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WA_Fn-UseC_-Telco-Customer-Churn
- Citation Author(s):
- Submitted by:
- Mengjing Hao
- Last updated:
- Mon, 02/19/2024 - 02:51
- DOI:
- 10.21227/0q5y-3529
- Data Format:
- License:
234 Views- Categories:
- Keywords:
Abstract
Nowadays, the high cost of customer acquisition makes telecom operators encounter the “ceiling”, and even fall into the dilemma of customer acquisition. As market saturation increases, telecom operators need to solve the problem of increasing subscriber stickiness and prolonging subscriber life cycle. Therefore, it is crucial to analyse and predict the churn of telecom users. The dataset is ”Telecom Operator Customer Dataset”. The dataset obtained from the official Kaggle competition website in this study, which comprised 21 fields. Because the customer ID serves as a unique identifier for each customer and is not relevant to the data analysis, we focus on the remaining 20 fields, which include 19 input variables and 1 target variable. The target variable is a dichotomous variable representing whether a customer has churned.
Nowadays, the high cost of customer acquisition makes telecom operators encounter the “ceiling”, and even fall into the dilemma of customer acquisition. As market saturation increases, telecom operators need to solve the problem of increasing subscriber stickiness and prolonging subscriber life cycle. Therefore, it is crucial to analyse and predict the churn of telecom users. The dataset is ”Telecom Operator Customer Dataset”. The dataset obtained from the official Kaggle competition website in this study, which comprised 21 fields. Because the customer ID serves as a unique identifier for each customer and is not relevant to the data analysis, we focus on the remaining 20 fields, which include 19 input variables and 1 target variable. The target variable is a dichotomous variable representing whether a customer has churned.
