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StandardScaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51b4d79ee8f10b1e6511e55c6f9d03f467a9f48538e7a6d0eb941de5bf8c0587
+size 1303

app.py

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

encoders .joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7fd52bc3fae6cf62b4c8b9ceef029bb68e19523253480aeda8580ea92faa815d
+size 852

rf_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ee0a2c33a3af1181465f543f11989f6b7ad0df9c3dc2a89f78c9c21bd94b392
+size 13516809