Data Quality Model for Machine Learning

Abstract: Context: - Machine learning is a part of artificial intelligence, this area is now continuously growing day by day. Most internet related services such as Social media service, Email Spam, E-commerce sites, Search engines are now using machine learning. The Quality of machine learning output relies on the input data, so the input data is crucial for machine learning and good quality of input data can give a better outcome to the machine learning system. In order to achieve quality data, a data scientist can use a data quality model on data of machine learning. Data quality model can help data scientists to monitor and control the input data of machine learning. But there is no considerable amount of research done on data quality attributes and data quality model for machine learning. Objectives: - The primary objectives of this paper are to find and understand the state-of-art and state-of-practice on data quality attributes for machine learning, and to develop a data quality model for machine learning in collaboration with data scientists. Methods: - This paper mainly consists of two studies: - 1) Conducted a literature review in the different database in order to identify literature on data quality attributes and data quality model for machine learning. 2) An in-depth interview study was conducted to allow a better understanding and verifying of data quality attributes that we identified from our literature review study, this process is carried out with the collaboration of data scientists from multiple locations. Totally of 15 interviews were performed and based on the results we proposed a data quality model based on these interviewees perspective. Result: - We identified 16 data quality attributes as important from our study which is based on the perspective of experienced data scientists who were interviewed in this study. With these selected data quality attributes, we proposed a data quality model with which quality of data for machine learning can be monitored and improved by data scientists, and effects of these data quality attributes on machine learning have also been stated. Conclusion: - This study signifies the importance of quality of data, for which we proposed a data quality model for machine learning based on the industrial experiences of a data scientist. This research gap is a benefit to all machine learning practitioners and data scientists who intended to identify quality data for machine learning. In order to prove that data quality attributes in the data quality model are important, a further experiment can be conducted, which is proposed in future work.