This project analyzes customer churn within the telecommunications industry using the IBM Telco dataset. We implemented a dual-modeling approach: 1) A classification analysis using Logistic Regression and Random Forest to predict at-risk customers (achieving an AUC of 0.84), and 2) A linear regression audit to deconstruct the pricing drivers of monthly charges (R² ≈ 0.999). The findings provide actionable insights for targeted customer retention and pricing transparency.

This uses anonymized mobile massage data with combined data from income and consent forms with optional surveys attached to the consent forms of each client. The idea is to make the data provide information for best massage services to offer, idea region, age group, pressure, and other information to predict the next year income using the library prophet for R as well as dplyr and ggplot2 for graphical plots. Date variables are no joke if you enter them wrong. Many hours spent getting correct AI generated code to turn a month/day/year of 4 digit year into a 2 digit year. But that was cut out of this document so you can avoid the upset. Useful information to help guide this mobile massage provider into more income by targeting preferred idea client to get those who return more often and pay more per household.

Pitch as part of the Coursera Developing Data Products course.

A simple page explaining linear regression made for the "developing data products" Coursera course.