This dataset comes from an Iranian telecom company, with each row representing a customer over a year period. Along with a churn label, there is information on the customers' activity, such as call failures and subscription length.
You can access the report I prepared for my project by clicking on this text
| Column | Explanation |
|---|---|
| Call Failure | number of call failures |
| Complaints | binary (0: No complaint, 1: complaint) |
| Subscription Length | total months of subscription |
| Charge Amount | ordinal attribute (0: lowest amount, 9: highest amount) |
| Seconds of Use | total seconds of calls |
| Frequency of use | total number of calls |
| Frequency of SMS | total number of text messages |
| Distinct Called Numbers | total number of distinct phone calls |
| Age Group | ordinal attribute (1: younger age, 5: older age) |
| Tariff Plan | binary (1: Pay as you go, 2: contractual) |
| Status | binary (1: active, 2: non-active) |
| Age | age of customer |
| Customer Value | the calculated value of customer |
| Churn | class label (1: churn, 0: non-churn) |
You have just been hired by a telecom company. A competitor has recently entered the market and is offering an attractive plan to new customers. The telecom company is worried that this competitor may start attracting its customers.
You have access to a dataset of the company's customers, including whether customers churned. The telecom company wants to know whether you can use this data to predict whether a customer will churn. They also want to know what factors increase the probability that a customer churns.
You will need to prepare a report that is accessible to a broad audience. It should outline your motivation, steps, findings, and conclusions.
Since I made this project through the DataCamp Workspace, you can also examine the project from there.