Background:

Waze’s free navigation app makes it easier for drivers around the world to get to where they want to go. Waze’s community of map editors, beta testers, translators, partners, and users helps make each drive better and safer.

Project goal:

Waze leadership has asked your data team to develop a machine learning model to predict user churn. An accurate model will help prevent churn, improve user retention, and grow Waze’s business.

Scenario:

Your team is still in the early stages of their user churn project. So far, you’ve completed a project proposal, and used Python to inspect and organize Waze’s user data. Now, the data is ready for exploratory data analysis (EDA) and further data visualization.

Course 3 tasks:

• Clean data
• Handle outliers
• Perform EDA
• Visualize data
• Share an executive summary with the Waze data team

Project goal:

The TikTok data team is developing a machine learning model for classifying claims made in videos submitted to the platform.

Background:

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.

Course 4 tasks:

• Import relevant packages and TikTok data

• Explore the project data

• Implement a hypothesis test

• Communicate insights with stakeholders within TikTok

Project goal:

Waze leadership has asked your data team to develop a machine learning model to predict user churn. Churn quantifies the number of users who have uninstalled the Waze app or stopped using the app. This project focuses on monthly user churn. An accurate model will help prevent churn, improve user retention, and grow Waze’s business.

Scenario:

Your team is more than halfway through their user churn project. Earlier you completed a project proposal, used Python to analyze and visualize Waze’s user data, and conducted a hypothesis test. As a next step, leadership asks your team to build a regression model to predict user churn based on a variety of variables.

Course 5 tasks:

• Check model assumptions

• Build a binomial logistic regression model

• Evaluate the model

• Share an executive summary with the Waze leadership team

Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. And, the data shared in this project has been created for pedagogical purposes.