Project for real-time anomaly detection using kafka and python

It's assumed that zookeeper and kafka are running in the localhost, it follows this process:

• Train an unsupervised machine learning model for anomalies detection
• Save the model to be used in real-time predictions
• Generate fake streaming data and send it to a kafka topic
• Read the topic data with several subscribers to be analyzed by the model
• Predict if the data is an anomaly, if so, send the data to another kafka topic
• Subscribe a slack bot to the last topic to send a message in slack channel if an anomaly arrives

This could be illustrated as:

Article explaining how to run this project: medium

Generate fake transactions into a kafka topic:

Predict and send anomalies to another kafka topic

Producer and anomaly detection running at the same time

Send notifications to Slack

• First train the anomaly detection model, run the file:

model/train.py

• Create the required topics

kafka-topics.sh --zookeeper localhost:2181 --topic transactions --create --partitions 3 --replication-factor 1
kafka-topics.sh --zookeeper localhost:2181 --topic anomalies --create --partitions 3 --replication-factor 1

• Check the topics are created

kafka-topics.sh --zookeeper localhost:2181 --list

• Check file settings.py and edit the variables if needed

• Start the producer, run the file

streaming/producer.py

• Start the anomalies detector, run the file

streaming/anomalies_detector.py

• Start sending alerts to Slack, make sure to register the env variable SLACK_API_TOKEN, then run

streaming/bot_alerts.py