Welcome to the Waze Project!
Your Waze data analytics team is still in the early stages of their user churn project. Previously, you were asked to complete a project proposal by your supervisor, May Santner. You have received notice that your project proposal has been approved and that your team has been given access to Waze's user data. To get clear insights, the user data must be inspected and prepared for the upcoming process of exploratory data analysis (EDA).
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.
You check your inbox and notice a new message from Chidi Ga, your team’s Senior Data Analyst. Chidi is pleased with the work you have already completed and requests your assistance with exploratory data analysis (EDA) and further data visualization. Harriet Hadzic, Waze's Director of Data Analysis, will want to review a Python notebook that shows your data exploration and visualization.
Your team is nearing the midpoint of their user churn project. So far, you’ve completed a project proposal, and used Python to explore and analyze Waze’s user data. You’ve also used Python to create data visualizations. The next step is to use statistical methods to analyze and interpret your data.
You receive a new email from Sylvester Esperanza, your project manager. Sylvester tells your team about a new request from leadership: to analyze the relationship between mean amount of rides and device type. You also discover follow-up emails from three other team members: May Santner, Chidi Ga, and Harriet Hadzic. These emails discuss the details of the analysis. They would like a statistical analysis of ride data based on device type. In particular, leadership wants to know if there is a statistically significant difference in mean amount of rides between iPhone® users and Android™ users. A final email from Chidi includes your specific assignment: to conduct a two-sample hypothesis test (t-test) to analyze the difference in the mean amount of rides between iPhone users and Android users.
Your team is more than halfway through their user churn project. Earlier, you completed a project proposal, used Python to explore and analyze Waze’s user data, created data visualizations, and conducted a hypothesis test. Now, leadership wants your team to build a regression model to predict user churn based on a variety of variables.
You check your inbox and discover a new email from Ursula Sayo, Waze's Operations Manager. Ursula asks your team about the details of the regression model. You also notice two follow-up emails from your supervisor, May Santner. The first email is a response to Ursula, and says that the team will build a binomial logistic regression model. In her second email, May asks you to help build the model and prepare an executive summary to share your results.
Your team is close to completing their user churn project. Previously, you completed a project proposal, and used Python to explore and analyze Waze’s user data, create data visualizations, and conduct a hypothesis test. Most recently, you built a binomial logistic regression model based on multiple variables.
Leadership appreciates all your hard work. Now, they want your team to build a machine learning model to predict user churn. To get the best results, your team decides to build and test two tree-based models: random forest and XGBoost.