non_zero_good_price = application_train[application_train['AMT_GOODS_PRICE'].isnull() == False]\ncredit_to_good_price = non_zero_good_price['AMT_CREDIT'] / non_zero_good_price['AMT_GOODS_PRICE']\n\nplt.boxplot(credit_to_good_price);\nplt.title('Credit amount to goods price.');","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:41.671988Z","iopub.execute_input":"2021-07-02T07:07:41.672406Z","iopub.status.idle":"2021-07-02T07:07:42.045750Z","shell.execute_reply.started":"2021-07-02T07:07:41.672359Z","shell.execute_reply":"2021-07-02T07:07:42.044922Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We can see that most of the loans have the amount which is similar to the goods price, but there are some outliers.","metadata":{}},{"cell_type":"markdown","source":"**NAME_HOUSING_TYPE**  \n`plt.countplot()`: 범주형 변수의 bar graph","metadata":{}},{"cell_type":"code","source":"sns.countplot(application_train['NAME_HOUSING_TYPE']);\nplt.xticks(rotation=45);\nplt.title('Counts of housing type')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:42.046833Z","iopub.execute_input":"2021-07-02T07:07:42.047255Z","iopub.status.idle":"2021-07-02T07:07:42.603263Z","shell.execute_reply.started":"2021-07-02T07:07:42.047224Z","shell.execute_reply":"2021-07-02T07:07:42.602379Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Counts of housing type')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAZ0AAAFeCAYAAABeo8K4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAABDE0lEQVR4nO3debzmY/3H8dfbvjPMGGuWiCzZJoQQWbOWBmULaSEtSpZfdhUlUihFlgiRfZCQoixDdpUhMmOsY1+b8fn98bluvo4zZ86ZOef7vc857+fjcT/Oua/7e9/39b23z/e6vtf1uRQRmJmZ1WG6pitgZmaDh4OOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMaiBpuKS/SHpZ0nGd3H6GpKNqrtNVknat8znNHHSsLUn6nKTRkl6RNL78QK5Tw/OGpKX64KH3Ap4F5oqI/frg8XssIjaLiDN7+3ElrS9pbG8/rg0MDjrWdiR9CzgB+D4wHPgAcDKwdYPVmlaLAQ+EZ2PbIOegY21F0tzAEcDeEfGHiHg1Iv4XEZdHxHfKNjNLOkHSE+VygqSZy227Sbqpw2O+03op3VgnSbqydHXdKumD5ba/lLvcXVpY20saKukKSS9ImiDpr5I6/d5IWkvS7ZJeLH/Xaj0nsCuwf3ncT05m94d0Vq+uHrvc9mj1MSUdJum35f9ZJP1W0nNlH26XNLzc9mdJe1ZfN0k/lvS8pP9I2qzymEtUugf/VF7D33byGswOXAUsVPb1FUkLSXpN0nyV7VaV9IykGctz3yzp52X//ilpw+pnQtJppcU7TtJRkqafzGtobc5Bx9rNx4BZgIu72OZgYE1gZWAlYHXg/3rwHDsAhwNDgDHA0QARsW65faWImCMizgf2A8YCw8hW10HA+1orkuYFrgROBOYDfgJcKWm+iNgNOAc4tjzun3pSr64euxv7uiswN7Boue+Xgdcns+0awL+AocCxwGmSVG47F7itPMZhwM6dPUBEvApsBjxR9nWOiHgC+DMwsrLpzsB5EfG/ynM/XJ77UOAPZb8BzgAmAksBqwAbA3t2Y9+tDTnoWLuZD3g2IiZ2sc3ngSMi4umIeIb8oe70R3AyLo6I28pznEMGr8n5H7AgsFhpcf11Ml1knwIeioizI2JiRPwO+CewZS/Ua1oe+3/ka7pUREyKiDsi4qXJbPtYRPwqIiYBZ5L7PVzSB4CPAodExFsRcRNwWQ/2i/J4OwGUVsqOwNmV258GTiiv8flk8PtUaZVtDnyjtHqfBo4nA7T1Qw461m6eA4ZKmqGLbRYCHqtcf6yUddeTlf9fA+boYtsfka2OP0p6RNIB3axTq14L90K9puWxzwauAc4rXZHHSppxSs8fEa+Vf+cozz+hUgbweDeeu+pSYDlJSwAbAS9GxG2V28d1COat93QxYEZgfOkefAH4JTB/D5/f2oSDjrWbvwNvAtt0sc0T5I9RywdKGcCrwGytGyQtMC2ViYiXI2K/iFgS2Ar4VvV8Qxd1atVr3LQ8fzcf+z37DLyzz6XlcHhELAesBWwB7NLD5x8PzCup+hyLdrH9+1qCEfEGcAHZ2tmZ97ZyABaudOXBu+/p4+TnYWhEzFMuc0XE8j3cB2sTDjrWViLiReAQ4CRJ20iarZxs3kzSsWWz3wH/J2mYpKFl+9ZJ7buB5SWtLGkW8vxDTzwFLNm6ImkLSUuVH8QXgUnA253cbxTwIeVQ7xkkbQ8sB1zRw+fvzJQe+y5gh/I6jQC2q9T/E5JWLF1aL5HdbZ3Vf7Ii4jFgNHCYpJkkfYyuu/aeAuZTDgqpOgvYjQzeHYPO/MC+ZR8+C3wYGBUR44E/AsdJmkvSdJI+KGm9nuyDtQ8HHWs7EXEc8C1ycMAz5NHuPsAlZZOjyB/Be4B7gTtLGRHxb3L025+Ah4D3jGTrhsOAM0tXzkhg6fJYr5CtsJMj4oZO6vwc2YrYj+wi3B/YIiKe7eHzv083Hvt7wAeB58nzW+dW7r4AcCEZcB4EbuT9P/jd8XlykMdz5Gt9PtkC6ay+/yQPDB4pr+NCpfxmMuDdWQJZ1a3ka/0sOYBiu7LfkC2zmYAHyj5eSJ5vsn5InjZgZj0l6XzgnxFxaA/vdz1wbkT8ulK2G7BnRPT55F9rnls6ZjZFkj5aurWmk7QpOVH3kp4+BrAq2UqyQaqrEUJmZi0LAH8gh1+PBb4SEf/o7p0lnUkODvl6RLzcJzW0fsHda2ZmVht3r5mZWW0cdMzMrDY+p1MMHTo0Fl988aarYWbWr9xxxx3PRsSw7m7voFMsvvjijB49uulqmJn1K5I6zrnqkrvXzMysNg46ZmZWGwcdMzOrjYOOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2zkgwBat956ymq9Ajd/xol6arYGY2WW7pmJlZbRx0zMysNg46ZmZWGwcdMzOrjYOOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2DjpmZlYbBx0zM6tNnwUdSYtKukHSA5Lul/T1Un6YpHGS7iqXzSv3OVDSGEn/krRJpXzTUjZG0gGV8iUk3VrKz5c0UymfuVwfU25fvK/208zMuq8vWzoTgf0iYjlgTWBvScuV246PiJXLZRRAuW0HYHlgU+BkSdNLmh44CdgMWA7YsfI4x5THWgp4HtijlO8BPF/Kjy/bmZlZw/os6ETE+Ii4s/z/MvAgsHAXd9kaOC8i3oyI/wBjgNXLZUxEPBIRbwHnAVtLErABcGG5/5nANpXHOrP8fyGwYdnezMwaVMs5ndK9tQpwaynaR9I9kk6XNKSULQw8Xrnb2FI2ufL5gBciYmKH8vc8Vrn9xbK9mZk1qM+DjqQ5gIuAb0TES8ApwAeBlYHxwHF9XYcu6raXpNGSRj/zzDNNVcPMbNDo06AjaUYy4JwTEX8AiIinImJSRLwN/IrsPgMYByxaufsipWxy5c8B80iaoUP5ex6r3D532f49IuLUiBgRESOGDRs2rbtrZmZT0Jej1wScBjwYET+plC9Y2Wxb4L7y/2XADmXk2RLA0sBtwO3A0mWk2kzkYIPLIiKAG4Dtyv13BS6tPNau5f/tgOvL9mZm1qAZprzJVFsb2Bm4V9JdpewgcvTZykAAjwJfAoiI+yVdADxAjnzbOyImAUjaB7gGmB44PSLuL4/3XeA8SUcB/yCDHOXv2ZLGABPIQGVmZg3rs6ATETcBnY0YG9XFfY4Gju6kfFRn94uIR3i3e65a/gbw2Z7U18zM+p4zEpiZWW0cdMzMrDYOOmZmVhsHHTMzq42DjpmZ1cZBx8zMauOgY2ZmtXHQMTOz2jjomJlZbRx0zMysNg46ZmZWGwcdMzOrjYOOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2DjpmZlYbBx0zM6uNg46ZmdXGQcfMzGrjoGNmZrVx0DEzs9o46JiZWW0cdMzMrDYOOmZmVhsHHTMzq42DjpmZ1abPgo6kRSXdIOkBSfdL+nopn1fStZIeKn+HlHJJOlHSGEn3SFq18li7lu0fkrRrpXw1SfeW+5woSV09h5mZNasvWzoTgf0iYjlgTWBvScsBBwDXRcTSwHXlOsBmwNLlshdwCmQAAQ4F1gBWBw6tBJFTgC9W7rdpKZ/cc5iZWYP6LOhExPiIuLP8/zLwILAwsDVwZtnsTGCb8v/WwFmRbgHmkbQgsAlwbURMiIjngWuBTcttc0XELRERwFkdHquz5zAzswbVck5H0uLAKsCtwPCIGF9uehIYXv5fGHi8crexpayr8rGdlNPFc5iZWYP6POhImgO4CPhGRLxUva20UKIvn7+r55C0l6TRkkY/88wzfVkNMzOjj4OOpBnJgHNORPyhFD9VusYof58u5eOARSt3X6SUdVW+SCflXT3He0TEqRExIiJGDBs2bOp20szMuq0vR68JOA14MCJ+UrnpMqA1Am1X4NJK+S5lFNuawIuli+waYGNJQ8oAgo2Ba8ptL0laszzXLh0eq7PnMDOzBs3Qh4+9NrAzcK+ku0rZQcAPgQsk7QE8Bowst40CNgfGAK8BXwCIiAmSjgRuL9sdERETyv9fBc4AZgWuKhe6eA4zM2tQnwWdiLgJ0GRu3rCT7QPYezKPdTpweiflo4EVOil/rrPnMDOzZjkjgZmZ1cZBx8zMauOgY2ZmtXHQMTOz2jjomJlZbRx0zMysNg46ZmZWGwcdMzOrjYOOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2DjpmZlYbBx0zM6uNg46ZmdXGQcfMzGrjoGNmZrVx0DEzs9o46JiZWW0cdMzMrDYOOmZmVhsHHTMzq42DjpmZ1cZBx8zMatOtoCPpuu6UmZmZdWWGrm6UNAswGzBU0hBA5aa5gIX7uG5mZjbAdBl0gC8B3wAWAu7g3aDzEvDzvquWmZkNRF12r0XETyNiCeDbEbFkRCxRLitFRJdBR9Lpkp6WdF+l7DBJ4yTdVS6bV247UNIYSf+StEmlfNNSNkbSAZXyJSTdWsrPlzRTKZ+5XB9Tbl+85y+LmZn1hW6d04mIn0laS9LnJO3SukzhbmcAm3ZSfnxErFwuowAkLQfsACxf7nOypOklTQ+cBGwGLAfsWLYFOKY81lLA88AepXwP4PlSfnzZzszM2kB3BxKcDfwYWAf4aLmM6Oo+EfEXYEI367E1cF5EvBkR/wHGAKuXy5iIeCQi3gLOA7aWJGAD4MJy/zOBbSqPdWb5/0Jgw7K9mZk1bErndFpGAMtFRPTCc+5TWkmjgf0i4nlyUMItlW3G8u5Ahcc7lK8BzAe8EBETO9l+4dZ9ImKipBfL9s/2Qt3NzGwadHeezn3AAr3wfKcAHwRWBsYDx/XCY041SXtJGi1p9DPPPNNkVczMBoXutnSGAg9Iug14s1UYEVv15Mki4qnW/5J+BVxRro4DFq1sukgpYzLlzwHzSJqhtHaq27cea6ykGYC5y/ad1edU4FSAESNG9EYrzszMutDdoHNYbzyZpAUjYny5ui3ZggK4DDhX0k/I4dlLA7eRQ7SXlrQEGUx2AD4XESHpBmA78jzPrsCllcfaFfh7uf36XuoWNDOzadStoBMRN/b0gSX9DlifnFg6FjgUWF/SykAAj5LzgIiI+yVdADwATAT2johJ5XH2Aa4BpgdOj4j7y1N8FzhP0lHAP4DTSvlpwNmSxpADGXboad3NzKxvdCvoSHqZDBQAMwEzAq9GxFyTu09E7NhJ8WmdlLW2Pxo4upPyUcCoTsofIUe3dSx/A/js5J7HzMya092Wzpyt/8vw462BNfuqUmZmNjD1OMt0pEuATaa0rZmZWVV3u9c+Xbk6HTlv540+qZGZmQ1Y3R29tmXl/4nkIICte702ZmY2oHX3nM4X+roiZmY28HU399oiki4uWaOflnSRpEX6unJmZjawdHcgwW/ISZcLlcvlpczMzKzbuht0hkXEbyJiYrmcAQzrw3qZmdkA1N2g85yknVpr3EjaicnkMzMzM5uc7gad3YGRwJNkdujtgN36qE5mZjZAdXfI9BHArmXtGyTNSy7qtntfVczMzAae7rZ0PtIKOAARMQFYpW+qZGZmA1V3g850koa0rpSWTndbSWZmZkD3A8dxwN8l/b5c/yydZIQ2MzPrSnczEpwlaTSwQSn6dEQ80HfVMjOzgajbXWQlyDjQmJnZVOvx0gZmZmZTy0HHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2DjpmZlYbBx0zM6uNg46ZmdXGQcfMzGrjoGNmZrVx0DEzs9o46JiZWW36LOhIOl3S05Luq5TNK+laSQ+Vv0NKuSSdKGmMpHskrVq5z65l+4ck7VopX03SveU+J0pSV89hZmbN68uWzhnAph3KDgCui4ilgevKdYDNgKXLZS/gFHhnhdJDgTWA1YFDK0HkFOCLlfttOoXnMDOzhvVZ0ImIvwATOhRvDZxZ/j8T2KZSflakW4B5JC0IbAJcGxETIuJ54Fpg03LbXBFxS0QEcFaHx+rsOczMrGF1n9MZHhHjy/9PAsPL/wsDj1e2G1vKuiof20l5V89hZmYNa2wgQWmhRJPPIWkvSaMljX7mmWf6sipmZkb9Qeep0jVG+ft0KR8HLFrZbpFS1lX5Ip2Ud/Uc7xMRp0bEiIgYMWzYsKneKTMz6566g85lQGsE2q7ApZXyXcootjWBF0sX2TXAxpKGlAEEGwPXlNtekrRmGbW2S4fH6uw5zMysYTP01QNL+h2wPjBU0lhyFNoPgQsk7QE8Bowsm48CNgfGAK8BXwCIiAmSjgRuL9sdERGtwQlfJUfIzQpcVS508RxmZtawPgs6EbHjZG7asJNtA9h7Mo9zOnB6J+WjgRU6KX+us+cwM7PmOSOBmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2DjpmZlYbBx0zM6uNg46ZmdXGQcfMzGrjoGNmZrVx0DEzs9o46JiZWW0cdMzMrDYOOmZmVhsHHTMzq42DjpmZ1cZBx8zMauOgY2ZmtXHQMTOz2jjomJlZbRx0zMysNg46ZmZWGwcdMzOrjYOOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltGgk6kh6VdK+kuySNLmXzSrpW0kPl75BSLkknShoj6R5Jq1YeZ9ey/UOSdq2Ur1Yef0y5r+rfSzMz66jJls4nImLliBhRrh8AXBcRSwPXlesAmwFLl8tewCmQQQo4FFgDWB04tBWoyjZfrNxv077fHTMzm5J26l7bGjiz/H8msE2l/KxItwDzSFoQ2AS4NiImRMTzwLXApuW2uSLilogI4KzKY5mZWYOaCjoB/FHSHZL2KmXDI2J8+f9JYHj5f2Hg8cp9x5ayrsrHdlJuZmYNm6Gh510nIsZJmh+4VtI/qzdGREiKvq5ECXh7AXzgAx/o66czMxv0GmnpRMS48vdp4GLynMxTpWuM8vfpsvk4YNHK3RcpZV2VL9JJeWf1ODUiRkTEiGHDhk3rbpmZ2RTUHnQkzS5pztb/wMbAfcBlQGsE2q7ApeX/y4Bdyii2NYEXSzfcNcDGkoaUAQQbA9eU216StGYZtbZL5bHMzKxBTXSvDQcuLqOYZwDOjYirJd0OXCBpD+AxYGTZfhSwOTAGeA34AkBETJB0JHB72e6IiJhQ/v8qcAYwK3BVuZiZWcNqDzoR8QiwUiflzwEbdlIewN6TeazTgdM7KR8NrDDNlTUzs17VTkOmzcxsgHPQMTOz2jjomJlZbRx0zMysNg46ZmZWGwcdMzOrjYOOmZnVxkHHzMxq46BjZma1cdAxM7PaOOiYmVltHHTMzKw2DjpmZlYbBx0zM6uNg46ZmdXGQcfMzGrjoGNmZrVx0DEzs9o46JiZWW0cdMzMrDYOOmZmVpsZmq6AWV9Z+2drN12FHrv5azc3XQWzPuWWjpmZ1cZBx8zMauPuNbN+6sZ112u6Cj223l9ubLoK1jC3dMzMrDYOOmZmVhsHHTMzq42DjpmZ1WbADiSQtCnwU2B64NcR8cOGq9R2/nvEik1Xocc+cMi9TVfBzKbBgGzpSJoeOAnYDFgO2FHScs3WyszMBmpLZ3VgTEQ8AiDpPGBr4IFGa2Vm3fbz/S5vugo9ss9xWzZdhX5hoAadhYHHK9fHAms0VBczs/c5eqftmq5Cjxz82wt75XEUEb3yQO1E0nbAphGxZ7m+M7BGROzTYbu9gL3K1WWAf9VYzaHAszU+X90G8v4N5H0D719/V/f+LRYRw7q78UBt6YwDFq1cX6SUvUdEnAqcWlelqiSNjogRTTx3HQby/g3kfQPvX3/X7vs3IAcSALcDS0taQtJMwA7AZQ3Xycxs0BuQLZ2ImChpH+Aacsj06RFxf8PVMjMb9AZk0AGIiFHAqKbr0YVGuvVqNJD3byDvG3j/+ru23r8BOZDAzMza00A9p2NmZm3IQcfMrJdJUtN1aFcOOgOcpJklDSn/D2m6Pr1J0uyt9EaSVpK06JTu0y78o/R+kmaRtGT5f1FJ8zZdp54qn8m5IiIkrdB0fXqbpJlKmrGp/j0ZsAMJDCRNB6wLzFs+IJ+V9OmIeLHhqvWW+YAjJY0DVgS+0nB9ukWSyo/SZsAHI+LnTdepTawArCFpAWBToD/mlVkF2FvS1cC3JX0mIv7ddKV6g6QZgLWABSS9Sr5Xx0bESz15HLd0BrCIeJvMsvBV4HDgNwMo4BAR/wX+BuwO3BIR/1Rq6891CThbA98H/tN0fdrIg8BqwDeBiyLiSehfrcKIuAkI4FfA4RHx71bLYACYBLwAfBH4JXB9RLzU0+9bW385beq1vqjlh/lS4A5gbknLNlqxXtDhR+hW4ABgY0m7R3pb0qwNVa9T1TpLmg34HPB54HpJ60g6RNLQxirYoMpn9VXgHOA0YA5JW5XyKJO821aHz+Q5wBnAfpKWiIhJzdSq97Ra58AYQMCdwLKSZi0Ht91/LA+ZHrgkrQK8AjxPHmAcC/wb+AWZJmjWiLiluRpOPUnrAB8G/h4R90naGDgGOBr4B3k0dnhEvN5gNYE8VwEsExF3S1oGmIWs5zhgfuAx4OPAXyPiG41VtEHl/ZwLeCIi7pL0bfIzehHwBrlEyTkR8b8Gq9klSWsBiwF/jojxko4ENgc2ILsO142IHzRZx6lR6Q5eBHiSbPGsQuatfCwifiBpIWDeiLhvSo/nls4A0zrikrQeOTn2+8APgQWA7wEfAo4CbgTmaKia06Ts21nASsCNkraMiD8C+5HdiJcDf2mHgFMsCqwn6ZfAVcDDwL7kAcD3S6DZA1hc0tyN1bJmlc/qx4ALyOVHflAS9B5HBuPdyM/xU20ecNYFfg3sCvxc0uYR8T3y/b6E7I76Z3M1nHol4GwBXA+cB/woIu4EzgcWK0vHXEN3xwhEhC8D7EIOHjgCWBlYCNgb+C15sn3OcvvqTddzKvftQ8DZwCfL9ZHA3cBW5fr8wIearmcn9d4feAs4vlLW6mn4FHAfsGXT9WzgdVkHOBJYp1zfALgW2KlcXxBYvul6TmEfPlJ+dD9crn+TDDKbl+vLAEtX3/P+dAGWBs4E1gM+APwZOKnc9kHgQDKrf/cer+kd8qVXPxzTl79XAK8B85frS5CDCS4C1utwn37zJSBb5l8FbiO7p2Yq5Z8BHgE+03QdJ/faAsOBPclW59eA4aV8QbL/f7P+9n70xmsD/Ah4EdiiXJ8J+ARwM/C1puvZnf0gEwo/D+xdKd+XbI1v21/f0/J9Wxj4O3AhMGcpH0K2es7s7D2d0sXndAaASp/r3FFGp0m6DJgrItYv1z9I9i//JSLubq62PVPZtwWAlyPiVUmfAz5Gjlz7fWSC15HA+Ij4a6MV7kDSpuSIrP9ExLnl3NPW5Hmn58mW22kR8XSD1axN5f1cOCLGlbL9yRbrlpHnQmYkz3G9EhG3NVnfzlT2YT7gTeBVclDIjuQI0QvLdt8C/hjdOM/RTiqDBlrXtwG+Qx7o/Ski3ipzqC4jDwLvjR4EEgedAaL8uO0J3APcExGXSPoTMCkiNinbzBrtc56j20p/8iHkyJkg93N3YElyf8+JiIllW/XkC9AXKj9KK5LnKv5AHjG+Rc4l2gDYhAw++0fExY1VtgFlftIBZGtmxoj4jqSfkKv7bh8RYxutYDeUH+IvkUHnWuBq4KNk4DkvIn7XXO2mXuWzuzGwIfAU8DtgTbKb/sfADRHxpqQZWt+7Hmm6CedLrzSD1wLuJ8/hXEF+6Fu33QTcWP7vd818YFWyRbMoebL9EbLZPwvZhXEysFDT9eyk3uuS/eBbluuLk/38vwBmKGUL9df3ZRpel5XIA4WlgBOAP/FuN+mJ5FDcmZuu5xT2ofWZnAv4GTkoZ2byfOnOZABaAJiu6bpO5f5tTq5JtkXZl1+X8l2AW8hzkFP9mXVGgn6sclS/JPB/ZH/4cPKIBEnzRMQ6klaHHIXSWGWn3hvkF3sdchj0hpHzcJYq5YtExBNNVnAyZiS/vE+Qo+n+S3ZPHAWcJenzwHjot+9Lj1Q+q3NQDhTIg6XtI7trlouIfSUtGxFvNlrZKZuXHMXV6jrdKfLIf36yVXBdlImt/dQIYDtymPc8ZIuOiDhL0kTg2Wn6zDYdVX2ZqiMRdbi+ETkc815gWCnbAvgBZXBBf7l0sm8rlX0bTc4rghxF80dggabr27He5HySRcnhoyuRLbMdKtstBqzQdH0beF1ag1yWKq/JI8A8pWwjckLlPE3Xdwr7MF35uwLZArgdWLKUbV3K5m66vtO6r2QX2nXkKLXFSvmWwM698Rxu6fRDERFlItpq5ByG28kPyWvA7Mqkid8HDop+Nhu67NsWwKfJk+2/JefeHA+sJWlxckjqgdFGR5Ol3luSLc7ryOHpu5MT6E6WNHNEnBkRjzVZz7qV12VDMu/fKOCv5CTlTwCfkPQyOYLt0Ih4obmaTl7Zh83JXGMvRMTxkkaTvQorS/ow2Yo9IPppmilJI8gDpdHASeRotcsj4rEyB+k4Sotnmp+rRDHrR8rs7TOBG8hRXHuTH5jVyOHDzwK/jIhL2+HEek8oM0WfTXZJDSHnOHyRPPH+MWB24IKIuLad9q3M1r6I7O/eg2xpbhsRz0r6JPAbYI1oz67APiNpJXLfLyYDzYXkOZ15gG+Q3Y4XR8Tl7fR+VklaghwscCw5+fPhiNhF0lfI86izkOdRr2rXfeiKpPXJia3/Jc+pXU0ewJ5ItkiXBv4vIq7slefrZ6/PoFUZVTKEHCUzKSKuk/QlYBvgx+X6nOT7+lJ/+QJU9m1ZYHWyG+2XZZj07uQX+4CIeETSdNHDXE99rQxHn5cc9vt34NvALhExRtInIuIGSfNGxIRGK1qTyvu5GDl5cMGIOKf8uO1G5gE8NyKea72f7fpZlbQ8efCzVEScUcpuA/4VETuX67NH5o3rNyrv0axkz8E1wEPkHLI5yYOEu8hM7jNHRO8lpm26D9GXHvW3bkFmJb4F+EOlfE/yx+5TTddxKvapdeDzSTItzC3kyKAFS/n8wGFky2dO2uQcVaXea5B934uSKU/G8+6otA3I7qTFmq5vA6/PpsDTZFfjf3j3fNw6wO/JwDxL0/Wcwnu7Pnn0fys5ym5EZZt7gavL//11lNo2ZK/CnZSMAuSgpIPIwR4b9sXzuqXTT5RWwIFkt9pbZP/q2Ig4sNy+F3B3RNzaXC17pnK0tQo5U3+fiHhI0gXAWODYiHhS0nBg9oh4pNEKd6DMAbcjmeDxPEmbkLPT3yC7Pr8HHBwRlzVYzdqVz2rrh+t24BRyAMWnIyf3rgu8GG08SVnSJ8jAeQ75fduN7HK6PCL+UbZZKyL+1lglp0Hp9vw5OdhoA3I49JqRvQlLkkO/z4+IXs8X54SfbUrS9NI7CRGHA6eTXTh3kq2BnwILSjoBICJO7S8BR5l1mRJwFgaWJYfPfqhs8iVySO3hkhaIiKfaLeAUi5NLFLRWLL2ZHBL9PzL10Hci4rLW+zhQSZqxZBFofVYPIN/LmSMHsuxDTuy9RtJsEdF2WTGUC5RVjSQTyD5bfngvA2YFRkpaDaA/BRxV1rwpQeUrwH8jYlREfJscPHCjpA+V79qxfRFwwEGnLZUf5bWA+cvs7eXJlsCc5NHX22S/+Cllm2WaqmtPlQ//pyR9RdKa5GivW8mkj3tLWjcinie/FPOTgbYtVA4C5iuttDPJQQ5fLvV+JSIejoh9I+KYiLgaBvY8nBJs1gLWkfQZcn7HieRggXXKj9hb5HmDB8nhxm2lBJydJH1I0vqSfhYRXyG7Sy+GdwLMleT8qx6tlNk0STMDa5f/P0QO5X8MmEfSpwEi4nCyq+3vyvWe3uqzCjXdr+hLp32ts5Bf0takwvVK+bZkt81IYHpyTP1cTdd3KvZvDrJP/GXgI6VsATLQXAJsUMpmaLqundR9K3JRvDOAjUvZ58m5RBs0Xb8GXo/pyHk2o8j1gVqJS1ckW+cH8m725bbNvECOjnyRXHbiY5Xyy8h1jlrX52m6rlOxbwuSB0eXk+fX5gdmI/Op/RjYurLt0n1dH7d02kwZzfMGOV5+ObLL5r/KPEcXk6lDvg2MjNRvjroq3Uxrkqn87ycDKZFzbs4jT9h+W5lQsK1aCKWf/zAyyeE8wNHK1UrPIReQO72MLhwUKiMJJ5InoP8GzKJMPHsvOf/mI8Cnywivtno/4T2fyX+ROfJmIfOpARARWwGvSLqrFPWb71tLRIwnuwbXIHsVXoiI18jzw08AG0narmz+cF/Xx0GnjZQum7clbUCe19geeJSch/NRgIi4lPwy994QxppERCgX7DqGDJ6bk4ub/bhsMoTs+98zIiZEm0xsrfSHr0S+LyuTR4+/A/aS9OWI+A2wVmTX4KBQPqvrAIeSJ6LPJ+fitH7AxlDmXEUbDimuDGRZiexG+zaZz+/3ZVAIkhaLiM3I/SPabLh+d5Sh6muQgzvuBn4oacnIzOZXkiMu74V69s+j19qMcmbwzsBNEfF7SUPJ8x5vAC+Qo6U2iTaajd9d5QTmecDJ8e6ch6XIrAPjyQWi9o42WUK78qM0S2l9tvrHzwW+ERGPS7oamEBmi2777Mi9SZlF+2tkVvOfl/M7u5Ctm5mAjcmFzP7VYDW7VILmCeRqmOeXss+RA0JOIbugNo+I0Y1VchqU870/A74ZEfdLWpucwDwDGYBWIRcWHFdXndzSaROVo+ktyQ/F/MrUKc+SJ9nHk8NOD+2PAaeYAXgG+IqkuQAiYgw5OGI0sF+7BBx4p2W2CXCapO+V/98iz6XtJWllMrvw8YMp4FS6pJYtl49IWjByOekzyEzn/yEXYWvbgFM8TOaD27ZVEBHnkucXZyDz5vW7gKM0FzmH7wPlQkTcTJ6neoFs+fy1zoADbuk0rnI0PU+U3FOS9iNT438PeCBykbLpyImR/2vdp8Fqd0tl3xYG3oxMCTMfGURnIY++2jZXVekK/BFwKnlifHZyyPo/yC6lhYEjSpfngFd5PxeNiMdL2fpkfrlR5IJlT3d2n9orOxmVfRhKDlR5UtJC5Ht6WkQcVNl2+nbp4u2ujq+3MlfhvmRPyYURcWfltvkis0LU+h456LQBZTLBL5Mf/Bsj4npJB5PrdhwN3NUf+5IBJH2K3Ic7yHkbO5W5HAeSI9b2asfBEMoccBeQ5yO+XwYIfJw8D/U9skttodLF1lY/rH2pvJ8Hk+s0TSATsa5LdgnfCFwWEc80V8PJqwScbchuwUnAJRFxsqQFyX26NCK+1WQ9p1Zl/zYhP6cPka3OSeT+vk5+nhttubl7rWFlRNQxZFP3o8BBknaNiKPJ0V1HkEfY/U7pL/8B2XXxd+Bzkq6JiKfIfZ5ATrBsV3cBe0haugwQuIpMQLpkRExqHe0PooCzFvl+7kQOuR1JtgRvIgcRbEyey2lL5Qd5A/K7tj2lxSpp/zLC6+PAjpKWqXQh9htl/z5FZpi/kcw08Etyft8J5ECdbZX5GRvjpQ0aUrrLZiQDzefJGfjDybQb25eDlkMkfTAiXm6wqtPiFfIHahkycedQ4K+Sro6ITSV9I3LiYNspLZjvkcv1/kTSYWQ/+HAyHcqgUTnfOJxs0SxNDnv/Htm1dhx5wv3Wcg6ync1JZrdeiwwyXwaOVSZkPaCMVmvLz+SUSJqbHD04EvgwsAg5Ou0EMivEscAcjf+eRBtMXhpMF96/INT05BfhMmD+UnYTeUJ2kabrO5X7NiswW2X/Tga+UK5/hxxMsErT9e1iP6av/L8QOcN+AtnSWbu6rwP5Unk/Z+tQfhrvTuo9nTxQWq7p+k5hH+aslE1X6rx+uX5i+c4t2nR9p3b/KtfnIw8Kbil/h5Mtur+Q+Qsbr7NbOjWLiJC0MZnD6S7gwcglCRYn56zcRU62Oy762Yiosm/bkJMn55D0SzKj8FhgSUm7kZl7Px59lNepJ6rnYpTLKPwvIp6LiEnKybgTI+IJSUeS+7A6OYlwUCjv5xbAvpLuBcZFxE/IGe2fL0OkVwD2iIgHmqzr5FT2YXdJb5ETIm8mU/LsXrqaliHz5D3eYFWnStm/T5Dd1G9FLiExPfBoZPLc1oTQE6NN5kp5IEHNlMkCf0pORpubHCf/M3Io7k/JZJHfj4g/NFbJqaRcQfGXZGtmKPB14CwyRcy25AS1k9th35SLri0VEX8u/eDHkfMWxkbEfmWbGSJiYmX7fSgpRaKfdsH0hHItmcPJxeneIE9GX00uynY++Zn9dURc2Fglp0DS6mT30oHkgIe5ydRSd5FTEzYEDo+Iyxuq4lSpDBoYQR7YnUseFL0dEZtIuhJ4lexC/FK0U6bzpptag+lCHlH9G9i9XJ+H/OCfTZ6YXZDSpUY/674h5zqcD1xUKVuL/IKvXK7P0i77Rk5ivI3s/z6bbIEtQZ6A/UVluxkq/y8IDG267jW8NiIXX3udzDbcKl+UXEFzKXLI+1zt8n5OZj8WKZ/J31bKtiFXL124XB/azvswhf1bD/gV782ddjUZZEWuJLx8u+2fR6/V60kyKeLXASLn5dxOHn0tGBHjo3SpRfmk9COPkak05pK0kTKF/d/IL/1iAFFm9Te5b6XrgYg4i2yV7Uv28d8euTriZ4BlJJ1ZtpvYum95f9r9RPk0i/Qw+fp8WZkHj8jup8fJcx9vRBnq3saf1SDPZ6xYun2JiEvICb2rl20mlPJ23YeuLE6u37R8pWx3YBh5XvKOiLgf2mv/HHT6UGvYpaSFJH0gciLkJsAYSVeUuR/DyFndszRY1R6r7NsakjYkf4iOIlfR3I48D7ARmbbnucYqWqFcMmINSUNKP/jdwC/IZJVrS5qpBJWRwIcktV0a/r5SeT+XLUPdiYhvkIMGHiwHEh8lu6PeaKyiXajswyqSlgNmjIgfAr8mhwp/tbyny5AtcKIfzX+r7F91aY09yWH9nyijDJch0xC1zZIg79N0U2ugX8i0NneQo9NOLWWzA38kh+P+ljyx3nhdp2LftiJn6P+A7Pf/bCnfj2zB/Yp3l8FtdElfsvtyOrJv/0LyR2eNctteZLfRRsBMpWzGpl/fBl6jLci5YWeSS2isUMqPIddwOqVV1q4XclLkA8D+ZGqlj5byb5Gpea7h3aVC+t0y03S+tMbnyBboeeT5tm2brmeX+9B0BQbyhTxxeRc57PaL5Ki008n+1tnKl/iKyvZt0+/ajX37ELkMwQJkk/4+MuvyTuX2/cnlcNdq+gecPHd2YHndVydHop0NLFDZZk8yNf8m/el96MXXaLVyoLAged7jeTLVf2to9MFkF+qs5XrbvUbk3JQ7yXNzXyAHsPwHWLfcvg85fL9frntEzsG5k0y/dEl5v1rnh7ctn+vWgV/bBlR3r/WtCcAeZEr83chutNXJL3MA3wVC0lnQXv2u3fAaOcluKXJU00gy8HxH0p7kaLD/kT9gMzZTRVDm2PokeQS4MHl+aX1