| dc.contributor.author | Ndung'u, Rachel N. | |
| dc.date.accessioned | 2022-06-20T09:05:02Z | |
| dc.date.available | 2022-06-20T09:05:02Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | International Journal of Computer Applications Technology and Research Volume 11–Issue 06, 231-235, 2022, ISSN:-2319–8656 DOI:10.7753/IJCATR1106.1008 | en_US |
| dc.identifier.issn | 2319–8656 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/6104 | |
| dc.description.abstract | The world today is on revolution 4.0 which is data-driven. The majority of organizations and systems are using data to solve problems through use of digitized systems. Data lets intelligent systems and their applications learn and adapt to mined insights without been programmed. Data mining and analysis requires smart tools, techniques and methods with capability of extracting useful patterns, trends and knowledge, which can be used as business intelligence by organizations as they map their strategic plans. Predictive intelligent systems can be very useful in various fields as solutions to many existential issues. Accurate output from such predictive intelligent systems can only be ascertained by having well prepared data that suits the predictive machine learning function. Machine learning models learns from data input using the ‘garbage-in-garbage-out’ concept. Cleaned, pre-processed and consistent data would produce accurate output as compared to inconsistent, noisy and erroneous data. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Data Preparation; Data pre-processing; Machine Learning; Predictive models | en_US |
| dc.title | Data Preparation for Machine Learning Modelling | en_US |
| dc.type | Article | en_US |
