Your first machine learning project should be simple and complete

Instead of saying you are building a churn model, define who would use the output and what they could do differently. A decision gives you a prediction horizon, useful features, a cost of errors and a way to judge whether the work matters.

Keep the first scope narrow. Tabular classification or regression with a well-understood dataset is enough to practise the complete workflow.

Before modelling, write a provisional success rule: what must improve over the baseline, which error is most costly and what action the prediction would trigger. You can revise the numbers later, but naming the trade-off prevents a high score from becoming the only definition of success.

A baseline tells you whether your model adds value. It may be the majority class, a simple average or a basic linear model. Record it before trying more flexible methods.

Split the data carefully and keep the test set separate. If time or groups matter, use a split that reflects how the model would encounter future data.

Use the training data to fit models and the validation data to compare experiments. Leave the test set untouched until you have chosen the final approach; checking it repeatedly quietly turns it into another validation set and makes the reported result less trustworthy.

Build a reproducible preprocessing and modelling pipeline. Then change one meaningful variable at a time: a feature set, model family or parameter group. Record each result so your final choice has a reason.

Inspect errors, not only the headline score. The examples a model gets wrong often reveal missing context, data quality problems or groups that need separate attention.

A single metric can hide where a model works and where it breaks. Compare performance across the slices that matter to the decision: common and rare cases, important user groups, recent and older records, or low- and high-value outcomes. Use only slices that contain enough examples to interpret responsibly.

Then inspect the decision threshold, not only the model ranking. Show how raising or lowering it changes false positives, false negatives and the number of cases someone would need to review. The best threshold is the one that fits the real constraint, not the default value from a library.

Explain whether the result is good enough for the stated use, which errors remain risky and what you would test next. Include a simple visual or interface only if it helps someone understand or use the output.

Finally, rehearse a three-minute walkthrough: problem, data, baseline, improvement, evaluation, limitation and next step. That story is the real portfolio asset.