elena-krismer / south-african-heart-disease Goto Github PK

• Describe if the attributes are discrete/continuous, Nominal/Ordinal/In-terval/Ratio,
• Give an account of whether there are data issues (i.e. missing values or corrupted data) and describe them if so.�
• Include basic summary statistics of the attributes.

If your data set contains many similar attributes, you may restrict yourself to describing a few representative features (apply common sense

Summarize here the most important things you have learned about the data and give also your thoughts on whether your primary machine learning aim appears to be feasible based on your visualization.

Data visualization(s) based on suitable visualization techniques including a principal component analysis (PCA). Touch upon the following subjects, use visualizations when it appears sensible. Keep in mind the ACCENT principles and Tufte's guidelines when you visualize the data.�

• Are there issues with outliers in the data,�
• do the attributes appear to be normal distributed,�
• are variables correlated,�
• does the primary machine learning modeling aim appears to be feasible based on your visualizations.

There are three aspects that need to be described when you carry out the PCA analysis for the report:�The amount of variation explained as a function of the number of PCA components included,�the principal directions of the considered PCA components (either nd a way to plot them or interpret them in terms of the features),�the data projected onto the considered principal components. If your attributes have different scales you should include the step where the data is standardized by the standard deviation prior to the PCA analysis.

• Explain what your data is about. I.e. what is the overall problem of interest?�
• Provide a reference to where you obtained the data.�
• Summarize previous analysis of the data. (i.e. go through one or two of the original source papers and read what they did to the data and summarize their results).�
• You will be asked to apply (1) classification and (2) regression on your data in the next report. For now, we want you to consider how this should be done. Therefore: Explain, in the context of your problem of interest, what you hope to accomplish/learn from the data using these techniques?. Explain which attribute you wish to predict in the regression based on which other attributes? Which class label will you predict based on which other attributes in the classification task? If you need to transform the data in order to carry out these tasks, explain roughly how you plan to do this.

One of these tasks (1){(5) is likely more relevant than the rest and will be denoted the main machine learning aim in the following. The purpose of the following questions, which asks you to describe/visualize the data, is to allow you to reject on the feasibility of this task