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from flask import Flask, jsonify | ||
import pandas as pd | ||
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app = Flask(__name__) | ||
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# Load data | ||
data_file = '../data/merged_data_raw.csv' | ||
df = pd.read_csv(data_file) | ||
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@app.route('/') | ||
def home(): | ||
welcome = "Welcome" | ||
return welcome | ||
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@app.route('/api/summary', methods=['GET']) | ||
def summary(): | ||
total_transactions = len(df) | ||
total_fraud_cases = df[df['class'] == 1].shape[0] | ||
fraud_percentage = (total_fraud_cases / total_transactions) * 100 if total_transactions > 0 else 0 | ||
return jsonify({ | ||
'total_transactions': total_transactions, | ||
'total_fraud_cases': total_fraud_cases, | ||
'fraud_percentage': fraud_percentage | ||
}) | ||
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@app.route('/api/fraud_trends', methods=['GET']) | ||
def fraud_trends(): | ||
# Group by date and count fraud cases | ||
trends = df[df['class'] == 1].groupby('purchase_time').size().reset_index(name='fraud_cases') | ||
return trends.to_json(orient='records') | ||
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@app.route('/api/geographical_analysis', methods=['GET']) | ||
def geographical_analysis(): | ||
geo_data = df.groupby('country')['ip_address'].sum().reset_index() | ||
return geo_data.to_json(orient='records') | ||
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@app.route('/api/device_browser_analysis', methods=['GET']) | ||
def device_browser_analysis(): | ||
device_browser_data = df.groupby(['device_id', 'browser'])['class'].sum().reset_index() | ||
return device_browser_data.to_json(orient='records') | ||
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if __name__ == '__main__': | ||
app.run(debug=True) |
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from dash import Dash, dcc, html, Input, Output | ||
import requests | ||
import pandas as pd | ||
import plotly.express as px | ||
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app = Dash(__name__, suppress_callback_exceptions=True) | ||
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# Layout of the dashboard | ||
app.layout = html.Div([ | ||
dcc.Location(id='url', refresh=False), | ||
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html.H1("Fraud Insights Dashboard"), | ||
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# Summary Boxes | ||
html.Div(id='summary-boxes'), | ||
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# Line chart for fraud trends | ||
dcc.Graph(id='fraud-trends-chart'), | ||
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# Geographical analysis | ||
dcc.Graph(id='geographical-chart'), | ||
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# Device and browser analysis | ||
dcc.Graph(id='device-browser-chart'), | ||
]) | ||
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# Callback to update summary boxes | ||
@app.callback(Output('summary-boxes', 'children'), | ||
Input('url', 'pathname')) | ||
def update_summary_boxes(pathname): | ||
response = requests.get('http://127.0.0.1:5000/api/summary') | ||
data = response.json() | ||
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return html.Div([ | ||
html.Div(f"Total Transactions: {data['total_transactions']}", style={'padding': 10}), | ||
html.Div(f"Total Fraud Cases: {data['total_fraud_cases']}", style={'padding': 10}), | ||
html.Div(f"Fraud Percentage: {data['fraud_percentage']:.2f}%", style={'padding': 10}), | ||
]) | ||
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# Callback to update fraud trends chart | ||
@app.callback(Output('fraud-trends-chart', 'figure'), | ||
Input('url', 'pathname')) | ||
def update_fraud_trends_chart(pathname): | ||
response = requests.get('http://127.0.0.1:5000/api/fraud_trends') | ||
data = pd.read_json(response.text) | ||
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fig = px.line(data, x='purchase_time', y='fraud_cases', title='Fraud Cases Over Time') | ||
return fig | ||
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# Callback to update geographical analysis chart | ||
@app.callback(Output('geographical-chart', 'figure'), | ||
Input('url', 'pathname')) | ||
def update_geographical_chart(pathname): | ||
response = requests.get('http://127.0.0.1:5000/api/geographical_analysis') | ||
data = pd.read_json(response.text) | ||
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fig = px.choropleth(data, locations='ip_address', locationmode='country', | ||
color='country', title='Fraud Cases by Location') | ||
return fig | ||
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# Callback to update device and browser analysis chart | ||
@app.callback(Output('device-browser-chart', 'figure'), | ||
Input('url', 'pathname')) | ||
def update_device_browser_chart(pathname): | ||
response = requests.get('http://127.0.0.1:5000/api/device_browser_analysis') | ||
data = pd.read_json(response.text) | ||
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fig = px.bar(data, x='device_id', y='class', color='browser', | ||
title='Fraud Cases by Device and Browser') | ||
return fig | ||
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if __name__ == '__main__': | ||
app.run_server(debug=True) |
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Flask | ||
Dash | ||
pandas | ||
plotly |