def get_pred(invoice_id):
# 1. lms_limits
# 2. Cibil
db = db_init()
# scf invoice get business_id for that invoice
scf_invoice = db['scf_invoice']
data = scf_invoice.find({
"invoice_id": invoice_id
}, no_cursor_timeout=True).sort("_id", pymongo.DESCENDING).limit(1)
for i, row in enumerate(data):
business_id = str(row['business_id'])
print("business_id : " + str(business_id))
lms_loan = db['lms_limits_master']
data = lms_loan.find({
"business_id": business_id
}, no_cursor_timeout=True).sort("_id", pymongo.DESCENDING).limit(1)
for i, row in enumerate(data):
id = str(row['_id'])
for col in lms_limits_cols:
if col in row:
if str(row[col]) == "nan":
data_df.loc[0, col] = "0"
else:
data_df.loc[0, col] = str(row[col])
else:
data_df.loc[0, col] = "0"
print(col + " :: " + "0")
return cibil_flat_data_func(business_id)
def predict_default(business_id):
db = db_init()
# scf invoice get business_id for that invoice
cas_business = db['cas_business']
data = cas_business.find({"business_id": business_id}, no_cursor_timeout=True).sort("_id",
pymongo.DESCENDING).limit(1)
for i, row in enumerate(data):
date_of_birth = str(row['business_partners'][0]['date_of_birth'])
print("date_of_birth : " + str(date_of_birth))
latest_cibil_score = row['latest_cibil_score']
date_of_birth = parse(date_of_birth)
print(date_of_birth)
now = datetime.datetime.now()
print(now)
age = now - date_of_birth
print(age.days)
from datetime import date, timedelta
age = age.days / 365.2425
print(int(age))
# 1. get age from 0th business partner - cas business
# 2. latest_cibil_score - cas business
print(latest_cibil_score)
cibil_score_group = group_cibil_score(latest_cibil_score)
# scf invoice get business_id for that invoice
lms_limits_master = db['lms_limits_master']
data = lms_limits_master.find({"business_id": business_id}, no_cursor_timeout=True).sort("_id",
pymongo.DESCENDING).limit(
1)
for i, row in enumerate(data):
limit_amount_sanctioned = str(row['limit_amount_sanctioned'])
no_credit_bounces = row['no_credit_bounces']
no_credit_bounces_last_6_months = row['no_credit_bounces_last_6_months']
no_credit_bounces_last_3_months = row['no_credit_bounces_last_3_months']
back_to_back_bounces_last_3_months = row['back_to_back_bounces_last_3_months']
lms_limit_master = ["limit_amount_sanctioned",
"no_credit_bounces",
"no_credit_bounces_last_6_months",
"no_credit_bounces_last_3_months",
"back_to_back_bounces_last_3_months"]
cas_business = ["age", "latest_cibil_score"]
cols = lms_limit_master.append(cas_business)
all_data_df = pd.DataFrame(columns=cols)
all_data_df.loc[0, 'cibil_score_group'] = cibil_score_group
all_data_df.loc[0, 'age'] = age
all_data_df.loc[0, 'limit_amount_sanctioned'] = limit_amount_sanctioned
all_data_df.loc[0, 'no_credit_bounces'] = no_credit_bounces
all_data_df.loc[0, 'no_credit_bounces_last_6_months'] = no_credit_bounces_last_6_months
all_data_df.loc[0, 'no_credit_bounces_last_3_months'] = no_credit_bounces_last_3_months
all_data_df.loc[0, 'back_to_back_bounces_last_3_months'] = back_to_back_bounces_last_3_months
all_data_df = pd.concat(
[all_data_df, pd.get_dummies(all_data_df['cibil_score_group'], prefix='category', dummy_na=True)], axis=1).drop(
['cibil_score_group'], axis=1)
# print(all_data_df)
# load and predict
all_cols = ["limit_amount_sanctioned",
