Upload 3 files

app.py

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+import pandas as pd
+from sklearn.pipeline import Pipeline 
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler,OneHotEncoder
+import streamlit as st
+import joblib
+
+df=pd.read_csv('PS_20174392719_1491204439457_log.csv')
+model = joblib.load('best_model.pkl')
+
+preprocessor=ColumnTransformer(
+    transformers=[
+        ('num',StandardScaler(),['step','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']),
+        ('cat',OneHotEncoder(),['type'])
+    ]
+)
+pipeline=Pipeline(steps=[('preprocessor',preprocessor),('regressor',model)])
+pipeline.fit(df[['step','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest','type']],df[['isFraud']])
+def price_pred(step,amount,oldbalanceOrg,newbalanceOrig,oldbalanceDest,newbalanceDest,type):
+    input_data=pd.DataFrame({
+        'step':[step],
+        'amount':[amount],
+        'oldbalanceOrg':[oldbalanceOrg],
+        'newbalanceOrig':[newbalanceOrig],
+        'oldbalanceDest':[oldbalanceDest],
+        'newbalanceDest':[newbalanceDest],
+        'type':[type]
+    })
+    prediction=pipeline.predict(input_data)[0]
+    return prediction
+
+def main():
+    st.title('Fraud Detection')
+    st.write('Enter process detail and predict fraud or not')
+
+    
+    type=st.selectbox('Type',df['type'].unique())
+    amount=st.number_input('Amount',int(df['amount'].min()),int(df['amount'].max()))
+    oldbalanceOrg=st.number_input('oldbalanceOrg',int(df['oldbalanceOrg'].min()),int(df['oldbalanceOrg'].max()))
+    newbalanceOrig=st.number_input('newbalanceOrig',int(df['newbalanceOrig'].min()),int(df['newbalanceOrig'].max()))
+    oldbalanceDest=st.number_input('oldbalanceDest',int(df['oldbalanceDest'].min()),int(df['oldbalanceDest'].max()))
+    newbalanceDest=st.number_input('newbalanceDest',int(df['newbalanceDest'].min()),int(df['newbalanceDest'].max()))
+    step=st.number_input('step',int(df['step'].min()),int(df['step'].max()))
+
+
+    if st.button('Predict'):
+        prediction=price_pred(step,amount,oldbalanceOrg,newbalanceOrig,oldbalanceDest,newbalanceDest,type)
+        if prediction==1:
+            st.write(f'The Proces is FRAUD')
+        elif prediction==0:
+            st.write(f'The Proces is NOT FRAUD')
+        
+if __name__=='__main__':
+    main()

best_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30d724a23d06ae64ea913b62139bba3fc63174846056e2319f13f3f38e114943
+size 27838713

requirements.txt

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+joblib
+scikit-learn
+streamlit