1. Dene the problem - Write down the problem statement and understand what you 're trying to solve. In this case, your objective is to predict whether a student will get an admit or not. So, it means, that this is a binary classication problem. 2. Generate your own hypothesis - Next, list down all the things that you think can affect your objective viz. List down all the possible features with respect to your target feature. In your case, you ask this question What are the factors that can affect a student's admission? Take a piece of paper and write down your own hypothesis. Examples may be the following features GRE Score TOEFL Score Statement of Purpose [SOP] Letter of Recommendation {LOR} Academic Performance (GPA) Extra Curricular Activities (Sports, Math Olympiad etc.) Outstanding Achievements Projects and Research Make sure that you write down at least 1020 for any problem statement. This helps you get a deeper understanding of what you are trying to solve and prompts you to think beyond the available dataset. 3. The Dataset You will be using UCLA hypothetical data for graduate admissions (see Excel workbook provided}. You can now map your hypothesis with the given dataset. Identify each feature as either (a) Continuous Variable or (b) Categorical Variable