Case Study: LinkedIn LinkedIn is the largest professional social networking site, with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion monthly interactions. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the predictive analytics developed by data scientists at LinkedIn: LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems: This tool helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression. Recommendation Systems Personalized for News Feed: The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, decision trees, and neural networks for recommendation systems. CNN's to Detect Inappropriate Content: To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on its platform. Any form of spam, harassment, or inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a neural networks-based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community. 1. Based on the case study described above, what type of machine learning model does LinkedIn use in its prediction analytics process? 2. You have a meeting with the CEO of LinkedIn, and you are supposed to convince them to hire you to change the model they are using, which you described in question 1. Your task is to for one of the two other machine learning models available (any other different from the one you identified they are already using, and you described in.

1. Case Study: LinkedIn
LinkedIn is the largest professional social networking site, with nearly 800 million members in
more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking
around 1 billion monthly interactions. The data science team at LinkedIn works with this
massive pool of data to generate insights to build strategies, apply algorithms and statistical
inferences to optimize engineering solutions, and help the company achieve its goals. Here are
some of the predictive analytics developed by data scientists at LinkedIn:
LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems: This tool
helps recruiters build and manage a talent pool to optimize the chances of hiring candidates
successfully. This sophisticated product works on search and recommendation engines. The
LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The
results delivered have to be relevant and specific. The initial search model was based on linear
regression.
Recommendation Systems Personalized for News Feed: The LinkedIn news feed is the heart and
soul of the professional community. A member's newsfeed is a place to discover conversations
among connections, career news, posts, suggestions, photos, and videos. Every time a member
visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the
feed by sorting through posts and ranking the most relevant results on top. The algorithms help
LinkedIn understand member preferences and help provide personalized news feeds. The
algorithms used include logistic regression, decision trees, and neural networks for
recommendation systems.
CNN's to Detect Inappropriate Content: To provide a professional space where people can trust
and express themselves professionally in a safe community has been a critical goal at LinkedIn.
LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior
on its platform. Any form of spam, harassment, or inappropriate content is immediately flagged
and taken down. These can range from profanity to advertisements for illegal services. LinkedIn
uses a neural networks-based machine learning model. This classifier trains on a training dataset
containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list
consists of accounts having content from "blocklisted" phrases or words and a small portion of
manually reviewed accounts reported by the user community.
1. Based on the case study described above, what type of machine learning model does LinkedIn
use in its prediction analytics process?
2. You have a meeting with the CEO of LinkedIn, and you are supposed to convince them to hire
you to change the model they are using, which you described in question 1. Your task is to for
one of the two other machine learning models available (any other different from the one you
identified they are already using, and you described in question 1). Describe the main benefits of
the new model you are suggesting to LinkedIn, justifying why the model you are proposing is
better than the one they are already using