"no_credit_bounces",
"age",
"no_credit_bounces_last_6_months",
"category_8.0",
"no_credit_bounces_last_3_months",
"category_-1.0",
"back_to_back_bounces_last_3_months",
"category_6.0"]
if cibil_score_group == 8:
all_data_df['category_6.0'] = 0
all_data_df['category_-1.0'] = 0
elif cibil_score_group == -1:
all_data_df['category_6.0'] = 0
all_data_df['category_8.0'] = 0
elif cibil_score_group == 6:
all_data_df['category_8.0'] = 0
all_data_df['category_-1.0'] = 0
else:
all_data_df['category_8.0'] = 0
all_data_df['category_6.0'] = 0
all_data_df['category_-1.0'] = 0
predict_df = all_data_df[all_cols]
print(predict_df)
import pickle
filename = 'service_api_proba/proba_8_cols.sav'
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.predict_proba(predict_df)
print(result)
print("Probablity of giving : " + str(result[0][1]))
response = str(result[0][1])
return response
def cibil_flat_data_func(business_id):
db = db_init()
cas_bus_partner_cibil_analysis = db['cas_bus_partner_cibil_analysis']
data = cas_bus_partner_cibil_analysis.find({
"business_id": business_id
}).sort("_id", pymongo.DESCENDING).limit(1)
df = pd.DataFrame(list(data))
print(df.columns)
# print(df.dtypes)
# print(df['Account'])
df3 = df.drop_duplicates('business_id')
accounts_column_names = [
"business_id", "cibil_pull_date", "last_account_opened_months",
"monthly_emi_for_active_accounts",
"sanctioned_amt_for_active_accounts",
"no_of_loans_opened_last_3_months",
"value_of_loans_opened_last_12_months", "total_number_of_accounts",
"no_of_loans_paid_off_successfully",
"value_of_loans_opened_last_3_months", "number_of_active_accounts",
"total_value_of_accounts", "account_type",
"no_of_loans_opened_last_12_months", "latest_disbursed_date",
"value_of_loans_paid_off_successfully",
"current_bal_for_active_accounts",
"amt_written_off_total_liabilities_ratio",
"no_acs_written_off_total_liabilities_ratio", "amt_bl_paid_off",
"last_loan_drawn_in_months", "no_acs_overdue_total_accounts_ratio",
"no_of_bl_paid_off", "credit_card_usage_total_limits_ratio",
"no_of_running_bl_pl", "no_of_enquiries_last_3_months",
"no_of_enquiries_last_12_months"
]
cols_scoring = [
"amt_written_off_total_liabilities_ratio",
"no_acs_written_off_total_liabilities_ratio", "amt_bl_paid_off",
"last_loan_drawn_in_months", "no_acs_overdue_total_accounts_ratio",
"no_of_bl_paid_off", "credit_card_usage_total_limits_ratio",
"no_of_running_bl_pl"
]
enquiry = [
"no_of_enquiries_last_3_months", "no_of_enquiries_last_12_months"
]
df_mongo_buckets = pd.DataFrame(columns=accounts_column_names)
i = 0
print(df3.dtypes)
print(df3)
# df3['business_id'] = df3['business_id'].astype(str)
print(df3.dtypes)
for row in df3.iterrows():
account = row[1]['Account']
id = row[1]['_id']
if 'cibil_pull_date' in row[1]:
cibil_pull_date = row[1]['cibil_pull_date']
if not str(cibil_pull_date) == "nan":
if "$date" in cibil_pull_date:
cibil_pull_date = cibil_pull_date['$date']
if 'business_id' in row[1]:
business_id = str(row[1]['business_id'])
else:
business_id = str(0)
print(business_id)
if 'business_pan' in row[1]:
business_pan = row[1]['business_pan']
else:
business_pan = 0
for data in account:
# print(data)
################################
print(row[1])
