PART 1: CLASSIFICATION WITH NEURAL NETWORKS

This part involves the following file:
heart-v.arff
in the directory:
/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/arff/UCI/

For the neural network training runs build a table with the following headings:

1. Describe the data encoding that is required for this task. How many outputs and how many inputs will there be?

2. Develop a script to generate the necessary training, validation and test files. You might want to normalize the numeric attributes with Weka beforehand.

3. Using Javanns carry out 5 train and rest runs for a network with 10 hidden nodes. Comment on the variation in the training runs and the degree of overfit- ting.

4. Experiment with different numbers of hidden nodes. What seems to be the right number of hidden nodes for this problem?

5. For 10 hidden nodes, explore different values of the learning rate. What do you conclude?

6. [Optional] Change the learning function to backprop-momentum. Explore dif- ferent combinations of learning rate and momentum. What do you conclude?

7. Perform a run with 10 hidden nodes and no validation data. Stop training when the MSE is no longer changing. Get the classification error on the training and test data. Comment on the degree of overfitting.

8. Compare the classification accuracy of the neural classifiers with the classifica- tion accuracy of Weka J48 and MultilayerPerceptron.

PART 2: NUMERIC PREDICTION WITH NEURAL NETWORKS

This part involves the following file:

heart-v.arff
in the directory:
/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/arff/UCI/

The task is to predict the value of the Age variable.

Build a similar table of runs to the one in the previous question.

1. Describe the data encoding that is required for this task. How many outputs and how many inputs will there be? What scaling or normalization is required?

2. Modify your script from part 1 to generate the necessary training, validation and test files.

3. Using Javanns carry out 5 train and rest runs for a network with 5 hidden nodes. Comment on the variation in the training runs and the degree of overfitting.

For this and subsequent tasks you can work with the network error directly. There is no need to re-scale the network outputs to the original range.

4. Experiment with different numbers of hidden nodes. What seems to be the right number of hidden nodes for this problem?

5. For 5 hidden nodes, explore different values of the learning rate. What do you conclude?

6. [Optional] Change the learning function to backprop-momentum. Explore dif- ferent combinations of learning rate and momentum. What do you conclude?

7. Perform a run with 5 hidden nodes and no validation data. Stop training when the MSE is no longer changing. Get the classification error on the training and test data. Comment on the degree of overfitting.

8. Compare the classification accuracy of the neural classifiers with the classifica- tion accuracy of Weka M5P and MultiLayerPerceptron..

PART 3: DATA MINING

Choose EITHER the bank data OR the movies data.
The relevant files are: bank-full.csv bank-names.txt
OR
IMDB-movie-data.csv
in the directory
/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/other
The bank data is related to direct marketing campaigns of a Portuguese banking in- stitution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

The movie data is was collected from the IMDb web site which claims to be "the world's most popular and authoritative source for movie, TV and celebrity content". It was collected to answer the question " How can we tell the greatness of a movie before it is released in cinema?" There is a full description at "https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset"
IMDB-movie-data.csv has some changes from the kaggle file, mostly to make the genre information more usable.

Your task is to analyze the data with appropriate data mining techniques and identify any "golden nuggets" in the data. You are expected to use classification, clustering, association finding, attribute selection and visualization in your analysis, or to explain why a particular technique is not relevant.

Attachment:- sample format and data files.zip