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Mansur, Muzzammil
- On The Analysis of Customer Engagements with A Telecommunication Company in Sokoto-North western Nigeria Using Machine Learning Techniques
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Authors
Affiliations
1 Department of Computer Science,Usmanu Danfodiyo University, Sokoto, NG
2 Undergraduate Student, Department of Computer Science,Usmanu Danfodiyo University, Sokoto, NG
3 Department of Computer Science, Waziri Ummaru Federal Polytechnic. Birnin-Kebbi, NG
1 Department of Computer Science,Usmanu Danfodiyo University, Sokoto, NG
2 Undergraduate Student, Department of Computer Science,Usmanu Danfodiyo University, Sokoto, NG
3 Department of Computer Science, Waziri Ummaru Federal Polytechnic. Birnin-Kebbi, NG
Source
International Journal of Advanced Networking and Applications, Vol 13, No 6 (2022), Pagination: 5152-5158Abstract
This study was intended to analyse data mining techniques on the customer engagements with telecommunication companies in Nigeria. This study was guided by the following objectives; to provide an overview, on how prediction is being made in a telecommunication company using data mining. MTN Nigeria was chosen as a case study to identify fraud telecommunication companies in Nigeria; to identify the challenges of data mining faced by telecommunication companies in Nigeria. The study employed the descriptive and explanatory design; primary means were applied in order to collect data. Primary data sources were used and data was analyzed using orange data mining software. The study findings revealed that data mining significantly impacts on the performance of telecommunication industries. In this paper, we made an attempt in to the analysis of telecommunication company data to assess the impact of customer engagements.Keywords
MTN, Machine Learning, K-Nearest Neighbor, K-Mean, Decision Tree.Full Text
References
- Aregbeyen, A. (2011). The Determinants of Bank Selection Choices by Customers: Recent and Extensive Evidence from Nigeria. International Journal of Business and Social Science.Vol. 2, No. 22, pp.276-288.
- BenlanHea, Y. & QianWan, X. (2014). Prediction of customer attrition of commercial banks based on SVM model. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014, Procedia Computer Science Vol. 31, pp.423 – 430.
- Ezawa, K. & Norton, S. (1995). Knowledge discovery in telecommunication services data using Bayesian Network models. Pp 100.
- Han, J., Altman, R. B., Kumar, V., Mannila, H., & Pregibon, D (2002). Emerging scientific applications in data mining. Communications of the ACM; 45(8): 54-58.
- Ngai, E.W.T. Li Xiu, D.C.K. % Chau, (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications. Vol. 36: pp. 2592–2602.
- Oshini Goonetilleke, T.L & Caldera, H.A. (2013). Mining Life Insurance Data for Customer Attrition Analysis. Journal of Industrial and Intelligent Information.Vol. 1: 52-58.
- Rehman,H. U. & Ahmed, S. (2008). An Empirical Analysis of the determinants of bank selection in Pakistan; A customer view. Pakistan Economic and Social Review. Vol. 46, no.2, pp.147-160.
- Siddiqi, K. O. (2011). Interrelations between Service Quality Attributes, Customer Satisfaction and Customer Loyalty in the Retail Banking Sector in Bangladesh. International Journal of Business and Management. Vol. 6, No. 3, pp.12-36.
- Soeini, R. A. & Rodpysh, K.V. (2012). Evaluations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn reduction: Case Study Insurance Industry”, International Conference on Information and Computer Applications. Vol. 24: 290297.
- ZHOA Shan, M. LIU Ai-Jun, L. (2007), "A predictive Model of Churn in Telecommunications Base on Data Mining"., IEEE International Conference on Control and Automation", Guangzhou, China.
- MTN official website retrieved from https://www.mtnonline.com/ on 11/01/2022
- ORANGE official website retrieved from https://orangedatamining.com/ on 15/01/2022.
- On the Analysis of Some Machine Learning Algorithms for the Prediction of Diabetes
Abstract Views :111 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
2 Department of Computer Science, Waziri Ummaru Federal Polytechnic, Birnin-Kebbi, NG
1 Department of Computer Science, Usmanu Danfodiyo University, Sokoto, NG
2 Department of Computer Science, Waziri Ummaru Federal Polytechnic, Birnin-Kebbi, NG
Source
International Journal of Advanced Networking and Applications, Vol 14, No 1 (2022), Pagination: 5294-5299Abstract
Diabetes or Diabetes Mellitus (DM) is noxious diseases in the world. Diabetes is caused by obesity or high blood glucose level, lack of exercise and so forth. It can be manage if it’s detected at early state. Machine learning is the construction of computer system or program that can adapt and learn from their experience. PIMA dataset is used in this research works. The dataset contains some 9 attributes of 768 patients. There are different kinds of machine learning algorithms but in this research works we choose three algorithms which are under supervised learning. The algorithms are Logistic regression, Decision tree and Random forest. Each of these algorithms model were trained and tested. We later use some measure to compare and analyze the performance of the machine learning algorithms. The performance measures used are Accuracy, F-measure, Recall and Precision. Logistic Regression has the highest accuracy score which is 77%, also have the highest precision score 0.77 and have the highest f-measure 0.64. Decision Tree has the highest recall score 0.58.Keywords
Diabetes, Machine Learning, Logistic Regression, Decision Tree, Random Forest.Full Text
References
- Deeraj Shetty, Kishor Rit, Sohail Shaikh, Nikita Patil, "Diabetes Disease Prediction Using Data Mining ".International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017.
- Tejas N. Joshi, Prof. Pramila M. Chawan, "Diabetes Prediction Using Machine Learning Techniques". Int. Journal of Engineering Research and Application, Vol. 8, Issue 1, (Part -II) January 2018, pp.-09-13.
- Jitranjan Sahoo, Manoranjan Dash & Abhilash Pati, “Diabetes Prediction Using Machine Learning Classification Algorithms”, International Research Journal of Engineering and Technology, Vol. 7, Issue 8, August 2020.
- Nonso Nnamoko, Abir Hussain, David England, "Predicting Diabetes Onset: an Ensemble Supervised Learning Approach ". IEEE Congress on Evolutionary Computation (CEC), 2018.
- Mitushi Soni, ‘Diabetes Prediction using Machine Learning Techniques’, International Journal of Engineering Research & Technology, Vol. 9, Issue 9, September 2020.