Do students train cricket prediction models and use PKL files?

Students can use historical cricket data to frame predictive or classification questions, prepare features, train multiple candidate models and compare performance. Depending on the task, a model may estimate a probability, classify a situation, predict a performance range or support a larger decision system. Learners evaluate metrics, inspect errors and document limitations before choosing a final model. The selected pipeline can be exported as a PKL artifact and loaded by an API service so that predictions can be generated from validated requests. Students are also taught that a PKL file is not proof of a good model by itself; the artifact must be linked to reproducible training code, evaluation evidence, compatible dependencies and a clear description of expected inputs and outputs.