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Full-Text Articles in Physical Sciences and Mathematics
Customer Churn Prediction, Deepshikha Wadikar
Customer Churn Prediction, Deepshikha Wadikar
Dissertations
Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …
Predicting Happiness - Comparison Of Supervised Machine Learning Techniques Performance On A Multiclass Classification Problem, Dorota Nieciecka
Predicting Happiness - Comparison Of Supervised Machine Learning Techniques Performance On A Multiclass Classification Problem, Dorota Nieciecka
Dissertations
In the modern world, especially in contemporary economies and politics, a population's subjective well-being is a frequent subject of the public debate. As comparisons of happiness levels in different countries are published, different circumstances and their effect on the value of the subjective well-being reported by people are also analysed. However, a significant amount of the research related to subjective well-being and its determinants is still based upon survey answers and employing conventional statistical methods providing details regarding correlations and causality between different factors and subjective well-being. Application of Supervised Machine Learning techniques for prediction of subjective well-being may provide …
A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson
A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson
Dissertations
Epilepsy is one of the most common neurological disorders, and afflicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to significant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artificial neural network and sup port vector machine classifiers, using EEG spectral features to predict the onset …