Machine Learning Challenges - Data Integration and Transformation

AI Machine Learning model accuracy depends on the quality of data. In data science, when we say quality of data, it means data consistency, data completeness and data correctness which are all part of data integrity. In this session we will talk about how machine learning models can be adopted for data integration. Also, in case of some of the machine learning models, we assume data is normally distributed or data elements are appropriately scaled. However, it is not always true. Hence, data has to be transformed by normalizing data without losing its integrity. This is a big challenge in data science. Data integrity is maintained with the help of integrity constraints or the rules that are designed to keep data consistent and correct. In this session we will discuss some of the techniques and methods used for data integration, data transformation and normalization while ensuring data integrity. We will walk you through the steps involved with the help of examples.