Rapiedminer help

You will be working on a real dataset about SPAM emails. This dataset consists of the frequency of different words used in the emails. Each row represents one email. The last attribute “is_spam” represents whether the email is a SPAM email or not. 1. Import the data from [login to view URL] file into RapidMiner. 2. Measure the accuracy of Decision Tree without any data pre-processing. Also try to change the parameters of the classifier, and see which parameters give best results. 3. Measure accuracy after normalizing the data (using range and z-score normalizations). 4. Measure accuracy after discretizing the numeric attributes in 2, 3, and 4 bins (each). 5. Measure accuracy after reducing dimensions (use “Weight by ...” and “Select by weights” operators). Try different weighting schemes. 6. Measure accuracy by using all of the pre-processing steps excluding dimensionality reduction (tasks 3 to 4) 7. Measure accuracy by using all of the pre-processing steps including dimensionality reduction (tasks 3 to 5) 8. Try to find a combination of pre-processing steps which gives the best results. 9. Measure accuracy using Neural Network classifier and suitable data pre-processing steps. 10. Measure accuracy using any 3 classifiers not used in previous tasks using suitable data pre-processing steps. 11. For measuring accuracy in all the steps listed above, use 10-fold cross validation (X-Validation). Use your student id as the seed in randomization (wherever possible) 1. Rapidminer process files (rmp file) for each task. Name of the file should be “[login to view URL]”. In case a task has multiple sub tasks (like tasks 3, 4, 5, 8, and 10) the names should be like “[login to view URL]”, “[login to view URL]” “[login to view URL]”, ... 2. Write a report about the dataset and its characteristics. Discuss in detail which pre-processing steps were useful and which classifier produced the best results. The report should also include results of all the tasks in form of a table. You can generate the plots for comparing results using Microsoft Excel. 3. PowerPoint presentation of your project.