yrtA+klYBiIhfkyPtnu9n70NveYhs8X2YDDDLku/xeZJGRGbIuBq4tXVerA29SJ7jWIQ8V/dRskVwsaR1yZbAw8BWkuZoqpLTYEXyPVqZ9y+tcTG5TPgpktaKNu429DydXlJyNw2LiHvKdUXEfaWf9WTgVxExRtJp5BHXohHxb2Ua9UbTUkyJpFnJOSwTW0M1Iwc8jJW0Kzls9gFl1uWbgTsi57p8BxgSuWBUE/WegcwWPBvZ/bcv8DFynZejy2Wrcn5nW2CbiGiL8099SSWRZWtIuHIxtpeAf0g6ABgVEU9J+j15vqs1AORLkuaPNk2CGRFPAJRzjGdFxMuSHiaTkU4fmXD2d8DrEfFKk3WdGhFxYplXtD+wXWTWjI2BdSVdERG/Led9nu76kZrloNN71iZTvD8ITGwdLUcudPVP4GNlMt3G5Kqf/y63v0wu29yWlFmhvwv8SdIN8f4s168Bp5Yf+G8Bu0TEP8oP20SyRdFEvVV+UK8mz1HcBJzEu8kPf0Ee0e9JHhGfMEgCzlDgBkmblx+tjpmU7wIOkHQEueTEt8rBU2u7dk3m2VrFFDILxhqS9iUXQBwZEXeXz8QTzdWyV7xOHqTuKeliOiytERFnN1m57nD3Wu95hEyoN3tEhN7NVwWZ7HIMecLvlIi4o4kKTqUXyW6yTYB1yo9PtEbSRMTvgV2BOchJkzeW8saOhpUZovdXpm6/nQwuPyTPQ51NDqPdkzwpfgywW0Rc1NqngSxydN51wF8kLVxaPNXustHkPI9FyLWbbir3m1T+Nt71WBnFNXs5kGsd3LW+c6eQ+7EAubje3WWbxuveHZP7HFYO5A4hz8GdRh4s3V5n/aaVMxL0ImW6lGWA7Vs/zNUPuqRZI+L1juXtqtL9sj6Z3HG68veWUv7OUXKHI81GSTqKbHWNIgcQPEMOdpiTTHo4L5lleAXyR2nAz72B975Hkn5AJu9cIyLGqZJ9ocN92vKzqswi8TVyoMBTEXFYKX/f57Bd96EzrbpK+jiZVPUecmXW8R22m56c2ze2P+0fOOj0iuqbXr7Mt5UTe5Pdrr8o/ePHA4eRgyLGkSk3/lqOkttmnypBcnpy5NyK5Ii0h8iJdHMCv4xctndJYFJEPNZYhWtU+TGbMXKFTyQdQ2Y47zLwtJvyg3w8mVViB3JawloR8Xq5vW0OgKaGpPXIibmXACPInpLLI+K2cnu/3j93r/WCancT2XWzVOu2alO5XX6cu6PSVbER8PvIfGlbAc+SrYd1yg9YW+yTMmHnl8vInUnkap8XkskOnyKH0e4MnFB+gB8ZhAFnC+AwSccDRMR3yRF7N0lapD8EnGIOMr/fEuQ50m1KD8KHoX9N+OxI0tLkej/7R8QBwDfJHHfrlNvVn/cPHHT6wvXARsphw/0q0FRVPtj3AEso1xmZFLmc7xJk99RcjVXw/WYjg/1RknYi52jMA7wcmezwADK9+0rkjO1BowSczYGjyLREW0i6StLQ8n5eDIyWNHM7nteqnMNZpozemo0cCn0wsFFEPFZa5F+XNE9jFZ1GZT+XIZcn+Fw5qLuXnKi7vXLNn375e1LloNNDlS/AspLmV2Yfbn2xpyvnB74KfFzSik3Wtacq+/YxSTsrM9j+hxwyu0HZ56VK2entNNorIh4hh5IeS66c+CUyy+6hklaMiP+SEwbXap1YHgyUZgY+S+7/omQOwLeAKyUNi1yeeb2IeLMdf9QqrbSTgMUi4iLgcnLdppkkbUWuiXN5ZD7DfqPynZsfmCcirgC+Tc7x+27Z7GkyI8TMjVSyl/mczlQoX4DvkalTlgMOiYj7ym2tFUEPAq6JTHrZbyjXV/8p+SU+nDyPI3JI+AhyhN6h0U6p0juQtCx5kvlJMvfb/cA+kctkDwqdDGKZg8zzdx6wYUS8IukZMqv2juT5rbbstindZucDe7bOa5Tyn5OLlM0G/Dwirmqnc4zdJWlL8pzpK2TWh6PJBdi+QfYmvAz8KCKubqiKvcrzdHpI0hLkkMUtyGG38wHjWif3yhf3TUnnRJmL0x+UI645yOHPW5IjvJ4C/h4Rz0j6E/kFmDUiHmnnL3dE/FPSIeTotFXJ5SO+02yt6tNhBNQK5OCPK8kfr8fIBfVmIPMBnt4aWNAuyiCPHSOzIEBmkni4ciJ9poh4KyL2Kddnj7JAWbt+Jien9BwcTB7c3UMOINiHbOW8TGb6eL4VcNr5e9dd7l7rOZGZlNckA88eEfE8maV4SGujVsBpxz7yFkmzSRperi5GTjy7jfyB/hmwRQk42wNLR6b2fwTa/8sduQLojRGxLdml9mjTdaqD3p1HtR553mN+4EjynNZy5HpOB5EB5/yIuLmpunZG0kLk79INyvlWkAk8Q9Ka5Uf3LWVW5f2U83Reb6zC0+5NskX+VDlo/SJ53vFr5ITmUcAikvYdCAEH3NKZospR44rAC2R24hHATsCqEfGkpA2A/yOHcD5fvX+7fkhKMFwW2EXSo+SCUPuRXYOrA3tHxKPK3GSHkTmf+pVK6/Oucn1AfGk7U87NPBM5jH0ZMv3PdyPiQmU6m33I9/sHZN6uuSLizgar/D6lF+F2YM2I+JukeyTdHRE7S7qHzOO3jqR/kOd3vtpurbSp8DY5AXtlSa9FLn/yS3JxuUmSriUTBd89UD67PqfTDWXkz0+Bz0XE7eX6dmQf7I3AocD/tfN5js6UE8y/IL/M342IU5VpUn5CZiGYk5zFf0h/27fBpLyPBwFnR+b32448GX0/mcbmReVaOL8mR3u1XW4uSTORs+y/SM5PGU7OB7sDuCwi9pc0Etis3OWictK932odFJXRlp8lUxA9Q+YI/FpEXNNk/fqKg84UKBd9Op/MMnCfpFam4hfJL8izZILLfnMSs1pPZX6qDwNDgZ9ExN+VkytHkK2el+PdvFVtv2+DURm8Mjs5yGPPiDhEOWN/C/KH7DRy+enfAFtFm2VgKAMFdia/Z0eR5+H+LyJ+UwZA3AVcGhH7le1ni4jX+vNnUu9OZF6AHNq/MNkS/RAZZK9rsn59yUGnEx1+lBcmh+I+QQ7R3IRcMOmCiLiwuVpOnUp34SpkULk/Il6VtD85Ae0b5BDpT0TEOQ1W1bpB701tswo5yOXOiDhS0mfJE9Szkt04P46IK5ur7ftJmosc5NBqmZ1J1vXPwA2R2cvnILNKXBURu/e3YFP5zg0HZo4cvo9yGfDbgWMi4tRGK1kjn9PpRPmAbECes/mxpH+TweZX5FHjZ8gjk36n7NtG5Jf7L2Q23jUi4tgy5uF8ckTeng1W07qh/Ji9Xbp7FyW7z44Evinp0Ig4XNJrwKeB+1oBp51+tCPipXJO8UDyczeS7EnYHphL0sTIJUCWJrvf2vY86eSU79zWZBco5ZzUWeSw6ENaB3etQUf9bf96yqPXJu8Z4AeS9oqIk8glYC8n5zrsAjzYaO2mknIOy2eBT0fEDsDvgduV66QcS7Z0doyI6xuspnVD+THblBwcMDYy/c/dZJboJST9sASa64EVJO1UWkbt9qN2BTlMf0IZIXkLcBU5bH9HSctGxCsRcWM7jwatKueoWv8vQQ6B3g3YkBxstCnwp0rAmS6KBqpbKwedDiQNlTR3ZPqJVcnAs38ZSbI68GPgiIj4Y7M17RlJ0ykXY/sWuV8LAETE/uQKhP9Sprq/OSJubbCq1gVJ85TBHi0jgQMj4spynmASOd/jJGBxSYuR7+91wLXRBhNAJS0paW9JuygnI/+NPBB6W9IpAOX7dT15nuqdEWr94UdZ0oeAkyRtokzb8zY5NPqlyMXjfkguZb9j6z7t8L7UxUGnonxBf0SmfJmrBJ6Pk0kSvxY5Oe0LEXFJPzriatVzlsgsvN8hV/ccIekjAJGJBU8jT2Ram5I0GzmsfSaVdWTIg4f5W5uUv4tGrrHyxYh4LHLY+LnRBhkZJC0H/JE8Yb4iOZdoD/K8zjeApSSdCBARVwFHRsTDjVR2KpT9O5dscd4VuUjj88C9ZGqs4WVY9GnkkvWDzqAfSNCxf1vSPmRL4CLgbxHxvKSfkisQLhYR4xqq6lQrI5n2BsYCd5JHvseSM9WviMp8jXbq77f3U05AnoPsjjoN2IA8kDgyIm6QtBZwOvCpdvuxljQ3mTPtNxHxm1K2CLkfd0TEQeX6eeQ5qC/3p89jGRRxCfDbiDi9w207AOuTrZ77yPdsr4i4tuZqNm5QDySojCpZn8w8/Ao5b2UkOQ9nqKQXyeGoq/bTgLMumUNtV3JY6pcj4hfKdX8OBz4j6aFyRNYvui8GI7070fV5SWuSKf0nkfn/fgmcIeky8pzBfu0WcIo3yTQ8FwBImiVyEbI9gb9LujcifidpR3ICa3/7PL5Ojmy9CN5ZaO3tcqrmPEmvkOeElyczmQzK86aDOuiUgLMx2aV2PvAxMkP0WsBrZL/rBmSCy3ug/VsCkuaITObYWtVzHvI8zuJk1oFtyqYvk0PBh7UCjrWnyii1JYFxkXPCXiBbrwDnkOs4zQn8Otp3XtWs5Ai09YErI+INZR61x5XJO1sZ2x8nf7z7m9nJXpJ1yP2bVM6lTkdmiH4jcu5Rv16EbVoNuqBTxsp/MN7N/rwV8NNWc1jSyeTiX1uXczfzRsSE1pe4Db/I7ygj006S9B/g0dI3PjM5PHocsGnk7PSNyGG0+7VDP79NXqU1vglwMnCvpBvILqmfkwdJswPnRiXBbDt+Titd1Z+RNC4yPdGk1s1ktui2P7CbnIh4QdLPeO/+qQSfDYCRkm4le1QGrUE1kECZWXdbYB9lQkTINVeGVDY7gFzLYnaAiJhQ/rb1l6CcwPw1eb7mb+QJ5nUi4vfkRLuZydFBW5Ipfa6IiNcaqq51Uwk4a5Bzw0aSwWZhsvV6H9kdvDow02QfpL38ARhPrvK6YflBXgv4MjlvrO2/a1NwMe/u3wbkd25t8hzq7yLi5X6+f9Ns0A0kKK2Bdck0L8eRTf7rgN0j4lJJ65DzHLaMiPGNVbQHypyAB8nsAluVEWv7AbNFxBHl+lHkqoSzAz+LiFH99YhyMCkj1i4DhkTEaqVsI+CTZCLIHwLTlRFR/ULpbdie7B68nUzDdEREXNpoxXpJ2b+RZCv0TjIF0Q9bo14H+3duUASdMu/mxdZ5DuXs5vPJlOnfI1sFp5FDiT9GJr9sq3QhU6JM6HglcFhEnCzpYDI33D3kyoMnkM36J0tf+qD/8Lc7SR+IiP8qM5xfCFwYEQeX2zYDNgJOjogxTdZzainzjkEO5390oH0mS/B5m0x9M3ag7d/UGvBBR5mB9wHglMiUNtORX+CXyLVjViVndD8HzA3MHpnvqd99QJTLS19LdqfNB3ydTI/ycWBlcrmCfrOw3GCldxN43kxmjv5R6T49Fbg+Ig4p280XbbRkuFl3DPigA1CGmF5GrguzNnm0v58ymef2ZOD5VUTc2Fwte4cy6eP1wAmRubda6dOHRptlF7b30ruZh1t/VyczC5wdEScqszGfSya+PKjZ2ppNnUExei0iblEmRbwW+GdEfL6UjytzG0S2dPq9iPiHpE8CV0l6KSKOLzcNiP0biFojJEugWYGcuX5BRNwm6UvAWZJmjYhjJH2ebJGb9UuDoqXTImklsuvp2xFxWqV81sgUMQNGGfH0J3Ii2uP9ratwsJC0OHADuXT0AcpsxFsBt5ILlT1Xzt9cSU7sHTQp8G1gGhQtnZYyaW4jYJSk2SOileNpQAUcgIi4VZnA86Wm62JdmkhOXVhf0tERcbCkieQKmSKzDYwhh+I+0Fw1zXrHoGrptHRoBYwdqLODKxML+92giMFE0reB6ckUKSrnGz9FtngWIZNj7hkltb/fS+vPBmXQgUzO51aANaGks1k9Is4r1zci51EdSGaKeDUivitpGDnw5dmIuKmxCpv1osEcdNwKsNqVibz/Bj5ATuy8jTzPuAWZJ++vwNeANyNi784fxaz/GlRpcKpagcYBx+oUEW8BW5PZltcmz9tcQU5KXi0i7gZOBGYtc3PMBpRBG3TMmlICy9bACmRm6J3LTQuUbBn3AV+PCA8csAFn0HavmTWtpC76E/CViDhX0jzAKxExsdmamfWdQTVk2qydRMTtZSLvlSVjxIlN18msr7mlY9awwTKE3wwcdMzagofw22DhgQRm7eFlyKH8TVfErC+5pWNmZrVxS8fMzGrjoGNmZrVx0DEzs9o46JiZWW0cdGzAkBSSjqtc/7akwzpsc5ek8zqUnSHpNUlzVspOKI83tFyfVO7buhzQRT3+LGlE5friku6rXF9H0m2S/lkue3Woy3YdHu+V8nc6SSdKuk/SvZJul7REue3RSl27fB0k7STpHkn3S7pb0q9LNoTO9uXisr9jJL1Y2f8bJB1T2W4xSY9Imqfs/7/KY98saZnK6/KvymNcOLnX0AYuZySwgeRN4NOSfhARz3a8UdKHyXVrPl4W8Xu1cvMYMh/abyVNB2wAjKvc/npErDytFZS0AHAusE1E3FkCxTWSxkXElVO4+/bAQsBHIuJtSYsAr3ay3WRfB0mbAt8ENivLtU8P7AoMB17o+EARsW253/rkirtblOuzAndJOiMiHgR+CnwvIl4oo74/HxGjS0D9Ebk20DvlU9hPG8Dc0rGBZCJwKvmj2pkdgbOBP5IBpuo88kcdYH3g5vJ4vW1v4IyIuBOgBIX9gcm2nCoWBMa3MhZExNiIeL6T7bp6HQ4mg8e48hiTIuL0iPhXT3airLb7TeAkSZsDc0bEOZ1s+hdgqZ48tg1sDjo20JwEfF7S3J3ctj0ZXH5HBqCqfwPDJA0pt53X4fZZO3SvbU/XzmltC4yqlC8P3NFh29GlfEouALYsj3ucpFW62HZyr8PywJ3deK4piohRwPPAmcBXJ7PZlsC9levnVF7DH/VGPax/cfeaDSgR8ZKks4B9gddb5eUcy7MR8V9J44DTJc0bERMqd/8DsAOwBvClDg/d0+61d7qRJC1OrpnTrV2YXFlEjC3nRzYol+skfTYirnvfHSbzOlRJWpFs+c0JHBQR53ezjlUnAbN20lI6R9LrwKPkonQt7l4b5NzSsYHoBGAPYPZK2Y7AspIeBR4G5gI+0+F+5wNHAtf2YdLNB4DVOpStBtxf/n8OGNK6QdK8wDvnZSLizYi4KiK+A3wf2KaL5zqB978O9wOrlse6twTSq4BZe74rALxdLh19PiJWjohtIuLxqXxsG4AcdGzAKa2XC8gfXMrAgJHAihGxeEQsTp7T2bHD/R4jz3mc3IfVOwnYTdLKpW7zAccAx5bb/wxsr1zWGmA34Iay7aqSFir/Twd8hFyBtFMdX4fiB8CPyyCElqkNOGY95u41G6iOA/Yp/38cGBcRT1Ru/wuwnKQFq3eKiF9O5vFmLednWq6OiO6c/H+PiBgvaSfgV2WItoATIuLycvsVklYD7pA0iWyVfbncff5yv5nL9duAn0/hKauvAxExStIw4Koycu0FcqXSa3q6L1Op1e0G2d35yZqe19qEE36amVlt3L1mZma1cfea2VSSdDGwRIfi70ZEXV1VvWqg7Y+1J3evmZlZbdy9ZmZmtXHQMTOz2jjomJlZbRx0zMysNg46ZmZWm/8HmCYHKbcxhekAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Contact information**  \nThere are 6 features showing that client provieded some contact information, let's see how many ways of contact clients usually provied.  \n정보를 종합해 새로운 col 만들기","metadata":{}},{"cell_type":"code","source":"application_train['contact_info'] = application_train['FLAG_MOBIL'] + application_train['FLAG_EMP_PHONE'] + application_train['FLAG_WORK_PHONE'] + application_train['FLAG_CONT_MOBILE'] + application_train['FLAG_PHONE'] + application_train['FLAG_EMAIL']\n\nsns.countplot(application_train['contact_info']);\nplt.title('Count of ways to contact clients')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:42.607954Z","iopub.execute_input":"2021-07-02T07:07:42.608491Z","iopub.status.idle":"2021-07-02T07:07:42.836874Z","shell.execute_reply.started":"2021-07-02T07:07:42.608435Z","shell.execute_reply":"2021-07-02T07:07:42.835929Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","output_type":"stream"},{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Count of ways to contact clients')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"Most clients provide 3 ways to contact them and usually minimus is 2, if we don't consider several people who left only 1.","metadata":{}},{"cell_type":"markdown","source":"## deliquencies\nIt is very important to see how many times clients was late with payments or defaulted his loans.  \nI suppose into about his social circle is also important. I'll divide values into 2 groups: 0, 1 and more than 1.  \n\n고객들이 얼마나 많이 연체했거나 대출금을 갚지 못했는지 보는 것은 매우 중요하다.  \n나는 그의 사교계에 대한 정보 또한 중요하다고 생각한다. 값을 0, 1 및 1 이상의 두 그룹으로 나누겠습니다.  \n\n**OBS_30_CNT_SOCIAL_CIRCLE** : 30DPD(만기일 경과) 기본값을 통해 클라이언트 소셜 환경에 대한 관찰 횟수","metadata":{}},{"cell_type":"code","source":"application_train.loc[application_train['OBS_30_CNT_SOCIAL_CIRCLE'] > 1, 'OBS_30_CNT_SOCIAL_CIRCLE'] = '1+'\napplication_train.loc[application_train['DEF_30_CNT_SOCIAL_CIRCLE'] > 1, 'DEF_30_CNT_SOCIAL_CIRCLE'] = '1+'\napplication_train.loc[application_train['OBS_60_CNT_SOCIAL_CIRCLE'] > 1, 'OBS_60_CNT_SOCIAL_CIRCLE'] = '1+'\napplication_train.loc[application_train['DEF_60_CNT_SOCIAL_CIRCLE'] > 1, 'DEF_60_CNT_SOCIAL_CIRCLE'] = '1+'","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:42.838888Z","iopub.execute_input":"2021-07-02T07:07:42.839384Z","iopub.status.idle":"2021-07-02T07:07:43.151867Z","shell.execute_reply.started":"2021-07-02T07:07:42.839335Z","shell.execute_reply":"2021-07-02T07:07:43.150831Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize = (30, 8))\nplt.subplot(1, 4, 1)\nsns.countplot(application_train['OBS_30_CNT_SOCIAL_CIRCLE']);\nplt.subplot(1, 4, 2)\nsns.countplot(application_train['DEF_30_CNT_SOCIAL_CIRCLE']);\nplt.subplot(1, 4, 3)\nsns.countplot(application_train['OBS_60_CNT_SOCIAL_CIRCLE']);\nplt.subplot(1, 4, 4)\nsns.countplot(application_train['DEF_60_CNT_SOCIAL_CIRCLE']);","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:43.153460Z","iopub.execute_input":"2021-07-02T07:07:43.153805Z","iopub.status.idle":"2021-07-02T07:07:45.032615Z","shell.execute_reply.started":"2021-07-02T07:07:43.153772Z","shell.execute_reply":"2021-07-02T07:07:45.031602Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2160x576 with 4 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Continuous variables  \n**AMT_INCOME_TOTAL**","metadata":{}},{"cell_type":"code","source":"sns.boxplot(application_train['AMT_INCOME_TOTAL']);\nplt.title('AMT_INCOME_TOTAL boxplot');","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:45.034215Z","iopub.execute_input":"2021-07-02T07:07:45.034625Z","iopub.status.idle":"2021-07-02T07:07:45.217379Z","shell.execute_reply.started":"2021-07-02T07:07:45.034581Z","shell.execute_reply":"2021-07-02T07:07:45.216495Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"sns.boxplot(application_train[application_train['AMT_INCOME_TOTAL'] < np.percentile(application_train['AMT_INCOME_TOTAL'], 90)]['AMT_INCOME_TOTAL'])\nplt.title('AMT_INCOME_TOTAL boxplot on data within 90 percentile')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:45.218699Z","iopub.execute_input":"2021-07-02T07:07:45.219052Z","iopub.status.idle":"2021-07-02T07:07:45.801697Z","shell.execute_reply.started":"2021-07-02T07:07:45.218989Z","shell.execute_reply":"2021-07-02T07:07:45.800631Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","output_type":"stream"},{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_INCOME_TOTAL boxplot on data within 90 percentile')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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['mean', 'median', 'count']})","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:45.803081Z","iopub.execute_input":"2021-07-02T07:07:45.803404Z","iopub.status.idle":"2021-07-02T07:07:45.841036Z","shell.execute_reply.started":"2021-07-02T07:07:45.803372Z","shell.execute_reply":"2021-07-02T07:07:45.839439Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"       AMT_INCOME_TOTAL                  \n                   mean    median   count\nTARGET                                   \n0         169077.722266  148500.0  282686\n1         165611.760906  135000.0   24825","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">AMT_INCOME_TOTAL</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n      <th>count</th>\n    </tr>\n    <tr>\n      <th>TARGET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>169077.722266</td>\n      <td>148500.0</td>\n      <td>282686</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>165611.760906</td>\n      <td>135000.0</td>\n      <td>24825</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(application_train['AMT_INCOME_TOTAL'])\nplt.title('AMT_INCOME_TOTAL histogram')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:45.842785Z","iopub.execute_input":"2021-07-02T07:07:45.843332Z","iopub.status.idle":"2021-07-02T07:07:46.046342Z","shell.execute_reply.started":"2021-07-02T07:07:45.843268Z","shell.execute_reply":"2021-07-02T07:07:46.044920Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_INCOME_TOTAL histogram')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(application_train[application_train['AMT_INCOME_TOTAL'] < np.percentile(application_train['AMT_INCOME_TOTAL'], 90)]['AMT_INCOME_TOTAL'])\nplt.title('AMT_INCOME_TOTAL histogram on data within 90 percentile')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:46.047638Z","iopub.execute_input":"2021-07-02T07:07:46.047960Z","iopub.status.idle":"2021-07-02T07:07:46.640358Z","shell.execute_reply.started":"2021-07-02T07:07:46.047927Z","shell.execute_reply":"2021-07-02T07:07:46.639033Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_INCOME_TOTAL histogram on data within 90 percentile')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(np.log1p(application_train['AMT_INCOME_TOTAL']))\nplt.title('AMT_INCOME_TOTAL histogram on data with log1p transformation')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:46.641947Z","iopub.execute_input":"2021-07-02T07:07:46.642375Z","iopub.status.idle":"2021-07-02T07:07:46.855705Z","shell.execute_reply.started":"2021-07-02T07:07:46.642341Z","shell.execute_reply":"2021-07-02T07:07:46.854480Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_INCOME_TOTAL histogram on data with log1p transformation')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We can see following things from the information above:\n- income feature has some huge outliers. This could be due to rich individuals or due to errors in data.\n- average income is almost similar for those who repay the loans and those who dont\n- if we leave only data within 90 percentile, it is almost normally distributed (90퍼센타일 내의 그래프는 정규분포 꼴)\n- log transformation also helps","metadata":{}},{"cell_type":"markdown","source":"**AMT_CREDIT**","metadata":{}},{"cell_type":"code","source":"sns.boxplot(application_train['AMT_CREDIT'], orient='v')\nplt.title('AMT_CREDIT boxplot')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:46.857100Z","iopub.execute_input":"2021-07-02T07:07:46.857425Z","iopub.status.idle":"2021-07-02T07:07:47.051045Z","shell.execute_reply.started":"2021-07-02T07:07:46.857393Z","shell.execute_reply":"2021-07-02T07:07:47.049751Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/opt/conda/lib/python3.7/site-packages/seaborn/_core.py:1319: UserWarning: Vertical orientation ignored with only `x` specified.\n  warnings.warn(single_var_warning.format(\"Vertical\", \"x\"))\n","output_type":"stream"},{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_CREDIT boxplot')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"sns.boxplot(application_train[application_train['AMT_CREDIT'] < np.percentile(application_train['AMT_CREDIT'], 95)]['AMT_CREDIT'], orient='v')\nplt.title('AMT_CREDIT boxplot on data within 90 percentile')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:47.053169Z","iopub.execute_input":"2021-07-02T07:07:47.053532Z","iopub.status.idle":"2021-07-02T07:07:47.405242Z","shell.execute_reply.started":"2021-07-02T07:07:47.053499Z","shell.execute_reply":"2021-07-02T07:07:47.404189Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n/opt/conda/lib/python3.7/site-packages/seaborn/_core.py:1319: UserWarning: Vertical orientation ignored with only `x` specified.