Waze User Churn Project

The goal of this project was to use Python to build a dataframe, perform a preliminary inspection of the dataset, and inform the other team members of the results.

• At this stage, we were focused on inspecting user data to uncover any important relationships in the driving data for retained and churned users.
• This also involved creating a pandas dataframe for data learning, future EDA, and statistical findings.
• The insights from this project will help guide the next steps for the analytics department.

Key Insights

• This dataset contains approximately 82% retained users and 18% churned users.
• The label column is missing 700 values.
• Retained users used the app over twice as many days as churned users in the last month.
• Churned users had more drives overall in fewer days, and their trips were longer in both distance and duration.
• The users in this dataset drove a lot of mileage overall. This may be worth exploring more to determine a more complete user profile.