  The research on mortality is an active area of research for any country where
the conclusions are driven from the provided data and conditions. The domain
knowledge is an essential but not a mandatory skill (though some knowledge is
still required) in order to derive conclusions based on data intuition using
machine learning and data science practices. The purpose of conducting this
project was to derive conclusions based on the statistics from the provided
dataset and predict label(s) of the dataset using supervised or unsupervised
learning algorithms. The study concluded (based on a sample) life expectancy
regardless of gender, and their central tendencies; Marital status of the
people also affected how frequent deaths were for each of them. The study also
helped in finding out that due to more categorical and numerical data, anomaly
detection or under-sampling could be a viable solution since there are
possibilities of more class labels than the other(s). The study shows that
machine learning predictions aren't as viable for the data as it might be
apparent.
