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import joblib
import pandas as pd
import streamlit as st
model = joblib.load('model.joblib')
unique_values = joblib.load('unique_values.joblib')
unique_education = unique_values["education"]
unique_self_employed = unique_values["self_employed"]
def main():
st.title("Loan Approve Prediction")
with st.form("questionaire"):
education = st.selectbox("Education", unique_education)
self_employed = st.selectbox("Self Employed", unique_self_employed)
no_of_dependents = st.slider("Number of Dependents", min_value=0 ,max_value=10)
income_annum = st.slider("Income Per Year",min_value=200000, max_value=1000000)
loan_amount = st.slider("Loan Amount", min_value=300000, max_value=40000000)
loan_term = st.slider("Loan Term (Year)", min_value=2, max_value=20)
cibil_score = st.slider("CIBIL Score", min_value=300, max_value=900)
residential_assets_value = st.slider("Residential Assets Value", min_value=-100000, max_value=30000000)
commercial_assets_value = st.slider("Commercial Assets Value", min_value=0,max_value=20000000)
luxury_assets_value = st.slider("Luxury Assets Value", min_value=0,max_value=50000000)
bank_asset_value = st.slider("Bank Assets Value", min_value=0,max_value=20000000)
clicked = st.form_submit_button("Loan Approve Prediction")
if clicked:
result=model.predict(pd.DataFrame({"no_of_dependents": [no_of_dependents],
"education": [education],
"self_employed": [self_employed],
"income_annum": [income_annum],
"loan_amount": [loan_amount],
"loan_term": [loan_term],
"cibil_score": [cibil_score],
"residential_assets_value": [residential_assets_value],
"commercial_assets_value": [commercial_assets_value],
"luxury_assets_value": [luxury_assets_value],
"bank_asset_value": [bank_asset_value]
}))
result = 'Approved' if result[0] == 1 else 'Declined'
st.success('The prediction of loan approval is {}'.format(result))
if __name__=='__main__':
main()
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