Define the decision before opening a notebook
Imagine a support team that wants to flag incoming tickets for urgent review. The useful question is not whether machine learning can classify text. It is whether a prediction, made when a ticket arrives, helps the team prioritize work. Write a one-sentence specification: predict whether a new ticket requires urgent review using information available at submission time.
Define who assigns the label and what urgent means. A label based on inconsistent personal judgment produces a different problem from one based on a documented escalation rule. Start with a de-identified, authorized dataset and a small scope, such as one support queue. Avoid using sensitive customer details merely because they are available.
Inspect rows and choose a realistic split
Count records, missing values, label frequencies, duplicate tickets, and distinct customers. Read examples from both classes. If urgent tickets are uncommon, accuracy alone can conceal a model that never flags anything. Also inspect whether several rows describe the same incident; those rows should not casually appear on both sides of an evaluation split.
Choose the split that resembles deployment. A random, stratified split may suit independent examples from a stable population. A chronological split better represents predicting future tickets, while a customer-grouped split tests generalization to unfamiliar customers. Reserve a final test set before experimenting, and record the exact split rule, not just the random seed.
Establish a baseline and one simple pipeline
First fit a DummyClassifier using the training labels. Its predictions ignore the input features, giving you a reference for whether learned features add value. Compare a simple text model against that baseline, rather than comparing only several sophisticated models with one another. The official baseline documentation describes the available strategies. [1](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)
For text, a reasonable first experiment combines a TF-IDF vectorizer and logistic regression in one pipeline. Fit the entire pipeline only on training data. Do not construct a vocabulary from every ticket before splitting. Keep the experiment small enough that you can explain how text becomes features and how those features produce a score.
Measure the errors that affect the queue
Produce a confusion matrix and report precision and recall for urgent tickets. Precision asks how many flagged tickets really require urgent review; recall asks how many urgent tickets were found. Neither tells the whole operational story. A team with limited reviewers may favor fewer false alarms, while a safety-critical queue may place greater weight on missed urgent cases.
Choose a probability threshold using validation data and a stated review capacity. For a hypothetical queue receiving 200 tickets daily, examine how many tickets each candidate threshold would flag. This is a planning exercise, not a promised workload estimate. Do not adjust the threshold repeatedly against the final test set.
Read failures instead of only tuning parameters
Review a sample of false positives and false negatives with someone who understands support operations. Sort errors into categories: vague language, missing context, mislabeled examples, unfamiliar products, or inappropriate reliance on particular phrases. Write down which failures require better labels rather than a different algorithm.
Check performance across meaningful slices such as language, product, and ticket length, while noting when a slice has too few examples for a stable conclusion. Remove post-resolution features such as final priority or escalation outcome. Scikit-learn identifies both leakage and inconsistent preprocessing as common causes of misleading results; pipelines help enforce the correct fitting boundary. [2](https://scikit-learn.org/stable/common_pitfalls.html)
Package a project someone else can inspect
Your deliverable should include a README, data provenance, label definition, split policy, dependency versions, baseline comparison, and a short error-analysis report. Save the fitted preprocessing and classifier together. Include a tiny synthetic input example so another learner can exercise inference without accessing private tickets.
Finish with a decision memo: what the model does, which cases it should not handle, and whether the evidence justifies a supervised pilot. State that scores are estimates, not certainty. A successful first project can conclude that the dataset is inadequate. Demonstrating why deployment would be premature is more valuable than presenting a high score whose assumptions nobody can reproduce.
Sources & further reading
Last editorial review: 27 October 2026. Examples are educational, not live employer data or guaranteed outcomes.