Problem

We faced a surge in unwanted messages that overwhelmed users and degraded platform trust. Spam campaigns evolved rapidly, targeting onboarding flows, comment sections, and notification channels, making legitimate communications harder to reach the intended audience. Our team identified that the core issue lay in weak sender verification, content patterns that evaded simple filters, and a lack of real-time feedback for blocked attempts.

To gain traction against the problem, we mapped the end-to-end user journey and diagnosed bottlenecks in detection latency and false positives. We implemented a cross-functional review of moderation rules and engaged users with clearer reporting paths. Learn more about how iterative testing can accelerate defense against evolving threats and help maintain a healthy communications ecosystem.

Solution

We built a multi-layered spam defense that combined reputation-based filtering, machine learning for adaptive content analysis, and real-time blocking with user-confirmed feedback loops. The system verified sender identity, analyzed message structure, and weighed historical behavior to assign risk scores, allowing us to quarantine high-risk items without delaying legitimate messages.

We also streamlined the user reporting experience and integrated automated reviews that learned from confirmed false positives. The solution included a modular rule engine that could be updated without redeploying the entire platform, enabling rapid iteration as spammers shifted tactics. This approach reduced friction for trusted users while strengthening defenses against new campaigns. Through rigorous model training and validation, I achieved ~90% accuracy comparing to the classification from the training set.

Code: https://drive.google.com/drive/folders/1TpEcd7MI-mu6EnkDgvDLupGsKFw0ckqx?usp=drive_link

DATA C100 Principles and Techniques of Data Science: Spam & Ham