What This Document Is
This document is the eighth assignment for Pace University’s Data Mining (CS 619) course. It focuses on applying the Naive Bayes classification algorithm to a dataset concerning tennis game predictions based on weather conditions. The assignment requires both manual calculation and implementation using the WEKA data mining tool.
Why This Document Matters
This assignment is intended for students enrolled in the CS 619 Data Mining course. It serves as a practical exercise to reinforce understanding of the Naive Bayes algorithm, data preparation for WEKA, and interpreting classification results. Successful completion demonstrates the ability to apply theoretical knowledge to a real-world predictive modeling problem.
Common Limitations or Challenges
This assignment focuses specifically on the Naive Bayes algorithm and its application to a single dataset. It does not cover other classification methods or more complex data mining techniques. Students will need prior knowledge of probability and basic data manipulation skills.
What This Document Provides
The full document includes: a dataset of tennis play conditions, instructions for manual Naive Bayes calculations to predict tennis play based on outlook and wind, guidance on converting the data into the ARFF format for use with WEKA, and questions prompting analysis of WEKA’s output including training/test split, classification accuracy, mean absolute error, and confusion matrix interpretation. This preview only describes the assignment’s scope and purpose. It does *not* include the dataset, detailed WEKA instructions, or answers to the assignment questions.
