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| import pandas as pd | |
| import streamlit as st | |
| import pickle | |
| import json | |
| # load the combined pipeline | |
| with open('pipeline.pkl', 'rb') as file: | |
| pipeline = pickle.load(file) | |
| # load the list of numeric columns | |
| with open('normal_dist.json', 'r') as file: | |
| normal_dist = json.load(file) | |
| with open('skewed_dist.json', 'r') as file: | |
| skewed_dist = json.load(file) | |
| # load the list of categorical columns (object type) | |
| with open('cat_cols.json', 'r') as file: | |
| cat_cols = json.load(file) | |
| # load the list of categorical columns (number type) | |
| with open('cat_num_cols.json', 'r') as file: | |
| cat_num_cols = json.load(file) | |
| with open('columns_to_drop.json', 'r') as file: | |
| columns_to_drop = json.load(file) | |
| def run(): | |
| # membuat title | |
| st.title('Credit Default Risk Prediction') | |
| st.subheader('Predicting Credit Default') | |
| st.markdown('---') | |
| st.write("# Customer Information") | |
| # Buat form | |
| with st.form(key='form_credit'): | |
| st.write("#### Client's Personal Information") | |
| SK_ID_CURR = st.number_input('Client ID', min_value=0, max_value=9999999, value=0) | |
| NAME_CONTRACT_TYPE = st.selectbox('Contract Type', ('Cash loans', 'Revolving loans')) | |
| CODE_GENDER = st.radio('Clients gender', ('F', 'M')) | |
| FLAG_OWN_CAR = st.radio('Flag if the client owns a car', ('N', 'Y')) | |
| FLAG_OWN_REALTY= st.radio('Flag if client owns a house or flat', ('N', 'Y')) | |
| CNT_CHILDREN= st.number_input("Client's Children Count", min_value=0, max_value=20, value=0) | |
| st.markdown('---') | |
| st.write("#### Client's Financial Information") | |
| AMT_INCOME_TOTAL= st.number_input('Client Income', min_value=0, max_value=9999999, value=30000) | |
| AMT_CREDIT=st.number_input('Client Credit Amount', min_value=0, max_value=9999999, value=30000) | |
| AMT_ANNUITY=st.number_input('Client Annuity', min_value=0, max_value=9999999, value=3000) | |
| AMT_GOODS_PRICE=st.number_input('price of the goods for which the loan is given', min_value=0, max_value=9999999, value=50000) | |
| NAME_TYPE_SUITE=st.selectbox('Who was accompanying client when they were applying for the loan', ('Unaccompanied', 'Family', 'Spouse, partner', 'Group of people', 'Other_B', 'Children', 'Other_A')) | |
| NAME_INCOME_TYPE=st.selectbox('Clients income type', ('Working', 'State servant', 'Pensioner', 'Commercial associate', 'Businessman', 'Student', 'Unemployed')) | |
| NAME_EDUCATION_TYPE=st.selectbox('Level of highest education the client achieved', ('Higher education', 'Secondary / secondary special', 'Incomplete higher', 'Lower secondary', 'Academic degree')) | |