\n  warnings.warn(single_var_warning.format(\"Vertical\", \"x\"))\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_CREDIT boxplot on data within 90 percentile')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"application_train.groupby('TARGET').agg({'AMT_CREDIT': ['mean', 'median', 'count']})","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:47.406703Z","iopub.execute_input":"2021-07-02T07:07:47.406999Z","iopub.status.idle":"2021-07-02T07:07:47.440089Z","shell.execute_reply.started":"2021-07-02T07:07:47.406969Z","shell.execute_reply":"2021-07-02T07:07:47.439036Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"           AMT_CREDIT                  \n                 mean    median   count\nTARGET                                 \n0       602648.282002  517788.0  282686\n1       557778.527674  497520.0   24825","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">AMT_CREDIT</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n      <th>count</th>\n    </tr>\n    <tr>\n      <th>TARGET</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>602648.282002</td>\n      <td>517788.0</td>\n      <td>282686</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>557778.527674</td>\n      <td>497520.0</td>\n      <td>24825</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.hist(application_train['AMT_CREDIT'])\nplt.title('AMT_CREDIT histogram')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:47.441898Z","iopub.execute_input":"2021-07-02T07:07:47.442342Z","iopub.status.idle":"2021-07-02T07:07:47.650554Z","shell.execute_reply.started":"2021-07-02T07:07:47.442301Z","shell.execute_reply":"2021-07-02T07:07:47.649473Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_CREDIT histogram')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(application_train[application_train['AMT_CREDIT'] < np.percentile(application_train['AMT_CREDIT'], 90)]['AMT_CREDIT'])\nplt.title('AMT_INCOME_TOTAL histogram on data within 90 percentile')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:47.651970Z","iopub.execute_input":"2021-07-02T07:07:47.652293Z","iopub.status.idle":"2021-07-02T07:07:48.014732Z","shell.execute_reply.started":"2021-07-02T07:07:47.652264Z","shell.execute_reply":"2021-07-02T07:07:48.013710Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_INCOME_TOTAL histogram on data within 90 percentile')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAZEAAAEVCAYAAAAhANiZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdvUlEQVR4nO3de7xcVX338c+XhKtcAiRSSAIHJbZGlIsRg7ZqQSFcanhVpSBKsFEeRVq1Xhovj0QUn1gttHkENUpKQBQQFVKBIuUiogQIRVBA5MgtCQFCEsJNwMCvf6x1kp1x5sycdS4zE77v12temVl77b3Xmj0z373X3mdHEYGZmVmJTdrdADMz614OETMzK+YQMTOzYg4RMzMr5hAxM7NiDhEzMyvmELG2ktQjKSSNbjD9M5K+M9Lt2pjk93ePdrejj6RdJT0paVQ/dRq2WdIxkn46fC3cuFW/U82+f63oqBCRdI2k1ZI2ryk/K3d0ek35abn8uPzGPJkfz0h6vvL69ibrXfeBlTQ7vz6yMn10LuuplO0n6VJJj0laJelGSe+rTB8j6RuSHpL0tKRfV6fnOvdJek7S2JryW6rry/1/rtKfJyXd2k9//qpS76m8rOq8u+Z6h+d2PyVppaRzJU3I01p6P5tssy/19763IiK+HBHvb1Yvt6NpPWtsKH5QWhERD0TE1hHxfF7vgLZdRJwbEQeVrFvS5vl348H8uT1D0qaV6TtI+nH+Ttwv6d0l6+kUkt4iaWm1rNXvVKs6JkTyD+ZfAQG8vU6V3wHHVuqPBo4Efg/r3pitI2Jr4IPA9X2vI+JVA2zOKuALjfaUJO0PXAX8DNgD2BH4EHBInr4Z8N/AbsD+wHbAJ4E5kv6pZnH3AkdXlv1qYKs6q/2XSn+2joi9GjU+In5eeS/6+j6mMu8Dkt4JfA/4N2BsrvcscJ2k7Vt5P1vYZhuF4f5RtRE1C5gC7Am8AtgX+Fxl+unAc8BOwDHANyQN9PdjwLr6MxYRHfEAPg/8AjgV+EnNtLOArwEPA9vnssOBy4DrgONq6h8HXDeAdQewR34+GzgXuBWYkctG5zo9+fV1wOn9LG8m8AjwkpryvwOeBLbNr+8jfYBvqtT5GvDZmvWdBXyp8H3tycsaXSkTcD/wqZq6mwC/AU5u5f1sYZs1bXOlfTOAB4BHgc9Wps8GvpufbwF8F1gJPAbcRPqynwI8DzyT39+v5/pvyHXW5H/fUFnu7sC1wBOkwD+9sp6+Ns3Mbbo2l/8AeCgv71rgVTX9PSN/Jp/M78ufkUJ6NfBbYJ9+3of+2noN8MW8zCeAnwJj+1nWJ4HlwIPA37Ph5/sw4BbgcWAJMLsy3wO57pP5sT/wctIO08q8bc4l7ZDUW+8XgP+fn28KPAV8Nb/eMm+fHSrv7+h+tl2Qdl7uztv6dED1Po/91a3TxsXAuyqv3w0syc9fQgqQV1SmnwPMabCs2cCFwPl5u/wPsFdl+i7AD4EVpJ3Ff6wz73fztnh/fm/+I2+31cBFlfqHA7/K/fsl8JrKtPuATwC35c/P+aTvykuAPwAvVLbpLmz4nVq3LfLr7YAzSZ+fZcCXgFH9fYc75kiEdJRxbn4cLGmnmunPABcDR1Xqnz1MbQng/wInVQ91ASRtRfpyXdjP/G8DLouIp2rKf0jauPtXyhYB20p6ZT7yOYr0wRpOfw7sSvpRXCciXshtfFuLy2m2zQbiL3O7DgQ+L+mVderMIH3IJ5KO/j4I/CEiPgv8HDgx0pHSiZJ2AC4B5ua6pwKXSNoxL+t7wI152mzgvXXW92bglcDB+fVlwCTgpaQfjHNr6h9J2ikYSzqquz7XG0v6vJxar+MttBXSj9378ro3I/1o1FvWtDztbbmtb62p8hRpu40hBcqHJB2Rp70p/zsmv4/Xk3Y4/h/px+eVpPd+dr11k47M35Kfv44UuH3L3B+4KyJWVWeot+0qkw/Py3kN6b09mMYGUlc1zydI2o50ZLI2In5XmX4r64/m65lO+h7tQPpMXSRpU0mbAP+Z5x9P+lx/VNLBNfNeSNoW55ICa6u8vpcCpwFI2geYD/wf0ufjW8DCmiHkI4FppJ2j15B2rJ8ijY48GOtHER7spy+QdobWkkZY9gEOIgVcQx0RIpL+kjT0c0FE3Ewaoqo3Fnk2cKykMaQv+EXD1aaIWEjag6h9A7cnvW/L+5l9bL3pEbGWtDc3tmbSOaQv9tuAO0l7ALU+kc+/9D0WtNSRxu2jXhtzWW37/sQAtlmrvhARf4iIW0lfvHrDdX8kfYn2iIjnI+LmiHi8wfIOA+6OiHMiYm1EfJ90NPA3+ZzQ64DPR8RzEXEdsLDOMmZHxFMR8QeAiJgfEU9ExLOkH9K98o9Pnx/nNj0D/Bh4JiLOjjT2fz7pSzmgtlbq/EdE/C635QJg7wbLOjLX/U3+EZldnRgR10TEryPihYi4Dfg+6btUV0T0RsQVEfFsRKwgBVyj+tcDk3L4vYm0Rzte0tZ5np81Wk8DcyLisYh4ALiaxn0eSN3/Aj4iaZykPwP+MZdvBWxNOiqoWgNs0896b46ICyPij6T3ZgtgKunzNS4iTs6fsXuAb7N+JxjSEPFFeedtDOkH/4MRsToi/hgRfe/X8cC3IuKG/LlfQNpJmVpZ1tyIeDCH9H/20/+G8k7gocBH8+f+EVKQHdXffB0RIqQ9zJ9GxKP59fdy2Qbyl30cabjnJ31f7mH0ubyuLSplq0mHhzv3M9+j9abncc+xeXrVOaQf4ONofHT1tYgYU3n8yfszAH3rr9eHneu0r56WttkAPFR5/jTpC13rHOBy4Lx8YvRfao8UK3YhDdlV3U/aK9wFWBURT1emLamzjHVlkkZJmiPp95IeJw0hwIaB+3Dl+R/qvK7Xp2Zt7dPK+9O3rGpfNliupNdLulrSCklrSEdzDXcaJO0k6TxJy3K/v9uofv4+LiYFxptIofFL4I2UhUirfR5I3VNIw3m/ym27iLRz8jB5qLmm/rakoapG1r3XOQyWkrbBbsAu1R0/4DOk4dc/mZd0hLcqIlbXWcduwMdrljUxr6fPQN6rRnYjDUMur6znW6SjoobaHiKStiTtPb1Z6Uqmh4CPkfby6u2Nfhf4OMM3lLVORFwB9AInVMqeJu1xvaOfWf8bOETSS2rK30Hag1hUs577SWOmhwI/GnzLm7qL9GF/V7UwH4K/A7iyv5kLttmQyHtnX4iIyaRzCIez/mKL2ttRP0j6UlTtSjrKWw7skIcm+0yst8rK83eThh/eShpS68nlYvD6a+tALWfDvuxaM/17pKOuiRGxHfBN1veh3i29v5zLXx0R2wLvof8+/ww4gHTUdVN+fTCwH+k8Uj0jdivxfLR7YkSMj4iXkc713JwD4HfAaEmTKrPsBfR3dee69zp/fyaQtucS4N6aHb9tIuLQanMqz5eQPpNj6qxjCXBKzbK2ykesTbvcQp3qep4lnW/rW8+20eTCpLaHCHAE6cTaZNIh2N6ksdefU7kaq2Iuadin0QdyqH0W+FRN2aeA4yR9sm/cWtJeks7L088h/Uj/IF82uWkeC51LGiJZU2c9M4ED6pxHGXIREaRx889JerekLfKh/XdIe16nNVnEEbS2zUblZfc9NhtMuyX9taRX53NHj5P2IF/Ikx8GXlapfinwity/0ZL+Lrf3Jzm0FwOzJW2Wr7arDh3Vsw3pC7aSNPTx5cH0pUbDthYs6wLSZ3NyDsmTaqZvQ9rjfUbSfmw4BLmC9H6+rKb+k8AaSeNJJ+378zPSZ+COiHiOdFHA+0k/qCsazFO77YaNpPGSdlEylXzuEyB/934EnCzpJZLeSNpxOKefRb5W0t/mUYaPsn4n8UbgCUn/LGnLfCS7p6TX1VtIRCwnnXM7Q9L2+Tej73zSt4EP5qNI5bYdJqm/YbY+DwM71gy71pXb8FPgXyVtK2kTSS+X1HC4EzojRGaQxnAfiIiH+h7A14FjVHPpW0Ssiogr8w/hsIuIX5A+ENWyX5L2tg4A7pG0CphH+jEgj5m/lZTsN5B+8E4lXXX01Qbr+X1ELO6nKZ/Shn/r0cqQU3/9Op90MvljpB/GO0hX0LwxIlY2mb3VbTaLNIzT97hqMG0mXe10Ien9vJP0g9X3Bf934J1K1/7PzX04nHTUupIU/IdXht+OIZ3sXUm6AuV80g9AI2eThoaWkd6rRf3UHZAW2jqQZV1GuiLsKtJRdO17fgLpR/IJ0tV1F1TmfZo03POLPJwxlXTF1b6kcwOX0PxI+Zekz1HfTt4dpIti+tvp22DbNevjIL2c1MangAXArIio/uHiCaT2P0I6X/ShiOjvSORi0lWXq0nfp7/NR8zPk7bp3qRRhkdJO2n9/Zi/l7Rj9Nu8/o8C5N+FD5C+X6tJ2/W4VjobEb/N/bgnb9NdmsxyLOnCjTvyui6k/6H7dZfMmb2oSTof+G1E1O65m9UlaTbpIo/3tLst7dQJRyJmI07S6/Kh+iZKl8VOZxiv9jPbWL0oQkQb3gZkg0e72zYYSvcQqtevfm/zYkAaGruGNN4/lzRscUtbW2TWhTycZWZmxV4URyJmZjY8uvamX2PHjo2enp52N8PMrGvcfPPNj0bEuKFcZteGSE9PD4sX93dFrJmZVUmqvTPCoHk4y8zMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKde1frHejnlmXtG3d9805rG3rNrONl49EzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJivznqRaNeVYb4qzGzj5iMRMzMr5hAxM7NiDhEzMyvmEDEzs2IOETMzK+YQMTOzYg4RMzMr5hAxM7NiDhEzMyvmEDEzs2IOETMzK+YQMTOzYg4RMzMr5hAxM7NiDhEzMyvmEDEzs2IOETMzK+YQMTOzYg4RMzMr5hAxM7NiLYeIpFGSbpH0k/x6d0k3SOqVdL6kzXL55vl1b57eU1nGp3P5XZIOrpRPy2W9kmYNYf/MzGwYDeRI5CPAnZXXXwFOi4g9gNXAzFw+E1idy0/L9ZA0GTgKeBUwDTgjB9Mo4HTgEGAycHSua2ZmHa6lEJE0ATgM+E5+LeAA4MJcZQFwRH4+Pb8mTz8w158OnBcRz0bEvUAvsF9+9EbEPRHxHHBermtmZh2u1SORfwM+BbyQX+8IPBYRa/PrpcD4/Hw8sAQgT1+T668rr5mnUfmfkHS8pMWSFq9YsaLFppuZ2XBpGiKSDgceiYibR6A9/YqIeRExJSKmjBs3rt3NMTN70RvdQp03Am+XdCiwBbAt8O/AGEmj89HGBGBZrr8MmAgslTQa2A5YWSnvU52nUbmZmXWwpkciEfHpiJgQET2kE+NXRcQxwNXAO3O1GcDF+fnC/Jo8/aqIiFx+VL56a3dgEnAjcBMwKV/ttVlex8Ih6Z2ZmQ2rVo5EGvln4DxJXwJuAc7M5WcC50jqBVaRQoGIuF3SBcAdwFrgwxHxPICkE4HLgVHA/Ii4fRDtMjOzETKgEImIa4Br8vN7SFdW1dZ5BnhXg/lPAU6pU34pcOlA2mJmZu3nv1g3M7NiDhEzMyvmEDEzs2IOETMzKzaYq7PMOlrPrEvast775hzWlvWatYOPRMzMrJhDxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo1DRFJW0i6UdKtkm6X9IVcvrukGyT1Sjpf0ma5fPP8ujdP76ks69O5/C5JB1fKp+WyXkmzhqGfZmY2DFo5EnkWOCAi9gL2BqZJmgp8BTgtIvYAVgMzc/2ZwOpcflquh6TJwFHAq4BpwBmSRkkaBZwOHAJMBo7Odc3MrMONblYhIgJ4Mr/cND8COAB4dy5fAMwGvgFMz88BLgS+Lkm5/LyIeBa4V1IvsF+u1xsR9wBIOi/XvWMwHbPO0DPrknY3wcyGUUvnRPIRw6+AR4ArgN8Dj0XE2lxlKTA+Px8PLAHI09cAO1bLa+ZpVF6vHcdLWixp8YoVK1ppupmZDaOWQiQino+IvYEJpKOHvxjORvXTjnkRMSUipowbN64dTTAzs4oBXZ0VEY8BVwP7A2Mk9Q2HTQCW5efLgIkAefp2wMpqec08jcrNzKzDtXJ11jhJY/LzLYG3AXeSwuSdudoM4OL8fGF+TZ5+VT6vshA4Kl+9tTswCbgRuAmYlK/22ox08n3hEPTNzMyGWdMT68DOwIJ8FdUmwAUR8RNJdwDnSfoScAtwZq5/JnBOPnG+ihQKRMTtki4gnTBfC3w4Ip4HkHQicDkwCpgfEbcPWQ/NzGzYtHJ11m3APnXK72H91VXV8meAdzVY1inAKXXKLwUubaG9ZmbWQfwX62ZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlbMIWJmZsVa+TsRMxuAdt108r45h7Vlvfbi5iMRMzMr5hAxM7NiDhEzMyvmEDEzs2IOETMzK+YQMTOzYg4RMzMr5hAxM7NiDhEzMyvmEDEzs2IOETMzK+YQMTOzYg4RMzMr5hAxM7NiDhEzMyvmEDEzs2IOETMzK+YQMTOzYg4RMzMr5hAxM7Nio9vdADPrfj2zLmnLeu+bc1hb1mvr+UjEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKNQ0RSRMlXS3pDkm3S/pILt9B0hWS7s7/bp/LJWmupF5Jt0nat7KsGbn+3ZJmVMpfK+nXeZ65kjQcnTUzs6HVypHIWuDjETEZmAp8WNJkYBZwZURMAq7MrwEOASblx/HANyCFDnAS8HpgP+CkvuDJdT5QmW/a4LtmZmbDrWmIRMTyiPif/PwJ4E5gPDAdWJCrLQCOyM+nA2dHsggYI2ln4GDgiohYFRGrgSuAaXnathGxKCICOLuyLDMz62ADOiciqQfYB7gB2CkiludJDwE75efjgSWV2Zbmsv7Kl9Ypr7f+4yUtlrR4xYoVA2m6mZkNg5bvnSVpa+CHwEcj4vHqaYuICEkxDO3bQETMA+YBTJkypXh97brPj5nZxqalEJG0KSlAzo2IH+XihyXtHBHL85DUI7l8GTCxMvuEXLYMeEtN+TW5fEKd+mY2AN45snZo5eosAWcCd0bEqZVJC4G+K6xmABdXyo/NV2lNBdbkYa/LgYMkbZ9PqB8EXJ6nPS5pal7XsZVlmZlZB2vlSOSNwHuBX0v6VS77DDAHuEDSTOB+4Mg87VLgUKAXeBp4H0BErJL0ReCmXO/kiFiVn58AnAVsCVyWH2Zm1uGahkhEXAc0+ruNA+vUD+DDDZY1H5hfp3wxsGeztpiZWWfxX6ybmVkxh4iZmRVziJiZWTGHiJmZFXOImJlZMYeImZkVc4iYmVkxh4iZmRVziJiZWTGHiJmZFXOImJlZMYeImZkVc4iYmVkxh4iZmRVziJiZWTGHiJmZFXOImJlZsVb+e1wzM6vRM+uStqz3vjmHtWW9jfhIxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIo5RMzMrJhDxMzMijlEzMysmEPEzMyKOUTMzKyYQ8TMzIr53llm1rXadf8qW89HImZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlasaYhImi/pEUm/qZTtIOkKSXfnf7fP5ZI0V1KvpNsk7VuZZ0auf7ekGZXy10r6dZ5nriQNdSfNzGx4tHIkchYwraZsFnBlREwCrsyvAQ4BJuXH8cA3IIUOcBLwemA/4KS+4Ml1PlCZr3ZdZmbWoZqGSERcC6yqKZ4OLMjPFwBHVMrPjmQRMEbSzsDBwBURsSoiVgNXANPytG0jYlFEBHB2ZVlmZtbhSs+J7BQRy/Pzh4Cd8vPxwJJKvaW5rL/ypXXKzcysCwz6xHo+goghaEtTko6XtFjS4hUrVozEKs3MrB+lIfJwHooi//tILl8GTKzUm5DL+iufUKe8roiYFxFTImLKuHHjCptuZmZDpTREFgJ9V1jNAC6ulB+br9KaCqzJw16XAwdJ2j6fUD8IuDxPe1zS1HxV1rGVZZmZWYdregNGSd8H3gKMlbSUdJXVHOACSTOB+4Ejc/VLgUOBXuBp4H0AEbFK0heBm3K9kyOi72T9CaQrwLYELssPMzPrAk1DJCKObjDpwDp1A/hwg+XMB+bXKV8M7NmsHWZm1nn8F+tmZlbMIWJmZsUcImZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlbMIWJmZsUcImZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlbMIWJmZsUcImZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlbMIWJmZsUcImZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlbMIWJmZsUcImZmVswhYmZmxRwiZmZWzCFiZmbFHCJmZlbMIWJmZsUcImZmVswhYmZmxRwiZmZWzCFiZmbFOiZEJE2TdJekXkmz2t0eMzNrriNCRNIo4HTgEGAycLSkye1tlZmZNdMRIQLsB/RGxD0R8RxwHjC9zW0yM7MmRre7Adl4YEnl9VLg9bWVJB0PHJ9fPinpLmAs8Oiwt7B93L/u5v51t47rn74yqNl3G6JmrNMpIdKSiJgHzKuWSVocEVPa1KRh5/51N/evu23s/RsKnTKctQyYWHk9IZeZmVkH65QQuQmYJGl3SZsBRwEL29wmMzNroiOGsyJiraQTgcuBUcD8iLi9xdnnNa/S1dy/7ub+dbeNvX+DpohodxvMzKxLdcpwlpmZdSGHiJmZFeuaEGl2WxRJm0s6P0+/QVJPG5pZrIX+/ZOkOyTdJulKSUN+vfdwavW2NpLeISkkddVlla30T9KReRveLul7I93GwWjh87mrpKsl3ZI/o4e2o50lJM2X9Iik3zSYLklzc99vk7TvSLexo0VExz9IJ9t/D7wM2Ay4FZhcU+cE4Jv5+VHA+e1u9xD376+BrfLzD21s/cv1tgGuBRYBU9rd7iHefpOAW4Dt8+uXtrvdQ9y/ecCH8vPJwH3tbvcA+vcmYF/gNw2mHwpcBgiYCtzQ7jZ30qNbjkRauS3KdGBBfn4hcKAkjWAbB6Np/yLi6oh4Or9cRPpbmm7R6m1tvgh8BXhmJBs3BFrp3weA0yNiNUBEPDLCbRyMVvoXwLb5+XbAgyPYvkGJiGuBVf1UmQ6cHckiYIyknUemdZ2vW0Kk3m1RxjeqExFrgTXAjiPSusFrpX9VM0l7Rt2iaf/yEMHEiLhkJBs2RFrZfq8AXiHpF5IWSZo2Yq0bvFb6Nxt4j6SlwKXAP4xM00bEQL+fLyod8Xci1jpJ7wGmAG9ud1uGiqRNgFOB49rclOE0mjSk9RbSUeS1kl4dEY+1s1FD6GjgrIj4V0n7A+dI2jMiXmh3w2x4dcuRSCu3RVlXR9Jo0iH1yhFp3eC1dNsXSW8FPgu8PSKeHaG2DYVm/dsG2BO4RtJ9pHHnhV10cr2V7bcUWBgRf4yIe4HfkUKlG7TSv5nABQARcT2wBenmhRsD35apH90SIq3cFmUhMCM/fydwVeSzYl2gaf8k7QN8ixQg3TSeDk36FxFrImJsRPRERA/pnM/bI2Jxe5o7YK18Pi8iHYUgaSxpeOueEWzjYLTSvweAAwEkvZIUIitGtJXDZyFwbL5KayqwJiKWt7tRnaIrhrOiwW1RJJ0MLI6IhcCZpEPoXtJJsqPa1+KBabF/XwW2Bn6Qrxd4ICLe3rZGD0CL/etaLfbvcuAgSXcAzwOfjIiuOFJusX8fB74t6WOkk+zHdctOnKTvkwJ+bD6ncxKwKUBEfJN0judQoBd4Gnhfe1ramXzbEzMzK9Ytw1lmZtaBHCJmZlbMIWJmZsUcImZmVswhYmbWBZrdKLJO/RG54aevzjIz6wKS3gQ8SbqP155N6k4i/fHnARGxWtJLh+vvy3wkYmbWBerdKFLSyyX9l6SbJf1c0l/kSSN2w0+HiJlZ95oH/ENEvBb4BHBGLh+xG352xV+sm5nZhiRtDbyB9XexANg8/ztiN/x0iJiZdadNgMciYu8605aS/vOsPwL3Suq74edNw9EIMzPrMhHxOCkg3gXr/hvfvfLkixihG346RMzMukC+UeT1wJ9LWippJnAMMFPSrcDtrP8fJy8HVuYbfl7NMN7w05f4mplZMR+JmJlZMYeImZkVc4iYmVkxh4iZmRVziJiZWTGHiJmZFXOImJlZsf8FP4Ewq3Eg+PIAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.hist(np.log1p(application_train['AMT_CREDIT']))\nplt.title('AMT_CREDIT histogram on data with log1p transformation')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.015945Z","iopub.execute_input":"2021-07-02T07:07:48.016249Z","iopub.status.idle":"2021-07-02T07:07:48.212868Z","shell.execute_reply.started":"2021-07-02T07:07:48.016219Z","shell.execute_reply":"2021-07-02T07:07:48.211826Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'AMT_CREDIT histogram on data with log1p transformation')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"This feature shows the amount of the loan in question. We can see following things from the information above:\n- income feature has some outliers. Maybe mortage?\n- average credit amoint is almost similier for those who repay the loans and those who dont\n- if we leave only data within 95 percentile, it is almost normally distributed\n- log transformation also helps","metadata":{}},{"cell_type":"markdown","source":"**DAYS_BIRTH**","metadata":{}},{"cell_type":"code","source":"application_train['age'] = application_train['DAYS_BIRTH'] / -365\nplt.hist(application_train['age'])\nplt.title('Histogram of age in years.')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.214131Z","iopub.execute_input":"2021-07-02T07:07:48.214445Z","iopub.status.idle":"2021-07-02T07:07:48.412735Z","shell.execute_reply.started":"2021-07-02T07:07:48.214414Z","shell.execute_reply":"2021-07-02T07:07:48.411804Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Histogram of age in years.')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We can see that age distribution is almost normal and most of the people are between 30 and 40 years.","metadata":{}},{"cell_type":"markdown","source":"**DAYS_EMPLOYED**","metadata":{}},{"cell_type":"code","source":"application_train.loc[application_train['DAYS_EMPLOYED'] == 365243, 'DAYS_EMPLOYED'] = 0\napplication_train['years_employed'] = application_train['DAYS_EMPLOYED'] / -365\n\nplt.hist(application_train['years_employed'])\nplt.title('Length of working at current workplace in years.')","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.414261Z","iopub.execute_input":"2021-07-02T07:07:48.414691Z","iopub.status.idle":"2021-07-02T07:07:48.612677Z","shell.execute_reply.started":"2021-07-02T07:07:48.414625Z","shell.execute_reply":"2021-07-02T07:07:48.611619Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Length of working at current workplace in years.')