Many teams struggle to turn raw telemetry or log data into actionable insights without heavy infrastructure. The core pain points are: unclear label distribution, insufficient data for reliable models, difficulty extracting meaningful patterns from free‑text fields, and a lack of quick, reproducible ways to explore the dataset. A practical workflow solves these issues by first cleaning and filtering the data, then training lightweight Naive Bayes baselines to predict categorical fields like output type and tool name directly from the context column. These models run in pure Python, need no external libraries beyond pandas and scikit‑learn compatible utilities, and produce metrics, confusion matrices, and top‑token lists that immediately reveal which words drive each class. When data volume or class variety is too low, the script gracefully skips training and logs a clear warning, preventing wasted effort. Complementing the models, a simple keyword search helper lets analysts query any term across multiple text columns and retrieve preview snippets, enabling rapid hypothesis testing without writing custom SQL or Elasticsearch queries. All artifacts—model metrics, reports, cleaned exports, and search demo results—are saved to a defined output directory, making the process fully reproducible and shareable. The final summary JSON captures dataset statistics, distribution plots, and file pointers, giving stakeholders a single point of reference for model performance and data health. By following this straightforward, code‑first approach, teams can quickly assess feasibility, identify bottlenecks, and decide whether to invest in more complex modeling or additional data collection.
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