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The research on mortality is an active area of research for any country wherethe conclusions are driven from the provided data and conditions. The domainknowledge is an essential but not a mandatory skill (though some knowledge isstill required) in order to derive conclusions based on data intuition usingmachine learning and data science practices. The purpose of conducting thisproject was to derive conclusions based on the statistics from the provideddataset and predict label(s) of the dataset using supervised or unsupervisedlearning algorithms. The study concluded (based on a sample) life expectancyregardless of gender, and their central tendencies; Marital status of thepeople also affected how frequent deaths were for each of them. The study alsohelped in finding out that due to more categorical and numerical data, anomalydetection or under-sampling could be a viable solution since there arepossibilities of more class labels than the other(s). The study shows thatmachine learning predictions aren't as viable for the data as it might beapparent.

Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA