# Nishchol Mishra et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (3), 2012,

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2 Multiple related predictive models produce insights for making strategic company decisions such as exploring new markets, acquisitions, and retentions; finding up-selling and cross-selling opportunities; and discovering areas that can improve security and fraud detection. Predictive analytics indicates not only what to do, but also how and when to do it, and to explain what-if scenarios [20]. The major objective of data mining is to build a model that can be used to predict the occurrence of an event. The model builders will extract knowledge from historic data and represent it in such a form that the resulting model can be applied to new situations. The process of analysing data sets extracts useful information on which to apply one or more data mining techniques in order to discover previously unknown patterns within the data, or find trends in the data which can then be used to predict future trends or behaviours. Data mining can be divided into two main categories: supervised (predictive) and unsupervised (descriptive). In supervised learning, data is modelled from training data to find patterns within the data which can then be used to predict a label or value, given some set of parameters. Supervised learning is the process of creating predictive models using a set of historical data that contains the results we are trying to predict. The type of data determines whether this is done using a Regression or a Classification algorithm. Regression is a statistical methodology that was developed by Sir Frances Galton ( ), a mathematician who was also a cousin of Charles Darwin. Regression analysis can be used to model the relationship between one or more independent or predictor variables and a dependent or response variable (which is continuous valued) [17]. The simplest form of regression, linear regression, uses the formula of a straight line (y = mx + c) and determines the appropriate values for m and c to predict the value of y based on the input parameter, x. Advance techniques, such as multiple regression, allow the use of more than one input variable and allow for the fitting of more complex models, such as higher order polynomial equations [17]. Regression is a well-established and reliable statistical technique. Classification is the set of data mining techniques used to fit discrete (categorical) data to a known structure in order to be able to form predictions for the class label of unlabelled data. Typically, classification algorithms are done in three phases, the first two phases, training and testing, use labelled data, that is, data which has known class labels. Training uses a portion of the data to fit a classifying model to the data. The testing phase then uses the models to try and predict the class labels, and validates the predictions using the actual values in order to determine how accurate the model is. The feedback from this determines how well the models work, and whether new models should be built. Once an acceptable model is built that passes the testing phase, the classifier is deployed on unlabelled data. This is called the deployment phase. Common classification algorithms include Bayesian classification, decision trees, back-propagation and neural networks, and genetic and evolutionary learners. Unsupervised learning refers to the problem of trying to find hidden structure in unlabeled data. Unlike supervised algorithms, unsupervised algorithms do not learn from historical data with known labels, hence, they perform without any supervision. Standard unsupervised techniques include clustering, characterization, association rule mining, and change and deviation detecting techniques. Predictive Analytics consists of a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events [14]. Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions. Predictive analytics use data-mining techniques in order to make predictions about future events, and make recommendations based on these predictions [19] [14]. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. 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 [16]. Generally, the term predictive analytics is used to mean predictive modeling, "scoring" data with predictive models, and forecasting. However, people are increasingly using the term to describe related analytical disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making but have different purposes and the statistical techniques underlying them vary. Predictive models analyze past performance to assess whereas Descriptive models quantify relationships in data [15] [16]. Decision models describe the relationship between all the elements of a decision the known data (including results of predictive models), the decision and the forecast results of the decision in order to predict the results of decisions involving many variables [16]. These models can be used in optimization, maximizing certain outcomes while minimizing others. III. PREDICTIVE ANALYTICS TECHNIQUES Predictive models analyze identify patterns in historical and transactional data to determine various risks and oppurtunities. Forecasting models capture relationships between many factors to allow assessment of the risks or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Three basic techniques for Predictive analytics are Data profiling and Transformations, Sequential Pattern Analysis and Time Series Tracking [16]. Data profiling and transformations are functions that change the row and column attributes and analyses dependencies, data formats, merge fields, aggregate records, and make rows and columns [15]. Sequential pattern analysis identifies relationships between the rows of data. Sequential pattern analysis involves identifying frequently observed sequential occurrence of items across ordered transactions over time. Time Series Tracking is an ordered sequence of values at variable time intervals at the same distance [15]. Time series analysis gives the fact that the data points taken over time. 4435