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"There was a strange valeu -365243. it could mean empty values or some errors, so I replace it with zero. A lot of people dont work, but lets look deeper into this.","metadata":{}},{"cell_type":"code","source":"application_train.groupby(['NAME_INCOME_TYPE']).agg({'years_employed': ['mean', 'median', 'count', 'max'], 'age': ['median']})","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.614440Z","iopub.execute_input":"2021-07-02T07:07:48.614870Z","iopub.status.idle":"2021-07-02T07:07:48.692273Z","shell.execute_reply.started":"2021-07-02T07:07:48.614825Z","shell.execute_reply":"2021-07-02T07:07:48.691031Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"                     years_employed                                     age\n                               mean    median   count        max     median\nNAME_INCOME_TYPE                                                           \nBusinessman                7.874795  6.280822      10  14.917808  47.498630\nCommercial associate       5.833627  4.147945   71617  48.071233  39.780822\nMaternity leave            7.224110  8.273973       5  10.306849  39.350685\nPensioner                  0.002448 -0.000000   55362  35.246575  60.413699\nState servant              9.454441  7.205479   21703  48.172603  40.693151\nStudent                    6.835160  5.069863      18  21.931507  36.663014\nUnemployed                 0.000000 -0.000000      22  -0.000000  45.860274\nWorking                    6.446909  4.427397  158774  49.073973  39.876712","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"4\" halign=\"left\">years_employed</th>\n      <th>age</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n      <th>count</th>\n      <th>max</th>\n      <th>median</th>\n    </tr>\n    <tr>\n      <th>NAME_INCOME_TYPE</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Businessman</th>\n      <td>7.874795</td>\n      <td>6.280822</td>\n      <td>10</td>\n      <td>14.917808</td>\n      <td>47.498630</td>\n    </tr>\n    <tr>\n      <th>Commercial associate</th>\n      <td>5.833627</td>\n      <td>4.147945</td>\n      <td>71617</td>\n      <td>48.071233</td>\n      <td>39.780822</td>\n    </tr>\n    <tr>\n      <th>Maternity leave</th>\n      <td>7.224110</td>\n      <td>8.273973</td>\n      <td>5</td>\n      <td>10.306849</td>\n      <td>39.350685</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>0.002448</td>\n      <td>-0.000000</td>\n      <td>55362</td>\n      <td>35.246575</td>\n      <td>60.413699</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>9.454441</td>\n      <td>7.205479</td>\n      <td>21703</td>\n      <td>48.172603</td>\n      <td>40.693151</td>\n    </tr>\n    <tr>\n      <th>Student</th>\n      <td>6.835160</td>\n      <td>5.069863</td>\n      <td>18</td>\n      <td>21.931507</td>\n      <td>36.663014</td>\n    </tr>\n    <tr>\n      <th>Unemployed</th>\n      <td>0.000000</td>\n      <td>-0.000000</td>\n      <td>22</td>\n      <td>-0.000000</td>\n      <td>45.860274</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>6.446909</td>\n      <td>4.427397</td>\n      <td>158774</td>\n      <td>49.073973</td>\n      <td>39.876712</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Well, it seems that a lot of non-working people are pensioners연금수령자, which is normal. As for working people - they seem to work for several years at one place.  \n\nThere are so many features and so many possible angles from which we can analyze them. Let's see this for example.","metadata":{}},{"cell_type":"code","source":"application_train.groupby(['NAME_EDUCATION_TYPE', 'NAME_INCOME_TYPE']).agg({'AMT_INCOME_TOTAL': ['mean', 'median', 'count', 'max']})","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.694773Z","iopub.execute_input":"2021-07-02T07:07:48.695281Z","iopub.status.idle":"2021-07-02T07:07:48.810562Z","shell.execute_reply.started":"2021-07-02T07:07:48.695229Z","shell.execute_reply":"2021-07-02T07:07:48.809325Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"                                                   AMT_INCOME_TOTAL  \\\n                                                               mean   \nNAME_EDUCATION_TYPE           NAME_INCOME_TYPE                        \nAcademic degree               Commercial associate    274688.372093   \n                              Pensioner               203919.230769   \n                              State servant           258847.826087   \n                              Working                 226312.500000   \nHigher education              Businessman             652500.000000   \n                              Commercial associate    242283.998486   \n                              Maternity leave         195000.000000   \n                              Pensioner               171455.114974   \n                              State servant           207116.161080   \n                              Student                 231000.000000   \n                              Unemployed              189000.000000   \n                              Working                 194013.110125   \nIncomplete higher             Commercial associate    206232.296029   \n                              Pensioner               149437.155405   \n                              State servant           173589.989610   \n                              Student                 157500.000000   \n                              Unemployed              126000.000000   \n                              Working                 170659.490600   \nLower secondary               Commercial associate    168173.628261   \n                              Pensioner               106468.010464   \n                              State servant           143250.000000   \n                              Working                 140070.652174   \nSecondary / secondary special Commercial associate    181368.704601   \n                              Maternity leave          58500.000000   \n                              Pensioner               130863.482560   \n                              State servant           159979.349061   \n                              Student                 138681.818182   \n                              Unemployed               70753.846154   \n                              Working                 154261.801730   \n\n                                                                       \\\n                                                       median   count   \nNAME_EDUCATION_TYPE           NAME_INCOME_TYPE                          \nAcademic degree               Commercial associate  247500.00      43   \n                              Pensioner             155700.00      26   \n                              State servant         225000.00      23   \n                              Working               202500.00      72   \nHigher education              Businessman           495000.00      10   \n                              Commercial associate  202500.00   24025   \n                              Maternity leave       135000.00       3   \n                              Pensioner             148500.00    8188   \n                              State servant         180000.00    8863   \n                              Student               175500.00       6   \n                              Unemployed            180000.00       6   \n                              Working               174134.25   33762   \nIncomplete higher             Commercial associate  180000.00    3400   \n                              Pensioner             135000.00     518   \n                              State servant         157500.00     770   \n                              Student               157500.00       1   \n                              Unemployed             63000.00       3   \n                              Working               157500.00    5585   \nLower secondary               Commercial associate  144000.00     460   \n                              Pensioner              90000.00    1529   \n                              State servant         119250.00     102   \n                              Working               135000.00    1725   \nSecondary / secondary special Commercial associate  157500.00   43689   \n                              Maternity leave        58500.00       2   \n                              Pensioner             112500.00   45101   \n                              State servant         135000.00   11945   \n                              Student               144000.00      11   \n                              Unemployed             63000.00      13   \n                              Working               135000.00  117630   \n\n                                                                 \n                                                            max  \nNAME_EDUCATION_TYPE           NAME_INCOME_TYPE                   \nAcademic degree               Commercial associate     900000.0  \n                              Pensioner                747000.0  \n                              State servant            900000.0  \n                              Working                  630000.0  \nHigher education              Businessman             2250000.0  \n                              Commercial associate   13500000.0  \n                              Maternity leave          360000.0  \n                              Pensioner               2250000.0  \n                              State servant           3150000.0  \n                              Student                  562500.0  \n                              Unemployed               337500.0  \n                              Working                 9000000.0  \nIncomplete higher             Commercial associate    2250000.0  \n                              Pensioner                540000.0  \n                              State servant            540000.0  \n                              Student                  157500.0  \n                              Unemployed               270000.0  \n                              Working                 1350000.0  \nLower secondary               Commercial associate     945000.0  \n                              Pensioner               1125000.0  \n                              State servant            450000.0  \n                              Working                 1125000.0  \nSecondary / secondary special Commercial associate   18000090.0  \n                              Maternity leave           67500.0  \n                              Pensioner               1935000.0  \n                              State servant           2025000.0  \n                              Student                  225000.0  \n                              Unemployed               135000.0  \n                              Working               117000000.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th colspan=\"4\" halign=\"left\">AMT_INCOME_TOTAL</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th>mean</th>\n      <th>median</th>\n      <th>count</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>NAME_EDUCATION_TYPE</th>\n      <th>NAME_INCOME_TYPE</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">Academic degree</th>\n      <th>Commercial associate</th>\n      <td>274688.372093</td>\n      <td>247500.00</td>\n      <td>43</td>\n      <td>900000.0</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>203919.230769</td>\n      <td>155700.00</td>\n      <td>26</td>\n      <td>747000.0</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>258847.826087</td>\n      <td>225000.00</td>\n      <td>23</td>\n      <td>900000.0</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>226312.500000</td>\n      <td>202500.00</td>\n      <td>72</td>\n      <td>630000.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">Higher education</th>\n      <th>Businessman</th>\n      <td>652500.000000</td>\n      <td>495000.00</td>\n      <td>10</td>\n      <td>2250000.0</td>\n    </tr>\n    <tr>\n      <th>Commercial associate</th>\n      <td>242283.998486</td>\n      <td>202500.00</td>\n      <td>24025</td>\n      <td>13500000.0</td>\n    </tr>\n    <tr>\n      <th>Maternity leave</th>\n      <td>195000.000000</td>\n      <td>135000.00</td>\n      <td>3</td>\n      <td>360000.0</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>171455.114974</td>\n      <td>148500.00</td>\n      <td>8188</td>\n      <td>2250000.0</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>207116.161080</td>\n      <td>180000.00</td>\n      <td>8863</td>\n      <td>3150000.0</td>\n    </tr>\n    <tr>\n      <th>Student</th>\n      <td>231000.000000</td>\n      <td>175500.00</td>\n      <td>6</td>\n      <td>562500.0</td>\n    </tr>\n    <tr>\n      <th>Unemployed</th>\n      <td>189000.000000</td>\n      <td>180000.00</td>\n      <td>6</td>\n      <td>337500.0</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>194013.110125</td>\n      <td>174134.25</td>\n      <td>33762</td>\n      <td>9000000.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"6\" valign=\"top\">Incomplete higher</th>\n      <th>Commercial associate</th>\n      <td>206232.296029</td>\n      <td>180000.00</td>\n      <td>3400</td>\n      <td>2250000.0</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>149437.155405</td>\n      <td>135000.00</td>\n      <td>518</td>\n      <td>540000.0</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>173589.989610</td>\n      <td>157500.00</td>\n      <td>770</td>\n      <td>540000.0</td>\n    </tr>\n    <tr>\n      <th>Student</th>\n      <td>157500.000000</td>\n      <td>157500.00</td>\n      <td>1</td>\n      <td>157500.0</td>\n    </tr>\n    <tr>\n      <th>Unemployed</th>\n      <td>126000.000000</td>\n      <td>63000.00</td>\n      <td>3</td>\n      <td>270000.0</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>170659.490600</td>\n      <td>157500.00</td>\n      <td>5585</td>\n      <td>1350000.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">Lower secondary</th>\n      <th>Commercial associate</th>\n      <td>168173.628261</td>\n      <td>144000.00</td>\n      <td>460</td>\n      <td>945000.0</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>106468.010464</td>\n      <td>90000.00</td>\n      <td>1529</td>\n      <td>1125000.0</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>143250.000000</td>\n      <td>119250.00</td>\n      <td>102</td>\n      <td>450000.0</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>140070.652174</td>\n      <td>135000.00</td>\n      <td>1725</td>\n      <td>1125000.0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">Secondary / secondary special</th>\n      <th>Commercial associate</th>\n      <td>181368.704601</td>\n      <td>157500.00</td>\n      <td>43689</td>\n      <td>18000090.0</td>\n    </tr>\n    <tr>\n      <th>Maternity leave</th>\n      <td>58500.000000</td>\n      <td>58500.00</td>\n      <td>2</td>\n      <td>67500.0</td>\n    </tr>\n    <tr>\n      <th>Pensioner</th>\n      <td>130863.482560</td>\n      <td>112500.00</td>\n      <td>45101</td>\n      <td>1935000.0</td>\n    </tr>\n    <tr>\n      <th>State servant</th>\n      <td>159979.349061</td>\n      <td>135000.00</td>\n      <td>11945</td>\n      <td>2025000.0</td>\n    </tr>\n    <tr>\n      <th>Student</th>\n      <td>138681.818182</td>\n      <td>144000.00</td>\n      <td>11</td>\n      <td>225000.0</td>\n    </tr>\n    <tr>\n      <th>Unemployed</th>\n      <td>70753.846154</td>\n      <td>63000.00</td>\n      <td>13</td>\n      <td>135000.0</td>\n    </tr>\n    <tr>\n      <th>Working</th>\n      <td>154261.801730</td>\n      <td>135000.00</td>\n      <td>117630</td>\n      <td>117000000.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that most of the loans are taken by working people with secondary education.","metadata":{}},{"cell_type":"markdown","source":"## Transforming and merging data","metadata":{}},{"cell_type":"code","source":"application_train['AMT_INCOME_TOTAL'] = np.log1p(application_train['AMT_INCOME_TOTAL'])\napplication_train['AMT_CREDIT'] = np.log1p(application_train['AMT_CREDIT'])\n\napplication_train['OWN_CAR_AGE'] = application_train['OWN_CAR_AGE'].fillna(0)\n\n# AMT_ANNUITY: 대출 연금\napplication_train['app AMT_CREDIT / AMT_ANNUITY'] = application_train['AMT_CREDIT'] / application_train['AMT_ANNUITY']\n\n# EXT_SOURCE; Normalized score from external data source\napplication_train['app EXT_SOURCE mean'] = application_train[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis = 1)\napplication_train['app EXT_SOURCE_1 / DAYS_BIRTH'] = application_train['EXT_SOURCE_1'] / application_train['DAYS_BIRTH']\napplication_train['app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY'] = application_train['AMT_INCOME_TOTAL'] / 12. - application_train['AMT_ANNUITY']\napplication_train['app AMT_INCOME_TOTAL / AMT_ANNUITY'] = application_train['AMT_INCOME_TOTAL'] / application_train['AMT_ANNUITY']\napplication_train['app AMT_INCOME_TOTAL - AMT_GOODS_PRICE'] = application_train['AMT_INCOME_TOTAL'] - application_train['AMT_GOODS_PRICE']","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.811974Z","iopub.execute_input":"2021-07-02T07:07:48.812273Z","iopub.status.idle":"2021-07-02T07:07:48.979151Z","shell.execute_reply.started":"2021-07-02T07:07:48.812245Z","shell.execute_reply":"2021-07-02T07:07:48.978083Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# train에서 했던 작업 test에도\napplication_test.loc[application_test['OBS_30_CNT_SOCIAL_CIRCLE'] > 1, 'OBS_30_CNT_SOCIAL_CIRCLE'] = '1+'\napplication_test.loc[application_test['DEF_30_CNT_SOCIAL_CIRCLE'] > 1, 'DEF_30_CNT_SOCIAL_CIRCLE'] = '1+'\napplication_test.loc[application_test['OBS_60_CNT_SOCIAL_CIRCLE'] > 1, 'OBS_60_CNT_SOCIAL_CIRCLE'] = '1+'\napplication_test.loc[application_test['DEF_60_CNT_SOCIAL_CIRCLE'] > 1, 'DEF_60_CNT_SOCIAL_CIRCLE'] = '1+'\n\nnp.log1p(application_test['AMT_INCOME_TOTAL'])\nnp.log1p(application_test['AMT_CREDIT'])\n\napplication_test['age'] = application_test['DAYS_BIRTH'] / -365\napplication_test.loc[application_test['DAYS_EMPLOYED'] == 365243, 'DAYS_EMPLOYED'] = 0\napplication_test['years_employed'] = application_test['DAYS_EMPLOYED'] / -365\n\napplication_test['AMT_INCOME_TOTAL'] = np.log1p(application_test['AMT_INCOME_TOTAL'])\napplication_test['AMT_CREDIT'] = np.log1p(application_test['AMT_CREDIT'])\napplication_test['OWN_CAR_AGE'] = application_test['OWN_CAR_AGE'].fillna(0)\n\n# 바로 앞 cell\napplication_test['app AMT_CREDIT / AMT_ANNUITY'] = application_test['AMT_CREDIT'] / application_test['AMT_ANNUITY']\napplication_test['app