Spaces:
Runtime error
Runtime error
File size: 5,396 Bytes
fc7772a |
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 |
import joblib
import streamlit as st
import pandas as pd
import numpy as np
def main():
st.title("Credit Application Form")
Occupation = st.selectbox("Occupation", options=["Accountant", "Architect", "Developer", "Doctor", "Engineer",
"Entrepreneur", "Journalist", "Lawyer", "Manager", "Mechanic",
"Media_Manager", "Musician", "Scientist", "Teacher", "Writer"])
Payment_Behaviour = st.selectbox("Payment Behavior",
options=["High_spent_Large_value_payments", "High_spent_Medium_value_payments",
"High_spent_Small_value_payments", "Low_spent_Large_value_payments",
"Low_spent_Medium_value_payments", "Low_spent_Small_value_payments"])
Month = st.select_slider("Month", options=[1, 2, 3, 4, 5, 6, 7, 8])
Num_Bank_Accounts = st.select_slider("Number of Bank Accounts", options=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
Num_Credit_Card = st.select_slider("Number of Credit Cards", options=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
Num_of_Loan = st.select_slider("Number of Loan", options=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Interest_Rate = st.select_slider("Interest Rate", np.arange(1, 35, 1))
# Interest_Rate = st.number_input("Interest Rate", min_value=1, max_value=34)
Age = st.number_input("Age")
Annual_Income = st.number_input("Annual Income", value=1.0)
Monthly_Inhand_Salary = st.number_input("Monthly Inhand Salary")
Num_of_Delayed_Payment = st.number_input("Number of Delayed Payments", format='%u', value=0)
Changed_Credit_Limit = st.number_input("Changed Credit Limit")
Num_Credit_Inquiries = st.number_input("Number of Credit Inquiries", format='%u', value=0)
Credit_History_Age = st.number_input("Credit History Age")
Outstanding_Debt = st.number_input("Outstanding Debt")
Credit_Utilization_Ratio = st.number_input("Credit Utilization Ratio")
Total_EMI_per_month = st.number_input("Total EMI per Month")
Amount_invested_monthly = st.number_input("Amount Invested Monthly")
Monthly_Balance = st.number_input("Monthly_Balance")
Credit_Mix = st.radio("Credit Mix", options=("Bad", "Good", "Standard"), index=1)
submit_button = st.button("Submit")
if submit_button:
# Process the form data
process_form_data(Month, Age, Occupation, Annual_Income, Monthly_Inhand_Salary, Num_Bank_Accounts,
Num_Credit_Card, Interest_Rate, Num_of_Loan,
Num_of_Delayed_Payment, Changed_Credit_Limit, Num_Credit_Inquiries, Credit_Mix,
Payment_Behaviour, Credit_History_Age, Outstanding_Debt,
Credit_Utilization_Ratio, Total_EMI_per_month, Amount_invested_monthly, Monthly_Balance)
def process_form_data(Month, Age, Occupation, Annual_Income, Monthly_Inhand_Salary, Num_Bank_Accounts,
Num_Credit_Card, Interest_Rate, Num_of_Loan, Num_of_Delayed_Payment,
Changed_Credit_Limit, Num_Credit_Inquiries, Credit_Mix, Payment_Behaviour,
Credit_History_Age, Outstanding_Debt, Credit_Utilization_Ratio, Total_EMI_per_month,
Amount_invested_monthly, Monthly_Balance):
# Feature Engineering
Monthly_Savings = Monthly_Inhand_Salary - Annual_Income / 12
Total_Accounts = int(Num_Bank_Accounts) + int(Num_Credit_Card)
Savings_to_Income_Ratio = Monthly_Savings / Annual_Income
encoders = joblib.load('v2\encoders.joblib')
model = joblib.load('v2\Random_Forest.joblib')
dataDict = {"Month": Month, 'Occupation': Occupation, 'Num_Bank_Accounts': Num_Bank_Accounts, 'Num_Credit_Card': Num_Credit_Card,
'Interest_Rate': Interest_Rate, 'Num_of_Loan': Num_of_Loan, 'Credit_Mix': Credit_Mix, 'Credit_History_Age': Credit_History_Age,
'Num_of_Delayed_Payment': Num_of_Delayed_Payment, 'Payment_of_Min_Amount': Num_of_Delayed_Payment,
'Payment_Behaviour': Payment_Behaviour, 'Age': Age, 'Annual_Income': Annual_Income, 'Monthly_Inhand_Salary': Monthly_Inhand_Salary,
'Outstanding_Debt': Outstanding_Debt, 'Credit_Utilization_Ratio': Credit_Utilization_Ratio,'Changed_Credit_Limit': Changed_Credit_Limit,
'Num_Credit_Inquiries': Num_Credit_Inquiries, 'Total_EMI_per_month': Total_EMI_per_month, 'Amount_invested_monthly': Amount_invested_monthly,
'Monthly_Balance': Monthly_Balance, 'Total_Accounts': Total_Accounts, 'Savings_to_Income_Ratio': Savings_to_Income_Ratio}
df = pd.DataFrame([dataDict])
cols = ['Month', 'Num_Bank_Accounts', 'Num_Credit_Card', 'Interest_Rate', 'Num_of_Loan']
df[cols] = df[cols].astype(int)
decodedData = df.copy()
for col in ['Occupation', 'Payment_Behaviour', 'Credit_Mix']:
encoder = encoders[col]
decodedData[col] = encoder.transform(decodedData[col])
result = model.predict(decodedData)
result = encoders['Credit_Score'].inverse_transform(result)
st.write(df)
st.write(decodedData)
st.write(result)
if __name__ == "__main__":
# Load the models and encoders
encoders = joblib.load('spaces\encoders.joblib')
model = joblib.load('spaces\Random_Forest.joblib')
# Run the Streamlit app
main() |