1. The World Happiness Dataset. See also the World Happiness Dataset with Continents information added by Paul Sader and converted into arff. Thanks, Paul!!

   Use the attribute SWL-index 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. Since the SWL-ranking can be derived from SWL-index, remove SWL-ranking from consideration. Also, remove the attribute country as each of its values identifies an instance uniquely. Note that the access-to-education-score attribute contains missing values, marked with ".". Remember to replace them with "?" in your arff file.

2. Together with your project partner, choose one dataset from the following options:

   • 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.

   • 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.

   • A dataset of your choice. This dataset can be one available on a public, online data repository (including but not limited to the datasets used on Projects 1 or 2) or any other valid source. The dataset should contain at least 500 data instances with at least 5 different attributes (ideally some numeric and some nominal). For ideas, see the Data Sets listed on the course webpage/syllabus.

• 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:
    • Linear Regression (under "functions")
    • M5P (under "trees"): 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.

• Ideas for making this project even more interesting:
  • Start by choosing a dataset in a domain that excites you.
  • Before you start running experiments, look at the raw data in detail. Figure out 5 or more specific, interesting questions that you want to answer with your experiments. These questions may be phrased as conjectures that you want to confirm/refute with your experimental results. Sample questions on the World Happiness are: what's the relative importance of access to education, per-capita income, and life expectancy in satisfaction with life? What factor is most important? Are there general trends by continent?

    If you pursue this last question, add continent information to your dataset. The first group/individual to submit a correct dataset including continent information to the course mailing list will receive a 25 points bonus. To make it standard, let's assume there are 6 continents: Antarctica, Americas, Europe, Asia, Africa, Australia.

    Paul Sader was the first to submit a correct dataset with continent information added to it. Thanks, Paul!

  • Design your preprocessing and experiments around answering these 5 questions.
  • Analyze your resulting models in the light of your 5 questions.

• WRITTEN REPORT.

  Your project report should contain discussions of all the parts of the 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:
     • 5 or more specific questions/conjectures about the dataset domain that you aim to answer/validate with your experiments. See the section on "Ideas for making this project even more interesting" above.
     • 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.
       • Detailed analysis of the models obtained. Examine the linear regression formulas and the trees obtained. Elaborate on interesting attribute weights in those formulas, as well as presence/absence of predicting attributes in the trees. Do these models answer any of your 5 questions? If so, what's the answer? If not, why not and what modifications to the experiments are needed to answer those questions?
     • Investigate the pruning method used in M5P (i.e., when the option UNPRUNED=FALSE is chosen from the M5P GUI), which is explained in your textbook. 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.

  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 as well as interesting aspects of this model.
     • Strengths and weaknesses of your project.

• ORAL REPORT. We will discuss the results from the individual projects during the class on Tuesday, Dec. 5. 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] for those of you who are working alone on this project. If you are taking this course for grad. credit, state this fact at the beginning of your report. 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

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.