An Improved Telecommunication Churn Prediction System by PPFCM Clustering Hybrid Model

2020 ◽  
pp. 169-175
Author(s):  
J. Vijaya ◽  
S. Srimathi ◽  
S. Karthikeyan ◽  
S. Siddarth
Keyword(s):  
Hybrid Model ◽  
Prediction System ◽  
Churn Prediction

scholarly journals Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

Online word-of-mouth as a predictor of television rating

2015 ◽  
Vol 39 (6) ◽  
pp. 831-847 ◽  
Author(s):  
Ching-Chiang Yeh
Keyword(s):  
Hybrid Model ◽  
Word Of Mouth ◽  
Prediction Models ◽  
Prediction System ◽  
Data Set ◽  
Content Type ◽  
Predictive Variable ◽  
Online Word Of Mouth ◽  
Tv Ratings ◽  
Practical Implications

Purpose – Despite the growing importance of online word-of-mouth (WOM) with regard to television (TV) ratings, it is usually excluded from early prediction models. The purpose of this paper is to investigate the role of online WOM in TV ratings predictions, focussing on whether the incorporation of online WOM could improve predictions of TV ratings, and extracts meaningful rules for decision-making. Design/methodology/approach – The author uses online WOM as a potential predictive variable in the TV ratings prediction model. The author matches a list of programs based on TV ratings for the movie channel with internet user reviews and TV ratings information from Yahoo! Movies (YM) and XYZ Company. The data set includes 71 movies, for which the data were analyzed with a hybrid model. Findings – Grey relational analysis shows that online WOM is a useful ex ante determinant of TV ratings. As a predictive variable, it plays an essential role in enhancing TV ratings predictions. The experimental results also indicate that the proposed model surpasses other listed methods in terms of both accuracy and reduction of variables, while the proposed procedure yields a set of easily understandable decision rules that facilitate the interpretation of TV ratings information. Practical implications – This paper identifies critical predictors of TV ratings and suggests that online WOM messages are a credible source. A hybrid model is developed to illustrate an intelligent prediction system for TV ratings. Originality/value – The study demonstrates the effectiveness of online WOM and its impact on TV ratings. It offers an intelligent prediction system for TV ratings with practical implications for managers within the TV industry.

scholarly journals An Approach for a Next-Word Prediction for Ukrainian Language

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Khrystyna Shakhovska ◽  
Iryna Dumyn ◽  
Natalia Kryvinska ◽  
Mohan Krishna Kagita
Keyword(s):  
Markov Chains ◽  
Hybrid Model ◽  
Prediction System ◽  
Text Generation ◽  
Word Prediction ◽  
Current Output ◽  
Prediction Task ◽  
Limited Support ◽  
Text Prediction ◽  
Sequential Nature

Text generation, in particular, next-word prediction, is convenient for users because it helps to type without errors and faster. Therefore, a personalized text prediction system is a vital analysis topic for all languages, primarily for Ukrainian, because of limited support for the Ukrainian language tools. LSTM and Markov chains and their hybrid were chosen for next-word prediction. Their sequential nature (current output depends on previous) helps to successfully cope with the next-word prediction task. The Markov chains presented the fastest and adequate results. The hybrid model presents adequate results but it works slowly. Using the model, user can generate not only one word but also a few or a sentence or several sentences, unlike T9.

scholarly journals Employee Churn Rate Prediction and Performance Using Machine Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 824-826
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Employee Behavior ◽  
Churn Prediction ◽  
Design Expert ◽  
And Performance ◽  
Business Cost ◽  
Vital Resource

A person working for an organization is the vital resource which is known as an employee. If one of them leaves company suddenly, this could affect and cost massive amount to respective company. And recruitment would consume not only time and money but also the newly joined person needs some time for making particular business cost-effective. This model will help to predict rate at which employees are quitting jobs based on obtained analytic data accessible and use different machine learning algorithms to decrease prediction error. Personalized or individual employee’s prediction is different with respect to environment they are working in. While it has become apparent that employee churn prediction responds differently to salary, depending on their location, lifestyle, and environment, the linked knowledge and understanding remain fragmented. In this paper, we aim to design expert prediction system to deal with problems associated with lack of knowledge of employee behavior, to aware organizations about the importance of employee, to prevent unnecessary employee churn, and to improve growth of both separately

An intelligent hybrid model for chord prediction

1999 ◽  
Vol 4 (2) ◽  
pp. 115-119 ◽  
Author(s):  
URAQUITAN SIDNEY CUNHA ◽  
GEBER RAMALHO
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prior Knowledge ◽  
Hybrid Model ◽  
Neural Network Model ◽  
Prediction System ◽  
Rule Based ◽  
Long Term Experience ◽  
Better Than

For a system to be able to generate realtime accompaniment to previously unknown songs, it must predict their harmonic development, i.e. the chords to be played. We claim that such a system must combine long-term experience, to identify typical chord sequences (e.g. II–V and II–V–I), with ‘on-the-fly’ adaptation to track-recurrent structures (e.g. choruses and refrains) of the particular song being played. We have implemented a prediction system using a neural network model that encompasses prior knowledge about typical chord sequences. The results achieved are very encouraging, and rather better than those reported in the literature. However, our predictor could not adapt its behaviour to the idiosyncrasies of each song, since online learning is difficult in neural networks. In this paper, we propose an extension to our previous work by the inclusion of a rule-based sequence tracker, which detects recurrent chord sequences while the song is being performed. We show that this hybrid model, which combines a neural network predictor with a rule-based sequence tracker, improves the system's performance.

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