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app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+
+st.title("Müşteri devamlılık tahmin modeli")
+model=pickle.load(open("bank.pkl","rb"))
+
+crscore=st.number_input("CreditScore", value=600)
+geography=st.selectbox("Geography",{"France","Spain","Germany"})
+gender=st.selectbox("Gender",{"Male","Female"})
+age=st.number_input("Age")
+tenure=st.number_input("Tenure")
+balance=st.number_input("Balance")
+nop=st.number_input("NumOfProducts")
+hcc=st.number_input("HasCrCard")
+iam=st.number_input("IsActiveMember")
+es=st.number_input("EstimatedSalary")
+
+# DataFrame oluşturma
+df = pd.DataFrame({
+    "CreditScore": [crscore],
+    "Geography": [geography],
+    "Gender": [gender],
+    "Age": [age],
+    "Tenure": [tenure],
+    "Balance": [balance],
+    "NumOfProducts": [nop],
+    "HasCrCard": [hcc],
+    "IsActiveMember": [iam],
+    "EstimatedSalary": [es],
+})
+
+d={"Male":1,"Female":0}
+df["Gender"]=df["Gender"].map(d)
+
+d={"France":0,"Spain":1,"Germany":2}
+df["Geography"]=df["Geography"].map(d)
+
+if st.button("Tahmin Et"):
+    tahmin=model.predict(df)
+
+#class_names={   
+#                0: "düşük maliyetli",
+#                1: "orta maliyet",
+ #               2: "yüksek maliyet",
+ #               3: "çok yüksek maliyet"
+#            }
+
+#st.success(class_names[tahmin])
+st.write(tahmin.argmax())

bank.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e564bc4dbed85235de196c351143986cd6df6afab3c82f7107cc1d9585dfcf6b
+size 136488

requirements.txt

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+streamlit 
+scikit-learn