Outlier free Real Estate Predictive Model

Authors

  • Banerji G Professor & Head – Computer Science, Institute of Innovation in Technology & Management, New Delhi
  • Saxena K Professor & Head – Department of Computer Applications, Samrat Ashok Technological Institute, Vidisha, M.P., India

Keywords:

AODE (Average one dependence estimators), Classification based outlier detection, clustering based outlier detection, statistical based outlier detection component

Abstract

Studying human behaviour is a difficult task for many reasons:-The reasons are ranging from an intentional task to a cognitive sign, which are processes other than those of interest occurring at the same time. The processes can be psychological, social constraint and all cognitive. The processes operated in the background and have either some time no effect on the collective data or they may reflect the results occasionally. All those undesired behaviours produce measurable responses sometimes happens to be correct by chance. Some responses however may attract attention due to their unusual aspects , denoted as outliers. If not properly handled the outliers in the design phase the resultants may affect the resultant inferences or t he experimental outcome at initial stage. Thus, it is required to treat the outliers before it is too late in the design phase itself. The influence of outliers is more importance if the sample size is small with the examined statistics, which is less robust. In this paper, we have detected the outlier patterns in the Real Estate era and removed it up to a great degree, which not only reflects the drastic change in the results but also improves the rules formation. Thus, we provide a structure and comprehensive overview of the research on outlier detection.

References

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Published

2024-02-26

How to Cite

Banerji, G., & Saxena, K. (2024). Outlier free Real Estate Predictive Model. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(06), 831–835. Retrieved from https://ijact.in/index.php/j/article/view/148

Issue

Section

Original Research Article