| NAME_FAMILY_STATUS=st.selectbox('Family status of the client', ('Married', 'Single / not married', 'Civil marriage', 'Widow', 'Separated')) | |
| NAME_HOUSING_TYPE=st.selectbox('What is the housing situation of the client', ('House / apartment', 'With parents', 'Rented apartment', 'Municipal apartment', 'Office apartment', 'Co-op apartment')) | |
| REGION_POPULATION_RELATIVE=st.slider('Normalized population of region where client lives', min_value=0.000000, max_value=0.080000, value=0.0200, step=0.000001) | |
| st.markdown('---') | |
| DAYS_BIRTH_= st.number_input('How many days before the client was born', min_value=7000, max_value=30000, value=15000) | |
| DAYS_BIRTH = -1 * DAYS_BIRTH_ | |
| DAYS_EMPLOYED_= st.number_input('How many days before the application the person started current employment', min_value=0, max_value=30000, value=5000) | |
| DAYS_EMPLOYED = -1* DAYS_EMPLOYED_ | |
| DAYS_REGISTRATION_= st.number_input('How many days before the application did client change his registration', min_value=0, max_value=30000, value=5000) | |
| DAYS_REGISTRATION = -1* DAYS_REGISTRATION_ | |
| DAYS_ID_PUBLISH_ = st.number_input('How many days before the application did client change the identity document with which he applied for the loan', min_value=0, max_value=30000, value=5000) | |
| DAYS_ID_PUBLISH = -1* DAYS_ID_PUBLISH_ | |
| st.markdown('---') | |
| OWN_CAR_AGE=st.number_input('Age of client\'s car', min_value=0, max_value=100, value=0) | |
| st.markdown('---') | |
| FLAG_MOBIL_=st.radio('Did client provide mobile phone', ('Yes', 'No'), index=0) | |
| if FLAG_MOBIL_ == 'No': | |
| FLAG_MOBIL = 0 | |
| else: | |
| FLAG_MOBIL = 1 | |
| FLAG_EMP_PHONE_=st.radio('Did client provide emp phone', ('Yes', 'No'), index=0) | |
| if FLAG_EMP_PHONE_ == 'No': | |
| FLAG_EMP_PHONE = 0 | |
| else: | |
| FLAG_EMP_PHONE = 1 | |
| FLAG_WORK_PHONE_=st.radio('Did client provide work phone', ('Yes', 'No'), index=0) | |
| if FLAG_WORK_PHONE_ == 'No': | |
| FLAG_WORK_PHONE = 0 | |
| else: | |
| FLAG_WORK_PHONE = 1 | |
| FLAG_CONT_MOBILE_=st.radio('Was mobile phone reachable', ('Yes', 'No'), index=0) | |
| if FLAG_CONT_MOBILE_ == 'No': | |
| FLAG_CONT_MOBILE = 0 | |
| else: | |
| FLAG_CONT_MOBILE = 1 | |
| FLAG_PHONE_=st.radio('Did client provide home phone', ('Yes', 'No'), index=0) | |
| if FLAG_PHONE_ == 'No': | |
| FLAG_PHONE = 0 | |
| else: | |
| FLAG_PHONE = 1 | |
| FLAG_EMAIL_=st.radio('Did client provide email', ('Yes', 'No'), index=0) | |
| if FLAG_EMAIL_ == 'No': | |
| FLAG_EMAIL = 0 | |
| else: | |
| FLAG_EMAIL = 1 | |
| st.markdown('---') | |
| OCCUPATION_TYPE= st.selectbox('What kind of occupation does the client have', ('Low-skill Laborers', 'Drivers', 'Sales staff', | |
| 'High skill tech staff', 'Core staff', 'Laborers', | |
| 'Managers', 'Accountants', 'Medicine staff', | |
| 'Security staff', 'Private service staff', 'Secretaries', | |
