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app.py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+Pros = {'Engineer' : 1, 
+              'Healthcare' : 2, 
+              'Executive' : 3, 
+              'Doctor' : 4, 
+              'Artist' : 5,
+              'Lawyer' : 6, 
+              'Entertainment' : 7, 
+              'Homemaker' : 8, 
+              'Marketing' : 9
+              }
+
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
+
+unique_Gender =  unique_values["Gender"]
+unique_Ever_Married =  unique_values["Ever_Married"]
+unique_Graduated	 =  unique_values["Graduated"]
+unique_Spending_Score	 =  unique_values["Spending_Score"]
+unique_Var_1 = unique_values["Var_1"]
+unique_Profession = unique_values["profession"]
+
+def main():
+    st.title("Customer segmentation")
+
+    with st.form("questionaire"):
+        Gender = st.selectbox("Gender", unique_Gender)
+        Ever_Married = st.selectbox("Married", unique_Ever_Married)
+        Age = st.slider("Age", min_value=18, max_value=89)
+        Graduated = st.selectbox("Graduated", unique_Graduated)
+        Profession = st.selectbox("Profession", unique_Profession)
+        Work_Experience = st.slider("Work_Experience", min_value=0, max_value=14)
+        Spending_Score = st.selectbox("Spending_Score", unique_Spending_Score)
+        Family_Size = st.slider("Family_Size", min_value=1, max_value=9)
+        Var_1 = st.selectbox("Var_1", unique_Var_1)
+        ID = st.slider("ID", min_value=458982, max_value=467974)
+
+        clicked = st.form_submit_button("Predict Segmentation")
+        if clicked:
+            result=model.predict(pd.DataFrame({"Gender": [Gender],
+                                               "Ever_Married": [Ever_Married],
+                                               "Age": [Age],
+                                               "ID": [ID],
+                                               "Graduated": [Graduated],
+                                               "Profession": [Pros[Profession]],
+                                               "Work_Experience": [Work_Experience],
+                                               "Spending_Score": [Spending_Score],
+                                               "Family_Size": [Family_Size],
+                                               "Var_1": [Var_1],
+                                               }))
+            if result[0] == "A":
+                result = "A"
+            elif result[0] == "B":
+                result = "B"
+            elif result[0] == "C":
+                result = "C"
+            else:
+                result = "D"
+            st.success('The predicted Segmentation is {}'.format(result))
+
+if __name__=='__main__':
+    main()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f25fcd7ba6383eb00e9286cef1f0d8b344490c1ebeb673fa2a4779c5d2599213
+size 1244126

requirements.txt

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+joblib
+pandas
+scikit-learn==1.2.2
+xgboost==1.7.6
+altair<5

unique_values.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:280e2ba696c2e70aa1d9668ffab3f7d95d3ab0b4db5749074859941fb1391579
+size 1763