5 the limits in terms of scalability for systems. The major problem associated with scaling algorithms is that communications and synchronization overheads go up and so a lot of efficiency can be lost, especially where the computation doesn't fit nicely into a map/reduce model [22]. Data Ecosystems and Exchanges: The emergence of data exchanges is clearly related to the problems with ownership of data. They allow data to be exchanged under a clear set of rules with ownership and conditions contractually determined. They also allow for a company to have a viable business model as a data provider and so provide useful data to the whole ecosystem without having to also compete on how the data is used [22]. IX. CONCLUSION The future of Data Mining lies in Predictive Analytics. This study mainly focuses on opportunities, applications, trends & challenges of Predictive Analytics in Knowledge discovery domain. Predictive Analytics is an area of interest to almost all communities and organizations. Predictive analytics is using business intelligence data for forecasting and modeling. Proper data mining algorithms and predictive modeling can refine search for targeted customers. Predictive Analytics can aid in choosing marketing methods, and marketing more efficiently. Predictive Analytics can be also helpful in Social Media Analytics. REFERENCES [1] V. H. Bhat, P. G. Rao, S. Krishna, and P. D. Shenoy, An Efficient Framework for Prediction in Healthcare, Most, Springer-Verlag Berlin Heidelberg, pp , [2] R. Banjade and S. Maharjan, Product Recommendations using Linear Predictive Modeling, [3] Debahuti Mishra et al., Predictive Data Mining: Promising Future and Applications, Int. J. of Computer and Communication Technology, Vol. 2, No. 1, pp , [4] N. Chinchor, J. Thomas, and P. Wong, Multimedia Analysis+ Visual Analytics= Multimedia Analytics, IEEE Computer Graphics, [5] R. Maciejewski et al., Forecasting Hotspots - A Predictive Analytics Approach., IEEE transactions on visualization and computer graphics, vol. 17, no. 4, pp , May [6] A. Guazzelli, K. Stathatos, and M. Zeller, Efficient deployment of predictive analytics through open standards and cloud computing, ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp , [7] J. Yue, A. Raja, D. Liu, X. Wang, and W. Ribarsky, A blackboardbased approach towards predictive analytics, in Proceedings AAAI Spring Symposium on Technosocial Predictive Analytics, pp , [8] R. M. Riensche et al., Serious Gaming for Predictive Analytics, in AAAI Spring Symposium on Technosocial Predictive Analytics. Association for the Advancement of Artificial Intelligence (AAAI), San Jose, CA, no. Zyda, pp , [9] Sanfilippo et al., Technosocial Predictive Analytics in Support of Naturalistic Decision Making, Symposium A Quarterly Journal In Modern Foreign Literatures, no. June, pp , [10] V. H. Bhat, P. G. Rao, and P. D. Shenoy, An Efficient Prediction Model for Diabetic Database Using Soft Computing Techniques, Architecture,, Springer-Verlag Berlin Heidelberg, pp , [11] Z. Huang et al., Managing Complex Network Operation with Predictive Analytics, Proceedings of the AAAI Spring Symposium on Technosocial Predictive Analytics, Science, pp , [12] C. Mccue, Data mining and Predictive analytics in public safety and Security, Analysis, IEEE Computer Society, pp.12-18, August, [13] M. A. Razi and K. Athappilly, A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models, Expert Systems with Applications, vol. 29, no. 1, pp , [14] [15] [16] [17] Jiawei Han and Micheline Kamber,2006. [18] Wayne W. Eckerson, Predictive Analytics : Extending the Value of YourData Warehousing Investment, [19] [20] M Zaman, Predictive analytics; the future of business intelligencewww.mahmoudyoussef.com [21] [22]

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