EXT_SOURCE mean'] = application_test[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis = 1)\napplication_test['app EXT_SOURCE_1 / DAYS_BIRTH'] = application_test['EXT_SOURCE_1'] / application_test['DAYS_BIRTH']\napplication_test['app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY'] = application_test['AMT_INCOME_TOTAL'] / 12. - application_test['AMT_ANNUITY']\napplication_test['app AMT_INCOME_TOTAL / AMT_ANNUITY'] = application_test['AMT_INCOME_TOTAL'] / application_test['AMT_ANNUITY']\napplication_test['app AMT_INCOME_TOTAL - AMT_GOODS_PRICE'] = application_test['AMT_INCOME_TOTAL'] - application_test['AMT_GOODS_PRICE']","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:48.981020Z","iopub.execute_input":"2021-07-02T07:07:48.981458Z","iopub.status.idle":"2021-07-02T07:07:49.077737Z","shell.execute_reply.started":"2021-07-02T07:07:48.981412Z","shell.execute_reply":"2021-07-02T07:07:49.076880Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"# encoding\nfor col in ['FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE', 'OBS_30_CNT_SOCIAL_CIRCLE', \n            'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'NAME_CONTRACT_TYPE', 'CODE_GENDER', \n            'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', \n            'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'WEEKDAY_APPR_PROCESS_START']:\n    \n    unique_values = list(set(list(application_train[col].astype(str).unique()) + list(application_test[col].astype(str).unique())))\n    le.fit(unique_values)\n    application_train[col] = le.transform(application_train[col].astype(str))\n    application_test[col] = le.transform(application_test[col].astype(str))","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:49.078786Z","iopub.execute_input":"2021-07-02T07:07:49.079214Z","iopub.status.idle":"2021-07-02T07:07:55.967197Z","shell.execute_reply.started":"2021-07-02T07:07:49.079183Z","shell.execute_reply":"2021-07-02T07:07:55.966142Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"train = application_train","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:55.968647Z","iopub.execute_input":"2021-07-02T07:07:55.969095Z","iopub.status.idle":"2021-07-02T07:07:55.973295Z","shell.execute_reply.started":"2021-07-02T07:07:55.969052Z","shell.execute_reply":"2021-07-02T07:07:55.972497Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:55.974711Z","iopub.execute_input":"2021-07-02T07:07:55.975296Z","iopub.status.idle":"2021-07-02T07:07:56.103341Z","shell.execute_reply.started":"2021-07-02T07:07:55.975251Z","shell.execute_reply":"2021-07-02T07:07:56.102633Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"   SK_ID_CURR  TARGET  NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  \\\n0      100002       1                   0            1             0   \n1      100003       0                   0            0             0   \n2      100004       0                   1            1             1   \n3      100006       0                   0            0             0   \n4      100007       0                   0            1             0   \n\n   FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n0                1             0         12.218500   12.915581      24700.5   \n1                0             0         12.506181   14.072865      35698.5   \n2                1             0         11.119898   11.813037       6750.0   \n3                1             0         11.813037   12.652947      29686.5   \n4                1             0         11.707678   13.148033      21865.5   \n\n   AMT_GOODS_PRICE  NAME_TYPE_SUITE  NAME_INCOME_TYPE  NAME_EDUCATION_TYPE  \\\n0         351000.0                6                 7                    4   \n1        1129500.0                1                 4                    1   \n2         135000.0                6                 7                    4   \n3         297000.0                6                 7                    4   \n4         513000.0                6                 7                    4   \n\n   NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  REGION_POPULATION_RELATIVE  \\\n0                   3                  1                    0.018801   \n1                   1                  1                    0.003541   \n2                   3                  1                    0.010032   \n3                   0                  1                    0.008019   \n4                   3                  1                    0.028663   \n\n   DAYS_BIRTH  DAYS_EMPLOYED  DAYS_REGISTRATION  DAYS_ID_PUBLISH  OWN_CAR_AGE  \\\n0       -9461           -637            -3648.0            -2120          0.0   \n1      -16765          -1188            -1186.0             -291          0.0   \n2      -19046           -225            -4260.0            -2531         26.0   \n3      -19005          -3039            -9833.0            -2437          0.0   \n4      -19932          -3038            -4311.0            -3458          0.0   \n\n   FLAG_MOBIL  FLAG_EMP_PHONE  FLAG_WORK_PHONE  FLAG_CONT_MOBILE  FLAG_PHONE  \\\n0           1               1                0                 1           1   \n1           1               1                0                 1           1   \n2           1               1                1                 1           1   \n3           1               1                0                 1           0   \n4           1               1                0                 1           0   \n\n   FLAG_EMAIL  OCCUPATION_TYPE  CNT_FAM_MEMBERS  REGION_RATING_CLIENT  \\\n0           0                8              1.0                     2   \n1           0                3              2.0                     1   \n2           0                8              1.0                     2   \n3           0                8              2.0                     2   \n4           0                3              1.0                     2   \n\n   REGION_RATING_CLIENT_W_CITY  WEEKDAY_APPR_PROCESS_START  \\\n0                            2                           6   \n1                            1                           1   \n2                            2                           1   \n3                            2                           6   \n4                            2                           4   \n\n   HOUR_APPR_PROCESS_START  REG_REGION_NOT_LIVE_REGION  \\\n0                       10                           0   \n1                       11                           0   \n2                        9                           0   \n3                       17                           0   \n4                       11                           0   \n\n   REG_REGION_NOT_WORK_REGION  LIVE_REGION_NOT_WORK_REGION  \\\n0                           0                            0   \n1                           0                            0   \n2                           0                            0   \n3                           0                            0   \n4                           0                            0   \n\n   REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY  \\\n0                       0                       0                        0   \n1                       0                       0                        0   \n2                       0                       0                        0   \n3                       0                       0                        0   \n4                       0                       1                        1   \n\n   ORGANIZATION_TYPE  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  \\\n0                  5      0.083037      0.262949      0.139376   \n1                 39      0.311267      0.622246           NaN   \n2                 11           NaN      0.555912      0.729567   \n3                  5           NaN      0.650442           NaN   \n4                 37           NaN      0.322738           NaN   \n\n   APARTMENTS_AVG  BASEMENTAREA_AVG  YEARS_BEGINEXPLUATATION_AVG  \\\n0          0.0247            0.0369                       0.9722   \n1          0.0959            0.0529                       0.9851   \n2             NaN               NaN                          NaN   \n3             NaN               NaN                          NaN   \n4             NaN               NaN                          NaN   \n\n   YEARS_BUILD_AVG  COMMONAREA_AVG  ELEVATORS_AVG  ENTRANCES_AVG  \\\n0           0.6192          0.0143           0.00         0.0690   \n1           0.7960          0.0605           0.08         0.0345   \n2              NaN             NaN            NaN            NaN   \n3              NaN             NaN            NaN            NaN   \n4              NaN             NaN            NaN            NaN   \n\n   FLOORSMAX_AVG  FLOORSMIN_AVG  LANDAREA_AVG  LIVINGAPARTMENTS_AVG  \\\n0         0.0833         0.1250        0.0369                0.0202   \n1         0.2917         0.3333        0.0130                0.0773   \n2            NaN            NaN           NaN                   NaN   \n3            NaN            NaN           NaN                   NaN   \n4            NaN            NaN           NaN                   NaN   \n\n   LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  \\\n0          0.0190                   0.0000             0.0000   \n1          0.0549                   0.0039             0.0098   \n2             NaN                      NaN                NaN   \n3             NaN                      NaN                NaN   \n4             NaN                      NaN                NaN   \n\n   APARTMENTS_MODE  BASEMENTAREA_MODE  YEARS_BEGINEXPLUATATION_MODE  \\\n0           0.0252             0.0383                        0.9722   \n1           0.0924             0.0538                        0.9851   \n2              NaN                NaN                           NaN   \n3              NaN                NaN                           NaN   \n4              NaN                NaN                           NaN   \n\n   YEARS_BUILD_MODE  COMMONAREA_MODE  ELEVATORS_MODE  ENTRANCES_MODE  \\\n0            0.6341           0.0144          0.0000          0.0690   \n1            0.8040           0.0497          0.0806          0.0345   \n2               NaN              NaN             NaN             NaN   \n3               NaN              NaN             NaN             NaN   \n4               NaN              NaN             NaN             NaN   \n\n   FLOORSMAX_MODE  FLOORSMIN_MODE  LANDAREA_MODE  LIVINGAPARTMENTS_MODE  \\\n0          0.0833          0.1250         0.0377                  0.022   \n1          0.2917          0.3333         0.0128                  0.079   \n2             NaN             NaN            NaN                    NaN   \n3             NaN             NaN            NaN                    NaN   \n4             NaN             NaN            NaN                    NaN   \n\n   LIVINGAREA_MODE  NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  \\\n0           0.0198                       0.0                 0.0   \n1           0.0554                       0.0                 0.0   \n2              NaN                       NaN                 NaN   \n3              NaN                       NaN                 NaN   \n4              NaN                       NaN                 NaN   \n\n   APARTMENTS_MEDI  BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  \\\n0           0.0250             0.0369                        0.9722   \n1           0.0968             0.0529                        0.9851   \n2              NaN                NaN                           NaN   \n3              NaN                NaN                           NaN   \n4              NaN                NaN                           NaN   \n\n   YEARS_BUILD_MEDI  COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  \\\n0            0.6243           0.0144            0.00          0.0690   \n1            0.7987           0.0608            0.08          0.0345   \n2               NaN              NaN             NaN             NaN   \n3               NaN              NaN             NaN             NaN   \n4               NaN              NaN             NaN             NaN   \n\n   FLOORSMAX_MEDI  FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  \\\n0          0.0833          0.1250         0.0375                 0.0205   \n1          0.2917          0.3333         0.0132                 0.0787   \n2             NaN             NaN            NaN                    NaN   \n3             NaN             NaN            NaN                    NaN   \n4             NaN             NaN            NaN                    NaN   \n\n   LIVINGAREA_MEDI  NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI  \\\n0           0.0193                    0.0000                0.00   \n1           0.0558                    0.0039                0.01   \n2              NaN                       NaN                 NaN   \n3              NaN                       NaN                 NaN   \n4              NaN                       NaN                 NaN   \n\n   FONDKAPREMONT_MODE  HOUSETYPE_MODE  TOTALAREA_MODE  WALLSMATERIAL_MODE  \\\n0                   3               0          0.0149                   5   \n1                   3               0          0.0714                   0   \n2                   0               1             NaN                   7   \n3                   0               1             NaN                   7   \n4                   0               1             NaN                   7   \n\n   EMERGENCYSTATE_MODE  OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  \\\n0                    0                         1                         1   \n1                    0                         2                         0   \n2                    2                         0                         0   \n3                    2                         1                         0   \n4                    2                         0                         0   \n\n   OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  \\\n0                         1                         1                 -1134.0   \n1                         2                         0                  -828.0   \n2                         0                         0                  -815.0   \n3                         1                         0                  -617.0   \n4                         0                         0                 -1106.0   \n\n   FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  \\\n0                0                1                0                0   \n1                0                1                0                0   \n2                0                0                0                0   \n3                0                1                0                0   \n4                0                0                0                0   \n\n   FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  \\\n0                0                0                0                0   \n1                0                0                0                0   \n2                0                0                0                0   \n3                0                0                0                0   \n4                0                0                1                0   \n\n   FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n0                         0.0                        0.0   \n1                         0.0                        0.0   \n2                         0.0                        0.0   \n3                         NaN                        NaN   \n4                         0.0                        0.0   \n\n   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n0                         0.0                        0.0   \n1                         0.0                        0.0   \n2                         0.0                        0.0   \n3                         NaN                        NaN   \n4                         0.0                        0.0   \n\n   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  contact_info  \\\n0                        0.0                         1.0             4   \n1                        0.0                         0.0             4   \n2                        0.0                         0.0             5   \n3                        NaN                         NaN             3   \n4                        0.0                         0.0             3   \n\n         age  years_employed  app AMT_CREDIT / AMT_ANNUITY  \\\n0  25.920548        1.745205                      0.000523   \n1  45.931507        3.254795                      0.000394   \n2  52.180822        