Reference no: EM132308740

Assignment - Weka Data Mining

Task - Assessment Description: Weka Data Mining Practical and Report

This assignment consists of two parts.

Part 1: Comparison of classification algorithms on a nominal data set. There are two steps to perform in part 1.

Step 1: In this step, you are required to perform a data mining task to evaluate two different classification algorithms on a nominal data set. Load the contact-lenses.arff data set into Weka and compare the performance on this data set for the following algorithms:

• Decision Tree - J48 algorithm
• Naive Bayes

Step 2: From step 1 outputs, write a report that shows the performance of the two algorithms and comment on their accuracy using the confusion matrix and other performance metrics used in Weka. In your report consider:

• Is there a difference in performance between the algorithms?
• Which algorithm performs best?

Your report should include the necessary screenshots, tables, graphs, etc. to make your report understandable to the reader.

Part 2: Classification on a numeric data set.

Load the cpu.arff data set into Weka. Run the Linear Regression algorithm on this data set and answer the following questions:

1. Write down the linear regression model generated by the algorithm.

2. List the weights that have been assigned to the attributes.

3. What is the value of the root mean squared error? Explain the significance of this in the regression model.

4. What is the value of the correlation coefficient? Explain the significance of this in the linear regression model.

5. What measure has been taken in this algorithm to ensure that the training and testing data sets are not biased or over-fitted?

Rationale - This assessment task will assess the following learning outcome/s:

• be able to identify and analyse business requirements for the identification of patterns and trends in data sets.
• be able to appraise the different approaches and categories of data mining problems.
• be able to compare and evaluate output patterns.
• be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements.
• be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set.

These tasks aim to assess your progress towards:

• be able to identify and analyse business requirements for the identification of patterns and trends in data sets;
• be able to appraise the different approaches and categories of data mining problems;
• be able to compare and evaluate output patterns;
• be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements;
• be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set;
• be able to explain the importance of current and future trends likely to affect data mining and visualisation.