Machine Learning Case Studies
Machine Learning Case Studies
Short description explaining what the learner will understand after completing this study map.
Thinking Through a Machine Learning Case
A machine learning case rarely tests one concept in isolation. A practical scenario may require you to identify the problem, inspect the available data, determine the learning approach, choose a suitable algorithm family, evaluate results, and explain what should happen next.
Core idea: follow the scenario logically from problem to outcome. Do not choose an algorithm before understanding the target, labels, data quality, and type of result required.
Case-Study Decision Clues
Tap each card to review clues that help identify the correct next step in a machine learning scenario.
Case Study 1 — Predicting House Prices
A property company wants to estimate the selling price of a house from features such as size, location, age, and number of rooms. Historical examples include the actual selling price. This is labelled data with a numerical target, so the task is supervised regression.
Case Study 2 — Customer Segmentation
A retailer has customer purchase information but no predefined customer categories. It wants to discover groups of customers with similar behaviour. Because there are no target labels and the goal is to discover groups, this is an unsupervised clustering problem.
Useful Clue
No predefined categories are supplied. The system must discover the groups from similarities in the data.
Possible Algorithms
K-means or hierarchical clustering can be considered when the goal is to group similar observations.
Case Study 3 — Email Classification
An organization has emails labelled as spam or not spam and wants to classify new messages. The known categories make this a supervised classification problem. Suitable algorithm families can include K-nearest neighbors, decision trees, or logistic regression.
Case Study 4 — A Model Performs Poorly
A team trains a model and obtains excellent results on its training data, but performance drops substantially on unseen test data. This pattern can indicate overfitting. The team should review the model, features, data, settings, and evaluation process rather than relying on training performance alone.
Case-Study Explorer
Explore common scenario clues and connect them to the appropriate machine learning decision.
Key Takeaways
Focus on the evidence in the scenario: target type, labels, desired output, data quality, model behaviour, and stakeholder needs.
Core pattern: define the objective → understand and prepare the data → identify the learning type → choose a suitable algorithm → train and test → evaluate → communicate.
Common mistake: do not jump directly from a scenario keyword to an algorithm. First determine whether the required output is numerical, categorical, or an undiscovered grouping, and whether labels are available.
Knowledge Check
These questions combine concepts from across the learning material. Read each scenario carefully and identify the most appropriate interpretation or next step.