Project Title: Real-Time Anomaly Detection System

???? Objective:

To build a system that can detect anomalies in streaming data in real time. This is useful for fraud detection, network security, industrial monitoring, and more.

???? Core Components:

Data Ingestion:

Tools: Apache Kafka, Flume, or direct API.

Collects real-time data from sources like sensors, transactions, or logs.

Preprocessing:

Cleansing and normalization of data streams.

Handling missing values, timestamp alignment.

Feature Engineering:

Extraction of time-based features (rolling stats, lag features).

Dimensionality reduction (PCA, Autoencoders).

Anomaly Detection Algorithms:

Statistical: Z-score, Moving average, IQR.

Machine Learning: Isolation Forest, One-Class SVM.

Deep Learning: LSTM Autoencoders for sequence data.

Real-time scoring with thresholds or prediction intervals.

Stream Processing Framework:

Tools: Apache Spark Streaming, Apache Flink, or Kafka Streams.

Processes data in mini-batches or continuous flows.

Alerting & Visualization:

Trigger notifications (email, Slack, dashboard) on anomalies.

Real-time dashboards via Grafana, Kibana, or custom UI.

Evaluation Metrics:

Precision, recall, F1-score (if labeled data exists).

ROC-AUC, detection delay.

???? Deployment:

Containerized using Docker.

Deployed via Kubernetes or cloud platforms (AWS/GCP/Azure).

Scalable microservice architecture for handling large data volumes.