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app.py

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+# Import necessary libraries
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+import matplotlib.pyplot as plt
+
+
+
+# Load pre-trained model and scaler
+with open('standard_scaler.pkl', 'rb') as scaler_file:
+    scaler = pickle.load(scaler_file)
+
+with open('random_search_model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+
+# Function to preprocess user input and make predictions
+def predict_fraud(user_input):
+    # Separate the amount column
+    user_input_amount = user_input[-1]
+    user_input_features = user_input[:-1]
+
+    # Scale the amount column
+    user_input_amount_scaled = scaler.transform(np.array(user_input_amount).reshape(1, -1))
+    
+    # Combine scaled amount with other features
+    user_input_scaled = np.concatenate((user_input_features, user_input_amount_scaled), axis=1)
+    prediction = model.predict(user_input_scaled)[0]
+    probability = model.predict_proba(user_input_scaled)[0][1]
+    return prediction, probability
+
+# Function to generate charts
+def generate_charts(prediction, probability, amount):
+    fig, axes = plt.subplots(1, 2, figsize=(12, 6))
+
+    # Pie chart for prediction distribution
+    prediction_labels = ['Non-Fraudulent', 'Fraudulent']
+    prediction_values = [1 - prediction, prediction]
+    axes[0].pie(prediction_values, labels=prediction_labels, autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightcoral'])
+    axes[0].set_title('Prediction Distribution')
+
+    # Bar chart for probability distribution
+    axes[1].bar(['Probability'], [probability], color='lightgreen' if prediction == 0 else 'lightcoral')
+    axes[1].set_title('Probability of Fraud')
+
+    # Display amount
+    st.write(f"Transaction Amount: ${amount}")
+
+    # Display charts
+    st.pyplot(fig)
+
+# Streamlit app
+def main():
+    st.title("Fraud Detection Prediction App")
+
+    # User input fields
+    user_input = []
+    for i in range(1, 29):
+        user_input.append(st.number_input(f'V{i}', value=0.0))
+
+    min_amount = st.number_input('Minimum Amount', value=0.0)
+    max_amount = st.number_input('Maximum Amount', value=10000.0)
+
+    amount = st.number_input('Amount', min_value=min_amount, max_value=max_amount, value=(min_amount + max_amount) / 2)
+
+    # Make prediction on button click
+    if st.button('Predict'):
+        user_input_array = np.array(user_input)
+        user_input_array = np.append(user_input_array, amount)
+        prediction, probability = predict_fraud(user_input_array)
+        
+        # Display prediction result
+        st.write(f"Prediction: {'Fraudulent Transaction' if prediction == 1 else 'Non-Fraudulent Transaction'}")
+        st.write(f"Probability: {probability:.2%}")
+
+        # Generate and display charts
+        generate_charts(prediction, probability, amount)
+
+if __name__ == "__main__":
+    main()

random_search_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84a5ee1ab2f4b04a768d3bac50d605fe1160149c91036c6704f6025344f00929
+size 3025399

requirements.txt

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+scikit-learn
+streamlit
+pandas
+numpy
+matplotlib
+plotly

standard_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e54d791dea453fe62fdf8d07dc528e7b44fd30fbc370a581b7e9c2620fc568ac
+size 129