df_mongo_buckets.loc[i, 'business_id'] = str(business_id)
# df_mongo_buckets.loc[i, 'business_pan'] = business_pan
df_mongo_buckets.loc[i, 'cibil_pull_date'] = cibil_pull_date
# print(business_id)
# print(business_pan)
# print(id)
# print(type(business_pan))
# print(type(row[1]['Scoring']))
scoring = str(row[1]['Scoring'])
# print(type(scoring))
if scoring != "nan":
# print(scoring)
# scoring = ast.literal_eval(scoring)
if 'Scoring' in row[1]:
# print(row[1])
scoring = row[1]['Scoring']
if "amt_written_off_total_liabilities_ratio" in scoring:
df_mongo_buckets.loc[
i,
'amt_written_off_total_liabilities_ratio'] = scoring[
'amt_written_off_total_liabilities_ratio']
if "no_acs_written_off_total_liabilities_ratio" in scoring:
df_mongo_buckets.loc[
i,
'no_acs_written_off_total_liabilities_ratio'] = scoring[
'no_acs_written_off_total_liabilities_ratio']
if "amt_bl_paid_off" in scoring:
df_mongo_buckets.loc[
i, 'amt_bl_paid_off'] = scoring['amt_bl_paid_off']
if "last_loan_drawn_in_months" in scoring:
df_mongo_buckets.loc[
i, 'last_loan_drawn_in_months'] = scoring[
'last_loan_drawn_in_months']
if "no_acs_overdue_total_accounts_ratio" in scoring:
df_mongo_buckets.loc[
i,
'no_acs_overdue_total_accounts_ratio'] = scoring[
'no_acs_overdue_total_accounts_ratio']
if "no_of_bl_paid_off" in scoring:
df_mongo_buckets.loc[
i,
'no_of_bl_paid_off'] = scoring['no_of_bl_paid_off']
if "credit_card_usage_total_limits_ratio" in scoring:
df_mongo_buckets.loc[
i,
'credit_card_usage_total_limits_ratio'] = scoring[
'credit_card_usage_total_limits_ratio']
if "no_of_running_bl_pl" in scoring:
df_mongo_buckets.loc[i,
'no_of_running_bl_pl'] = scoring[
'no_of_running_bl_pl']
###################
enquiry = str(row[1]['Enquiry'])
if enquiry != "nan":
if 'Enquiry' in row[1]:
enquiry = ast.literal_eval(enquiry)
if "no_of_enquiries_last_3_months" in enquiry:
df_mongo_buckets.loc[
i, 'no_of_enquiries_last_3_months'] = enquiry[
'no_of_enquiries_last_3_months']
if "no_of_enquiries_last_12_months" in enquiry:
df_mongo_buckets.loc[
i, 'no_of_enquiries_last_12_months'] = enquiry[
'no_of_enquiries_last_12_months']
#################################
if "last_account_opened_months" in data:
df_mongo_buckets.loc[i, 'last_account_opened_months'] = data[
'last_account_opened_months']
if "monthly_emi_for_active_accounts" in data:
df_mongo_buckets.loc[i,
'monthly_emi_for_active_accounts'] = data[
'monthly_emi_for_active_accounts']
if "sanctioned_amt_for_active_accounts" in data:
df_mongo_buckets.loc[
i, 'sanctioned_amt_for_active_accounts'] = data[
'sanctioned_amt_for_active_accounts']
if "no_of_loans_opened_last_3_months" in data:
df_mongo_buckets.loc[
i, 'no_of_loans_opened_last_3_months'] = data[
'no_of_loans_opened_last_3_months']
if "total_number_of_accounts" in data:
df_mongo_buckets.loc[i, 'total_number_of_accounts'] = data[
'total_number_of_accounts']
if "no_of_loans_paid_off_successfully" in data:
df_mongo_buckets.loc[
i, 'no_of_loans_paid_off_successfully'] = data[
'no_of_loans_paid_off_successfully']
if "value_of_loans_opened_last_3_months" in data:
df_mongo_buckets.loc[
i, 'value_of_loans_opened_last_3_months'] = data[
'value_of_loans_opened_last_3_months']
if "value_of_loans_opened_last_12_months" in data:
df_mongo_buckets.loc[
