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app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+
+# import preproses
+preproses = pickle.load(open("preproses.pkl", "rb"))
+# import model
+model = pickle.load(open("model.pkl", "rb"))
+
+#title
+st.title("Online Payments Fraud Detection")
+st.write("Created by Sihar Pangaribuan")
+
+# User imput
+step = st.number_input(label='Unit of time (hour)', min_value=1, max_value=143, value=1, step=1)
+type = st.selectbox(label='Select type of online transaction', options=['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'])
+amount = st.number_input(label='Input amount of the transaction', min_value=0.1, max_value=10000000.0, value=0.1, step=0.1)
+nameOrig = st.text_input('Input customer origin Id', value='')
+oldbalanceOrg = st.number_input(label='Balance before the transaction', min_value=1.0, max_value=38939424.03, value=1.0, step=1.0)
+newbalanceOrig = st.number_input(label='balance after the transaction', min_value=0.0, max_value=38946233.02, value=0.0, step=0.1)
+nameDest = st.text_input('Input customer destination Id', value='')
+oldbalanceDest = st.number_input(label='Input initial balance of recipient before the transaction', min_value=0.0, max_value=42207404.59, value=0.0, step=0.1)
+newbalanceDest = st.number_input(label='Input the new balance of recipient after the transaction', min_value=0.0, max_value=42207404.59, value=0.0, step=0.1)
+
+# Convert ke data frame
+data = pd.DataFrame({'step': [step],
+                'type': [type],
+                'amount': [amount],
+                'nameOrig': [nameOrig],
+                'oldbalanceOrg': [oldbalanceOrg],
+                'newbalanceOrig': [newbalanceOrig],
+                'nameDest': [nameDest],
+                'oldbalanceDest': [oldbalanceDest],
+                'newbalanceDest': [newbalanceDest]
+            })
+
+data = preproses.transform(data)
+# model predict
+
+if st.button('Predict'):
+    prediction = model.predict(data).tolist()[0]
+
+    if prediction == 1:
+        prediction = 'Froud'
+    else:
+        prediction = 'Not Froud'
+
+    st.write('The Prediction is: ')
+    st.write(prediction)

preproses.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:278b2ad56b02da6f89d999a414066c4d1e2db9ee30f28b76eeeae5924fda25e8
+size 3399