Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study

Sayed, Hend El; Abdel-Fattah, Manal A.; Kholief, Sherif

doi:10.14569/ijacsa.2018.091196

Cited by 20 publications

(19 citation statements)

References 5 publications

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“…The authors suggested Naïve Bayes is superior than other classifiers and then the size of the dataset could influence the output of the classifiers. Sayed and et al [11] discovered in their paper that the Spark ML has an attraction over Spark MLlib in the performance and accuracy of big data analytics problems. Al-Saqqa and et al [12] examined about sentiment classification of big data using Spark's MLlib.…”

Section: Related Workmentioning

confidence: 99%

“…This creates CNN more analogous to the Network of biological neurons and decreases the complexity of both the weight and the network model. A fully connected soft-max layer is utilized as the classification layer for sentence level sentiment classification in CNN proposed by Kim [11]. This classification layer, however, has become too simple for the task of classifying sentiments.…”

Section: Proposed Cnn-svm Using Spark DLmentioning

confidence: 99%

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An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework

Raviya¹,

S²

2021

IJACSA

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Today, we live in the Big Data age. Social networks, online shopping, mobile data are main sources generating huge text data by users. This "text data" will provide companies with useful insight on how customers view their brand and encourage them to make business strategies actively in order to maintain their trade. Hence, it is essential for the enterprises to analyse the sentiments of social media big data to make predictions. Because of the variety and existence of data, the study of sentiment on broad data has become difficult. However, it includes opensource Big Data platforms and machine learning techniques to process large text information in real-time. The advancement in fields including Big Data and Deep Learning technology has influenced and overcome the traditional restrictions of distributed computing. The primary aim is to perform sentiment analysis on the pipelined architecture of Apache Spark ML to speed upward the computations and improve machine efficiency in different environments. Therefore, the Hybrid CNN-SVM model is designed and developed. Here, CNN is pipeline with SVM for sentiment feature extraction and classification in ML to improve the accuracy. It is more flexible, fast and scalable. In addition, Naive Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression classifiers have been used to measure the efficiency of the proposed system on multi-node environment. The experimental results demonstrate that in terms of different evaluation metrics, the hybrid sentiment analysis model outperforms the conventional models. The proposed method makes it convenient for effective handling of big sentiment datasets. It would be more beneficial for corporations, government and individuals to improve their great value.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Cnn-svm Using Spark DLmentioning

confidence: 99%

An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework

Raviya¹,

S²

2021

IJACSA

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“…H. Sayedand et al, 2018 [19] in their paper compared the hypothesis that the Spark ML bundle has preference over the Spark MLlib bundle in the issues of performance and accurateness when dealing with big data. They discovered that the MLlib is better in the training time operation and vice versa in the evaluation time operation.…”

Section: Related Workmentioning

confidence: 99%

Big Data Analysis Using Apache Spark MLlib and Hadoop HDFS with Scala and Java

Omar

Jumaa

2019

KJAR

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Nowadays with the technology revolution the term of big data is a phenomenon of the decade moreover, it has a significant impact on our applied science trends. Exploring well big data tool is a necessary demand presently. Hadoop is a good big data analyzing technology, but it is slow because the Job result among each phase must be stored before the following phase is started as well as to the replication delays. Apache Spark is another tool that developed and established to be the real model for analyzing big data with its innovative processing framework inside the memory and high-level programming libraries for machine learning, efficient data treating and etc. In this paper, some comparisons are presented about the time performance evaluation among Scala and Java in apache spark MLlib. Many tests have been done in supervised and unsupervised machine learning methods with utilizing big datasets. However, loading the datasets from Hadoop HDFS as well as to the local disk to identify the pros and cons of each manner and discovering perfect reading or loading dataset situation to reach best execution style. The results showed that the performance of Scala about 10% to 20% is better than Java depending on the algorithm type. The aim of the study is to analyze big data with more suitable programming languages and as consequences gaining better performance.

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“…The customer churn analysis for the banking application is performed using Spark ML package by dealing with big data. The prediction of customer churn is done based on their transaction data [2]. The customer churn analysis can be related to customer relationship management where the bank customers are converted into competitors.…”

Section: A Literature Reviewmentioning

confidence: 99%

Postulation of Customer Retention in Banking Sector using Machine Learning and Principal Component

Devi*,

Krishna,

Sowmya

et al. 2019

IJRTE

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Recently, there is a rapid growth in technological improvement in banking sector. The entire world is using the banking service for managing their financial and property assets. As of now, all the technological advancements are applied to banking sector to facilitate the customers with proper operational excellence. In this view, the bank has complete responsibility in serving the people with their modern application to save their time and wealth. So the customer value analysis is needed for the bank to enrich the marketing growth and turnover of the bank. But still, the prediction of customer churn remains a challenging issue for the banking sector for analyzing the profit growth. With this view, we focus on predicting the customer churn for the banking application. This paper uses the churn modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data set is applied to various classifiers like Logistic Regression, KNN, Kernel SVM, Naive Bayes, Decision Tree, Random Forest to analyze the confusion matrix. The Performance analysis is done by comparing the metrics like Precision, Recall, FScore and Accuracy. Second the data set is subjected to dimensionality reduction method using Principal component Analysis and then fitted to the above mentioned classifiers and their performance analysis is done. Third, the performance analysis is done for the dataset by comparing the metrics with and without applying the dimensionality reduction. A Performance analysis is done with various classification algorithms and comparative study is done with the performance metric such as accuracy, precision, recall, and f-score. The implementation is carried out with python code using Anaconda Navigator. Experimental results shows that before applying dimensionality reduction PCA, the Random Forest classifier is found to be effective with the accuracy of 86%, Precision of 0.85, Recall of 0.86 and FScore of 0.84. Experimental results shows that after applying dimensionality reduction, the 2 component PCA with the kernel SVM classifier is found to be effective with the accuracy of 81%, Precision of 0.81, Recall of 0.81 and FScore of 0.74. compared to other classifiers.

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Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study

Cited by 20 publications

References 5 publications

An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework

An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework

Big Data Analysis Using Apache Spark MLlib and Hadoop HDFS with Scala and Java

Postulation of Customer Retention in Banking Sector using Machine Learning and Principal Component

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