i, 'value_of_loans_opened_last_12_months'] = data[
'value_of_loans_opened_last_12_months']
if "number_of_active_accounts" in data:
df_mongo_buckets.loc[i, 'number_of_active_accounts'] = data[
'number_of_active_accounts']
if "total_value_of_accounts" in data:
df_mongo_buckets.loc[i, 'total_value_of_accounts'] = data[
'total_value_of_accounts']
if "total_number_of_accounts" in data:
df_mongo_buckets.loc[i, 'total_number_of_accounts'] = data[
'total_number_of_accounts']
if "no_of_loans_opened_last_12_months" in data:
df_mongo_buckets.loc[
i, 'no_of_loans_opened_last_12_months'] = data[
'no_of_loans_opened_last_12_months']
if "account_type" in data:
df_mongo_buckets.loc[i, 'account_type'] = data['account_type']
if "latest_disbursed_date" in data:
df_mongo_buckets.loc[
i, 'latest_disbursed_date'] = data['latest_disbursed_date']
if "value_of_loans_paid_off_successfully" in data:
df_mongo_buckets.loc[
i, 'value_of_loans_paid_off_successfully'] = data[
'value_of_loans_paid_off_successfully']
if "current_bal_for_active_accounts" in data:
df_mongo_buckets.loc[i,
'current_bal_for_active_accounts'] = data[
'current_bal_for_active_accounts']
i += 1
# df_mongo_buckets.to_csv(
# 'api_cibil_group_data3.csv')
#
# df = pd.read_csv(
# 'api_cibil_group_data3.csv')
# all columns
all_cols = [
'last_account_opened_months', 'monthly_emi_for_active_accounts',
'sanctioned_amt_for_active_accounts',
'no_of_loans_opened_last_3_months',
'value_of_loans_opened_last_12_months', 'total_number_of_accounts',
'no_of_loans_paid_off_successfully',
'value_of_loans_opened_last_3_months', 'number_of_active_accounts',
'total_value_of_accounts', 'no_of_loans_opened_last_12_months',
'latest_disbursed_date', 'value_of_loans_paid_off_successfully',
'current_bal_for_active_accounts',
'amt_written_off_total_liabilities_ratio',
'no_acs_written_off_total_liabilities_ratio', 'amt_bl_paid_off',
'last_loan_drawn_in_months', 'no_acs_overdue_total_accounts_ratio',
'no_of_bl_paid_off', 'credit_card_usage_total_limits_ratio',
'no_of_running_bl_pl', 'no_of_enquiries_last_3_months',
'no_of_enquiries_last_12_months'
]
unique_account_type = [
'5', '51', '10', '13', '2', '0', '6', '7', '9', '1', '17', '61', '52',
'39', '34', '32', '3', '53', '12', '4', '8', '59', '15', '33', '54',
'40', '55', '36', '14', '50', '43', 'None', 'blank', '41'
]
generate_all_cols = []
for rows in unique_account_type:
for col in all_cols:
generate_all_cols.append(str(col) + "_" + str(rows))
print(generate_all_cols)
df_cols = [
'last_account_opened_months_5', 'monthly_emi_for_active_accounts_5',
'sanctioned_amt_for_active_accounts_5',
'no_of_loans_opened_last_3_months_5',
'value_of_loans_opened_last_12_months_5', 'total_number_of_accounts_5',
'no_of_loans_paid_off_successfully_5',
'value_of_loans_opened_last_3_months_5', 'number_of_active_accounts_5',
'total_value_of_accounts_5', 'no_of_loans_opened_last_12_months_5',
'latest_disbursed_date_5', 'value_of_loans_paid_off_successfully_5',
'current_bal_for_active_accounts_5',
'amt_written_off_total_liabilities_ratio_5',
'no_acs_written_off_total_liabilities_ratio_5', 'amt_bl_paid_off_5',
'last_loan_drawn_in_months_5', 'no_acs_overdue_total_accounts_ratio_5',
'no_of_bl_paid_off_5', 'credit_card_usage_total_limits_ratio_5',
