Improvements to Malware Detection and Classification - Data Analysis using PCA - Computer Science Assignment Help

Assignment Task:

Part 1 – Data analysis using PCA and report on findings

a) You must clean and normalize the data to make it usable in “R”. 
- To run PCA on this data set, you will need to address the NAs appropriate prior to PCA. You will need to convert the category variables into something usable noting that PCA will only accept numerical values. Hint: Google the terms “dummy variables”. 
- Briefly report on any data manipulation that was required to make the data usable in R. 
- Export your “cleaned” data as follows. 
#Write to a csv file. This will need to be submitted write.csv(mydata,"mydata.csv") 
b) Perform PCA on the 9 features (1st 9 columns in mydata) using prcomp(.) in R. 
- Outline the individual and cumulative proportions of variance explained by each of the first 3 components. 
- Outline the coefficients (or loadings) for PC1 to PC3. 
c) Create a scree plot and outline how many principal components do you believe are adequate to explain at least 50% of the variability in your data. 
d) Create a biplot with PC1 and PC2 to help visualize the results of your PCA in the first two dimensions. Colour code the points with the variable, Verified.as.Malware. Write a short paragraph to explain what your biplot is showing. 
e) Based on the results from parts b) to d), describe which dimension(s) (if any) can assist with the classification of malware and what are the key features in this dimension that can drive this process. 
f) Based on the features that you have identified in part e), 
- Cross-tabulate each of your categorical feature(s) (if any) against the malware variable, i.e. Verified.as.Malware and determine the count and percentage

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