An analysis on classification models for customer churn prediction

Figures & data

Figure 1. Steps in the proposed model.

Figure 2. Dataset.

Figure 3. Distribution of data in categorical features.

Figure 4. Correlation heat map.

Figure 5. Weightage of classes of dependent feature – Churn.

Figure 6. Performance of classification algorithms.

Figure 7. Accuracy metrics of classification algorithms on Train data.

Figure 8. Accuracy metrics of classification algorithms on Test data.

Figure 9. Confusion matrices for train set and test set.

Figure 10. AUC-ROC graph.

Data availability

The data supporting the findings of this study is accessible and can be provided upon request from the corresponding author.