'no_of_running_bl_pl_5', 'no_of_enquiries_last_3_months_5',
'no_of_enquiries_last_12_months_5', 'last_account_opened_months_51',
'monthly_emi_for_active_accounts_51',
'sanctioned_amt_for_active_accounts_51',
'no_of_loans_opened_last_3_months_51',
'value_of_loans_opened_last_12_months_51',
'total_number_of_accounts_51', 'no_of_loans_paid_off_successfully_51',
'value_of_loans_opened_last_3_months_51',
'number_of_active_accounts_51', 'total_value_of_accounts_51',
'no_of_loans_opened_last_12_months_51', 'latest_disbursed_date_51',
'value_of_loans_paid_off_successfully_51',
'current_bal_for_active_accounts_51',
'amt_written_off_total_liabilities_ratio_51',
'no_acs_written_off_total_liabilities_ratio_51', 'amt_bl_paid_off_51',
'last_loan_drawn_in_months_51',
'no_acs_overdue_total_accounts_ratio_51', 'no_of_bl_paid_off_51',
'credit_card_usage_total_limits_ratio_51', 'no_of_running_bl_pl_51',
'no_of_enquiries_last_3_months_51',
'no_of_enquiries_last_12_months_51', 'last_account_opened_months_10',
'monthly_emi_for_active_accounts_10',
'sanctioned_amt_for_active_accounts_10',
'no_of_loans_opened_last_3_months_10',
'value_of_loans_opened_last_12_months_10',
'total_number_of_accounts_10', 'no_of_loans_paid_off_successfully_10',
'value_of_loans_opened_last_3_months_10',
'number_of_active_accounts_10', 'total_value_of_accounts_10',
'no_of_loans_opened_last_12_months_10', 'latest_disbursed_date_10',
'value_of_loans_paid_off_successfully_10',
'current_bal_for_active_accounts_10',
'amt_written_off_total_liabilities_ratio_10',
'no_acs_written_off_total_liabilities_ratio_10', 'amt_bl_paid_off_10',
'last_loan_drawn_in_months_10',
'no_acs_overdue_total_accounts_ratio_10', 'no_of_bl_paid_off_10',
'credit_card_usage_total_limits_ratio_10', 'no_of_running_bl_pl_10',
'no_of_enquiries_last_3_months_10',
'no_of_enquiries_last_12_months_10', 'last_account_opened_months_13',
'monthly_emi_for_active_accounts_13',
'sanctioned_amt_for_active_accounts_13',
'no_of_loans_opened_last_3_months_13',
'value_of_loans_opened_last_12_months_13',
'total_number_of_accounts_13', 'no_of_loans_paid_off_successfully_13',
'value_of_loans_opened_last_3_months_13',
'number_of_active_accounts_13', 'total_value_of_accounts_13',
'no_of_loans_opened_last_12_months_13', 'latest_disbursed_date_13',
'value_of_loans_paid_off_successfully_13',
'current_bal_for_active_accounts_13',
'amt_written_off_total_liabilities_ratio_13',
'no_acs_written_off_total_liabilities_ratio_13', 'amt_bl_paid_off_13',
'last_loan_drawn_in_months_13',
'no_acs_overdue_total_accounts_ratio_13', 'no_of_bl_paid_off_13',
'credit_card_usage_total_limits_ratio_13', 'no_of_running_bl_pl_13',
'no_of_enquiries_last_3_months_13',
'no_of_enquiries_last_12_months_13', 'last_account_opened_months_2',
'monthly_emi_for_active_accounts_2',
'sanctioned_amt_for_active_accounts_2',
'no_of_loans_opened_last_3_months_2',
'value_of_loans_opened_last_12_months_2', 'total_number_of_accounts_2',
'no_of_loans_paid_off_successfully_2',
'value_of_loans_opened_last_3_months_2', 'number_of_active_accounts_2',
'total_value_of_accounts_2', 'no_of_loans_opened_last_12_months_2',
'latest_disbursed_date_2', 'value_of_loans_paid_off_successfully_2',
'current_bal_for_active_accounts_2',
'amt_written_off_total_liabilities_ratio_2',
