Complete the following steps:
- Open the following training dataset ( ) in your Excel spreadsheet program and try to understand the attributes (dependent & independent variables).
- Open the following testing dataset ( ) in your Excel spreadsheet program and try to understand the attributes (dependent & independent variables).
- Import both of them into the RapidMiner repository as I did in my video lecture (Predictive Modeling – Linear Regression).
- Add them to a new process and rename them as “Training dataset” and “Scoring dataset” so you can tell them apart.
- Use a Set Role operator to designate the MPG attribute as the label for the training data.
- Add a linear regression operator and apply your model to your scoring dataset.
- Run your model.
- In the Results perspective, examine your attribute coefficients and the predictions for the cars in your scoring dataset.
Report your results:
- Which attributes have the most significant predictive power?
- Were any attributes dropped from the dataset as non-predictors? If so, which ones and why do you think they were not useful predictors?
- Compare the predicted MPG values to the actual MPG values in the scoring dataset. How close are the predictions?
- On average, how far off are your model’s predictions?
- What other attributes do you think would help your model better predict fuel efficiency
Complete on Word Doc as well
1st assign
2nd assign
