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import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
import streamlit as st
import altair as alt
try:
# Load the data
df = pd.read_csv("fraud_data.csv")
# Prepare the data for the model
X = df[['TransactionAmount', 'CustomerAge', 'TransactionFrequency']]
y = df['IsFraud']
except FileNotFoundError:
st.write("Error: Data file not found.")
st.stop()
except Exception as e:
st.write(f"An error occurred: {e}")
st.stop()
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train a Random Forest Classifier model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions on the testing set
y_pred = model.predict(X_test)
# Evaluate the model's performance
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred, output_dict=True)
# Create a Streamlit app
st.title("Fraud Detection System")
# Create tabs
tab1, tab2, tab3 = st.tabs(["Data Visualization", "Model Performance", "Fraud Prediction"])
# Tab 1: Data Visualization
with tab1:
st.write("### Fraud Data")
st.write(df)
# Scatter plot
st.write("### Scatter Plot of Features")
for col in ['TransactionAmount', 'CustomerAge', 'TransactionFrequency']:
st.write(f"**{col} vs Fraudulent Transactions**")
st.altair_chart(
alt.Chart(df).mark_circle().encode(
x=col,
y='IsFraud',
tooltip=[col, 'IsFraud']
).interactive(),
use_container_width=True
)
# Tab 2: Model Performance
with tab2:
st.write("### Model Performance")
st.write(f"Accuracy: {accuracy:.2f}")
st.write("Classification Report:")
st.json(report)
# Tab 3: Fraud Prediction
with tab3:
st.write("### Predict Fraudulent Transactions")
amount_input = st.number_input("Transaction Amount", min_value=1.0, value=100.0, step=1.0)
age_input = st.number_input("Customer Age", min_value=18, value=30, step=1)
frequency_input = st.slider("Transaction Frequency (past month)", min_value=1, max_value=100, value=5, step=1)
if st.button("Predict"):
# Create input array for prediction
input_data = [[amount_input, age_input, frequency_input]]
# Make prediction
prediction = model.predict(input_data)[0]
result = "Fraudulent" if prediction == 1 else "Legitimate"
st.write(f"### Prediction: {result}") |