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| import streamlit as st | |
| import numpy as np | |
| import pickle | |
| import streamlit.components.v1 as components | |
| from sklearn.preprocessing import LabelEncoder | |
| le = LabelEncoder() | |
| # Load the pickled model | |
| def load_model(): | |
| return pickle.load(open('Credit_Card_Classification_LogisticRegression.pkl','rb')) | |
| # Function for model prediction | |
| def model_prediction(model, features): | |
| predicted = str(model.predict(features)[0]) | |
| return predicted | |
| def transform(text): | |
| text = le.fit_transform(text) | |
| return text[0] | |
| def app_design(): | |
| # Add input fields for High, Open, and Low values | |
| image = 'credit.png' | |
| st.image(image, use_column_width=True) | |
| st.subheader("Enter the following values:") | |
| Gender = st.selectbox("Gender",('Male','Female')) | |
| if Gender == 'Male': | |
| Gender = 1 | |
| else: | |
| Gender = 0 | |
| Age= st.number_input("Age") | |
| Debt= st.number_input("Debt") | |
| Married= st.selectbox("Married",('Yes','No')) | |
| if Married == 'Yes': | |
| Married = 1 | |
| else: | |
| Married = 0 | |
| BankCustomer= st.number_input("Bank Customer") | |
| Industry= st.text_input("Industry") | |
| Industry = transform([Industry]) | |
| Ethnicity= st.text_input("Ethnicity") | |
| Ethnicity = transform([Ethnicity]) | |
| YearsEmployed = st.number_input("Years Employed") | |
| PriorDefault= st.selectbox("Prior Default",('Yes','No')) | |
| if PriorDefault == 'Yes': | |
| PriorDefault = 1 | |
| else: | |
| PriorDefault = 0 | |
| Employed= st.selectbox("Employed",('Yes','No')) | |
| if Employed == 'Yes': | |
| Employed = 1 | |
| else: | |
| Employed = 0 | |
| CreditScore = st.number_input("Credit Score") | |
| DriversLicense= st.selectbox("Drivers License",('Yes','No')) | |
| if DriversLicense == 'Yes': | |
| DriversLicense = 1 | |
| else: | |
| DriversLicense = 0 | |
| Citizen= st.selectbox("Citizen",('ByBirth','ByOtherMeans')) | |
| if Citizen == 'ByBirth': | |
| Citizen = 1 | |
| else: | |
| Citizen = 0 | |
| ZipCode= st.number_input("ZipCode") | |
| Income= st.number_input("Income") | |
| # Create a feature list from the user inputs | |
| features = [[Gender, Age,Debt,Married,BankCustomer,Industry,Ethnicity,YearsEmployed,PriorDefault,Employed,CreditScore,DriversLicense,Citizen,ZipCode,Income]] | |
| # Load the model | |
| model = load_model() | |
| # Make a prediction when the user clicks the "Predict" button | |
| if st.button('Predict Status'): | |
| predicted_value = model_prediction(model, features) | |
| if(predicted_value==1): | |
| st.success(f"The credit card is approved") | |
| else: | |
| st.success(f"The credit card is not approved") | |
| def main(): | |
| # Set the app title and add your website name and logo | |
| st.set_page_config( | |
| page_title="Credit Card Classification Model", | |
| page_icon=":chart_with_upwards_trend:", | |
| ) | |
| st.title("Welcome to our Credit Card Classification Model!") | |
| app_design() | |
| if __name__ == '__main__': | |
| main() |