Volume-2 Issue-2, November 2022 – Indian Journal of Data Mining (IJDM) https://www.ijdm.latticescipub.com Exploring Innovation | ISSN: 2582-9246 (Online) | A Periodical Journal | Reg. No: C/1383209 | Published by Lattice Science Publication (LSP) Fri, 29 Dec 2023 09:56:26 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://www.ijdm.latticescipub.com/wp-content/uploads/2021/03/cropped-IJDM-WFS-32x32.png Volume-2 Issue-2, November 2022 – Indian Journal of Data Mining (IJDM) https://www.ijdm.latticescipub.com 32 32 B1631113223 https://www.ijdm.latticescipub.com/portfolio-item/b1631113223/ Fri, 22 Dec 2023 05:22:03 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=1007 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

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]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Usage of Technology in Promoting Well-being of Senior CitizensCROSSMARK Color horizontal
Radhika Kapur

Dr. Radhika Kapur, Pedagogy and Organizational Culture in Nursery Schools, Delhi University, New Delhi, India.

Manuscript received on 01 April 2021 | Revised Manuscript received on 08 August 2022 | Manuscript Accepted on 15 December 2022 | Manuscript published on 30 December 2023 | PP: 6-11 | Volume-2 Issue-2 November 2022 | Retrieval Number: 100.1/ijdm.B1631113223 | DOI: 10.54105/ijdm.B1631.112222

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the present existence, with advancements taking place and with the advent of modernization, the utilization of technologies have acquired prominence. The internet is regarded as one of the eminent factors that is utilized in augmenting information in terms of all subjects and concepts. Furthermore, individuals are able to obtain answers to all types of questions that are overwhelming to them. The senior citizens make use of technologies and internet for number of purposes. In addition, they are required to obtain help from others as well in putting into operation different tasks and activities in a well-ordered manner. In cases of visual impairments and other types of health problems and illnesses, the senior citizens are unable to carry out job duties on their own, hence, they are required to obtain help from others. The senior citizens are not only incurring the feeling of satisfaction, but they are able to contribute efficiently in leading to up-gradation of overall standards of living, when they are making use of different types of technologies and internet. The senior citizens in some cases are overwhelmed by feelings by apprehensiveness and vulnerability. But understanding the concepts and getting engaged in regular practice will be facilitating in honing technical skills. Therefore, the role of technology is considered important in promoting well-being of senior citizens. The main concepts that are taken into account in this research paper are, understanding the meaning and significance of technologies, factors highlighting usage of technology in promoting well-being of senior citizens and measures to be implemented in augmenting technical skills by senior citizens.

Keywords: Communities, Competencies, Job Duties, Knowledge, Promoting, Senior Citizens, Technology, Well-being
Article of the Scope: Data Preparation

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]]> B1628113223 https://www.ijdm.latticescipub.com/portfolio-item/b1628113223/ Wed, 20 Dec 2023 10:03:00 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=1001 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

The post B1628113223 appeared first on Indian Journal of Data Mining (IJDM).

]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Bank Customer Churn PredictionCROSSMARK Color horizontal
Jufin P. A1, Amrutha N2

1Jufin P A, Department of Computer Science, St. Albert’s College (Autonomous), Ernakulam, India.

2Amrutha N, Department of Computer Science, St. Albert’s College (Autonomous), Ernakulam, India. 

Manuscript received on 25 October 2022 | Revised Manuscript received on 05 November 2022 | Manuscript Accepted on 15 November 2022 | Manuscript published on 30 December 2023 | PP: 1-5 | Volume-2 Issue-2 November 2022 | Retrieval Number: 100.1/ijdm.B1628113223 | DOI: 10.54105/ijdm.B1628.112222

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the current challenging era, there is a stiff competition happening between the banking industries. To strengthen the grade and level of services they provide, banks focus on customer retention as well as the customer churning. Customer churning becomes one of the duties of corporate intelligences to speculate the number of customers leaving from the bank or presumed to be churned. It also helps in predicting the number of customers retained. The primary objective of this paper is “Bank customer churn prediction” is to build a model that can distinguish and visualize which factors or attributes contribute to customer churn. In addition to that, this paper also discusses a comparison between various classification algorithms. Machine learning is a modern technology that has the potential to solve classification problems. Using supervised machine learning techniques, a best model is chosen that will assign a probability to the churn to simplify customer service to prevent customer churn. Few methodologies are compared in order to accomplish different accuracy levels. XGBoost is considered in order to check if a better model can be obtained that provides best result in terms of accuracy. The other three machine learning algorithms compared are Logistic regression, Support vector machine [SVM], and Random Forest.
Keywords: Customer Churning, Machine Learning, XG Boost, Logistic Regression, SVM, Random Forest.
Article of the Scope: Machine Learning

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