This paper explores various strategies and planning techniques for effectively solving problems using machine learning. With the increasing complexity of real-world challenges, it is crucial to leverage the power of machine learning algorithms to find optimal solutions. We present a comprehensive overview of key approaches, methodologies, and best practices in problem-solving using machine learning. The paper highlights the importance of careful planning and strategy formulation to achieve successful outcomes. It also discusses the challenges and potential pitfalls that may arise during the problem-solving process.
Authors and Affiliations
Khusboo Rawat
Research Scholar, Eklavya University Damoh,Madhya Pradesh,India
Dr. Gopi Sao
Head, Department of Mathematics Eklavya University,Damoh,Madhya Pradesh,India, Email: gopisao0104@gmail.com
Puja Verma
Research Scholar, Eklavya University Damoh,Madhya Pradesh,India
Machine Learning, Machine learning algorithms, Strategies and Planning
-
- Goodfellow, I., Bengio, Y., &Courville, A. (2016). Deep Learning.MIT Press.
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
- Chollet, F. (2017). Deep Learning with Python.Manning Publications.
- Raschka, S., &Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning.Springer.
- Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., &Elhadad, N. (2015). Intelligible Models for Healthcare: Predicting Pneumonia Risk and Hospital 30-day Readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
- Ribeiro, M. T., Singh, S., &Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
- Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning.arXiv preprint arXiv:1702.08608.
- Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841-887.
- Lipton, Z. C. (2016). The Mythos of Model Interpretability.arXiv preprint arXiv:1606.03490.
- DallaPozza, I., Goetz, O., &Sahut, J. M. (2018). Implementation effects in the relationship between CRM and its performance. Journal of Business Research, 89, 391–403. https://doi.org/10.1016/j.jbusres.2018.02.004
- Subramanian, R. S., &Prabha, Dr. D. (2017). A Survey on Customer Relationship Management.2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017.
- Coussement, K., Lessmann, S., &Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27–36. https://doi.org/10.1016/j.dss.2016.11.007
- De Caigny, A., Coussement, K., & De Bock, K. W. (2018).A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760–772. https://doi.org/10.1016/j.ejor.2018.02.009
- Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237,242–254. https://doi.org/10.1016/j.neucom.2016.12.009
- Martínez, A., Schmuck, C., Pereverzyev, S., Pirker, C., &Haltmeier, M. (2020). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), 588–596. https://doi.org/10.1016/j.ejor.2018.04.034
- Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31. https://doi.org/10.1016/j.dss.2014.03.001
- Ganesh Babu R., Elangovan K., Maurya S., Karthika P. (2021) Multimedia Security and Privacy on Real-Time Behavioral Monitoring in Machine Learning IoT Application Using Big Data Analytics. In: Kumar R., Sharma R., Pattnaik P.K. (eds) Multimedia Technologies in the Internet of Things Environment. Studies in Big Data, vol 79.Springer, Singapore.https://doi.org/10.1007/978-981-15-7965-3_9
- C. Jain, G. V. S. Sashank, V. N and S. Markkandan, "Low-cost BLE based Indoor Localization using RSSI Fingerprinting and Machine Learning," Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2021, pp. 363-367
Publication Details
Published in : Volume 9 | Issue 1 | March- April 2024
Date of Publication : 25-03-2024
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 27-37
Manuscript Number : IJSMER202403
Publisher : Rems Publication
ISSN : 2455-6203
Cite This Article :
Khusboo Rawat, Dr. Gopi Sao, Puja Verma " Strategies and Planning for Problem of Customers Using Machine
Learning ", International Journal of Science Management and Engineering Research (IJSMER), ISSN : 2455-6203, Volume 9 Issue 1, March- 2024 , pp. 27-37.
Available at doi : https://doi.org/
Journal URL : https://ejournal.rems.co.in/IJSMER202403 |