An Analysis of Different Classification Algorithms for Customer Churn Prediction.
Customer churn prediction is a very important classification problem in machine learning as it helps businesses to identify customers who are likely to cancel a subscription or service. This project aims to predict customer churn for a telecom company using a dataset from Kaggle.
The dataset contains 7043 rows and 21 columns. Each row represents a customer, each column contains customer’s attributes described on the column Metadata.
The following classifiers were used to predict customer churn:
- Support Vector Machine
- K-Nearest Neighbors
- Decision Tree
- Random Forest
- Multinomial Naive Bayes
- Multilayer Perceptron
- Convolutional Neural Network
Classifiers 1-6 were then compared using the following performance metrics:
The CNN model was seperately trained, and the following plots were obtained:
| Accuracy Plot | Loss Plot |
|---|---|
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The CNN model outperformed all other classifiers with an accuracy of 0.81 and a loss of 0.42. The Multilayer Perceptron model was the second best classifier with an accuracy of 0.80 and a loss of 0.43. The SVM model was the worst classifier with an accuracy of 0.73.