Real-Time User Behavior Analysis

1.1 Oveview

Real-time user behavior analysis enables CapsureLabs to monitor, interpret, and act on user actions across the platform as they occur. This functionality supports personalized experiences, improves security by detecting unusual activities, and allows data-driven insights for platform optimization.

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1.2 Real-Time User Behavior Analysis Methods

Event Capture and Streaming

Collect user actions across the platform, such as clicks, views, and interactions, using event capture and streaming technology.

Data Processing and Feature Engineering

Transform raw event data into structured inputs for real-time models.

Pattern Recognition Models

Deploy machine learning algorithms to identify and interpret common behavioral patterns.

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1.3 Architecture Overview

Event Producer

Application or service that generates user events.

Message Broker

Handles data stream (e.g., Kafka or RabbitMQ) to manage real-time events.

Streaming Processor

Processes incoming data for analysis (e.g., Apache Flink or Spark Streaming).

Predictive Model

A machine learning model that evaluates the processed data and outputs predictions.

Database

Storage for logging events, tracking user actions, and model output.

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1.4 Data Collection and Processing: Event Producer and Streaming Processor

from kafka import KafkaProducer
import json

producer = KafkaProducer(
    bootstrap_servers='localhost:9092',
    value_serializer=lambda v: json.dumps(v).encode('utf-8')
)

event = {
    "user_id": "123",
    "action": "click",
    "page": "homepage",
    "timestamp": "2024-10-05T12:00:00Z"
}

producer.send('user_behavior_topic', event)
producer.flush()

1.4.1 Apache Flink Setup for processing streamed events:

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.window import TimeWindow
from pyflink.datastream.functions import MapFunction

env = StreamExecutionEnvironment.get_execution_environment()

class ExtractBehaviorData(MapFunction):
    def map(self, value):
        return json.loads(value)['user_id'], json.loads(value)['action']

data_stream = env.add_source("kafka_source")
processed_stream = data_stream.map(ExtractBehaviorData)

1.4.2 Real-Time Pattern Recognition Model

from sklearn.ensemble import IsolationForest
import numpy as np

X_train = np.array([[10], [15], [20], [15], [50]]) 
model = IsolationForest(contamination=0.1)
model.fit(X_train)

X_new = np.array([[25]])
prediction = model.predict(X_new)  

1.4.3 Model Integration: Real-Time Prediction API

from flask import Flask, request, jsonify
import joblib
import numpy as np

app = Flask(__name__)

model = joblib.load('user_behavior_anomaly_model.pkl')

@app.route('/predict_behavior', methods=['POST'])
def predict_behavior():
    data = request.json
    features = np.array([data['clicks_per_minute'], data['pages_per_session']]).reshape(1, -1)
    
    prediction = model.predict(features)
    result = "Anomaly" if prediction[0] == -1 else "Normal"
    return jsonify({'behavior_prediction': result})

if __name__ == '__main__':
    app.run(debug=True)

1.4.4 cURL Request

curl -X POST -H "Content-Type: application/json" -d '{"clicks_per_minute": 18, "pages_per_session": 5}' http://localhost:5000/predict_behavior