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Documenting Data Analysis Procedures and Evaluating Machine Learning Models

After completing the data analyses and refining the literature review based on your understanding of the information that you use to develop the proposed models, in this stage of the project, you need to document the data analysis procedures and evaluate the developed machine learning models from three areas:

  • Effectiveness, by selecting and applying a set of valid measures to test the performance of the models
  • Efficiency, by measuring the time each model took to be trained or tested
  • Stability, by ensuring that each model generates robust and significant results

Before writing up and interpreting the findings, you need to document the statistical analyses that have been used to compare the developed models, to decide which model outperforms the others. Notably, there should be a section covering the analysis limitations, study implications, ethical considerations, project continuity, and critical insights on improving the work.

Now for the final report writing: I am suggesting a layout here, you might want to follow if you like. You may create and organize the sections as follows:

Title page

Introduction (describe the problem you are addressing, its context in real world, your perspective and the research questions) 

Literature review (or Related work) updated with corrections as suggested 

Dataset description including your workflow (as description or as chart/diagram) 

Results and Discussions: Subsections (1) Exploratory investigations (2) Machine Learning work (Classification or Regression studies whatever you are doing) (3) Performance measure (Try to mention the rules and formulae you are using, you may also like to put the Confusion Matrix figure etc.) [You may also create a separate discussion subsection if you want] 

Summary or Conclusion (describing what you have achieved and how does it compare with any published result, if it is there) 


There is as such no page limit. However, please do not insert your data files and codes in the body of the report. Try to include the results as Tables or Graphs as much as possible, and avoid putting them as texts, 

  • Combining the literature review with the quantitative analyses
  • Building and evaluating the proposed modes from different perspectives
  • Listing the limitations of the work
  • Revisiting the research questions and work implications to interpret the results

By the end of the module, you will be able to:

  • Highlight the differences between your results and similar ones in the literature review
  • Fully interpret the achieved results of your proposed methodology
  • Evaluate the shortcomings of your research and how to improve it
  • A revised version of the research questions
  • The main contribution of the work compared to past research
  • A link to the GitHub repository where the source code files are checked in—pls provide link herewill ad in repository
  • A more precise description of the applied methodology and the study design
  • The conducted analyses, including all activities and their business rules
  • A list of all the findings, including a detailed interpretation of the results of the applied techniques
  • The shortcomings of the work and concluding remarks on the continuity of the work
  • A references page, using APA style

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