In this case, you will learn to use RapidMiner to analyze the price in the ridesharing services. The case data file has 19,160 records of rideshares from Lyft and Uber in the Boston area, matched with weather data, day and time. There are several things you need to do with the data:
1. Preparing the data for modeling.
2. Setting up the training model and cross validation.
3. Evaluating the model results.
4. Applying the model to a new dataset.
You will find two datasets on Bb. For steps 1-3 in the case study, you will need the “Rideshare” dataset, the one with 19,160 observations. In step 4 (applying the model to a new dataset), you will need the “Rideshare New Data”.
After running regression on the data, you need to analyze the coefficents and p value of variables to find what factors influence the price of ridesharing services the most. Please include the results and your analysis in the slides.
