Predictive Maintenance · Supervised Learning · Probability CalibrationA synthetic yet realistic dataset was generated to simulate operational telemetry from network routers. The goal is to estimate the probability of a router failure occurring within the next 24 hours using real‑time operational variables. This project mimics a predictive maintenance scenario for network infrastructure, where failures are rare and cost‑sensitive.
Unplanned router outages lead to service disruptions, SLA penalties, and increased operational workload. By predicting failure probabilities rather than issuing hard binary alarms, network operations teams can prioritize preventive maintenance, allocate resources efficiently, and reduce downtime — all while maintaining control over alert thresholds.
Train a supervised classification model that outputs a well‑calibrated probability of failure (target = 1) based on five operational features. The model must:
A custom Python script created 10,000 records with a fixed random seed for full reproducibility.
| Variable | Type | Description |
|---|---|---|
active_sessions |
Integer | Number of currently active sessions on the router. |
crc_errors_per_second |
Float (0–0.1) | CRC errors per second, indicating physical layer degradation. |
buffer_memory_utilization_percent |
Float (0–100) | Percentage of buffer memory in use. |
unplanned_restarts_last_24h |
Integer (0–5) | Unexpected reboots in the last day. |
dropped_packets_due_to_buffer_full_last_hour |
Integer (0–1000) | Packets dropped due to buffer exhaustion. |
target |
Binary (0/1) | Failure event in the next 24 hours. |
The target was derived from a weighted combination of the features, with added noise, then binarized. Features of positive cases were subsequently inflated to improve separability — a realistic pattern often seen when failures stress the system.