Initial Push

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import json
+from datetime import datetime
+
+# Load trained model
+iso_forest = joblib.load("isolation_forest_model.pkl")
+
+# Load location mapping
+with open("location_mapping.json", "r") as f:
+    location_mapping = json.load(f)
+
+# Manual mapping for categorical variables
+transaction_type_mapping = {"Debit": 0, "Credit": 1}
+channel_mapping = {"ATM": 0, "Online": 1, "Branch": 2}
+day_of_week_mapping = {"Monday": 0, "Tuesday": 1, "Wednesday": 2, "Thursday": 3, "Friday": 4, "Saturday": 5, "Sunday": 6}
+
+st.title("Anomaly Detection for Bank Transactions")
+
+
+
+# User inputs
+date = st.date_input("Select Transaction Date")
+time = st.time_input("Select Transaction Time")
+location = st.selectbox("Select Location", options=list(location_mapping.keys()))
+transaction_type = st.radio("Transaction Type", options=["Debit", "Credit"])
+channel = st.radio("Transaction Channel", options=["ATM", "Online", "Branch"])
+transaction_duration = st.slider("Transaction Duration (seconds)", min_value=0, max_value=600, value=30)
+login_attempts = st.number_input("Login Attempts", min_value=0)
+transaction_amount = st.number_input("Transaction Amount", min_value=0.0, format="%.2f")
+
+if st.button("Check for Anomaly"):
+    # Convert date to day of the week
+    day_of_week = day_of_week_mapping[date.strftime('%A')]
+    
+    # Convert time to total seconds since midnight
+    total_seconds = time.hour * 3600 + time.minute * 60
+    
+    # Convert categorical values to numeric
+    location_encoded = location_mapping.get(location, -1)  # Default to -1 if not found
+    transaction_type_encoded = transaction_type_mapping[transaction_type]
+    channel_encoded = channel_mapping[channel]
+    
+    # Ensure the order of features matches training
+    input_data = pd.DataFrame([[
+        transaction_type_encoded, location_encoded, channel_encoded, total_seconds,  
+        transaction_duration, login_attempts, day_of_week, transaction_amount  # <-- Corrected order
+    ]], columns=[
+        "TransactionType", "Location", "Channel", "Time", 
+        "TransactionDuration", "LoginAttempts", "DayOfWeek", "TransactionAmount"  # <-- Corrected order
+    ])
+
+    # Predict anomaly
+    prediction = iso_forest.predict(input_data)[0]
+    anomaly_label = "Anomalous" if prediction == -1 else "Normal"
+    
+    # Display result
+    st.write(f"### The transaction is: **{anomaly_label}**")

isolation_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d06ad59646c63b0d8d88c3a64149e475c4dcb7daba8a3518d2ef339e7e7fd83d
+size 1607544

location_mapping.json

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+{
+    "Albuquerque": 0,
+    "Atlanta": 1,
+    "Austin": 2,
+    "Baltimore": 3,
+    "Boston": 4,
+    "Charlotte": 5,
+    "Chicago": 6,
+    "Colorado Springs": 7,
+    "Columbus": 8,
+    "Dallas": 9,
+    "Denver": 10,
+    "Detroit": 11,
+    "El Paso": 12,
+    "Fort Worth": 13,
+    "Fresno": 14,
+    "Houston": 15,
+    "Indianapolis": 16,
+    "Jacksonville": 17,
+    "Kansas City": 18,
+    "Las Vegas": 19,
+    "Los Angeles": 20,
+    "Louisville": 21,
+    "Memphis": 22,
+    "Mesa": 23,
+    "Miami": 24,
+    "Milwaukee": 25,
+    "Nashville": 26,
+    "New York": 27,
+    "Oklahoma City": 28,
+    "Omaha": 29,
+    "Philadelphia": 30,
+    "Phoenix": 31,
+    "Portland": 32,
+    "Raleigh": 33,
+    "Sacramento": 34,
+    "San Antonio": 35,
+    "San Diego": 36,
+    "San Francisco": 37,
+    "San Jose": 38,
+    "Seattle": 39,
+    "Tucson": 40,
+    "Virginia Beach": 41,
+    "Washington": 42
+}

requirements.txt

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+streamlit
+pandas
+joblib
+json
+datetime
+scikit-learn