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import joblib |
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import pandas as pd |
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import streamlit as st |
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model = joblib.load('model.joblib') |
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unique_values = joblib.load('unique_values.joblib') |
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def main(): |
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st.title("Customer Segmentation Prediction") |
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with st.form("questionnaire"): |
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Gender = st.selectbox("Gender", unique_values["Gender"]) |
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Ever_Married = st.selectbox("Ever Married", unique_values["Ever_Married"]) |
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Age = st.slider("Age", min_value=18, max_value=89) |
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Graduated = st.selectbox("Graduated", unique_values["Graduated"]) |
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Profession = st.selectbox("Profession", unique_values["Profession"]) |
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Work_Experience = st.slider("Work Experience", min_value=0, max_value=14) |
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Spending_Score = st.selectbox("Spending Score", unique_values["Spending_Score"]) |
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Family_Size = st.slider("Family Size", min_value=1, max_value=9) |
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Var_1 = st.selectbox("Var_1", unique_values["Var_1"]) |
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ID = st.slider("ID", min_value=458982, max_value=467974) |
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clicked = st.form_submit_button("Predict Segmentation") |
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if clicked: |
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result = model.predict(pd.DataFrame({"Gender": [Gender], |
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"Ever_Married": [Ever_Married], |
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"Age": [Age], |
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"ID": [ID], |
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"Graduated": [Graduated], |
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"Profession": [Pros[Profession]], |
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"Work_Experience": [Work_Experience], |
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"Spending_Score": [Spending_Score], |
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"Family_Size": [Family_Size], |
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"Var_1": [Var_1] |
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})) |
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if result[0] == 0: |
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result = "A" |
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elif result[0] == 1: |
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result = "B" |
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elif result[0] == 2: |
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result = "C" |
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else: |
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result = "D" |
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st.success('Predicted Segmentation: {}'.format(result)) |
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if __name__ == '__main__': |
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main() |
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