'no_acs_written_off_total_liabilities_ratio_2', 'amt_bl_paid_off_2',
'last_loan_drawn_in_months_2', 'no_acs_overdue_total_accounts_ratio_2',
'no_of_bl_paid_off_2', 'credit_card_usage_total_limits_ratio_2',
'no_of_running_bl_pl_2', 'no_of_enquiries_last_3_months_2',
'no_of_enquiries_last_12_months_2', 'last_account_opened_months_0',
'monthly_emi_for_active_accounts_0',
'sanctioned_amt_for_active_accounts_0',
'no_of_loans_opened_last_3_months_0',
'value_of_loans_opened_last_12_months_0', 'total_number_of_accounts_0',
'no_of_loans_paid_off_successfully_0',
'value_of_loans_opened_last_3_months_0', 'number_of_active_accounts_0',
'total_value_of_accounts_0', 'no_of_loans_opened_last_12_months_0',
'latest_disbursed_date_0', 'value_of_loans_paid_off_successfully_0',
'current_bal_for_active_accounts_0',
'amt_written_off_total_liabilities_ratio_0',
'no_acs_written_off_total_liabilities_ratio_0', 'amt_bl_paid_off_0',
'last_loan_drawn_in_months_0', 'no_acs_overdue_total_accounts_ratio_0',
'no_of_bl_paid_off_0', 'credit_card_usage_total_limits_ratio_0',
'no_of_running_bl_pl_0', 'no_of_enquiries_last_3_months_0',
'no_of_enquiries_last_12_months_0', 'last_account_opened_months_6',
'monthly_emi_for_active_accounts_6',
'sanctioned_amt_for_active_accounts_6',
'no_of_loans_opened_last_3_months_6',
'value_of_loans_opened_last_12_months_6', 'total_number_of_accounts_6',
'no_of_loans_paid_off_successfully_6',
'value_of_loans_opened_last_3_months_6', 'number_of_active_accounts_6',
'total_value_of_accounts_6', 'no_of_loans_opened_last_12_months_6',
'latest_disbursed_date_6', 'value_of_loans_paid_off_successfully_6',
'current_bal_for_active_accounts_6',
'amt_written_off_total_liabilities_ratio_6',
'no_acs_written_off_total_liabilities_ratio_6', 'amt_bl_paid_off_6',
'last_loan_drawn_in_months_6', 'no_acs_overdue_total_accounts_ratio_6',
'no_of_bl_paid_off_6', 'credit_card_usage_total_limits_ratio_6',
'no_of_running_bl_pl_6', 'no_of_enquiries_last_3_months_6',
'no_of_enquiries_last_12_months_6', 'last_account_opened_months_7',
'monthly_emi_for_active_accounts_7',
'sanctioned_amt_for_active_accounts_7',
'no_of_loans_opened_last_3_months_7',
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'last_loan_drawn_in_months_36',
'no_acs_overdue_total_accounts_ratio_36', 'no_of_bl_paid_off_36',
'credit_card_usage_total_limits_ratio_36', 'no_of_running_bl_pl_36',
'no_of_enquiries_last_3_months_36', 'no_of_enquiries_last_12_months_36',
'last_account_opened_months_14', 'monthly_emi_for_active_accounts_14',
'sanctioned_amt_for_active_accounts_14',
'no_of_loans_opened_last_3_months_14',
'value_of_loans_opened_last_12_months_14',
'total_number_of_accounts_14', 'no_of_loans_paid_off_successfully_14',
'value_of_loans_opened_last_3_months_14',
'number_of_active_accounts_14', 'total_value_of_accounts_14',
'no_of_loans_opened_last_12_months_14', 'latest_disbursed_date_14',
'value_of_loans_paid_off_successfully_14',
'current_bal_for_active_accounts_14',
'amt_written_off_total_liabilities_ratio_14',
'no_acs_written_off_total_liabilities_ratio_14', 'amt_bl_paid_off_14',
'last_loan_drawn_in_months_14',
'no_acs_overdue_total_accounts_ratio_14', 'no_of_bl_paid_off_14',
'credit_card_usage_total_limits_ratio_14', 'no_of_running_bl_pl_14',
