While carrying out the storm casualty’s project, the statistical
analyses techniques and/or the machine learning techniques that
were investigated were evaluated in detail. The first dataset
contained the storm details and it encompassed 51 variables and
56,921 cases. The data set was examined with the help of R. It helped
to identify that there were numerous missing values. For example, in
‘category’ there were 56,000 missing variables. For addressing the
issue, I eliminated the variables with over 40 % missing values. The
second database contained information relating to storm fatalities
during the year 2017. It was much smaller than the first dataset as it
contained only 775 cases and 11 variables. While examining the data
it was revealed that out of 775 victims, 490 were male. The third
dataset used in the project contained information relating to the
location along with individual storm episodes. It contained 43,579
cases and 11 variables. f R was used as the main tool to merge the
three datasets together so that a comprehensive insight into the
storms and associated casualties could be captured. R was used as
one of the main techniques in the project as it provided a robust
framework to carry out tasks such as handling of missing variables as
well as feature creation. In addition to R, SAS Enterprise Miner was
also used as a useful technique to compute diverse models in an
effective and efficient manner. The evaluation of the diverse
techniques in the project was extremely useful as it helped to merge
the available data and get a bigger picture relating to the storm
casualties pattern. The use of these techniques acted as the core
foundation upon which the five predictive models were based upon.
The predictive models used in the study include logistic regression,
neural network, support vector machine, random forest and ensemble
model.
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