Create Ml-Model


Ml-Model

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+import streamlit as st
+import pandas as pd
+import pickle
+
+st.image('images.jpeg')
+# Load the pickled model
+loaded_pickle_model = pickle.load(open("random_forest_model.pkl", "rb"))
+
+def predict_loan_approval(data):
+    # Use the loaded model to make predictions
+    prediction = loaded_pickle_model.predict(data)
+    return prediction
+
+def main():
+    st.title("Loan Approval Prediction")
+
+    # Input form for user to enter data
+    st.header("Input Data")
+    gender = st.selectbox("Gender", ["Male", "Female"])
+    married = st.selectbox("Married", ["Yes", "No"])
+    dependents = st.number_input("Dependents", min_value=0, max_value=10, value=0)
+    education = st.selectbox("Education", ["Graduate", "Not Graduate"])
+    self_employed = st.selectbox("Self Employed", ["Yes", "No"])
+    applicant_income = st.number_input("Applicant Income", value=0)
+    coapplicant_income = st.number_input("Coapplicant Income", value=0)
+    loan_amount = st.number_input("Loan Amount", value=0)
+    loan_amount_term = st.number_input("Loan Amount Term", value=0)
+    credit_history = st.selectbox("Credit History", [0.0, 1.0])
+    property_area = st.selectbox("Property Area", ["Urban", "Semiurban", "Rural"])
+
+    # Mapping input values 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': 0, 'Semiurban': 1, 'Rural': 2}
+
+    # Create a DataFrame from the input data
+    new_data = pd.DataFrame({
+        'Gender': [gender_map[gender]],
+        'Married': [married_map[married]],
+        'Dependents': [dependents],
+        'Education': [education_map[education]],
+        'Self_Employed': [self_employed_map[self_employed]],
+        'ApplicantIncome': [applicant_income],
+        'CoapplicantIncome': [coapplicant_income],
+        'LoanAmount': [loan_amount],
+        'Loan_Amount_Term': [loan_amount_term],
+        'Credit_History': [credit_history],
+        'Property_Area': [property_area_map[property_area]]
+    })
+
+    # Button to predict loan approval
+    if st.button("Predict Loan Approval"):
+        prediction = predict_loan_approval(new_data)
+        if prediction[0] == 1:
+            st.success("Loan is Approved 👍")
+        else:
+            st.error("Loan is Rejected 👎")
+
+if __name__ == "__main__":
+    main()