Predictive Analytic Modeling: A Walkthrough

Abstract

Abstract-Predictive analytic modeling is the key step involved in building a successful Data Mining solution. Predictive analytic modeling is a set of iterative activities in which a predictive model is built from the observed data set. Predictive analytics is the area of data mining concerned with the prediction of future probabilities and trends using archive data. It brings together management, information technology and modeling that eases researchers in making suitable predictions. Many methodologies have been proposed for model building, and these are based on some basic industrial engineering frameworks. The proposed research work discusses CRISP, DMAIC and SEMMA methodologies which provide the building blocks for an efficient predictive model. Keywords-Predictive Analytics, Data Mining, Modeling, CRISP, DMAIC and SEMMA. I. INTRODUCTIONToday's demanding atmosphere is forcing more organizations to explore and adopt predictive analytics. Data being the indispensible assets of an organization is increasing exponentially and so does the data storage need. Selling large amount of data has little value, but if one can add insight via analytics to data then we have the opportunity to monetize it. There is a sea change in the corporate world, researchers and IT organizations can seize upon the opportunity to reinvent themselves and transform from a support function into a profit center [1]. "We are sitting on a mountain of gold but we're not mining it as effectively as we could," says Michael Masciandaro, director of business intelligence at Rohm & Haas, a global specialty materials manufacturer. Data Mining is the technique that retrieves useful information from large amounts of data. Data mining is defined as a process involving the extraction of useful and interesting information from the underlying data [2]. Modeling is the main part of Predictive Analytics. Predictive Analytics modeling is used in representation of real world situations for rendering or description of reality. Limited, imprecise, but useful, a model helps us to make sense of the world [3]. The result of the models may give important information that can be used in decision making and future prediction. Predictive Analytic Modeling helps in optimizing existing processes, better understanding and predicting customer behavior, identify unexpected opportunities and recessions, and predict problems before they happen. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions [4]. Predictive analytic modeling involves searching for meaningful relationships among variables and representing those relationships in models. These variables are called predictors and constitute the core element of predictive analytics. A predictor could be measured for an indi...

Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India)

“…Data mining techniques have significantly changed the weather prediction process [29]. In the past decades lot of weather prediction models were designed and implemented using data mining techniques [31].…”

Predictive Modeling for Criminology Licensure Examination Success Through Mathematical Modelling

“…Verhagen (2020) highlights its application in predicting the location of archaeological sites, emphasizing the importance of accurate and representative datasets. Shah (2017) provides a comprehensive overview of the process of building a predictive model, discussing methodologies such as CRISP, DMAIC, and SEMMA. Francopoulo (2016) applies predictive modeling to the NLP domain, focusing on the usage of technical terms.…”

Gwlm–narx

“…The prediction of the rainfall is significantly needed in the areas that are subjected to the flash flooding and in these places, the prediction accuracy depends on the frequency of the rainfall in that particular area, and the prediction could be made in advance (Altunkaynak and Nigussie, 2015). Accurate rainfall prediction (Mohammad et al , 2017a, b; Shah et al , 2017a, b) is the major concern in most of the countries. Thus, accurate rainfall prediction plays a beneficial role in designing the policies, planning and final decisions, and above all, it assures a sustainable functioning of water resource systems (Cramer et al , 2017; Wadoux et al , 2017; Kashiwao et al , 2017; Adil et al , 2017; Mohammad et al , 2017a, b).…”

“…Modelling the rainfall prediction is a tedious process because the process of modelling injects complexity as the processes of the atmosphere seems to be complex (Sedki et al , 2009; Teschl et al , 2007; Wu and Jin, 2009; Wu et al , 2010, 2015). The accurate prediction of the rainfall enhances the quality of decision making and the corresponding activities (Huang and Davy, 2016; Thorey et al , 2015; Shah et al , 2017a, b). A broad range of rainfall prediction techniques is employed at national and regional levels.…”

“…The empirical method depends on the analysis of past historical data of weather (Azad and Dixit, 2019a, b). The mainly utilized empirical methods for rainfall prediction includes the auto-regressive integrated moving average, artificial neural networks (ANNs) (Vijaya et al , 2016; Shah et al , 2017a), multiple linear regression, K-nearest neighbours and support vector machines for regression methods (Ninu Preetha and Praveena, 2018; Adil et al , 2017). These types of algorithms possess the tendency to evaluate multiple points simultaneously in the search space and also, they are capable of determining the global solution for a given problem (Mohammad et al , 2017a, b).…”