An Overview of Machine Learning Techniques and Tools for Predictive Analytics
Predictive analytics is the use of raw facts or data, algorithms of statistics and techniques of machine learning to identify what is the possibility of future outcomes based on historical data. Our main goal is to get the knowledge of what has happened in the past and predict future scenarios. This paper gives a brief introduction of various machine learning techniques and tools which use these machine learning techniques to accurately predict the outcomes based on the given data and business requirement. Furthermore, this paper is aimed help beginners in the field of predictive analytics to choose between various tools and techniques available in the market which can maximize the accuracy and outcomes.
I.Rish, “An Empirical Study of Naïve Bayes Classifier”, IJCAI 2001 Empir Methods Artif Intell. 3.
Sasan Karamizadeh, Shahidan M. Abdullah et al, “Advantages and Drawbacks of Support Vector Functionality,” 2014 IEEE 2014 International Conference on Computer, Communication, and Control Technology (I4CT 2014), September 2-4, Langkawi, Kedah, Malaysia.
Jin Huang, Jingjing Lu and Charles X. Ling, “Comparing Naïve Bayes, Decision Trees, and SVM with AUC and Accuracy,” Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03)0-7695-1978-4/03
Arpit Bansal, Mayur Sharma, and Shalini Goel, “An Improved K-Means Clustering for Prediction Analysis using Classification Technique in Data Mining,” International Journal of Computer Applications (0975 – 8887) Volume 157 – No 6, January 2017
Aderibigbe Israel Adekitan and Odunayo Salau, “The impact of engineering students’ performance in the first three years on their graduation result using educational data mining,” Heliyon 5 (2019) e01250
 S. R. Vispute, S. Kanthekar, A. Kadam, C. Kunte and P. Kadam, "Automatic Personalized Marathi Content Generation," 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), Mumbai, 2014, pp. 294-299.
 S. R. Vispute and M. A. Potey, "Automatic text categorization of marathi documents using clustering technique," 2013 15th International Conference on Advanced Computing Technologies (ICACT), Rajampet, 2013, pp. 1-5.
 S. R. Vispute, S. Patil, S. Sangale, A. Padwal and A. Ukarde, "Parallel Processing System for Marathi Content Generation," 2015 International Conference on Computing Communication Control and Automation, Pune, 2015, pp. 575-579.
 Sandeep Kumar, Deepak Kumar, and Rashid Ali, “Factor Analysis Using Two Stages Neural Network Architecture”, International Journal of Machine Learning and Computing, Vol. 2, No. 6, December 2012
 Abhay Kumar, Ramnish Sinha, Daya Shankar Verma, “Modeling using K-Means Clustering Algorithm”, 1st Int’l Conf. on Recent Advances in Information Technology | RAIT-2012 |
 J. Han and M. Kamber, “Data mining Concepts and techniques”, 2nd edition, Morgan Kaufmann Publishers, pp. 401-404, 2007.
 Stephen J. Redmond, Conor Heneghan, “A method for initialising the K-means clustering algorithm using kd-trees”, Pattern Recognition Letters 28 (2007) 965-973.
 O. Dekel, O. Shamir, and L. Xiao. Learning to classify with missing and corrupted features. Machine Learning, 81(2):149–178, 2010.
 M.Reza, F.Derakhshi, M.Ghaemi, "Classifying Different Feature Selection Algorithms Based on the Search Strategies", International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME'2014), Dubai (UAE)
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :