| University | National University of Singapore (NUS) |
| Subject | Machine Learning |
- Explore, prepare, and transform the data to facilitate predictive modeling. Here are some hints: In exploratory modeling, it is useful to move fairly soon to at least an initial model without solving all data preparation issues. One example is the GPS information—other geographic information is available so you could defer the challenge of how to interpret/use the GPS information.
- How will you deal with missing data, such as cases where NaN is indicated? Think about what useful information might be held within the date and time fields. The data file contains a worksheet with some hints on how to extract features from the date/time field.
- Think also about the categorical variables, and how to deal with them. Should we turn them all into dummies? Use only some?
- Fit ANY TWO predictive models of your choice, one of which must be a neural network. Do they provide information on how the predictor variables relate to cancellations?
- Report the predictive performance of your model in terms of error rates. How well does the model perform? Can the model be used in practice?
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