| 'Cleaning staff', 'Cooking staff', 'HR staff', | |
| 'Waiters/barmen staff', 'Realty agents', 'IT staff')) | |
| CNT_FAM_MEMBERS= st.number_input("How many family members does client have", min_value=0, max_value=20, value=0) | |
| REGION_RATING_CLIENT= st.radio('Our rating of the region where client lives', (1, 2, 3), index=0) | |
| REGION_RATING_CLIENT_W_CITY= st.radio('Our rating of the region where client lives with taking city into account', (1, 2, 3), index=0) | |
| st.markdown('---') | |
| WEEKDAY_APPR_PROCESS_START= st.radio('Day of the week when the client apply for the loan', ('MONDAY', 'TUESDAY', 'WEDNESDAY', 'THURSDAY', 'FRIDAY', 'SATURDAY', 'SUNDAY')) | |
| HOUR_APPR_PROCESS_START= st.number_input('Hour when client apply for the loan', min_value=0, max_value=24, value=0, step=1, help="Input rounded value.") | |
| st.markdown('---') | |
| st.write("#### Client's Address in Region") | |
| REG_REGION_NOT_LIVE_REGION_=st.radio('Permanent address does not match contact address', ('No', 'Yes')) | |
| if REG_REGION_NOT_LIVE_REGION_ == 'No': | |
| REG_REGION_NOT_LIVE_REGION = 0 | |
| else: | |
| REG_REGION_NOT_LIVE_REGION = 1 | |
| REG_REGION_NOT_WORK_REGION_=st.radio('Permanent address does not match work address', ('No', 'Yes')) | |
| if REG_REGION_NOT_WORK_REGION_ == 'No': | |
| REG_REGION_NOT_WORK_REGION = 0 | |
| else: | |
| REG_REGION_NOT_WORK_REGION = 1 | |
| LIVE_REGION_NOT_WORK_REGION= st.radio('Contact address does not match work address', ('No', 'Yes')) | |
| if LIVE_REGION_NOT_WORK_REGION == 'No': | |
| LIVE_REGION_NOT_WORK_REGION = 0 | |
| else: | |
| LIVE_REGION_NOT_WORK_REGION = 1 | |
| st.markdown('---') | |
| st.write("#### Client's Address in City") | |
| REG_CITY_NOT_LIVE_CITY= st.radio('Contact address (city) does not match contact address', ('No', 'Yes')) | |
| if REG_CITY_NOT_LIVE_CITY == 'No': | |
| REG_CITY_NOT_LIVE_CITY = 0 | |
| else: | |
| REG_CITY_NOT_LIVE_CITY = 1 | |
| REG_CITY_NOT_WORK_CITY= st.radio('Permanent address (city) does not match work address', ('No', 'Yes')) | |
| if REG_CITY_NOT_WORK_CITY == 'No': | |
| REG_CITY_NOT_WORK_CITY = 0 | |
| else: | |
| REG_CITY_NOT_WORK_CITY = 1 | |
| LIVE_CITY_NOT_WORK_CITY= st.radio('Contact address (city) does not match work address', ('No', 'Yes')) | |
| if LIVE_CITY_NOT_WORK_CITY == 'No': | |
| LIVE_CITY_NOT_WORK_CITY = 0 | |
| else: | |
| LIVE_CITY_NOT_WORK_CITY = 1 | |
| ORGANIZATION_TYPE= st.selectbox('Type of organization where client works', ('Kindergarten', 'Self-employed', 'Transport: type 3', | |
| 'Business Entity Type 3', 'Government', 'Industry: type 9', | |
| 'School', 'Trade: type 2', 'XNA', 'Services', 'Bank', | |
| 'Industry: type 3', 'Other', 'Trade: type 6', 'Industry: type 12', | |
| 'Trade: type 7', 'Postal', 'Medicine', 'Housing', | |
