Fraud_Detection_Prediction_Tool_Machine_Learning/fraud_detection.py at main · Nikhil09052007/Fraud_Detection_Prediction_Tool_Machine_Learning · GitHub

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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)

oldbalanceDest = st.number_input("Old balance (Receiver)", min_value=0.0, value = 0.0)

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.")