'no_of_enquiries_last_3_months_14', 'no_of_enquiries_last_12_months_14',
'last_account_opened_months_50', 'monthly_emi_for_active_accounts_50',
'sanctioned_amt_for_active_accounts_50',
'no_of_loans_opened_last_3_months_50',
'value_of_loans_opened_last_12_months_50',
'total_number_of_accounts_50', 'no_of_loans_paid_off_successfully_50',
'value_of_loans_opened_last_3_months_50',
'number_of_active_accounts_50', 'total_value_of_accounts_50',
'no_of_loans_opened_last_12_months_50', 'latest_disbursed_date_50',
'value_of_loans_paid_off_successfully_50',
'current_bal_for_active_accounts_50',
'amt_written_off_total_liabilities_ratio_50',
'no_acs_written_off_total_liabilities_ratio_50', 'amt_bl_paid_off_50',
'last_loan_drawn_in_months_50',
'no_acs_overdue_total_accounts_ratio_50', 'no_of_bl_paid_off_50',
'credit_card_usage_total_limits_ratio_50', 'no_of_running_bl_pl_50',
'no_of_enquiries_last_3_months_50', 'no_of_enquiries_last_12_months_50',
'last_account_opened_months_43', 'monthly_emi_for_active_accounts_43',
'sanctioned_amt_for_active_accounts_43',
'no_of_loans_opened_last_3_months_43',
'value_of_loans_opened_last_12_months_43',
'total_number_of_accounts_43', 'no_of_loans_paid_off_successfully_43',
'value_of_loans_opened_last_3_months_43',
'number_of_active_accounts_43', 'total_value_of_accounts_43',
'no_of_loans_opened_last_12_months_43', 'latest_disbursed_date_43',
'value_of_loans_paid_off_successfully_43',
'current_bal_for_active_accounts_43',
'amt_written_off_total_liabilities_ratio_43',
'no_acs_written_off_total_liabilities_ratio_43', 'amt_bl_paid_off_43',
'last_loan_drawn_in_months_43',
'no_acs_overdue_total_accounts_ratio_43', 'no_of_bl_paid_off_43',
'credit_card_usage_total_limits_ratio_43', 'no_of_running_bl_pl_43',
'no_of_enquiries_last_3_months_43',
'no_of_enquiries_last_12_months_43', 'last_account_opened_months_None',
'monthly_emi_for_active_accounts_None',
'sanctioned_amt_for_active_accounts_None',
'no_of_loans_opened_last_3_months_None',
'value_of_loans_opened_last_12_months_None',
'total_number_of_accounts_None',
'no_of_loans_paid_off_successfully_None',
'value_of_loans_opened_last_3_months_None',
'number_of_active_accounts_None', 'total_value_of_accounts_None',
'no_of_loans_opened_last_12_months_None', 'latest_disbursed_date_None',
'value_of_loans_paid_off_successfully_None',
'current_bal_for_active_accounts_None',
'amt_written_off_total_liabilities_ratio_None',
'no_acs_written_off_total_liabilities_ratio_None',
'amt_bl_paid_off_None', 'last_loan_drawn_in_months_None',
'no_acs_overdue_total_accounts_ratio_None', 'no_of_bl_paid_off_None',
'credit_card_usage_total_limits_ratio_None',
'no_of_running_bl_pl_None', 'no_of_enquiries_last_3_months_None',
'no_of_enquiries_last_12_months_None',
'last_account_opened_months_blank',
'monthly_emi_for_active_accounts_blank',
'sanctioned_amt_for_active_accounts_blank',
'no_of_loans_opened_last_3_months_blank',
'value_of_loans_opened_last_12_months_blank',
'total_number_of_accounts_blank',
'no_of_loans_paid_off_successfully_blank',
'value_of_loans_opened_last_3_months_blank',
'number_of_active_accounts_blank', 'total_value_of_accounts_blank',
'no_of_loans_opened_last_12_months_blank',
'latest_disbursed_date_blank',
'value_of_loans_paid_off_successfully_blank',
