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Osemede, Kennedy (2017) An Investigation on How Classifiers Algorithm in Predictive Analytics Prevents Customers Forfeiture. Masters thesis, Dublin, National College of Ireland.
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Abstract
This research is focused on predicting if a customer will leave or not based on using various machine learning techniques. The goal is to determine what accuracy each of those models will produce when intercompared against each other to predict if a customer will either leave or not and the determine the major factor behind their decision to leave. To achieve the above-mentioned goal, Logistic Regression, Naive Bayes, Decision Tree and Support Vector Machine models were implemented on a Customer Churn Dataset to determine the accuracy, precision and F-measure score. Logistic Regression achieved the best classification accuracy of 79.08% and precision of 62.96% followed by Support Vector Machine which scored 77.24% Accuracy and 56.49% Precision.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Customer Service |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Caoimhe Ní Mhaicín |
| Date Deposited: | 28 Aug 2018 11:44 |
| Last Modified: | 28 Aug 2018 11:44 |
| URI: | https://norma.ncirl.ie/id/eprint/3086 |
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