← Back to Portfolio
KPI Anomaly Detection System
ML & AIAutomated monitoring system using Isolation Forest to detect unusual patterns in business KPIs.
Overview
💡 Challenge
Manual monitoring of dozens of KPIs is time-consuming and often misses subtle anomalies until they become major issues.
⚡ Solution
Implemented Isolation Forest algorithm to automatically flag anomalous KPI values, with customizable sensitivity and alert thresholds.
🎯 Impact
Reduces monitoring time by 80% and catches anomalies 2-3 days earlier than manual review.
Technical Details
🛠️ Tech Stack
PythonScikit-learnIsolation ForestPandasStreamlit
✨ Key Features
- ✓Automated anomaly scoring using Isolation Forest
- ✓Historical anomaly timeline visualization
- ✓Customizable sensitivity thresholds
- ✓Email alert integration
Key Learnings
- →Isolation Forest works well for multivariate KPI monitoring
- →Feature scaling is critical for anomaly detection accuracy
- →False positive management requires domain-specific tuning
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.