Spaces:
Sleeping
Sleeping
File size: 2,402 Bytes
72487d7 |
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 |
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() |