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README.md

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 # Scientific Context
+Behavioral observation and data collection is an integral part of maintaining animals in captivity. Large corporations, such as zoos, rely on staff members to observe animal behaviors as a means of assessing their health. However, without constant surveillance, key behavioral patterns can be overlooked. Machine learning provides a unique opportunity to reduce the time-commitment of staff, increase monitoring time, and produce visualizations that provide valuable insights. 
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+Currently, there are a few major products that utilize machine learning methods for behavioral observation in captivity. These models are insightful, but lack the accessibility and customization of an open source model. 
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+My dataset, consisting of Southern Sea Otter enclosure live feeds, will serve as an example of how scientists could use my model to gain behavioral information. From the model outputs, users can track, county, and study long term species patterns across multiple enclosures. These results can then aid in making care-related decisions.
 
 
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