0.616438                      0.001750   \n3  52.068493        8.326027                      0.000426   \n4  54.608219        8.323288                      0.000601   \n\n   app EXT_SOURCE mean  app EXT_SOURCE_1 / DAYS_BIRTH  \\\n0             0.161787                      -0.000009   \n1             0.466757                      -0.000019   \n2             0.642739                            NaN   \n3             0.650442                            NaN   \n4             0.322738                            NaN   \n\n   app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY  \\\n0                            -24699.481792   \n1                            -35697.457818   \n2                             -6749.073342   \n3                            -29685.515580   \n4                            -21864.524360   \n\n   app AMT_INCOME_TOTAL / AMT_ANNUITY  app AMT_INCOME_TOTAL - AMT_GOODS_PRICE  \n0                            0.000495                           -3.509878e+05  \n1                            0.000350                           -1.129487e+06  \n2                            0.001647                           -1.349889e+05  \n3                            0.000398                           -2.969882e+05  \n4                            0.000535                           -5.129883e+05  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SK_ID_CURR</th>\n      <th>TARGET</th>\n      <th>NAME_CONTRACT_TYPE</th>\n      <th>CODE_GENDER</th>\n      <th>FLAG_OWN_CAR</th>\n      <th>FLAG_OWN_REALTY</th>\n      <th>CNT_CHILDREN</th>\n      <th>AMT_INCOME_TOTAL</th>\n      <th>AMT_CREDIT</th>\n      <th>AMT_ANNUITY</th>\n      <th>AMT_GOODS_PRICE</th>\n      <th>NAME_TYPE_SUITE</th>\n      <th>NAME_INCOME_TYPE</th>\n      <th>NAME_EDUCATION_TYPE</th>\n      <th>NAME_FAMILY_STATUS</th>\n      <th>NAME_HOUSING_TYPE</th>\n      <th>REGION_POPULATION_RELATIVE</th>\n      <th>DAYS_BIRTH</th>\n      <th>DAYS_EMPLOYED</th>\n      <th>DAYS_REGISTRATION</th>\n      <th>DAYS_ID_PUBLISH</th>\n      <th>OWN_CAR_AGE</th>\n      <th>FLAG_MOBIL</th>\n      <th>FLAG_EMP_PHONE</th>\n      <th>FLAG_WORK_PHONE</th>\n      <th>FLAG_CONT_MOBILE</th>\n      <th>FLAG_PHONE</th>\n      <th>FLAG_EMAIL</th>\n      <th>OCCUPATION_TYPE</th>\n      <th>CNT_FAM_MEMBERS</th>\n      <th>REGION_RATING_CLIENT</th>\n      <th>REGION_RATING_CLIENT_W_CITY</th>\n      <th>WEEKDAY_APPR_PROCESS_START</th>\n      <th>HOUR_APPR_PROCESS_START</th>\n      <th>REG_REGION_NOT_LIVE_REGION</th>\n      <th>REG_REGION_NOT_WORK_REGION</th>\n      <th>LIVE_REGION_NOT_WORK_REGION</th>\n      <th>REG_CITY_NOT_LIVE_CITY</th>\n      <th>REG_CITY_NOT_WORK_CITY</th>\n      <th>LIVE_CITY_NOT_WORK_CITY</th>\n      <th>ORGANIZATION_TYPE</th>\n      <th>EXT_SOURCE_1</th>\n      <th>EXT_SOURCE_2</th>\n      <th>EXT_SOURCE_3</th>\n      <th>APARTMENTS_AVG</th>\n      <th>BASEMENTAREA_AVG</th>\n      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n      <th>YEARS_BUILD_AVG</th>\n      <th>COMMONAREA_AVG</th>\n      <th>ELEVATORS_AVG</th>\n      <th>ENTRANCES_AVG</th>\n      <th>FLOORSMAX_AVG</th>\n      <th>FLOORSMIN_AVG</th>\n      <th>LANDAREA_AVG</th>\n      <th>LIVINGAPARTMENTS_AVG</th>\n      <th>LIVINGAREA_AVG</th>\n      <th>NONLIVINGAPARTMENTS_AVG</th>\n      <th>NONLIVINGAREA_AVG</th>\n      <th>APARTMENTS_MODE</th>\n      <th>BASEMENTAREA_MODE</th>\n      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n      <th>YEARS_BUILD_MODE</th>\n      <th>COMMONAREA_MODE</th>\n      <th>ELEVATORS_MODE</th>\n      <th>ENTRANCES_MODE</th>\n      <th>FLOORSMAX_MODE</th>\n      <th>FLOORSMIN_MODE</th>\n      <th>LANDAREA_MODE</th>\n      <th>LIVINGAPARTMENTS_MODE</th>\n      <th>LIVINGAREA_MODE</th>\n      <th>NONLIVINGAPARTMENTS_MODE</th>\n      <th>NONLIVINGAREA_MODE</th>\n      <th>APARTMENTS_MEDI</th>\n      <th>BASEMENTAREA_MEDI</th>\n      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n      <th>YEARS_BUILD_MEDI</th>\n      <th>COMMONAREA_MEDI</th>\n      <th>ELEVATORS_MEDI</th>\n      <th>ENTRANCES_MEDI</th>\n      <th>FLOORSMAX_MEDI</th>\n      <th>FLOORSMIN_MEDI</th>\n      <th>LANDAREA_MEDI</th>\n      <th>LIVINGAPARTMENTS_MEDI</th>\n      <th>LIVINGAREA_MEDI</th>\n      <th>NONLIVINGAPARTMENTS_MEDI</th>\n      <th>NONLIVINGAREA_MEDI</th>\n      <th>FONDKAPREMONT_MODE</th>\n      <th>HOUSETYPE_MODE</th>\n      <th>TOTALAREA_MODE</th>\n      <th>WALLSMATERIAL_MODE</th>\n      <th>EMERGENCYSTATE_MODE</th>\n      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n      <th>DAYS_LAST_PHONE_CHANGE</th>\n      <th>FLAG_DOCUMENT_2</th>\n      <th>FLAG_DOCUMENT_3</th>\n      <th>FLAG_DOCUMENT_4</th>\n      <th>FLAG_DOCUMENT_5</th>\n      <th>FLAG_DOCUMENT_6</th>\n      <th>FLAG_DOCUMENT_7</th>\n      <th>FLAG_DOCUMENT_8</th>\n      <th>FLAG_DOCUMENT_9</th>\n      <th>FLAG_DOCUMENT_10</th>\n      <th>FLAG_DOCUMENT_11</th>\n      <th>FLAG_DOCUMENT_12</th>\n      <th>FLAG_DOCUMENT_13</th>\n      <th>FLAG_DOCUMENT_14</th>\n      <th>FLAG_DOCUMENT_15</th>\n      <th>FLAG_DOCUMENT_16</th>\n      <th>FLAG_DOCUMENT_17</th>\n      <th>FLAG_DOCUMENT_18</th>\n      <th>FLAG_DOCUMENT_19</th>\n      <th>FLAG_DOCUMENT_20</th>\n      <th>FLAG_DOCUMENT_21</th>\n      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n      <th>contact_info</th>\n      <th>age</th>\n      <th>years_employed</th>\n      <th>app AMT_CREDIT / AMT_ANNUITY</th>\n      <th>app EXT_SOURCE mean</th>\n      <th>app EXT_SOURCE_1 / DAYS_BIRTH</th>\n      <th>app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY</th>\n      <th>app AMT_INCOME_TOTAL / AMT_ANNUITY</th>\n      <th>app AMT_INCOME_TOTAL - AMT_GOODS_PRICE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>100002</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>12.218500</td>\n      <td>12.915581</td>\n      <td>24700.5</td>\n      <td>351000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.018801</td>\n      <td>-9461</td>\n      <td>-637</td>\n      <td>-3648.0</td>\n      <td>-2120</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>6</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0.083037</td>\n      <td>0.262949</td>\n      <td>0.139376</td>\n      <td>0.0247</td>\n      <td>0.0369</td>\n      <td>0.9722</td>\n      <td>0.6192</td>\n      <td>0.0143</td>\n      <td>0.00</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0369</td>\n      <td>0.0202</td>\n      <td>0.0190</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0252</td>\n      <td>0.0383</td>\n      <td>0.9722</td>\n      <td>0.6341</td>\n      <td>0.0144</td>\n      <td>0.0000</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0377</td>\n      <td>0.022</td>\n      <td>0.0198</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0250</td>\n      <td>0.0369</td>\n      <td>0.9722</td>\n      <td>0.6243</td>\n      <td>0.0144</td>\n      <td>0.00</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0375</td>\n      <td>0.0205</td>\n      <td>0.0193</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0149</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-1134.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>4</td>\n      <td>25.920548</td>\n      <td>1.745205</td>\n      <td>0.000523</td>\n      <td>0.161787</td>\n      <td>-0.000009</td>\n      <td>-24699.481792</td>\n      <td>0.000495</td>\n      <td>-3.509878e+05</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>100003</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12.506181</td>\n      <td>14.072865</td>\n      <td>35698.5</td>\n      <td>1129500.0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.003541</td>\n      <td>-16765</td>\n      <td>-1188</td>\n      <td>-1186.0</td>\n      <td>-291</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>39</td>\n      <td>0.311267</td>\n      <td>0.622246</td>\n      <td>NaN</td>\n      <td>0.0959</td>\n      <td>0.0529</td>\n      <td>0.9851</td>\n      <td>0.7960</td>\n      <td>0.0605</td>\n      <td>0.08</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0130</td>\n      <td>0.0773</td>\n      <td>0.0549</td>\n      <td>0.0039</td>\n      <td>0.0098</td>\n      <td>0.0924</td>\n      <td>0.0538</td>\n      <td>0.9851</td>\n      <td>0.8040</td>\n      <td>0.0497</td>\n      <td>0.0806</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0128</td>\n      <td>0.079</td>\n      <td>0.0554</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0968</td>\n      <td>0.0529</td>\n      <td>0.9851</td>\n      <td>0.7987</td>\n      <td>0.0608</td>\n      <td>0.08</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0132</td>\n      <td>0.0787</td>\n      <td>0.0558</td>\n      <td>0.0039</td>\n      <td>0.01</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0714</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>-828.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>45.931507</td>\n      <td>3.254795</td>\n      <td>0.000394</td>\n      <td>0.466757</td>\n      <td>-0.000019</td>\n      <td>-35697.457818</td>\n      <td>0.000350</td>\n      <td>-1.129487e+06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>100004</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.119898</td>\n      <td>11.813037</td>\n      <td>6750.0</td>\n      <td>135000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.010032</td>\n      <td>-19046</td>\n      <td>-225</td>\n      <td>-4260.0</td>\n      <td>-2531</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>NaN</td>\n      <td>0.555912</td>\n      <td>0.729567</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-815.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>52.180822</td>\n      <td>0.616438</td>\n      <td>0.001750</td>\n      <td>0.642739</td>\n      <td>NaN</td>\n      <td>-6749.073342</td>\n      <td>0.001647</td>\n      <td>-1.349889e+05</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>100006</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.813037</td>\n      <td>12.652947</td>\n      <td>29686.5</td>\n      <td>297000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.008019</td>\n      <td>-19005</td>\n      <td>-3039</td>\n      <td>-9833.0</td>\n      <td>-2437</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>6</td>\n      <td>17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>0.650442</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-617.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n      <td>52.068493</td>\n      <td>8.326027</td>\n      <td>0.000426</td>\n      <td>0.650442</td>\n      <td>NaN</td>\n      <td>-29685.515580</td>\n      <td>0.000398</td>\n      <td>-2.969882e+05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>100007</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.707678</td>\n      <td>13.148033</td>\n      <td>21865.5</td>\n      <td>513000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.028663</td>\n      <td>-19932</td>\n      <td>-3038</td>\n      <td>-4311.0</td>\n      <td>-3458</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>37</td>\n      <td>NaN</td>\n      <td>0.322738</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1106.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>54.608219</td>\n      <td>8.323288</td>\n      <td>0.000601</td>\n      <td>0.322738</td>\n      <td>NaN</td>\n      <td>-21864.524360</td>\n      <td>0.000535</td>\n      <td>-5.129883e+05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test = application_test","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:56.104471Z","iopub.execute_input":"2021-07-02T07:07:56.104973Z","iopub.status.idle":"2021-07-02T07:07:56.109137Z","shell.execute_reply.started":"2021-07-02T07:07:56.104937Z","shell.execute_reply":"2021-07-02T07:07:56.107932Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"train = train.fillna(0)\ntest = test.fillna(0)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:56.110584Z","iopub.execute_input":"2021-07-02T07:07:56.110947Z","iopub.status.idle":"2021-07-02T07:07:56.668125Z","shell.execute_reply.started":"2021-07-02T07:07:56.110913Z","shell.execute_reply":"2021-07-02T07:07:56.667026Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"# SK_ID_CURR; 샘플의 대출 아이디\nX = train.drop(['SK_ID_CURR', 'contact_info', 'TARGET'], axis = 1)\ny = train['TARGET']\n\nX_test = test.drop(['SK_ID_CURR'], axis = 1)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:56.669932Z","iopub.execute_input":"2021-07-02T07:07:56.670262Z","iopub.status.idle":"2021-07-02T07:07:56.835075Z","shell.execute_reply.started":"2021-07-02T07:07:56.670230Z","shell.execute_reply":"2021-07-02T07:07:56.833924Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"X.head()","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:56.836594Z","iopub.execute_input":"2021-07-02T07:07:56.837009Z","iopub.status.idle":"2021-07-02T07:07:56.963718Z","shell.execute_reply.started":"2021-07-02T07:07:56.836974Z","shell.execute_reply":"2021-07-02T07:07:56.962416Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"   NAME_CONTRACT_TYPE  CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n0                   0            1             0                1   \n1                   0            0             0                0   \n2                   1            1             1                1   \n3                   0            0             0                1   \n4                   0            1             0                1   \n\n   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n0             0         12.218500   12.915581      24700.5         351000.0   \n1             0         12.506181   14.072865      35698.5        1129500.0   \n2             0         11.119898   11.813037       6750.0         135000.0   \n3             0         11.813037   12.652947      29686.5         297000.0   \n4             0         11.707678   13.148033      21865.5         513000.0   \n\n   NAME_TYPE_SUITE  NAME_INCOME_TYPE  NAME_EDUCATION_TYPE  NAME_FAMILY_STATUS  \\\n0                6                 7                    4                   3   \n1                1                 4                    1                   1   \n2                6                 7                    4                   3   \n3                6                 7                    4                   0   \n4                6                 7                    4                   3   \n\n   NAME_HOUSING_TYPE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n0                  1                    0.018801       -9461           -637   \n1                  1                    0.003541      -16765          -1188   \n2                  1                    0.010032      -19046           -225   \n3                  1                    0.008019      -19005          -3039   \n4                  1                    0.028663      -19932          -3038   \n\n   DAYS_REGISTRATION  DAYS_ID_PUBLISH  OWN_CAR_AGE  FLAG_MOBIL  \\\n0            -3648.0            -2120          0.0           1   \n1            -1186.0             -291          0.0           1   \n2            -4260.0            -2531         26.0           1   \n3            -9833.0            -2437          0.0           1   \n4            -4311.0            -3458          0.0           1   \n\n   FLAG_EMP_PHONE  FLAG_WORK_PHONE  FLAG_CONT_MOBILE  FLAG_PHONE  FLAG_EMAIL  \\\n0               1                0                 1           1           0   \n1               1                0                 1           1           0   \n2               1                1                 1           1           0   \n3               1                0                 1           0           0   \n4               1                0                 1           0           0   \n\n   OCCUPATION_TYPE  CNT_FAM_MEMBERS  REGION_RATING_CLIENT  \\\n0                8              1.0                     2   \n1                3              2.0                     1   \n2                8              1.0                     2   \n3                8              2.0                     2   \n4                3              1.0                     2   \n\n   REGION_RATING_CLIENT_W_CITY  WEEKDAY_APPR_PROCESS_START  \\\n0                            2                           6   \n1                            1                           1   \n2                            2                           1   \n3                            2                           6   \n4                            2                           4   \n\n   HOUR_APPR_PROCESS_START  REG_REGION_NOT_LIVE_REGION  \\\n0                       10                           0   \n1                       11                           0   \n2                        9                           0   \n3                       17                           0   \n4                       11                           0   \n\n   REG_REGION_NOT_WORK_REGION  LIVE_REGION_NOT_WORK_REGION  \\\n0                           0                            0   \n1                           0                            0   \n2                           0                            0   \n3                           0                            0   \n4                           0                            0   \n\n   REG_CITY_NOT_LIVE_CITY  REG_CITY_NOT_WORK_CITY  LIVE_CITY_NOT_WORK_CITY  \\\n0                       0                       0                        0   \n1                       0                       0                        0   \n2                       0                       0                        0   \n3                       0                       0                        0   \n4                       0                       1                        1   \n\n   ORGANIZATION_TYPE  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  \\\n0                  5      0.083037      0.262949      0.139376   \n1                 39      0.311267      0.622246      0.000000   \n2                 11      0.000000      0.555912      0.729567   \n3                  5      0.000000      0.650442      0.000000   \n4                 37      0.000000      0.322738      0.000000   \n\n   APARTMENTS_AVG  BASEMENTAREA_AVG  YEARS_BEGINEXPLUATATION_AVG  \\\n0          0.0247            0.0369                       0.9722   \n1          0.0959            0.0529                       0.9851   \n2          0.0000            0.0000                       0.0000   \n3          0.0000            0.0000                       0.0000   \n4          0.0000            0.0000                       0.0000   \n\n   YEARS_BUILD_AVG  COMMONAREA_AVG  ELEVATORS_AVG  ENTRANCES_AVG  \\\n0           0.6192          0.0143           0.00         0.0690   \n1           0.7960          0.0605           0.08         0.0345   \n2           0.0000          0.0000           0.00         0.0000   \n3           0.0000          0.0000           0.00         0.0000   \n4           0.0000          0.0000           0.00         0.0000   \n\n   FLOORSMAX_AVG  FLOORSMIN_AVG  LANDAREA_AVG  LIVINGAPARTMENTS_AVG  \\\n0         0.0833         0.1250        0.0369                0.0202   \n1         0.2917         0.3333        0.0130                0.0773   \n2         0.0000         0.0000        0.0000                0.0000   \n3         0.0000         0.0000        0.0000                0.0000   \n4         0.0000         0.0000        0.0000                0.0000   \n\n   LIVINGAREA_AVG  NONLIVINGAPARTMENTS_AVG  NONLIVINGAREA_AVG  \\\n0          0.0190                   0.0000             0.0000   \n1          0.0549                   0.0039             0.0098   \n2          0.0000                   0.0000             0.0000   \n3          0.0000                   0.0000             0.0000   \n4          0.0000                   0.0000             0.0000   \n\n   APARTMENTS_MODE  BASEMENTAREA_MODE  YEARS_BEGINEXPLUATATION_MODE  \\\n0           0.0252             0.0383                        0.9722   \n1           0.0924             0.0538                        0.9851   \n2           0.0000             0.0000                        0.0000   \n3           0.0000             0.0000                        0.0000   \n4           0.0000             0.0000                        0.0000   \n\n   YEARS_BUILD_MODE  COMMONAREA_MODE  ELEVATORS_MODE  ENTRANCES_MODE  \\\n0            0.6341           0.0144          0.0000          0.0690   \n1            0.8040           0.0497          0.0806          0.0345   \n2            0.0000           0.0000          0.0000          0.0000   \n3            0.0000           0.0000          0.0000          0.0000   \n4            0.0000           0.0000          0.0000          0.0000   \n\n   FLOORSMAX_MODE  FLOORSMIN_MODE  LANDAREA_MODE  LIVINGAPARTMENTS_MODE  \\\n0          0.0833          0.1250         0.0377                  0.022   \n1          0.2917          0.3333         0.0128                  0.079   \n2          0.0000          0.0000         0.0000                  0.000   \n3          0.0000          0.0000         0.0000                  0.000   \n4          0.0000          0.0000         0.0000                  0.000   \n\n   LIVINGAREA_MODE  NONLIVINGAPARTMENTS_MODE  NONLIVINGAREA_MODE  \\\n0           0.0198                       0.0                 0.0   \n1           0.0554                       0.0                 0.0   \n2           0.0000                       0.0                 0.0   \n3           0.0000                       0.0                 0.0   \n4           0.0000                       0.0                 0.0   \n\n   APARTMENTS_MEDI  BASEMENTAREA_MEDI  YEARS_BEGINEXPLUATATION_MEDI  \\\n0           0.0250             0.0369                        0.9722   \n1           0.0968             0.0529                        0.9851   \n2           0.0000             0.0000                        0.0000   \n3           0.0000             0.0000                        0.0000   \n4           0.0000             0.0000                        0.0000   \n\n   YEARS_BUILD_MEDI  COMMONAREA_MEDI  ELEVATORS_MEDI  ENTRANCES_MEDI  \\\n0            0.6243           0.0144            0.00          0.0690   \n1            0.7987           0.0608            0.08          0.0345   \n2            0.0000           0.0000            0.00          0.0000   \n3            0.0000           0.0000            0.00          0.0000   \n4            0.0000           0.0000            0.00          0.0000   \n\n   FLOORSMAX_MEDI  FLOORSMIN_MEDI  LANDAREA_MEDI  LIVINGAPARTMENTS_MEDI  \\\n0          0.0833          0.1250         0.0375                 0.0205   \n1          0.2917          0.3333         0.0132                 0.0787   \n2          0.0000          0.0000         0.0000                 0.0000   \n3          0.0000          0.0000         0.0000                 0.0000   \n4          0.0000          0.0000         0.0000                 0.0000   \n\n   LIVINGAREA_MEDI  NONLIVINGAPARTMENTS_MEDI  NONLIVINGAREA_MEDI  \\\n0           0.0193                    0.0000                0.00   \n1           0.0558                    0.0039                0.01   \n2           0.0000                    0.0000                0.00   \n3           0.0000                    0.0000                0.00   \n4           0.0000                    0.0000                0.00   \n\n   FONDKAPREMONT_MODE  HOUSETYPE_MODE  TOTALAREA_MODE  WALLSMATERIAL_MODE  \\\n0                   3               0          0.0149                   5   \n1                   3               0          0.0714                   0   \n2                   0               1          0.0000                   7   \n3                   0               1          0.0000                   7   \n4                   0               1          0.0000                   7   \n\n   EMERGENCYSTATE_MODE  OBS_30_CNT_SOCIAL_CIRCLE  DEF_30_CNT_SOCIAL_CIRCLE  \\\n0                    0                         1                         1   \n1                    0                         2                         0   \n2                    2                         0                         0   \n3                    2                         1                         0   \n4                    2                         0                         0   \n\n   OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  DAYS_LAST_PHONE_CHANGE  \\\n0                         1                         1                 -1134.0   \n1                         2                         0                  -828.0   \n2                         0                         0                  -815.0   \n3                         1                         0                  -617.0   \n4                         0                         0                 -1106.0   \n\n   FLAG_DOCUMENT_2  FLAG_DOCUMENT_3  FLAG_DOCUMENT_4  FLAG_DOCUMENT_5  \\\n0                0                1                0                0   \n1                0                1                0                0   \n2                0                0                0                0   \n3                0                1                0                0   \n4                0                0                0                0   \n\n   FLAG_DOCUMENT_6  FLAG_DOCUMENT_7  FLAG_DOCUMENT_8  FLAG_DOCUMENT_9  \\\n0                0                0                0                0   \n1                0                0                0                0   \n2                0                0                0                0   \n3                0                0                0                0   \n4                0                0                1                0   \n\n   FLAG_DOCUMENT_10  FLAG_DOCUMENT_11  FLAG_DOCUMENT_12  FLAG_DOCUMENT_13  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  FLAG_DOCUMENT_17  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n0                 0                 0                 0                 0   \n1                 0                 0                 0                 0   \n2                 0                 0                 0                 0   \n3                 0                 0                 0                 0   \n4                 0                 0                 0                 0   \n\n   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n0                         0.0                        0.0   \n1                         0.0                        0.0   \n2                         0.0                        0.0   \n3                         0.0                        0.0   \n4                         0.0                        0.0   \n\n   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n0                         0.0                        0.0   \n1                         0.0                        0.0   \n2                         0.0                        0.0   \n3                         0.0                        0.0   \n4                         0.0                        0.0   \n\n   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR        age  \\\n0                        0.0                         1.0  25.920548   \n1                        0.0                         0.0  45.931507   \n2                        0.0                         0.0  52.180822   \n3                        0.0                         0.0  52.068493   \n4                        0.0                         0.0  54.608219   \n\n   years_employed  app AMT_CREDIT / AMT_ANNUITY  app EXT_SOURCE mean  \\\n0        1.745205                      0.000523             0.161787   \n1        3.254795                      0.000394             0.466757   \n2        0.616438                      0.001750             0.642739   \n3        8.326027                      0.000426             0.650442   \n4        8.323288                      0.000601             0.322738   \n\n   app EXT_SOURCE_1 / DAYS_BIRTH  app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY  \\\n0                      -0.000009                            -24699.481792   \n1                      -0.000019                            -35697.457818   \n2                       0.000000                             -6749.073342   \n3                       0.000000                            -29685.515580   \n4                       0.000000                            -21864.524360   \n\n   app AMT_INCOME_TOTAL / AMT_ANNUITY  app AMT_INCOME_TOTAL - AMT_GOODS_PRICE  \n0                            0.000495                           -3.509878e+05  \n1                            0.000350                           -1.129487e+06  \n2                            0.001647                           -1.349889e+05  \n3                            0.000398                           -2.969882e+05  \n4                            0.000535                           -5.129883e+05  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NAME_CONTRACT_TYPE</th>\n      <th>CODE_GENDER</th>\n      <th>FLAG_OWN_CAR</th>\n      <th>FLAG_OWN_REALTY</th>\n      <th>CNT_CHILDREN</th>\n      <th>AMT_INCOME_TOTAL</th>\n      <th>AMT_CREDIT</th>\n      <th>AMT_ANNUITY</th>\n      <th>AMT_GOODS_PRICE</th>\n      <th>NAME_TYPE_SUITE</th>\n      <th>NAME_INCOME_TYPE</th>\n      <th>NAME_EDUCATION_TYPE</th>\n      <th>NAME_FAMILY_STATUS</th>\n      <th>NAME_HOUSING_TYPE</th>\n      <th>REGION_POPULATION_RELATIVE</th>\n      <th>DAYS_BIRTH</th>\n      <th>DAYS_EMPLOYED</th>\n      <th>DAYS_REGISTRATION</th>\n      <th>DAYS_ID_PUBLISH</th>\n      <th>OWN_CAR_AGE</th>\n      <th>FLAG_MOBIL</th>\n      <th>FLAG_EMP_PHONE</th>\n      <th>FLAG_WORK_PHONE</th>\n      <th>FLAG_CONT_MOBILE</th>\n      <th>FLAG_PHONE</th>\n      <th>FLAG_EMAIL</th>\n      <th>OCCUPATION_TYPE</th>\n      <th>CNT_FAM_MEMBERS</th>\n      <th>REGION_RATING_CLIENT</th>\n      <th>REGION_RATING_CLIENT_W_CITY</th>\n      <th>WEEKDAY_APPR_PROCESS_START</th>\n      <th>HOUR_APPR_PROCESS_START</th>\n      <th>REG_REGION_NOT_LIVE_REGION</th>\n      <th>REG_REGION_NOT_WORK_REGION</th>\n      <th>LIVE_REGION_NOT_WORK_REGION</th>\n      <th>REG_CITY_NOT_LIVE_CITY</th>\n      <th>REG_CITY_NOT_WORK_CITY</th>\n      <th>LIVE_CITY_NOT_WORK_CITY</th>\n      <th>ORGANIZATION_TYPE</th>\n      <th>EXT_SOURCE_1</th>\n      <th>EXT_SOURCE_2</th>\n      <th>EXT_SOURCE_3</th>\n      <th>APARTMENTS_AVG</th>\n      <th>BASEMENTAREA_AVG</th>\n      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n      <th>YEARS_BUILD_AVG</th>\n      <th>COMMONAREA_AVG</th>\n      <th>ELEVATORS_AVG</th>\n      <th>ENTRANCES_AVG</th>\n      <th>FLOORSMAX_AVG</th>\n      <th>FLOORSMIN_AVG</th>\n      <th>LANDAREA_AVG</th>\n      <th>LIVINGAPARTMENTS_AVG</th>\n      <th>LIVINGAREA_AVG</th>\n      <th>NONLIVINGAPARTMENTS_AVG</th>\n      <th>NONLIVINGAREA_AVG</th>\n      <th>APARTMENTS_MODE</th>\n      <th>BASEMENTAREA_MODE</th>\n      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n      <th>YEARS_BUILD_MODE</th>\n      <th>COMMONAREA_MODE</th>\n      <th>ELEVATORS_MODE</th>\n      <th>ENTRANCES_MODE</th>\n      <th>FLOORSMAX_MODE</th>\n      <th>FLOORSMIN_MODE</th>\n      <th>LANDAREA_MODE</th>\n      <th>LIVINGAPARTMENTS_MODE</th>\n      <th>LIVINGAREA_MODE</th>\n      <th>NONLIVINGAPARTMENTS_MODE</th>\n      <th>NONLIVINGAREA_MODE</th>\n      <th>APARTMENTS_MEDI</th>\n      <th>BASEMENTAREA_MEDI</th>\n      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n      <th>YEARS_BUILD_MEDI</th>\n      <th>COMMONAREA_MEDI</th>\n      <th>ELEVATORS_MEDI</th>\n      <th>ENTRANCES_MEDI</th>\n      <th>FLOORSMAX_MEDI</th>\n      <th>FLOORSMIN_MEDI</th>\n      <th>LANDAREA_MEDI</th>\n      <th>LIVINGAPARTMENTS_MEDI</th>\n      <th>LIVINGAREA_MEDI</th>\n      <th>NONLIVINGAPARTMENTS_MEDI</th>\n      <th>NONLIVINGAREA_MEDI</th>\n      <th>FONDKAPREMONT_MODE</th>\n      <th>HOUSETYPE_MODE</th>\n      <th>TOTALAREA_MODE</th>\n      <th>WALLSMATERIAL_MODE</th>\n      <th>EMERGENCYSTATE_MODE</th>\n      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n      <th>DAYS_LAST_PHONE_CHANGE</th>\n      <th>FLAG_DOCUMENT_2</th>\n      <th>FLAG_DOCUMENT_3</th>\n      <th>FLAG_DOCUMENT_4</th>\n      <th>FLAG_DOCUMENT_5</th>\n      <th>FLAG_DOCUMENT_6</th>\n      <th>FLAG_DOCUMENT_7</th>\n      <th>FLAG_DOCUMENT_8</th>\n      <th>FLAG_DOCUMENT_9</th>\n      <th>FLAG_DOCUMENT_10</th>\n      <th>FLAG_DOCUMENT_11</th>\n      <th>FLAG_DOCUMENT_12</th>\n      <th>FLAG_DOCUMENT_13</th>\n      <th>FLAG_DOCUMENT_14</th>\n      <th>FLAG_DOCUMENT_15</th>\n      <th>FLAG_DOCUMENT_16</th>\n      <th>FLAG_DOCUMENT_17</th>\n      <th>FLAG_DOCUMENT_18</th>\n      <th>FLAG_DOCUMENT_19</th>\n      <th>FLAG_DOCUMENT_20</th>\n      <th>FLAG_DOCUMENT_21</th>\n      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n      <th>age</th>\n      <th>years_employed</th>\n      <th>app AMT_CREDIT / AMT_ANNUITY</th>\n      <th>app EXT_SOURCE mean</th>\n      <th>app EXT_SOURCE_1 / DAYS_BIRTH</th>\n      <th>app AMT_INCOME_TOTAL / 12 - AMT_ANNUITY</th>\n      <th>app AMT_INCOME_TOTAL / AMT_ANNUITY</th>\n      <th>app AMT_INCOME_TOTAL - AMT_GOODS_PRICE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>12.218500</td>\n      <td>12.915581</td>\n      <td>24700.5</td>\n      <td>351000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.018801</td>\n      <td>-9461</td>\n      <td>-637</td>\n      <td>-3648.0</td>\n      <td>-2120</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>6</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0.083037</td>\n      <td>0.262949</td>\n      <td>0.139376</td>\n      <td>0.0247</td>\n      <td>0.0369</td>\n      <td>0.9722</td>\n      <td>0.6192</td>\n      <td>0.0143</td>\n      <td>0.00</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0369</td>\n      <td>0.0202</td>\n      <td>0.0190</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0252</td>\n      <td>0.0383</td>\n      <td>0.9722</td>\n      <td>0.6341</td>\n      <td>0.0144</td>\n      <td>0.0000</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0377</td>\n      <td>0.022</td>\n      <td>0.0198</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0250</td>\n      <td>0.0369</td>\n      <td>0.9722</td>\n      <td>0.6243</td>\n      <td>0.0144</td>\n      <td>0.00</td>\n      <td>0.0690</td>\n      <td>0.0833</td>\n      <td>0.1250</td>\n      <td>0.0375</td>\n      <td>0.0205</td>\n      <td>0.0193</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0149</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-1134.