File size: 3,001 Bytes
4989c9d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
19843f0
 
 
4989c9d
 
9cd9658
4989c9d
 
 
 
 
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
import joblib
import pandas as pd
import streamlit as st

purpose_1 = {'all_other': 1,
            'credit_card': 2,
            'debt_consolidation': 3,
            'educational': 4,
            'home_improvement': 5,
            'major_purchase': 6,
            'small_business': 7,
            }

model = joblib.load('model_1.joblib')
unique_values = joblib.load('unique_values_1.joblib')
unique_Purpose =  unique_values["purpose"]

def main():
    st.title("Loan Data")

    with st.form("questionaire"):
        purpose = st.selectbox("Purpose", options = unique_Purpose )
        int_rate = st.slider("The interest rate of the loan", 0.0000,1.0000) 
        installments = st.number_input("The monthly installments owed") 
        log_annual_inc  = st.number_input("The natural log of the self-reported annual income of the borrower")
        dti = st.number_input("The debt to income ratio of the borrower")
        fico = st.slider("The FICO credit score of the borrower.", 0,1000)
        days_with_cr_line = st.number_input("The number of days the borrower has had a credit line.")
        revol_bal = st.number_input("The borrower's revolving balance")
        revol_util= st.number_input("The borrower's revolving line utilization rate")
        inq_last_6mths= st.slider("The borrower's number of inquiries by creditors in the last 6 months.", 0,100)
        delinq_2yrs = st.slider("The number of times the borrower had been 30+ days past due on a payment in the past 2 year", 0,100 )
        pub_rec= st.slider("The borrower's number of derogatory public records", 0,100 )
        not_fully_paid =  st.slider("not fully paid.", 0,100)
       
        # clicked==True only when the button is clicked
        clicked = st.form_submit_button("Predict income")
        if clicked:
            result=model.predict(pd.DataFrame({"purpose": [purpose_1],
                                               "int.rate": [int_rate],
                                               "installment": [installments],
                                               "log.annual.inc": [log_annual_inc],
                                               "dti": [dti],
                                               "fico": [fico],
                                               "days.with.cr.line": [days_with_cr_line],
                                               "revol.bal": [revol_bal],
                                               "revol.util": [revol_util],
                                               "inq.last.6mths": [inq_last_6mths],
                                               "delinq.2yrs":[delinq_2yrs],
                                               "pub.rec": [pub_rec],
                                               "not.fully.paid": [not_fully_paid]}))
            # Show prediction
            result = 'Pass' if result[1] == 0 else 'Not Pass'
            st.success("Your predicted loan is "+result) #แสดงผล
            
# Run main()
if __name__ == "__main__":
    main()