updated app

app.py

@@ -4,22 +4,22 @@ import pandas as pd
 
 st.title("Welcome to ABC Bank")
 
-model = joblib.load('model.joblib')
+model = joblib.load('model_final.joblib')
 #Even though we are not going to use gender to predict the loan status,
 #we will be getting the gender data for future plans/schemes.
-with st.form('Loan Form'):
+with st.form('Loan Form',clear_on_submit=True):
     col1,col2 = st.columns(2)
     with col1:
         Gender = st.selectbox('Gender',('Male','Female'))
-        Applicant_Income = st.number_input('Applicant Income',min_value=0)
+        Applicant_Income = st.number_input('Applicant Income',min_value=150)
         Coapplicant_Income = st.number_input('Co-applicant Income',min_value=0)
-        Loan_amount = st.number_input('Loan Amount',min_value=0)
-        Loan_Amount_Term = st.number_input('Loan Amount Term (Months)',min_value=0)
+        Loan_amount = st.number_input('Loan Amount (In thousands)',min_value=5)
+        Loan_Amount_Term = st.number_input('Loan Amount Term (Months)',min_value=12)
     with col2:
         Property_Area = st.selectbox('Property Area',('Urban','Rural','Semiurban'))
         Credit_History = st.number_input('Credit History',min_value=0,max_value=1)
         Self_Employed = st.selectbox('Self Employed',('Yes','No'))
-        Dependents = st.selectbox('Dependents',('0','1','2','3','3+'))
+        Dependents = st.selectbox('Dependents',('0','1','2','3+'))
         Education = st.selectbox('Education',('Graduate','Not Graduate'))
         Married = st.selectbox('Married',('Yes','No'))
 
@@ -35,14 +35,20 @@ with st.form('Loan Form'):
         'Credit_History': [Credit_History],
         'Property_Area': [Property_Area]}
         )
+    def emi_calculator(principle,term):
+        #interest = PNR/100
+        interest = (principle * 1000 * 8.5 * term )/float(12*100)
+        emi = ((principle*1000) + interest)/term
+        return emi
+    df['EMI'] = df.apply(lambda row: emi_calculator(row['Loan_Amount'],row['Loan_Amount_Term']),axis =1)
+    df['Balance_Income'] = df['Applicant_Income'] + df['Coapplicant_Income'] - df['EMI']
+    df.drop(columns = ['Applicant_Income','Coapplicant_Income','Loan_Amount','Loan_Amount_Term'])
+
     submit = st.form_submit_button('Predict')
     if submit:
         prediction = model.predict(df)
         if prediction:
             st.success('Congratulations, Your Home Loan is Approved!!')
         else:
-            st.error('We are extremely sorry to inform you that you Home Loan is not approved. Please reach out to nearest Branch for further clarification')
-
-    st.write(df)
-
+            st.error('We are extremely sorry to inform you that you Home Loan is not approved. Please reach out to nearest branch for further clarification')
 

model.joblib → model_final.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7167812dfb03f255d78695e8936eead9bd5fb0790ff99d819401ef85537b2a64
-size 5518
+oid sha256:8f507f76658ab5c5e1be35367b2683f70fd5a0e415702795dbbed8c2e2996e22
+size 104047