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b33b5ec cef64d3 b33b5ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | import pandas as pd
import numpy as np
import joblib
import streamlit as st
def predict(new_customer_obj, model_pipeline):
new_cust_df = pd.DataFrame([new_customer_obj])
st.dataframe(new_cust_df)
st.write(new_cust_df.info())
user1_prediction = model_pipeline.predict_proba(new_cust_df)[:, 1]
label = model_pipeline.predict(new_cust_df)[0]
return user1_prediction, label
def run():
# load pipeline
model_pipeline = joblib.load("./src/logistic_regression_pipeline.pkl")
# fetch dataset
# data = pd.read_excel('./src/default_of_credit_card_clients.xls', index_col=0)
# data.columns = data.iloc[0]
# data = data.drop(data.index[0])
# form creation
with st.form(key="prediction_form"):
limit_balance = st.number_input(label='limit balance', value=0, step=1, min_value=0)
# SEX
sex_options = {
"Male": "1",
"Female": "2"
}
sex_label = st.selectbox(
"Sex",
options=list(sex_options.keys()),
index=None
)
# EDUCATION
education_options = {
"Graduate School": "1",
"University": "2",
"High School": "3",
"Others": "4"
}
education_label = st.selectbox(
"Education",
options=list(education_options.keys()),
index=None
)
# MARITAL STATUS
marital_options = {
"Married": "1",
"Single": "2",
"Others": "3"
}
maritial_label = st.selectbox(
"Martial Status",
options=list(marital_options.keys()),
index=None
)
age = st.number_input("Age", value=1, step=1, min_value=1)
st.write(" === Customer Credit Payment History === ")
pay_options = {
"none": "0",
"pay duly": "-1",
"payment delay for 1 month": "1",
"payment delay for 2 month": "2",
"payment delay for 3 month": "3",
"payment delay for 4 month": "4",
"payment delay for 5 month": "5",
"payment delay for 6 month": "6",
"payment delay for 7 month": "7",
"payment delay for 8 month": "8",
"payment delay for 9 month and above": "9"
}
pay_4_label = st.selectbox(
"June 2005",
options=list(pay_options.keys()),
)
pay_3_label = st.selectbox(
"July 2005",
options=list(pay_options.keys()),
)
pay_2_label = st.selectbox(
"August 2005",
options=list(pay_options.keys()),
)
pay_0_label = st.selectbox(
"September 2005",
options=list(pay_options.keys()),
)
st.write(" === Amount of Bill Statement ===")
bill_amt_4 = st.number_input("June 2005", value=0, step=1, help="can be minus. ex: -123456")
bill_amt_3 = st.number_input("July 2005", value=0, step=1, help="can be minus. ex: -123456")
bill_amt_2 = st.number_input("August 2005", value=0, step=1, help="can be minus. ex: -123456")
bill_amt_1 = st.number_input("September 2005", value=0, step=1, help="can be minus. ex: -123456")
st.write(" === Amount of Previous Statement ===")
pay_amt_4 = st.number_input("June 2005", step=1, min_value=0, help="minimum 0")
pay_amt_3 = st.number_input("July 2005", step=1, min_value=0, help="minimum 0")
pay_amt_2 = st.number_input("August 2005", step=1, min_value=0, help="minimum 0")
pay_amt_1 = st.number_input("September 2005", step=1, min_value=0, help="minimum 0")
submitted = st.form_submit_button('Predict')
if submitted:
# error handling
if sex_label == None:
st.error('Sex cannot be empty')
st.stop()
if education_label == None:
st.error('Education cannot be empty')
st.stop()
if maritial_label == None:
st.error('Marital Status cannot be empty')
st.stop()
sex = sex_options[sex_label]
education = education_options[education_label]
marital_status = marital_options[maritial_label]
pay_0 = pay_options[pay_0_label]
pay_2 = pay_options[pay_2_label]
pay_3 = pay_options[pay_3_label]
pay_4 = pay_options[pay_4_label]
new_customer = {
"LIMIT_BAL": limit_balance,
"SEX": sex, # 1 = male, 2 = female
"EDUCATION": education, # 1 = graduate, 2 = university, 3 = high school
"MARRIAGE": marital_status, # 1 = married, 2 = single
"AGE": age,
"PAY_0": pay_0,
"PAY_2": pay_2,
"PAY_3": pay_3,
"PAY_4": pay_4,
"BILL_AMT1": bill_amt_1,
"BILL_AMT2": bill_amt_2,
"BILL_AMT3": bill_amt_3,
"BILL_AMT4": bill_amt_4,
"PAY_AMT1": pay_amt_1,
"PAY_AMT2": pay_amt_2,
"PAY_AMT3": pay_amt_3,
"PAY_AMT4": pay_amt_4
}
proba_default,predict_label = predict(new_customer, model_pipeline)
proba_label = "Not Default"
if predict_label == 1:
proba_label = "Default"
st.write(f"""
proba_default = {proba_default}
prediction of default payment next month \n
prediction probability ={(proba_default * 100)[0]:.3f}% \n
prediction label = {proba_label}
""")
if __name__ == '__main__':
run() |