MIT-xPRO-DSxCase-Study-2.4-Predicting-Wages-2. Case Study 2.4: Predicting Wages II Instructor: Victor Chernuzkov Activity Type: Optional Case Study Description: Predict wages using several machine learning methods and data splitting. Why this Case Study? Participants can apply several machine learning methods to any prediction problem. They can also obtain out-of-sample prediction to assess their models. Self-Help Package Contents: The video that covers this case study is given in Module 2, Segment 3.3. Self-Help-Package.zip Codebook.txt contains the name of the variables and a brief description. wage2015.Rdata: The dataset contains the variables used in the prediction analysis. Regression3.3.CaseStudy.R: performs a prediction analysis where the weekly wage is predicted using several demographic and job-related characteristics. Several machine learning methods are used for prediction and their performance is compared based on out-of-sample mean squared error and R-squared. The final part of the program aggregates the predictions obtained by each machine learning method. Regression.3.3.pdf is the set of slides that describes the estimation technique and present the results. .Rhistory Time Required: The time required to do this activity varies depending on your experience in the required programming background. We suggest planning somewhere between 1 & 3 hours. Remember, this is an optional activity for participants looking for hands-on experience.