ABSTRACT
Data analytics is used to make the decision by analyzing the raw data. Data analytics is categorized into four categories: descriptive, diagnostic, prescriptive, and predictive analytics. Predictive analysis is the most powerful tool for data analytics and involves advanced statistical, modeling, data mining, artificial intelligence, deep learning to dig into data, and allows analytics to make predictions. Predictive analysis can be classified into two types, namely regression analysis and classification. When the dependent variables in the form of binary, logistic regression is the best method to perform. Regression analysis is an effective mathematical tool in which the association between two or more elements of interest may be analyzed by the researcher. An efficient data classification mechanism is also valuable because it can help the system decide the required degree of control in order to protect the security and credibility of the data. To achieve the statistics for predictive analysis, machine learning acts as the core principle for it. Machine learning is used to help a computer to interpret information, comprehend connections, and use observations to solve problems and/or enrich information. Predictive analytics extensively used machine learning for data modeling due to its ability to accurately process a vast amount of data and recognize patterns.
