A Predictive Approach of Data Science to Transform Unstructured Web Data


Author(s): Rohan Kumar Mishra, Komal Sarraf, Anirban Bhar, Moumita Ghosh

In the Fourth Industrial Revolution era, there is a wealth of data available in the digital world, including internet of things (IoT) data, corporate data, health data, mobile data, urban data, security data, and many more. Making wise decisions can be achieved in many application sectors by gaining knowledge or insightful information from these facts. In the field of data science, advanced analytics techniques like machine learning modelling can offer more in-depth understanding of the data or actionable insights, which makes computing more intelligent and autonomous. It has always been a focus of study to apply predictive analytics to structured time-series data. Researchers have begun merging pertinent structured and unstructured data due to the abundance of textual content being generated across various sources on the web. An active field of research has long been predictive analytics over structured time-series data. Due to the abundance of textual data being produced by many online sources, researchers have begun merging pertinent structured and unstructured data to enhance predictions. In this article, we provide a data science paradigm for predictive analytics that makes use of unstructured data. We will examine data science and its relationship to artificial intelligence, machine learning, and deep learning in this essay. In this research, we attempted to exhibit the data science operations like data cleaning, data processing, data modelling, data visualisation, and data presenting approaches. The inclusion of these intellectual sciences in data science is valuable for perming many operations. Knowing your customers' demands and exceeding their future expectations through wise decision-making are essential for any firm looking to expand. The analytical algorithms or data operations used in data science make the data more useful for making decisions and enforcing decisions. We also emphasise the integration of mathematical and statistical techniques, logical thinking, and applications of artificial intelligence techniques in data science.