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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 | |
| from joblib import load | |
| # Load pre-trained model and scaler | |
| with open('standard_scaler.pkl', 'rb') as scaler_file: | |
| scaler = pickle.load(scaler_file) | |
| #with open('random_forest_model.pkl', 'rb') as model_file: | |
| #model = pickle.load(model_file) | |
| # Load the model | |
| model = load('model.joblib') | |
| def predict_fraud(user_input): | |
| user_input_amount = user_input['Amount'] | |
| user_input_features = user_input.drop(columns=['Amount']) | |
| # Scale the amount column | |
| user_input_amount_scaled = scaler.transform(np.array(user_input_amount).reshape(-1, 1)) | |
| # Reshape user_input_features if necessary | |
| if len(user_input_features.shape) == 1: | |
| user_input_features = user_input_features.values.reshape(1, -1) | |
| # Combine scaled amount with other features | |
| user_input_scaled = np.concatenate((user_input_features, user_input_amount_scaled), axis=1) | |
| # Ensure user_input_scaled is a 2D array | |
| if len(user_input_scaled.shape) == 1: | |
| user_input_scaled = user_input_scaled.reshape(1, -1) | |
| # Make predictions | |
| 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) | |
| user_input_df = pd.DataFrame([user_input]) # Convert list of dictionaries to DataFrame | |
| user_input_df['Amount'] = amount | |
| prediction, probability = predict_fraud(user_input_df) | |
| # 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() |