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README.md

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+---
+title: "Fraud Detection Model"
+emoji: "🔍"
+colorFrom: "red"
+colorTo: "pink"
+sdk: "streamlit"
+sdk_version: "1.19.0"  # Adjust to the version you're using
+app_file: "slapp.py"
+pinned: false
+---
+
+# Fraud Detection Model
+
+This Space hosts a fraud detection model that predicts the acceptability of transactions based on various features.
+
+## How to Use
+
+1. Enter the transaction details in the input fields.
+2. Click on the "Predict" button to see the results.

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:276fc889b0a67b07e440abb5c91c8e096c5dee3da739c33cf01db2b5e14afc96
+size 1211

requirements.txt

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+flask==1.1.2
+scikit-learn==1.0.2
+scipy
+numpy==1.23.5
+pandas==1.5.0
+matplotlib
+seaborn
+schedule
+jupyter
+mlflow
+requests
+jinja2==3.1.2
+streamlit==1.19.0
+altair==5.0.0
+
+

slapp.py

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+import streamlit as st
+import pickle
+import pandas as pd
+
+# Load the saved model
+try:
+    model = pickle.load(open('model.pkl', 'rb'))
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    model = None
+
+# Streamlit app
+st.title("Fraud Detection API")
+st.write("Enter the transaction details to check if it's acceptable or fraudulent.")
+
+# Create input fields for the features
+time = st.number_input('Time')
+v1 = st.number_input('V1')
+v2 = st.number_input('V2')
+v3 = st.number_input('V3')
+v4 = st.number_input('V4')
+v5 = st.number_input('V5')
+v6 = st.number_input('V6')
+v7 = st.number_input('V7')
+v8 = st.number_input('V8')
+v9 = st.number_input('V9')
+v10 = st.number_input('V10')
+v11 = st.number_input('V11')
+v12 = st.number_input('V12')
+v13 = st.number_input('V13')
+v14 = st.number_input('V14')
+v15 = st.number_input('V15')
+v16 = st.number_input('V16')
+v17 = st.number_input('V17')
+v18 = st.number_input('V18')
+v19 = st.number_input('V19')
+v20 = st.number_input('V20')
+v21 = st.number_input('V21')
+v22 = st.number_input('V22')
+v23 = st.number_input('V23')
+v24 = st.number_input('V24')
+v25 = st.number_input('V25')
+v26 = st.number_input('V26')
+v27 = st.number_input('V27')
+v28 = st.number_input('V28')
+amount = st.number_input('Amount')
+
+# Prepare a button for prediction
+if st.button('Predict'):
+    try:
+        # Create a DataFrame from the input data
+        transaction_data = pd.DataFrame({
+            'Time': [time],
+            'V1': [v1],
+            'V2': [v2],
+            'V3': [v3],
+            'V4': [v4],
+            'V5': [v5],
+            'V6': [v6],
+            'V7': [v7],
+            'V8': [v8],
+            'V9': [v9],
+            'V10': [v10],
+            'V11': [v11],
+            'V12': [v12],
+            'V13': [v13],
+            'V14': [v14],
+            'V15': [v15],
+            'V16': [v16],
+            'V17': [v17],
+            'V18': [v18],
+            'V19': [v19],
+            'V20': [v20],
+            'V21': [v21],
+            'V22': [v22],
+            'V23': [v23],
+            'V24': [v24],
+            'V25': [v25],
+            'V26': [v26],
+            'V27': [v27],
+            'V28': [v28],
+            'Amount': [amount]
+        })
+
+        # Perform prediction using the loaded model
+        prediction = model.predict(transaction_data)
+
+        # Prepare response
+        if prediction[0] == 0:
+            st.success('Prediction: Acceptable transaction')
+        else:
+            st.error('Prediction: Fraudulent transaction')
+
+    except Exception as e:
+        st.error(f'Error: {str(e)}')
+