Classification Of 3G Mobile Phone Customers

2011 ◽  
pp. 2247-2254
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Third Generation ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using lo-gistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population.

Classification Of 3G Mobile Phone Customers

2008 ◽  
pp. 2558-2565
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Third Generation ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using lo-gistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population.

Classification of 3G Mobile Phone Customers

2009 ◽  
pp. 2862-2870
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using logistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population

scholarly journals Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 10-16
Author(s):  
Rachmadania Irmanita ◽  
Sri Suryani Prasetiyowati ◽  
Yuliant Sibaroni
Keyword(s):  
Medical Treatment ◽  
Severe Malaria ◽  
Naive Bayes ◽  
Regression Tree ◽  
Naïve Bayes ◽  
Classification And Regression Tree ◽  
Second Step ◽  
Anopheles Mosquitoes ◽  
Classification And Regression

Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.

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