The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022
Each record represents one customer, and contains details about their demographics, location, tenure, subscription services, status for the quarter (joined, stayed, or churned), and more!
The Zip Code Population table contains complimentary information on the estimated populations for the California zip codes in the Customer Churn table
We need to predict whether the customer will churn, stay or join the company based on the parameters of the dataset.
| Machine Learning Models Applied | Accuracy |
|---|---|
| Random Forest | 78.11% |
| Logistic Regression | 78.28% |
| Naive Bayes Gaussian | 36.77% |
| Decision Tree | 77.29% |
| XGB_Classifier | 80.86% |
The ability to predict churn before it happens allows businesses to take proactive actions to keep existing customers from churning. This could look like:
Customer success teams reaching out to those high-risk customers to provide support or to gauge
what needs may not be being met.
The advantage of calculating a company's churn rate is that it provides clarity on how well the business is retaining customers, which is a reflection on the quality of the service the business is providing, as well as its usefulness.
This project follows the MIT LICENSE.
