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import joblib |
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import streamlit as st |
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import pandas as pd |
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def main(): |
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st.title("Bank Customer Churn") |
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credit_score = st.number_input("Credit Score") |
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country = st.selectbox("Country", options=['France', 'Spain', 'Germany']) |
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if country: |
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st.success(country) |
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gender = st.radio("Select Gender: ", ('Male', 'Female')) |
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if (gender == 'Male'): |
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st.success("Male") |
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else: |
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st.success("Female") |
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age = st.number_input("Age") |
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tenure = st.number_input("Tenure") |
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balance = st.number_input("Balance") |
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products_number = st.number_input("Products Number") |
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credit_card = st.number_input("Credit Card") |
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active_member = st.number_input("Active Member") |
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estimated_salary = st.number_input("Estimated Salary") |
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submit_button = st.button("Submit") |
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if submit_button: |
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process_form_data(credit_score, country, gender, age, tenure, |
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balance, products_number, credit_card, active_member, estimated_salary) |
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def process_form_data(credit_score, country, gender, age, tenure, |
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balance, products_number, credit_card, active_member, estimated_salary): |
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encoders = joblib.load('encoders .joblib') |
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model = joblib.load('rf_model.joblib') |
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scaler = joblib.load('StandardScaler.joblib') |
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dataDict = {'credit_score': credit_score, 'country': country, 'gender': gender, 'age': age, 'tenure':tenure, |
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'balance':balance, 'products_number': products_number, 'credit_card': credit_card, |
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'active_member': active_member, 'estimated_salary': estimated_salary} |
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df = pd.DataFrame([dataDict]) |
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decodedData = df.copy() |
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for col in ['country', 'gender']: |
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encoder = encoders[col] |
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decodedData[col] = encoder.transform(decodedData[col]) |
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decodedData = scaler.transform(decodedData) |
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result = model.predict(decodedData) |
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st.write(df) |
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if result == 1: |
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st.warning("Churn") |
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if result == 0: |
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st.success("Stay") |
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if __name__ == "__main__": |
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main() |
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