| 'Business Entity Type 2', 'Construction', 'Military', | |
| 'Industry: type 4', 'Trade: type 3', 'Legal Services', 'Security', | |
| 'Industry: type 11', 'University', 'Business Entity Type 1', | |
| 'Agriculture', 'Security Ministries', 'Transport: type 2', | |
| 'Industry: type 7', 'Transport: type 4', 'Telecom', 'Emergency', | |
| 'Police', 'Industry: type 1', 'Transport: type 1', 'Electricity', | |
| 'Industry: type 5', 'Hotel', 'Restaurant', 'Advertising', 'Mobile', | |
| 'Trade: type 1', 'Industry: type 8', 'Realtor', 'Cleaning', | |
| 'Industry: type 2', 'Trade: type 4', 'Industry: type 6', 'Culture', | |
| 'Insurance', 'Religion', 'Industry: type 13', 'Industry: type 10', | |
| 'Trade: type 5')) | |
| st.markdown('---') | |
| st.write("#### Credit Score") | |
| EXT_SOURCE_1= st.slider('Credit Score from Source 1', min_value=0.0, max_value=20.0, value=0.1, step=1.0, help="Input rounded value.") | |
| EXT_SOURCE_2= st.slider('Credit Score from Source 2', min_value=0.0, max_value=20.0, value=0.1, step=1.0, help="Input rounded value.") | |
| EXT_SOURCE_3= st.slider('Credit Score from Source 3', min_value=0.0, max_value=20.0, value=0.1, step=1.0, help="Input rounded value.") | |
| st.markdown('---') | |
| st.write("#### Client's social surroundings") | |
| OBS_30_CNT_SOCIAL_CIRCLE= st.number_input('Observation 30 days past due',min_value=0, max_value=50, value=0, step=1, help="Input rounded value.") | |
| DEF_30_CNT_SOCIAL_CIRCLE= st.number_input('Number of default 30 days past due', min_value=0, max_value=50, value=0, step=1, help="Input rounded value.") | |
| OBS_60_CNT_SOCIAL_CIRCLE= st.number_input('Observation 60 days past due', min_value=0, max_value=50, value=0, step=1, help="Input rounded value.") | |
| DEF_60_CNT_SOCIAL_CIRCLE= st.number_input('Number of default 60 days past due', min_value=0, max_value=50, value=0, step=1, help="Input rounded value.") | |
| DAYS_LAST_PHONE_CHANGE_ = st.number_input('How many days before application did client change phone', min_value=0, max_value=10000, value=1000) | |
| DAYS_LAST_PHONE_CHANGE = -1 * DAYS_LAST_PHONE_CHANGE_ | |
| st.markdown('---') | |
| st.write("#### Client's Document") | |
| flag_document_2= st.radio('Did client provide Identification?', ('No', 'Yes')) | |
| if flag_document_2 == 'No': | |
| FLAG_DOCUMENT_2 = 0 | |
| else: | |
| FLAG_DOCUMENT_2 = 1 | |
| FLAG_DOCUMENT_3_= st.radio('Did client provide proof of address?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_3_ == 'No': | |
| FLAG_DOCUMENT_3 = 0 | |
| else: | |
| FLAG_DOCUMENT_3 = 1 | |
| FLAG_DOCUMENT_4_= st.radio('Did client provide bank statement?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_4_ == 'No': | |
| FLAG_DOCUMENT_4 = 0 | |
| else: | |
| FLAG_DOCUMENT_4 = 1 | |
