Assignment: Application of Research

Assignment: Application of Research

Assignment: Application of Research

  • The sections below are specific to the application of the algorithm to the dataset of your choice


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  • Dataset description and link to exact location obtained from (1 paragraph)
  • Important notes, as applicable, describing what you learnt by applying the algorithm to the specific dataset (max 1 page)
  • Supporting diagrams and additional notes (e.g. suggested further research)

ADA-II HW Instructions for Algorithm Research assignments v.20190219 Complete research on the assigned algorithms, as shown by the respective assignment description under NYU Classes. Important changes to this document from its previous version are highlighted. → Your documents should be concise and only to the point (not a story-like narrative) → If any part of your document contains exact wording from a source this wording needs to be in quotes and a footnote number linking to your source document which should be listed in the references section C. If this is not included your submission grade will be reduced and your document will be further reviewed for plagiarism. Submit one set of these four (4) files for each algorithm, as noted in the respective assignment: Please note that “n” in the filename refers to the Homework code (3, for HW3, etc.) a) An MS Word document with your research results. Filename: HWn_AlgorithmName.DOCX • • Use single spacing, font Calibri, size 11 throughout. Include only the Section ID and your answers. Do not include the title of each section in your answer and do not use tables for section 1 or any other formats, e.g. your file should look like: 1. Simple Linear Regression 2. Method for building a model that characterizes the relationship between a dependent variable (y) and one explanatory (independent) variable (x). All data must be numerical. The dependent variable must be a ratio (not categorical)…… 3. …. b) A Jupyter Notebook with python code demonstrating use of the algorithm. Filename: HWn_AlgorithmName_code.IPYNB • • • Include a header with your name, the algorithm name and a link to the dataset used. Add comments and use Markdown to compile a professional looking notebook. Explain your findings as appropriate. c) A PDF version of your Jupyter Notebook. Filename: HWn_AlgorithmName_code.PDF To create a PDF file, 1. Add a blank line at the end of each cell in your notebook 2. Ensure that your code and comments do not exceed the width of a printable page (use a new line in the cell when necessary) 3. Print your Notebook as PDF (Command-P on the Mac) 1 d) The file with the dataset used, in its initial form (as downloaded from the link in the Notebook). Filename: HWn_YourLast_FirstName_AlgorithmName_datafile.CSV • Please note that if you make any data preprocessing and transformations, the respective code needs to be included in the Notebook. The code needs to run without any issues using the publicly available dataset. Contents of the MS Word document: Do not include the titles, only the section number: Section A – Research: 1. Algorithm name 2. Description in plain language (half page). Include cases used for. 3. Mathematical formula or formulae 4. Datasets it can be applied to: a. List types of data b. State minimum number of observations it requires, other constraints c. Describe restrictions (1 paragraph) 5. Assumptions: a. Describe when it cannot work effectively (1 – 2 paragraphs) b. Describe other potential limitations of the algorithm (1 – 2 paragraphs) 6. Implementation: a. Python Library used b. List function / method names used to run the algorithm 7. Evaluation: a. Assignment: Application of Research

Describe how to evaluate results when running it (approximately 1 paragraph or more) b. Describe meaning of results (approximately 1 paragraph or more) c. List function / method names used to evaluate the algorithm → All previous sections are notes of your research for the algorithm itself, not its application on a specific dataset. They should be comprehensive Section B – Application of Research (related to the dataset and code used for the algorithm): → The sections below are specific to the application of the algorithm to the dataset of your choice 2 8. Dataset description and link to exact location obtained from (1 paragraph) 9. Important notes, as applicable, describing what you learnt by applying the algorithm to the specific dataset (max 1 page) 10. Supporting diagrams and additional notes (e.g. suggested further research) Section C – references: Copy/paste the table below in your answer to add a simple listing of links to online or other resources used in each applicable section above: Section number: links. Include notes as needed. If needed, expand each section to use one line per link. Section Link(s)


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