'current_bal_for_active_accounts_blank',
'amt_written_off_total_liabilities_ratio_blank',
'no_acs_written_off_total_liabilities_ratio_blank',
'amt_bl_paid_off_blank', 'last_loan_drawn_in_months_blank',
'no_acs_overdue_total_accounts_ratio_blank', 'no_of_bl_paid_off_blank',
'credit_card_usage_total_limits_ratio_blank',
'no_of_running_bl_pl_blank', 'no_of_enquiries_last_3_months_blank',
'no_of_enquiries_last_12_months_blank',
'last_account_opened_months_41', 'monthly_emi_for_active_accounts_41',
'sanctioned_amt_for_active_accounts_41',
'no_of_loans_opened_last_3_months_41',
'value_of_loans_opened_last_12_months_41',
'total_number_of_accounts_41', 'no_of_loans_paid_off_successfully_41',
'value_of_loans_opened_last_3_months_41',
'number_of_active_accounts_41', 'total_value_of_accounts_41',
'no_of_loans_opened_last_12_months_41', 'latest_disbursed_date_41',
'value_of_loans_paid_off_successfully_41',
'current_bal_for_active_accounts_41',
'amt_written_off_total_liabilities_ratio_41',
'no_acs_written_off_total_liabilities_ratio_41', 'amt_bl_paid_off_41',
'last_loan_drawn_in_months_41',
'no_acs_overdue_total_accounts_ratio_41', 'no_of_bl_paid_off_41',
'credit_card_usage_total_limits_ratio_41', 'no_of_running_bl_pl_41',
'no_of_enquiries_last_3_months_41', 'no_of_enquiries_last_12_months_41'
]
df_cols.append('business_id')
df_buckets = pd.DataFrame(columns=df_cols)
i = 0
def add_data(j):
account_type = str(row['account_type'])
for col in all_cols:
# print(account_type)
full_col = col + "_" + account_type
# print(full_col + " : " + str(row[col]))
df_buckets.loc[j, full_col] = row[col]
for index, row in df_mongo_buckets.iterrows():
business_id = row['business_id']
all_business_id = df_buckets['business_id'].unique()
if business_id in all_business_id:
add_data(i)
else:
print(str(i))
i += 1
df_buckets.loc[i, 'business_id'] = business_id
add_data(i)
# df_buckets.to_csv('api_all_data_account_type21.csv', index=False)
# data_df.to_csv('op2.csv')
for index, row in df_buckets.iterrows():
for col in cibil_cols_required:
# print(col)
if col in row:
if str(row[col]) == "nan":
data_df.loc[0, col] = "0"
else:
data_df.loc[0, col] = str(row[col])
else:
data_df.loc[0, col] = "0"
# data_df = data_df.replace("nan", "0")
# data_df.to_csv('op.csv')
# get model
import pickle
filename = 'service_api_drawdowns/log_decission_tree_model.sav'
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.predict(data_df)
print(result)
# data_df['output_predicted'] = result
# data_df.to_csv('output_decision_tree.csv')
return str(result[0])
# -*- coding:utf-8 -*-
from flask import Flask
from flask_cors import CORS
from flask_restful import Resource, Api, reqparse
from model.product import Product
from model.order import Order
from model.orderhasproduct import OrderHasProduct
from model.db_init import db_init, db
from config import config
app = Flask(__name__, static_url_path='/static')
CORS(app)
app.config.from_object(config['default'])
app.config.update(RESTFUL_JSON=dict(ensure_ascii=False))
db_init(app)
api = Api(app)
class ProductEndpoint(Resource):
def get(self):
parser = reqparse.RequestParser()
parser.add_argument('pid', type=int, help='product id')
args = parser.parse_args()
pid = args['pid']
if not pid:
products = Product.query.all()