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This is a small Data Science project for fraud detection implying various methods and operations on a kaggle dataset.

Author: Nikhil09052007

fraud_detection.py

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+import streamlit as st
+import pandas as pd
+import joblib
+
+
+model = joblib.load("fraud_detection_pipeline.pkl")
+
+st.title("Fraud Detection Predictiion Application")
+
+st.markdown("Please enter the prediction details and use the predict button. ")
+
+st.divider()
+
+transaction_type= st.selectbox("Transaction Type",["PAYMENT","TRANSFER","CASH-OUT","DEPOSIT"])
+
+amount = st.number_input("Amount", min_value= 0.0, value=1000.0)
+
+oldbalanceOrg = st.number_input("Old BAlance (Sender)",min_value = 0.0, value=10000.0)
+
+newbalanceOrig = st.number_input("New Balance (Sender)", min_value=0.0,value=10000.0)
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+oldbalanceDest = st.number_input("Old balance (Receiver)", min_value=0.0, value = 0.0)
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+newbalanceDest = st.number_input("New balance (Receiver)",min_value=0.0,value= 0.0)
+
+
+# now we'l define the prediction button 
+
+if st.button("Predict"):
+    input_data = pd.DataFrame([{
+
+        "type": transaction_type,
+        "amount" : amount,
+        "oldbalanceOrg" : oldbalanceOrg,
+        "newbalanceOrig" : newbalanceOrig,
+        "oldbalanceDest" : oldbalanceDest,
+        "newbalanceDest" : newbalanceDest
+
+
+    }])
+
+
+    prediction = model.predict(input_data)[0]
+
+    st.subheader(f"Prediction : '{int(prediction)}'")
+
+    if prediction == 1:
+        st.error("This trasaction can be fraud")
+
+    else:
+        st.success("This transaction doesn't look like fraud.")
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