A high model score is useful only when the test resembles the problem you want to solve. Before adding another algorithm to your first data science project, check what information the model received and how you separated practice data from evaluation data.
Start with a prediction moment
Imagine a practice project that predicts whether an order will be returned. Write down the moment when the prediction must happen: immediately after the order is placed.
At that moment, the model might know the product category and order value. It cannot know whether a refund was later issued. A column called refund_processed may make the model look impressive, but it reveals part of the outcome you were trying to predict.
Make a small table before training:
| Candidate input | Available when the order is placed? | Decision |
|---|---|---|
| Product category | Yes | Consider as an input |
| Order value | Yes | Consider as an input |
| Refund processed | No | Exclude |
| Return completed date | No | Exclude |
This is an invented learning example. Use artificial or properly licensed data; keep customer identities out of a public portfolio.
Separate learning from evaluation
A model learns patterns from training data. Validation data helps you choose settings or compare approaches. A final test set provides a separate evaluation after those choices are made. If you repeatedly inspect the test result and change the model to improve it, that test set is influencing your decisions.
Choose a split that matches the intended use. For predictions about future orders, evaluate on a later time period rather than casually mixing earlier and later records. If several records belong to one customer and the goal is to predict for unseen customers, keep that customer’s records together when splitting. The right design depends on the question; a random split is not automatically appropriate.
Learn preprocessing from training data
Suppose you fill missing order values using an average. Calculate that average from training data, then apply it to the evaluation records. Learning it from the entire dataset allows information from evaluation data to influence the model-building process.
The same boundary matters for learned scaling and feature selection. A scikit-learn pipeline can keep preprocessing and model fitting together, including during cross-validation. It does not remove a refund column for you or choose a realistic time boundary: you must still design the inputs and split carefully.
Compare against a simple baseline
Consider a separate invented test set with 100 orders, of which 10 were returned. A rule that predicts “not returned” for every order gets 90 answers correct, but identifies none of the returns.
That arithmetic explains why accuracy alone can mislead. Record what happens to the cases you actually care about. In this example, examine missed returns and false alarms as well as the overall score. Decide which errors matter before presenting a model as useful.
What to include in your portfolio
Show the prediction question, the point in time when inputs are available, your split method, the baseline and the model’s errors. Explain one input you excluded and why. A reviewer should be able to understand the reasoning without relying on a screenshot of a large score.
Questions beginners often ask
Does high accuracy prove data leakage?
No. It is a reason to inspect the setup. Check the inputs, duplicated records, split design and evaluation process before drawing a conclusion.
Can a pipeline prevent every leak?
No. It helps keep learned preprocessing inside the training process. It cannot determine whether a business field reveals the outcome or whether your evaluation question matches real use.
What should I ask in a course demo?
Ask the trainer to explain a project’s split, baseline and common errors, and how learners receive feedback on those choices. ScholarLogic offers a Data Science course in Bangalore. Request a demo callback to confirm the current syllabus, batch availability and terms.
Further reading
- scikit-learn: common pitfalls and data leakage
- scikit-learn: getting started with pipelines
- scikit-learn: cross-validation and split design
