Comparison of Artificial Neural Network and SPSS Model in Predicting Customers Churn of Iran’s Insurance Industry

Pirmohammadi, Gholamreza; Zohouri, M. Mast

doi:10.5120/ijca2020920345

Cited by 4 publications

(8 citation statements)

References 7 publications

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“…Also SPSS is a statistical program with a number of tools and lists that may be generated from data input. The data is then analysed and generalized using neural network (NN) and Multiple Linear Regression (MLR) algorithms, and we may select the optimal technique for data analysis [16].…”

Section: Discussionmentioning

confidence: 99%

Statistical indicators to evaluate the performance of electrocoagulation with membrane bioreactor (EC-MBR) for treatment of organic matters in domestic wastewater

Zakoor

Gzar²

2022

ejuow

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An electrocoagulation with membrane bioreactor technique (EC-MBR) was developed to treat domestic wastewater and prevent membrane fouling. To support the new design, experiments were conducted on a few levels. The structure and distribution of organic matter removal utilizing the membrane are investigated using a laboratory-scale (EC-MBR) treatment of domestic wastewater. The study's goals were to assess the removal efficiency of organic matter (biological oxygen demand (BOD) and chemical oxygen demand (COD) in Al-Hawraa's wastewater, as well as its links to statistical indicators. It was chosen to sample and evaluate effluent from domestic wastewater using EC-MBR with operating temperature (25 0C), pH (7-8), DO (4-6) mg/L, beginning and final concentrations of BOD (184-6 mg/L), and COD (489-20 mg/L) using biological and electrochemical treatment procedures. According to the results, the organic matter removal efficiency may be calculated using the multilinear regression (MLR) and neural network (NN) models in the SPSS modeler. In addition, the results showed that the entire reactor had good BOD and COD maximum removal efficiencies of 96.7% and 95.9%, respectively. Finally, the highest accuracy of the MLR algorithm for COD and BOD is 99.6 for both, whereas the maximum accuracy of the NN algorithm for COD and BOD is 99.2 % and 99.1%, respectively. To choose the best algorithm for analysis and modeling the outcomes, a comparative study has been achieved to compare the results of two algorithms that used in this study. Therefore, for this study MLR algorithm was chosen.

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Section: Discussionmentioning

confidence: 99%

Statistical indicators to evaluate the performance of electrocoagulation with membrane bioreactor (EC-MBR) for treatment of organic matters in domestic wastewater

Zakoor

Gzar²

2022

ejuow

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“…The CNN machine learning algorithm is one of the most famous deep learning algorithms, whose main power is feature engineering without need for domain expertise [5]- [16]. In this research, the CNN algorithm is applied to build the customer churn prediction model.…”

Section: Customer Churn Prediction Frameworkmentioning

confidence: 99%

“…This competition arises due to the market saturation of abundant service providers and the products' offers diversity. Herein, churn prediction is a business use case, which applies various data mining techniques to detect the customers who are likely to cancel their subscription to a special service [5], [6]. Customer behavior changes in line with the defined business use case.…”

Section: Introductionmentioning

confidence: 99%

Smart evaluation for deep learning model: churn prediction as a product case study

Elgohary¹,

Galal

Mosa

et al. 2023

Bulletin EEI

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Customer churn prediction recently is one of the vital issues that confronts diverse business industries to sustain the customers base and profits. On the other hand, data scientists employ gigantic customer data to automate the data modelling process to offer these models as a generally portable service. This research has two main contributions: deep learning customer churn prediction model and smart evaluation prediction model service. So, this service harnesses any customer data to automate building, evaluation, and deployment of the churn prediction model. The research consists of three main parts. Firstly, it illustrates the dataset labelling which annotates customers data into churn or non-churn. Secondly, the deep learning churn prediction framework using convolutional neural network (CNN) algorithm. Finally, a case study is presented to show how churn prediction service is automatically trained and generated based on real customer data, where CNN parameters are adapted to achieve the most reliable performance in line with customers' behavior. The applied case study achieves accuracy 0.77, area under the curve (AUC) 0.84 and f1 score 0.83.

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“…Data were taken and tabulated by Excel 2019 software were used for data analysis. The regression analysis and multilayer prediction artificial neural network assessments were carried out by software (IBM SPSS 24) [11].…”

Section: Gathering and Processing Of Datamentioning

confidence: 99%

Evaluate the granite waste efficiency in the construction using statistical indicators

Tahir¹,

Yaseen²

2023

AJES

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Comparison of Artificial Neural Network and SPSS Model in Predicting Customers Churn of Iran’s Insurance Industry

Cited by 4 publications

References 7 publications

Statistical indicators to evaluate the performance of electrocoagulation with membrane bioreactor (EC-MBR) for treatment of organic matters in domestic wastewater

Statistical indicators to evaluate the performance of electrocoagulation with membrane bioreactor (EC-MBR) for treatment of organic matters in domestic wastewater

Smart evaluation for deep learning model: churn prediction as a product case study

Evaluate the granite waste efficiency in the construction using statistical indicators

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