ICACT 2019
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/20316
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Item An Unsupervised Machine Learning Approach for Churn Prediction(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Prasanth, S.; Rathnayaka, R.M.K.T.; Arumawadu, H.Customer churn is one of the critical issues faced by the firms nowadays. Telecommunication industry is no exception to this rule. In this industry, keeping the existing subscriber (customer) is more valuable than acquiring a new subscriber (attracting new customers costs approximately 5 times higher than retaining the existing customers). Therefore, predicting the attrition behavior of customers in advance is a significant task. This behavior has triggered most of the researchers to focus on developing the churn prediction model in several industries. Anyhow, in most of the time supervised machine learning techniques have been incorporated in this regard. But in here, an unsupervised machine learning approach has been proposed. A local telecommunication company can be approached for the purpose of conducting this research. Around 10,000 postpaid subscriber details with 20 attributes have been obtained and analyzed during this research. Further, Principal Component Analysis (PCA) and Kmeans clustering algorithm have been utilized with the intention of reducing the dimensionality between features and to find the churners and non-churners respectively. The results obtained from the PCA have revealed that, 16 principal components which represent all the 20 features are considered as most important aspects to cover the entire data. Moreover, totally 6 clusters have been generated and some particular features that tend to show high contributions were identified during the principal component analysis have been analyzed towards each cluster. The proposed approach has finally revealed that out of the 6 clusters three (3) representing 4888 are churners and the other three (3) representing 5112 are non-churners. It could be ensured that, this approach would assist the future researchers to have a promising start for combining the unsupervised technique with the supervised one.Item MLP Model Approach for Driver Fault Identification(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Ariyathilake, S.N.; Rathnayaka, R.M.K.T.The issue of the traffic accident has gain attention of the globe which has been a major challenge for the sustainable development of transportation and traffic. Crashes are events which occurred by involving different components: Driver, road, environment. Driver identification is directly connected to taking advanced actions on the road accident. Prevention of the road accident is the primary concern and necessary legal actions must be taken for the responsible party of the accident. In order to accurately predict the driver fault regarding an accident, this study aims to identify whether the driver is fault for the accident or not, by using a Multilayer Perceptron (MLP) model. The proposed model accurately predicts the driver fault while ensuring the accuracy of the decision. Proposed Multilayer perceptron model has achieved an accuracy of 97.77% with the accident data. To compare the results of the model, Decision Tree, Linear classifier and DNN classifier has used. Comparative results revealed that the most accurate model as the Multilayer perceptron approach. Necessary sensitivity analysis regarding the MLP was performed to find the best MLP model. Results revealed that by using 500 epochs with RMSprop accuracy was increased. T – Test was performed with 0.05 accuracy level for the selected methods and MLP method outperformed the other techniques. The research will provide the information needed to guide the relevant decision-makers in adopting suitable measures to prevent and to reduce the accident rate.