1. The Abalone Dataset available from the The University of California Irvine (UCI) Data Repository.

   Use the attribute age as the prediction target. After you run experiments predicting this attribute you may, if you wish, run additional experiments using a different predicting target of your choice.

2. The Automobile Dataset available from the The University of California Irvine (UCI) Data Repository.

   Use the attribute price as the prediction target. After you run experiments predicting this attribute you may, if you wish, run additional experiments using a different predicting target of your choice.

• Textbook: Read in great detail the following Sections from your textbook:
  • Numeric Predictions: Sections 4.6, 6.5, 5.8.

• Weka Code: Read the code of the relevant techiques implemented in the Weka system. Some of those techniques are enumerated below:
  • Numeric Predictions:
    • M5PRIME: Linear Regression, Regression Trees, Model Trees

Experiments:

• A main part of the project is the PREPROCESSING of your dataset. You should apply relevant filters to your dataset before doing the mining and/or using the results of previous mining tasks. For instance, you may decide to remove apparently irrelevant attributes, replace missing values if any, etc. Missing values in arff files are represented with the character "?". See the weka.filter.ReplaceMissingValuesFilter in Weka. Play with the filter and read the Java code implementing it. Your report should contain a detailed description of the preprocessing of your dataset and justifications of the steps you followed. If Weka does not provide the functionality you need to preprocess your data as you need to obtain useful patterns, preprocess the data yourself either by writing the necessary filters (you can incorporate them in Weka if you wish).

• To the extent possible/necessary, modify the attribute names and the nominal value names so that the resulting models are easy to read.

• You may restrict your experiments to a subset of the instances in the input data IF Weka cannot handle your whole dataset (this is unlikely). But remember that the more accurate your models, the better.

• INDIVIDUAL PROJECT AND WRITTEN REPORT.

  Your individual report should contain discussions of all the parts of the individual work you do for this project. In particular, it should elaborate on the the following topics:

  1. Code Description: Describe algorithmicly the Weka code of the 3 methods covered by this project: LINEAR REGRESSION, MODEL TREES, AND REGRESSION TREES; and filters that you use in the project. More precisely, explain the ALGORITHM underlying the code in terms of the input it receives, the output it produces, and the main steps it follows to produce this output. PLEASE NOTE THAT WE EXPECT A DETAIL DESCRIPTION OF THE ALGORITHMS USED NOT A LIST OF OBJECTS AND METHODS IMPLEMENTED IN THE CODE.

  2. Experiments: For EACH EXPERIMENT YOU RAN describe:
     • Instances: What data did you use for the experiment? That is, did you use the entire dataset of just a subset of it? Why?
     • Any pre-processing done to the data. That is, did you remove any attributes? Did you replace missing values? If so, what strategy did you use to select a replacement of the missing values?
     • Your system parameters.
     • For each of the methods covered by this project (i.e. LINEAR REGRESSION, MODEL TREES, AND REGRESSION TREES):
       • Results and detail ANALYSIS of results of the experiments you ran using different ways of testing (split ratio and N-fold cross-validation) the model.
       • Error measures of the resulting models.
       • Comparison of the results across the 3 numeric prediction methods used in this project.

  3. Summary of Results
     • For each of the datasets, what were the lowest error values obtained in your project? Include this model in your report.
     • Strengths and weaknesses of your project.

• GROUP PROJECT AND WRITTEN REPORT. Your group report should contain discussions of all the parts of the group project. In particular, it should elaborate on the the following topics:

  1. Experiments: as described in the individual part above. For the group part, start by running experiments that build upon the experience that you gained with the individual projects.

     Once that are done with these join experiments, investigate the pruning method used in M5PRIME (i.e. when the option UNPRUNED=FALSE is chosen from the M5PRIME GUI). Describe in detail how the pruning is done and run experiments to see how it modifies the resulting tree and how this affects the error values reported for the tree.

     Investigate also the smoothing method used in M5PRIME (i.e. when the option useUNSMOOTHED=FALSE is chosen from the M5PRIME GUI). Describe in detail how the smoothing is done and run experiments to see how it modifies the resulting tree and/or how it affects the error values reported for the tree.

  2. Summary of Results as described in the individual part above

• GROUP ORAL REPORT. We will discuss the results from the individual projects during the class on Monday, October 4. Your oral report should summarize the different sections of your written report as described above. Each group will have about 4 minutes to explain your results and to discuss your project in class. Be prepared!

Please submit the following files using the myWpi digital drop box:

1. [lastname]_proj3_report.[ext] containing your individual written reports. This file should be either a PDF file (ext=pdf), a Word file (ext=doc), or a PostScript file (ext=ps). For instance my file would be named (note the use of lower case letters only):
   • ruiz_proj3_report.pdf

   If you are taking this course for grad. credit, state this fact at the beginning of your report. In this case you submit only an individual report containing both the "individual" and the "group" parts, as you are working all by yourself on the projects.

2. [lastname1_lastname2]_proj3_report.[ext] containing your group written reports. This file should be either a PDF file (ext=pdf), a Word file (ext=doc), or a PostScript file (ext=ps). For instance my file would be named (note the use of lower case letters only):
   • ruiz_smith_proj3_report.pdf if I worked with Joe Smith on this project.

3. [lastname1_lastname2]_proj3_slides.[ext] (or [lastname]_proj3_slides.[ext] in the case of students taking this course for graduate credit) containing your slides for your oral reports. This file should be either a PDF file (ext=pdf) or a PowerPoint file (ext=ppt). Your group will have only 4 minutes in class to discuss the entire project (both individual and group parts, and classification and association rules).