Machine Learning Techniques for Better Data Driven Decisions Revisited
Shyam Baboo Bambiwal1, Rajveer Singh Yaduvanshi2, Vijay Kumar Pandey3
1Tarika Verma*, Research Scholar, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
2Nasib Singh Gill, Professor, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 460-464 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6766049420/2020©BEIESP | DOI: 10.35940/ijeat.D6766.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main goal of machine learning is to accurately predict the decisions to the problems without human expert intervention. These decisions depend upon patterns found and facts learnt during training tenure. However, prior incorporation of human knowledge is necessary for better prediction of the test data. The main aim is to make machines self-reliant for decision making. Providing machine with this vision makes it useful in every modern field. This makes the stepping stone to make computers behave as the humans do. Enhancing its speed and accuracy are the next step in this field. This paper presents a stock of techniques used to train the machines to respond to patterns present in the data sets so that useful information may be extracted for its potential use.
Keywords: Machine Learning Techniques, Supervised ML, Unsupervised ML, Reinforcement ML, Naïve Bayes, SVM, Decision Tree, Regression, Clustering, Association Rule, Apriori