Abstract a. Maximum number of words: 120 words 3. Introduction a. Objective of the coursework b. An overview of the coursework 4. Methodology a. Introduction to the chosen techniques b. Discussion of the reasons why you chose for comparison. 5. Simulations a. Introduction of the dataset b. Input encoding / input representation (How and why?) c. Procedures or how the implementation was carried out d. Explain the reasons why the particular software or tool was chosen, this may include comparisons with other tools Note: The dataset you use in the implementation algorithm should consist of at least 100 instances or more than 200 for a dataset with only a few attributes. Here are some online dataset repositories, feel free to use one or come up with your own: UCI KDD Archive: https://kdd.ics.uci.edu/ UCI Machine Learning Repository: https://www.ics.uci.edu/~mlearn/MLRepository.html Kdnuggets Dataset Achieve: https://www.kdnuggets.com/datasets/index.html 3 6. Results and Analysis a. Detailed analysis of the results, such as compare, contrast between the results obtained from the simulations of the two types of BI Analysis techniques with the appropriate screen dumps b. Problems encountered and the solutions. 7. Conclusions a. Conclusions and comments 8. References and Formatting See the end of this assessment brief for the format of the research paper. Formatting your page: Top & Bottom Margins: 2.5cm Left & Right Margins: 2.5cm All text after Abstract must be in a two column Format, single-spaced in 12 point Times New Roman. Please do not place any additional blank Lines between paragraphs. Columns are to be 7.6 cm wide, with a 0.8cmspace between them. Text must be fully justified.
Write an Introduction of the Kdnuggets Dataset Achieve: https://www.kdnuggets.com/datasets/index.html