Classification Project

Why are our customers churning? Some questions I have include:

Could the month in which they signed up influence churn? i.e. if a cohort is identified by tenure, is there a cohort or cohorts who have a higher rate of churn than other cohorts? (Plot the rate of churn on a line chart where x is the tenure and y is the rate of churn (customers churned/total customers)) (Definitely answer this question)
Are there features that indicate a higher propensity to churn? like type of internet service, type of phone service, online security and backup, senior citizens, paying more than x% of customers with the same services, etc.?
Is there a price threshold for specific services where the likelihood of churn increases once price for those services goes past that point? If so, what is that point for what service(s)? (Definitely answer this question)
If we looked at churn rate for month-to-month customers after the 12th month and that of 1-year contract customers after the 12th month, are those rates comparable? (Definitely answer this question)

Specific Deliverables:

a jupyter notebook where your work takes place
a csv file that predicts churn for each customer
acquire.py, prepare.py, model.py (you may decide to separate tasks into others, such as preprocessing.py or features.py)
a google slide presentation summarizing your model
a README.md file that contains a link to your google slides presentation, and instructions for how to use your python script(s)