The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees. They asked to provide data-driven suggestions based on understanding of the data. They have the following question:
"What’s likely to make the employee leave the company?"
Our goals in this project are to analyze the data collected by the HR department and to build a model that predicts whether or not an employee will leave the company.
If we can predict employees likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company.
TikTok is the leading destination for short-form mobile video. The platform is built to help imaginations thrive. TikTok's mission is to create a place for inclusive, joyful, and authentic content–where people can safely discover, create, and connect.
TikTok users have the ability to report videos and comments that contain user claims. These reports identify content that needs to be reviewed by moderators. This process generates a large number of user reports that are difficult to address quickly.
As videos that are labeled opinions will be less likely to go on to be reviewed by a human moderator. Videos that are labeled as claims will be further sorted by a downstream process to determine whether they should get prioritized for review.
We will build a machine learning model that can be used to determine whether a video contains a claim or whether it offers an opinion. With a successful prediction model, TikTok can reduce the backlog of user reports and prioritize them more efficiently.
Note: The data shared in this project has been created by google for pedagogical purposes in their 'Google Advanced Data Analytics' course.