| FLAG_DOCUMENT_5_= st.radio('Did client provide employment certificate?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_5_ == 'No': | |
| FLAG_DOCUMENT_5 = 0 | |
| else: | |
| FLAG_DOCUMENT_5 = 1 | |
| FLAG_DOCUMENT_6_= st.radio('Did client provide other document (code = 6)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_6_ == 'No': | |
| FLAG_DOCUMENT_6 = 0 | |
| else: | |
| FLAG_DOCUMENT_6 = 1 | |
| FLAG_DOCUMENT_7_= st.radio('Did client provide library card?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_7_ == 'No': | |
| FLAG_DOCUMENT_7 = 0 | |
| else: | |
| FLAG_DOCUMENT_7 = 1 | |
| FLAG_DOCUMENT_8_= st.radio('Did client provide car registration?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_8_ == 'No': | |
| FLAG_DOCUMENT_8 = 0 | |
| else: | |
| FLAG_DOCUMENT_8 = 1 | |
| FLAG_DOCUMENT_9_= st.radio('Did client provide passport?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_9_ == 'No': | |
| FLAG_DOCUMENT_9 = 0 | |
| else: | |
| FLAG_DOCUMENT_9 = 1 | |
| FLAG_DOCUMENT_10_= st.radio("Did client provide driver's license?", ('No', 'Yes')) | |
| if FLAG_DOCUMENT_10_ == 'No': | |
| FLAG_DOCUMENT_10 = 0 | |
| else: | |
| FLAG_DOCUMENT_10 = 1 | |
| FLAG_DOCUMENT_11_= st.radio('Did client provide other document (code = 11)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_11_ == 'No': | |
| FLAG_DOCUMENT_11 = 0 | |
| else: | |
| FLAG_DOCUMENT_11 = 1 | |
| FLAG_DOCUMENT_12_= st.radio('Did client provide other document (code = 12)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_12_ == 'No': | |
| FLAG_DOCUMENT_12 = 0 | |
| else: | |
| FLAG_DOCUMENT_12 = 1 | |
| FLAG_DOCUMENT_13_= st.radio('Did client provide other document (code = 13)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_13_ == 'No': | |
| FLAG_DOCUMENT_13 = 0 | |
| else: | |
| FLAG_DOCUMENT_13 = 1 | |
| FLAG_DOCUMENT_14_= st.radio('Did client provide other document (code = 14)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_14_ == 'No': | |
| FLAG_DOCUMENT_14 = 0 | |
| else: | |
| FLAG_DOCUMENT_14 = 1 | |
| FLAG_DOCUMENT_15_= st.radio('Did client provide other document (code = 15)', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_15_ == 'No': | |
| FLAG_DOCUMENT_15 = 0 | |
| else: | |
| FLAG_DOCUMENT_15 = 1 | |
| FLAG_DOCUMENT_16_= st.radio('Did client provide other document (code = 16)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_16_ == 'No': | |
| FLAG_DOCUMENT_16 = 0 | |
| else: | |
| FLAG_DOCUMENT_16 = 1 | |
| FLAG_DOCUMENT_17_= st.radio('Did client provide other document (code = 17)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_17_ == 'No': | |
| FLAG_DOCUMENT_17 = 0 | |
| else: | |
| FLAG_DOCUMENT_17 = 1 | |
| FLAG_DOCUMENT_18_= st.radio('Did client provide other document (code = 18)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_18_ == 'No': | |
| FLAG_DOCUMENT_18 = 0 | |
| else: | |
| FLAG_DOCUMENT_18 = 1 | |
| FLAG_DOCUMENT_19_= st.radio('Did client provide other document (code = 19)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_19_ == 'No': | |
| FLAG_DOCUMENT_19 = 0 | |
| else: | |
| FLAG_DOCUMENT_19 = 1 | |
| FLAG_DOCUMENT_20_= st.radio('Did client provide other document (code = 20)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_20_ == 'No': | |
| FLAG_DOCUMENT_20 = 0 | |
| else: | |
| FLAG_DOCUMENT_20 = 1 | |
| FLAG_DOCUMENT_21_= st.radio('Did client provide other document (code = 21)?', ('No', 'Yes')) | |
| if FLAG_DOCUMENT_21_ == 'No': | |
| FLAG_DOCUMENT_21 = 0 | |
| else: | |
| FLAG_DOCUMENT_21 = 1 | |
| st.markdown('---') | |
| st.write("#### Number of Enquiries") | |
| AMT_REQ_CREDIT_BUREAU_HOUR= st.slider('One hour before application', min_value=0, max_value=24, value=0, step=1, help="Input rounded value.") | |
| AMT_REQ_CREDIT_BUREAU_DAY= st.slider('One day before application', min_value=0, max_value=31, value=0, step=1, help="Input rounded value.") | |
| AMT_REQ_CREDIT_BUREAU_WEEK= st.slider('One week before application', min_value=0, max_value=10, value=0, step=1, help="Input rounded value.") | |
| AMT_REQ_CREDIT_BUREAU_MON= st.slider('One month before application', min_value=0, max_value=24, value=0, step=1, help="Input rounded value.") | |
| AMT_REQ_CREDIT_BUREAU_QRT= st.slider('Three months before application', min_value=0, max_value=12, value=0, step=1, help="Input rounded value.") | |
| AMT_REQ_CREDIT_BUREAU_YEAR= st.slider('One year before application', min_value=0, max_value=30, value=0, step=1, help="Input rounded value.") | |
| st.markdown('---') | |
| submitted = st.form_submit_button('Predict') | |
| # dataframe | |
| st.write("# Customer Summary") | |
| data_inf = { | |
| "SK_ID_CURR":SK_ID_CURR, | |
| "NAME_CONTRACT_TYPE":NAME_CONTRACT_TYPE, | |
| "CODE_GENDER":CODE_GENDER, | |
| "FLAG_OWN_CAR":FLAG_OWN_CAR, | |
| "FLAG_OWN_REALTY":FLAG_OWN_REALTY, | |
| "CNT_CHILDREN":CNT_CHILDREN, | |
| "AMT_INCOME_TOTAL":AMT_INCOME_TOTAL, | |
| "AMT_CREDIT":AMT_CREDIT, | |
| "AMT_ANNUITY":AMT_ANNUITY, | |
| "AMT_GOODS_PRICE":AMT_GOODS_PRICE, | |
| "NAME_TYPE_SUITE":NAME_TYPE_SUITE, | |
| "NAME_INCOME_TYPE":NAME_INCOME_TYPE, | |
| "NAME_EDUCATION_TYPE":NAME_EDUCATION_TYPE, | |
| "NAME_FAMILY_STATUS":NAME_FAMILY_STATUS, | |
| "NAME_HOUSING_TYPE":NAME_HOUSING_TYPE, | |
| "REGION_POPULATION_RELATIVE":REGION_POPULATION_RELATIVE, | |
| "DAYS_BIRTH":DAYS_BIRTH, | |
| "DAYS_EMPLOYED":DAYS_EMPLOYED, | |
| "DAYS_REGISTRATION":DAYS_REGISTRATION, | |
| "DAYS_ID_PUBLISH":DAYS_ID_PUBLISH, | |
| "OWN_CAR_AGE":OWN_CAR_AGE, | |
| "FLAG_MOBIL":FLAG_MOBIL, | |
| "FLAG_EMP_PHONE":FLAG_EMP_PHONE, | |
| "FLAG_WORK_PHONE":FLAG_WORK_PHONE, | |
| "FLAG_CONT_MOBILE":FLAG_CONT_MOBILE, | |
| "FLAG_PHONE":FLAG_PHONE, | |