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>25.920548</td>\n      <td>1.745205</td>\n      <td>0.000523</td>\n      <td>0.161787</td>\n      <td>-0.000009</td>\n      <td>-24699.481792</td>\n      <td>0.000495</td>\n      <td>-3.509878e+05</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12.506181</td>\n      <td>14.072865</td>\n      <td>35698.5</td>\n      <td>1129500.0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.003541</td>\n      <td>-16765</td>\n      <td>-1188</td>\n      <td>-1186.0</td>\n      <td>-291</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>39</td>\n      <td>0.311267</td>\n      <td>0.622246</td>\n      <td>0.000000</td>\n      <td>0.0959</td>\n      <td>0.0529</td>\n      <td>0.9851</td>\n      <td>0.7960</td>\n      <td>0.0605</td>\n      <td>0.08</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0130</td>\n      <td>0.0773</td>\n      <td>0.0549</td>\n      <td>0.0039</td>\n      <td>0.0098</td>\n      <td>0.0924</td>\n      <td>0.0538</td>\n      <td>0.9851</td>\n      <td>0.8040</td>\n      <td>0.0497</td>\n      <td>0.0806</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0128</td>\n      <td>0.079</td>\n      <td>0.0554</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0968</td>\n      <td>0.0529</td>\n      <td>0.9851</td>\n      <td>0.7987</td>\n      <td>0.0608</td>\n      <td>0.08</td>\n      <td>0.0345</td>\n      <td>0.2917</td>\n      <td>0.3333</td>\n      <td>0.0132</td>\n      <td>0.0787</td>\n      <td>0.0558</td>\n      <td>0.0039</td>\n      <td>0.01</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0714</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>-828.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>45.931507</td>\n      <td>3.254795</td>\n      <td>0.000394</td>\n      <td>0.466757</td>\n      <td>-0.000019</td>\n      <td>-35697.457818</td>\n      <td>0.000350</td>\n      <td>-1.129487e+06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.119898</td>\n      <td>11.813037</td>\n      <td>6750.0</td>\n      <td>135000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.010032</td>\n      <td>-19046</td>\n      <td>-225</td>\n      <td>-4260.0</td>\n      <td>-2531</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>0.000000</td>\n      <td>0.555912</td>\n      <td>0.729567</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.0000</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-815.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>52.180822</td>\n      <td>0.616438</td>\n      <td>0.001750</td>\n      <td>0.642739</td>\n      <td>0.000000</td>\n      <td>-6749.073342</td>\n      <td>0.001647</td>\n      <td>-1.349889e+05</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.813037</td>\n      <td>12.652947</td>\n      <td>29686.5</td>\n      <td>297000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.008019</td>\n      <td>-19005</td>\n      <td>-3039</td>\n      <td>-9833.0</td>\n      <td>-2437</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>6</td>\n      <td>17</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0.000000</td>\n      <td>0.650442</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.0000</td>\n      <td>7</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-617.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>52.068493</td>\n      <td>8.326027</td>\n      <td>0.000426</td>\n      <td>0.650442</td>\n      <td>0.000000</td>\n      <td>-29685.515580</td>\n      <td>0.000398</td>\n      <td>-2.969882e+05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.707678</td>\n      <td>13.148033</td>\n      <td>21865.5</td>\n      <td>513000.0</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.028663</td>\n      <td>-19932</td>\n      <td>-3038</td>\n      <td>-4311.0</td>\n      <td>-3458</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>37</td>\n      <td>0.000000</td>\n      <td>0.322738</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.00</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.0000</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1106.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>54.608219</td>\n      <td>8.323288</td>\n      <td>0.000601</td>\n      <td>0.322738</td>\n      <td>0.000000</td>\n      <td>-21864.524360</td>\n      <td>0.000535</td>\n      <td>-5.129883e+05</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Basic modelling, LGB","metadata":{}},{"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.20, random_state=42)\n\nparams = {\n    'boosting': 'dart',\n    'application': 'binary',\n    'learning_rate': 0.01,\n    'num_leaves': 34,\n    'max_depth': 5,\n    'feature_fraction': 0.9,\n    'scale_pos_weight': 2,\n    'reg_alpha': 0.05,\n    'reg_lambda': 0.1\n}\n\nmodel = lgb.train(params, lgb.Dataset(X_train, y_train), 1000, [lgb.Dataset(X_train, y_train), lgb.Dataset(X_valid, y_valid)], \n                  verbose_eval=10, early_stopping_rounds=20)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:07:56.965246Z","iopub.execute_input":"2021-07-02T07:07:56.965755Z","iopub.status.idle":"2021-07-02T07:17:12.293189Z","shell.execute_reply.started":"2021-07-02T07:07:56.965659Z","shell.execute_reply":"2021-07-02T07:17:12.292121Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines\n[LightGBM] [Info] Number of positive: 19876, number of negative: 226132\n[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.134409 seconds.\nYou can set `force_row_wise=true` to remove the overhead.\nAnd if memory is not enough, you can set `force_col_wise=true`.\n[LightGBM] [Info] Total Bins 13278\n[LightGBM] [Info] Number of data points in the train set: 246008, number of used features: 123\n[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines\n[LightGBM] [Warning] Find whitespaces in feature_names, replace with underlines\n[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.080794 -> initscore=-2.431606\n[LightGBM] [Info] Start training from score -2.431606\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/callback.py:182: UserWarning: Early stopping is not available in dart mode\n  _log_warning('Early stopping is not available in dart mode')\n","output_type":"stream"},{"name":"stdout","text":"[10]\tvalid_0's binary_logloss: 0.274237\tvalid_1's binary_logloss: 0.273404\n[20]\tvalid_0's binary_logloss: 0.270129\tvalid_1's binary_logloss: 0.269345\n[30]\tvalid_0's binary_logloss: 0.267778\tvalid_1's binary_logloss: 0.267063\n[40]\tvalid_0's binary_logloss: 0.266674\tvalid_1's binary_logloss: 0.266017\n[50]\tvalid_0's binary_logloss: 0.275525\tvalid_1's binary_logloss: 0.275014\n[60]\tvalid_0's binary_logloss: 0.292977\tvalid_1's binary_logloss: 0.292615\n[70]\tvalid_0's binary_logloss: 0.314411\tvalid_1's binary_logloss: 0.31415\n[80]\tvalid_0's binary_logloss: 0.311145\tvalid_1's binary_logloss: 0.310887\n[90]\tvalid_0's binary_logloss: 0.30934\tvalid_1's binary_logloss: 0.309062\n[100]\tvalid_0's binary_logloss: 0.332148\tvalid_1's binary_logloss: 0.331969\n[110]\tvalid_0's binary_logloss: 0.357422\tvalid_1's binary_logloss: 0.357314\n[120]\tvalid_0's binary_logloss: 0.350528\tvalid_1's binary_logloss: 0.350415\n[130]\tvalid_0's binary_logloss: 0.346057\tvalid_1's binary_logloss: 0.345941\n[140]\tvalid_0's binary_logloss: 0.37033\tvalid_1's binary_logloss: 0.370299\n[150]\tvalid_0's binary_logloss: 0.36681\tvalid_1's binary_logloss: 0.366768\n[160]\tvalid_0's binary_logloss: 0.383434\tvalid_1's binary_logloss: 0.383421\n[170]\tvalid_0's binary_logloss: 0.380777\tvalid_1's binary_logloss: 0.380793\n[180]\tvalid_0's binary_logloss: 0.382948\tvalid_1's binary_logloss: 0.382987\n[190]\tvalid_0's binary_logloss: 0.375227\tvalid_1's binary_logloss: 0.375266\n[200]\tvalid_0's binary_logloss: 0.371151\tvalid_1's binary_logloss: 0.371184\n[210]\tvalid_0's binary_logloss: 0.377797\tvalid_1's binary_logloss: 0.377843\n[220]\tvalid_0's binary_logloss: 0.375784\tvalid_1's binary_logloss: 0.375858\n[230]\tvalid_0's binary_logloss: 0.373698\tvalid_1's binary_logloss: 0.373771\n[240]\tvalid_0's binary_logloss: 0.364968\tvalid_1's binary_logloss: 0.365064\n[250]\tvalid_0's binary_logloss: 0.366564\tvalid_1's binary_logloss: 0.366677\n[260]\tvalid_0's binary_logloss: 0.361903\tvalid_1's binary_logloss: 0.362027\n[270]\tvalid_0's binary_logloss: 0.355885\tvalid_1's binary_logloss: 0.356022\n[280]\tvalid_0's binary_logloss: 0.353481\tvalid_1's binary_logloss: 0.353616\n[290]\tvalid_0's binary_logloss: 0.352014\tvalid_1's binary_logloss: 0.352177\n[300]\tvalid_0's binary_logloss: 0.351211\tvalid_1's binary_logloss: 0.351369\n[310]\tvalid_0's binary_logloss: 0.354383\tvalid_1's binary_logloss: 0.354546\n[320]\tvalid_0's binary_logloss: 0.352004\tvalid_1's binary_logloss: 0.352186\n[330]\tvalid_0's binary_logloss: 0.349811\tvalid_1's binary_logloss: 0.349996\n[340]\tvalid_0's binary_logloss: 0.353094\tvalid_1's binary_logloss: 0.353289\n[350]\tvalid_0's binary_logloss: 0.347821\tvalid_1's binary_logloss: 0.348014\n[360]\tvalid_0's binary_logloss: 0.346892\tvalid_1's binary_logloss: 0.347104\n[370]\tvalid_0's binary_logloss: 0.344781\tvalid_1's binary_logloss: 0.344997\n[380]\tvalid_0's binary_logloss: 0.341461\tvalid_1's binary_logloss: 0.341681\n[390]\tvalid_0's binary_logloss: 0.337201\tvalid_1's binary_logloss: 0.337434\n[400]\tvalid_0's binary_logloss: 0.331998\tvalid_1's binary_logloss: 0.332245\n[410]\tvalid_0's binary_logloss: 0.331311\tvalid_1's binary_logloss: 0.331575\n[420]\tvalid_0's binary_logloss: 0.330114\tvalid_1's binary_logloss: 0.33038\n[430]\tvalid_0's binary_logloss: 0.328531\tvalid_1's binary_logloss: 0.328822\n[440]\tvalid_0's binary_logloss: 0.327676\tvalid_1's binary_logloss: 0.327988\n[450]\tvalid_0's binary_logloss: 0.323208\tvalid_1's binary_logloss: 0.323536\n[460]\tvalid_0's binary_logloss: 0.324535\tvalid_1's binary_logloss: 0.324879\n[470]\tvalid_0's binary_logloss: 0.324999\tvalid_1's binary_logloss: 0.32536\n[480]\tvalid_0's binary_logloss: 0.322788\tvalid_1's binary_logloss: 0.323173\n[490]\tvalid_0's binary_logloss: 0.318745\tvalid_1's binary_logloss: 0.319159\n[500]\tvalid_0's binary_logloss: 0.316784\tvalid_1's binary_logloss: 0.317221\n[510]\tvalid_0's binary_logloss: 0.314052\tvalid_1's binary_logloss: 0.31451\n[520]\tvalid_0's binary_logloss: 0.313164\tvalid_1's binary_logloss: 0.31364\n[530]\tvalid_0's binary_logloss: 0.313628\tvalid_1's binary_logloss: 0.314129\n[540]\tvalid_0's binary_logloss: 0.312807\tvalid_1's binary_logloss: 0.31333\n[550]\tvalid_0's binary_logloss: 0.31118\tvalid_1's binary_logloss: 0.311725\n[560]\tvalid_0's binary_logloss: 0.309632\tvalid_1's binary_logloss: 0.310196\n[570]\tvalid_0's binary_logloss: 0.310389\tvalid_1's binary_logloss: 0.310955\n[580]\tvalid_0's binary_logloss: 0.311172\tvalid_1's binary_logloss: 0.311744\n[590]\tvalid_0's binary_logloss: 0.311015\tvalid_1's binary_logloss: 0.311596\n[600]\tvalid_0's binary_logloss: 0.310815\tvalid_1's binary_logloss: 0.311414\n[610]\tvalid_0's binary_logloss: 0.309824\tvalid_1's binary_logloss: 0.310443\n[620]\tvalid_0's binary_logloss: 0.306707\tvalid_1's binary_logloss: 0.30736\n[630]\tvalid_0's binary_logloss: 0.307002\tvalid_1's binary_logloss: 0.307679\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[640]\tvalid_0's binary_logloss: 0.305501\tvalid_1's binary_logloss: 0.306205\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[650]\tvalid_0's binary_logloss: 0.305801\tvalid_1's binary_logloss: 0.306528\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[660]\tvalid_0's binary_logloss: 0.304886\tvalid_1's binary_logloss: 0.305629\n[670]\tvalid_0's binary_logloss: 0.306515\tvalid_1's binary_logloss: 0.307268\n[680]\tvalid_0's binary_logloss: 0.305177\tvalid_1's binary_logloss: 0.305966\n[690]\tvalid_0's binary_logloss: 0.305389\tvalid_1's binary_logloss: 0.306207\n[700]\tvalid_0's binary_logloss: 0.303853\tvalid_1's binary_logloss: 0.304691\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[710]\tvalid_0's binary_logloss: 0.303018\tvalid_1's binary_logloss: 0.303881\n[720]\tvalid_0's binary_logloss: 0.304561\tvalid_1's binary_logloss: 0.305437\n[730]\tvalid_0's binary_logloss: 0.303418\tvalid_1's binary_logloss: 0.304326\n[740]\tvalid_0's binary_logloss: 0.302556\tvalid_1's binary_logloss: 0.303478\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[750]\tvalid_0's binary_logloss: 0.302101\tvalid_1's binary_logloss: 0.303052\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[760]\tvalid_0's binary_logloss: 0.3013\tvalid_1's binary_logloss: 0.30227\n[770]\tvalid_0's binary_logloss: 0.301363\tvalid_1's binary_logloss: 0.302358\n[780]\tvalid_0's binary_logloss: 0.301067\tvalid_1's binary_logloss: 0.302072\n[790]\tvalid_0's binary_logloss: 0.300399\tvalid_1's binary_logloss: 0.301417\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[800]\tvalid_0's binary_logloss: 0.300165\tvalid_1's binary_logloss: 0.30119\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[810]\tvalid_0's binary_logloss: 0.299776\tvalid_1's binary_logloss: 0.300817\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[820]\tvalid_0's binary_logloss: 0.298946\tvalid_1's binary_logloss: 0.300008\n[830]\tvalid_0's binary_logloss: 0.297066\tvalid_1's binary_logloss: 0.298162\n[840]\tvalid_0's binary_logloss: 0.295341\tvalid_1's binary_logloss: 0.296468\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[850]\tvalid_0's binary_logloss: 0.294687\tvalid_1's binary_logloss: 0.295828\n[860]\tvalid_0's binary_logloss: 0.294032\tvalid_1's binary_logloss: 0.295191\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[870]\tvalid_0's binary_logloss: 0.293007\tvalid_1's binary_logloss: 0.294191\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[880]\tvalid_0's binary_logloss: 0.29426\tvalid_1's binary_logloss: 0.295457\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[890]\tvalid_0's binary_logloss: 0.292752\tvalid_1's binary_logloss: 0.293979\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[900]\tvalid_0's binary_logloss: 0.292971\tvalid_1's binary_logloss: 0.294225\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[910]\tvalid_0's binary_logloss: 0.292869\tvalid_1's binary_logloss: 0.294142\n[920]\tvalid_0's binary_logloss: 0.293178\tvalid_1's binary_logloss: 0.294451\n[930]\tvalid_0's binary_logloss: 0.292528\tvalid_1's binary_logloss: 0.293823\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[940]\tvalid_0's binary_logloss: 0.291932\tvalid_1's binary_logloss: 0.293247\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[950]\tvalid_0's binary_logloss: 0.291645\tvalid_1's binary_logloss: 0.29299\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[960]\tvalid_0's binary_logloss: 0.291378\tvalid_1's binary_logloss: 0.292761\n[970]\tvalid_0's binary_logloss: 0.290796\tvalid_1's binary_logloss: 0.292197\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[980]\tvalid_0's binary_logloss: 0.290304\tvalid_1's binary_logloss: 0.291729\n[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n[990]\tvalid_0's binary_logloss: 0.290532\tvalid_1's binary_logloss: 0.291968\n[1000]\tvalid_0's binary_logloss: 0.289545\tvalid_1's binary_logloss: 0.291022\n","output_type":"stream"}]},{"cell_type":"code","source":"lgb.plot_importance(model, max_num_features=30, figsize=(24, 18))","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:17:12.294747Z","iopub.execute_input":"2021-07-02T07:17:12.295083Z","iopub.status.idle":"2021-07-02T07:17:12.995408Z","shell.execute_reply.started":"2021-07-02T07:17:12.295050Z","shell.execute_reply":"2021-07-02T07:17:12.994423Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:title={'center':'Feature importance'}, xlabel='Feature importance', ylabel='Features'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1728x1296 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"sub = test[['SK_ID_CURR']].copy()\nsub['TARGET'] = model.predict(X_test)\nsub.to_csv('sub.csv', index= False)","metadata":{"execution":{"iopub.status.busy":"2021-07-02T07:17:12.996682Z","iopub.execute_input":"2021-07-02T07:17:12.996968Z","iopub.status.idle":"2021-07-02T07:17:14.607192Z","shell.execute_reply.started":"2021-07-02T07:17:12.996940Z","shell.execute_reply":"2021-07-02T07:17:14.606167Z"},"trusted":true},"execution_count":49,"outputs":[]}]}