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Google Advanced Data Analytics

Course 1-6: In Course Project: Waze User Churn Analysis

Notes: Courses 1-6 are working on the same project case.

Background on the Waze Scenario

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. Waze partners with cities, transportation authorities, broadcasters, businesses, and first responders to help as many people as possible travel more efficiently and safely.

You’ll collaborate with your Waze teammates to analyze and interpret data, generate valuable insights, and help leadership make informed business decisions. Your team is about to start a new project to help prevent user churn on the Waze app. Churn quantifies the number of users who have uninstalled the Waze app or stopped using the app. This project focuses on monthly user churn. In your role, you will analyze user data and develop a machine learning model that predicts user churn.

This project is part of a larger effort at Waze to increase growth. Typically, high retention rates indicate satisfied users who repeatedly use the Waze app over time. Developing a churn prediction model will help prevent churn, improve user retention, and grow Waze’s business. An accurate model can also help identify specific factors that contribute to churn and answer questions such as:

  • Who are the users most likely to churn?
  • Why do users churn?
  • When do users churn?

For example, if Waze can identify a segment of users who are at high risk of churning, Waze can proactively engage these users with special offers to try and retain them. Otherwise, Waze may simply lose these users without knowing why.

Your insights will help Waze leadership optimize the company’s retention strategy, enhance user experience, and make data-driven decisions about product development.

Other Info

All 6 In-Course Projects use the same data set.

All In-Course Projects has answer keys uploads, too.


Course 7: End of Course Projects/Capstone: Salifort Motors Workforce Analysis

Background of the Salifort Motors Scenario

About the company

Salifort Motors is a fictional French-based alternative energy vehicle manufacturer. Its global workforce of over 100,000 employees research, design, construct, validate, and distribute electric, solar, algae, and hydrogen-based vehicles. Salifort’s end-to-end vertical integration model has made it a global leader at the intersection of alternative energy and automobiles.

Your business case

As a data specialist working for Salifort Motors, you have received the results of a recent employee survey. The senior leadership team has tasked you with analyzing the data to come up with ideas for how to increase employee retention. To help with this, they would like you to design a model that predicts whether an employee will leave the company based on their department, number of projects, average monthly hours, and any other data points you deem helpful.

The value of your deliverable

For this deliverable, you are asked to choose a method to approach this data challenge based on your prior course work. Select either a regression model or a tree-based machine learning model to predict whether an employee will leave the company. Both approaches are shown in the project exemplar, but only one is needed to complete your project.

Other Info

This capstone project will use Salifort Motors dataset.

Answer keys from Google were uploaded. But I'm using different structure for this project.

Model Deployment

The model has successfully been deployed on both local and cloud environments! To access the files, feel free to check them out here (located in the week 4 & week 5 folders)!