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Author: Ninad077

App_SVM.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+# Load the trained SVM model
+svc_model = joblib.load('mymodel.joblib')
+
+# Set up Streamlit app
+st.title('Loan Eligibility Prediction')
+
+# Create input form for user
+st.write('Enter Applicant Details:')
+gender = st.selectbox('Gender', ['Male', 'Female'])
+married = st.selectbox('Marital Status', ['Yes', 'No'])
+dependents = st.text_input('Number of Dependents', '0')
+education = st.selectbox('Education', ['Graduate', 'Not Graduate'])
+self_employed = st.selectbox('Self Employed', ['Yes', 'No'])
+applicant_income = st.text_input('Applicant Income', '')
+coapplicant_income = st.text_input('Coapplicant Income', '')
+loan_amount = st.text_input('Loan Amount', '')
+loan_amount_term = st.text_input('Loan Amount Term', '')
+credit_history = st.selectbox('Credit History', ['1', '0'])
+property_area = st.selectbox('Property Area', ['Urban', 'Rural', 'Semiurban'])
+
+# Map categorical variables to numerical values
+gender_map = {'Male': 1, 'Female': 0}
+married_map = {'Yes': 1, 'No': 0}
+education_map = {'Graduate': 1, 'Not Graduate': 0}
+self_employed_map = {'Yes': 1, 'No': 0}
+property_area_map = {'Urban': 1, 'Rural': 2, 'Semiurban': 0}
+
+# Convert categorical variables to numerical values
+gender_val = gender_map[gender]
+married_val = married_map[married]
+education_val = education_map[education]
+self_employed_val = self_employed_map[self_employed]
+property_area_val = property_area_map[property_area]
+
+# When user clicks the 'Predict' button
+if st.button('Predict'):
+    # Create input data array with numerical values
+    input_data = np.array([[gender_val, married_val,
+                        int(dependents), 
+                        education_val, 
+                        self_employed_val, 
+                        float(applicant_income), 
+                        float(coapplicant_income), 
+                        float(loan_amount), 
+                        float(loan_amount_term), 
+                        float(credit_history),
+                        property_area_val]])
+
+    
+    st.write("Input Data Shape:", input_data.shape)
+    st.write("Input Data:", input_data)
+
+    # Predict loan eligibility
+    eligibility = svc_model.predict(input_data)
+    
+    # Display prediction result
+    st.subheader(f'Eligibility: {eligibility[0]}')
+
+