DOI:10.20894/IJBI.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.065
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Real world data sets considerably is not in a proper manner. They may lead to have incomplete or missing values. Identifying a missed attributes is a challenging task. To impute the missing data, data preprocessing has to be done. Data preprocessing is a data mining process to cleanse the data. Handling missing data is a crucial part in any data mining techniques. Major industries and many real time applications hardly worried about their data. Because loss of data leads the company growth goes down. For example, health care industry has many datas about the patient details. To diagnose the particular patient we need an exact data. If these exist missing attribute values means it is very difficult to retain the datas. Considering the drawback of missing values in the data mining process, many techniques and algorithms were implemented and many of them not so efficient. This paper tends to elaborate the various techniques and machine learning approaches in handling missing attribute values and made a comparative analysis to identify the efficient method.
