Telecom Customer Churn Data for a 2018 Nigeria Data Science Bootcamp

A balanced dataset was used to help participants classify potential customers who might churn. This challenge was part of the pre-qualification for the 2018 Data Science Nigeria all-expense paid bootcamp and hackathon scheduled for October 10-15, 2018. The data is described as academic and focuses on the business problem of customer retention in telecommunications.

Use Cases

• Train a binary classification model to predict customer churn based on the described academic data.
• Benchmark machine learning algorithms on a balanced dataset for churn analysis.
• Study customer retention patterns in the Nigerian telecommunications sector implied by the dataset's context.

Strengths

• The dataset is explicitly described as balanced, which can mitigate class imbalance issues for model training.
• Data is contextualized for a specific, real-world business problem in telecommunications.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Data Science Nigeria bootcamp/hackathon pre-qualification challenge.

Collection Method

Likely gathered or synthesized for an academic machine learning competition.

Time Range

Associated with an event in October 2018.

Freshness

Last updated date is unknown.

Geography

Nigeria