| "FLAG_EMAIL":FLAG_EMAIL, | |
| "OCCUPATION_TYPE":OCCUPATION_TYPE, | |
| "CNT_FAM_MEMBERS":CNT_FAM_MEMBERS, | |
| "REGION_RATING_CLIENT":REGION_RATING_CLIENT, | |
| "REGION_RATING_CLIENT_W_CITY":REGION_RATING_CLIENT_W_CITY, | |
| "WEEKDAY_APPR_PROCESS_START":WEEKDAY_APPR_PROCESS_START, | |
| "HOUR_APPR_PROCESS_START":HOUR_APPR_PROCESS_START, | |
| "REG_REGION_NOT_LIVE_REGION":REG_REGION_NOT_LIVE_REGION, | |
| "REG_REGION_NOT_WORK_REGION":REG_REGION_NOT_WORK_REGION, | |
| "LIVE_REGION_NOT_WORK_REGION":LIVE_REGION_NOT_WORK_REGION, | |
| "REG_CITY_NOT_LIVE_CITY":REG_CITY_NOT_LIVE_CITY, | |
| "REG_CITY_NOT_WORK_CITY":REG_CITY_NOT_WORK_CITY, | |
| "LIVE_CITY_NOT_WORK_CITY":LIVE_CITY_NOT_WORK_CITY, | |
| "ORGANIZATION_TYPE":ORGANIZATION_TYPE, | |
| "EXT_SOURCE_1":EXT_SOURCE_1, | |
| "EXT_SOURCE_2":EXT_SOURCE_2, | |
| "EXT_SOURCE_3":EXT_SOURCE_3, | |
| "OBS_30_CNT_SOCIAL_CIRCLE":OBS_30_CNT_SOCIAL_CIRCLE, | |
| "DEF_30_CNT_SOCIAL_CIRCLE":DEF_30_CNT_SOCIAL_CIRCLE, | |
| "OBS_60_CNT_SOCIAL_CIRCLE":OBS_60_CNT_SOCIAL_CIRCLE, | |
| "DEF_60_CNT_SOCIAL_CIRCLE":DEF_60_CNT_SOCIAL_CIRCLE, | |
| "DAYS_LAST_PHONE_CHANGE":DAYS_LAST_PHONE_CHANGE, | |
| "FLAG_DOCUMENT_2":FLAG_DOCUMENT_2, | |
| "FLAG_DOCUMENT_3":FLAG_DOCUMENT_3, | |
| "FLAG_DOCUMENT_4":FLAG_DOCUMENT_4, | |
| "FLAG_DOCUMENT_5":FLAG_DOCUMENT_5, | |
| "FLAG_DOCUMENT_6":FLAG_DOCUMENT_6, | |
| "FLAG_DOCUMENT_7":FLAG_DOCUMENT_7, | |
| "FLAG_DOCUMENT_8":FLAG_DOCUMENT_8, | |
| "FLAG_DOCUMENT_9":FLAG_DOCUMENT_9, | |
| "FLAG_DOCUMENT_10":FLAG_DOCUMENT_10, | |
| "FLAG_DOCUMENT_11":FLAG_DOCUMENT_11, | |
| "FLAG_DOCUMENT_12":FLAG_DOCUMENT_12, | |
| "FLAG_DOCUMENT_13":FLAG_DOCUMENT_13, | |
| "FLAG_DOCUMENT_14":FLAG_DOCUMENT_14, | |
| "FLAG_DOCUMENT_15":FLAG_DOCUMENT_15, | |
| "FLAG_DOCUMENT_16":FLAG_DOCUMENT_16, | |
| "FLAG_DOCUMENT_17":FLAG_DOCUMENT_17, | |
| "FLAG_DOCUMENT_18":FLAG_DOCUMENT_18, | |
| "FLAG_DOCUMENT_19":FLAG_DOCUMENT_19, | |
| "FLAG_DOCUMENT_20":FLAG_DOCUMENT_20, | |
| "FLAG_DOCUMENT_21":FLAG_DOCUMENT_21, | |
| "AMT_REQ_CREDIT_BUREAU_HOUR":AMT_REQ_CREDIT_BUREAU_HOUR, | |
| "AMT_REQ_CREDIT_BUREAU_DAY":AMT_REQ_CREDIT_BUREAU_DAY, | |
| "AMT_REQ_CREDIT_BUREAU_WEEK":AMT_REQ_CREDIT_BUREAU_WEEK, | |
| "AMT_REQ_CREDIT_BUREAU_MON":AMT_REQ_CREDIT_BUREAU_MON, | |
| "AMT_REQ_CREDIT_BUREAU_QRT":AMT_REQ_CREDIT_BUREAU_QRT, | |
| "AMT_REQ_CREDIT_BUREAU_YEAR":AMT_REQ_CREDIT_BUREAU_YEAR | |
| } | |
| data_inf = pd.DataFrame([data_inf]) | |
| st.dataframe(data_inf.T, width=800, height=495) | |
| if submitted: | |
| # Predict using created pipeline | |
| y_pred_inf = pipeline.predict(data_inf) | |
| if y_pred_inf == 0: | |
| pred = 'Not Default' | |
| else: | |
| pred = 'Default' | |
| st.markdown('---') | |
| st.write('# Prediction : ', (pred)) | |
| st.markdown('---') | |
| if __name__ == '__main__': | |
| run() |