The machine learning and modeling case study is the most common type of interview question that tests a combination of modeling intuition and business application. This type of interview question is frequently broken down into different parts, in which an interviewer will first ask a very broad question about building a model for a product feature.

We want to approach the case study with an understanding of what the machine learning & modeling lifecycle should look like from beginning to end, as well as creating a structured format to make sure we’re delivering a solution that explains our thought process thoroughly.

For the machine learning lifecycle, we have around six different steps that we should touch on from beginning to end:

• Data Exploration & Pre-Processing
• Feature Selection & Engineering
• Model Selection
• Cross Validation
• Evaluation Metrics
• Testing and Roll Out

We’ll dive into how to tackle each part in the ensuing chapters.