Predictive Analysis
Predictive analysis can be defined as an exercise of extracting the information from existing data in order to predict something important about future.
Different predictive models and analysis are used to predict future which can be applied to different business to analyze something about current data and historical facts in order for getting better understanding about customers, products and partners and to identify possible risks and opportunities. It uses a number of techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.
Decision Trees
One approach for developing predictive classification model is a decision and classification tree, which represents
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The major limitations include:
• Inadequacy in applying regression and predicting continuous values
• Possibility of spurious relationships
• Unsuitability for estimation of tasks to predict values of a continuous attribute
• Difficulty in representing functions such as parity or exponential size
• Possibility of duplication with the same sub-tree on different paths
• Limited to one output per attribute, and inability to represent tests that refer to two or more different objects
Induction of Decision Trees
The implementation of the decision tree involves a data structure consisting of nodes and edges (or links), in which one node is identified as a parent to other nodes (the children) that are connected via the edges. When traversing the tree, each determination as to which path to take from any specific node is dependent on the answer to the node’s question. At each step along the path from the root of the tree to the leaves, the set of records that conform to the answers along the way continues to grow
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It uses gain ratio as splitting criteria. The splitting ceases when the number of instances to be split is below a certain threshold. Error–based pruning is performed after the growing phase. C4.5 can handle numeric attributes. It can induce from a training set that incorporates missing values by using corrected gain ratio criteria.
CART (Classification and Regression Tree)
CART is characterized by the fact that it constructs binary trees, namely each internal node has exactly two outgoing edges. The splits are selected using the twoing criteria and the obtained tree is pruned by cost–complexity Pruning.
When provided, CART can consider misclassification costs in the tree induction. It also enables users to provide prior probability distribution. An important feature of CART is its ability to generate regression trees. Regression trees are trees where their leaves predict a real number and not a class. In case of regression, CART looks for splits that minimize the prediction squared error (the least–squared deviation). The prediction in each leaf is based on the weighted mean for node.
CHAID (CHi-squared Automatic Interaction Detector)
Performs multi-level splits when computing classification
It is important for managers to make right decisions when it comes to a business. Managers need to make decisions based on surrounding factors and make decisions due to problems that arise. Managers also need to solve those problems which can give a business a competitive advantage. That is why it is important to have a Decision Support System in place, it can help a manager in better decision-making. This paper seeks to explain Information systems and Decision Support System (DSS). The following topics will be covered with regards to Information Systems and Decision Support System; background on what an Information system is, what a Decision Support Systems is, explain how the Decision Support System relates to users, discuss the types of Decision Support Systems, characteristics of a Decision Support System, explain the advantages as well as the disadvantages of the Decision Support Systems and lastly
Classification Text documents are arranged into groups of pre-labeled class. Learning schemes learn through training text documents and efficiency of these system is tested by using test text documents. Common algorithms include decision tree learning, naive Bayesian classification, nearest neighbor and neural network. This is called supervised learning.
The information can be made into categories which can be outputted to make choices for the future of the company. Information will be kept in the database and the database will contain tools to help process the information in multiple ways.
When I am teaching in the future, I am going to explain “data informed decision making” in three ways: what “data informed decision making” is , ways it can be used and ways it improves students.
Predictive and Text Analysis on Big Data – Being able to forecast data and analyse critical information for the company.
54). The first step in forecasting is to develop the opportunity or threat with different alternative conclusions, which is most useful when using a brainstorming method (Ginter et al., 2013, p. 54). In addition, there is a need to identify the associations between the tendencies, changes, predicaments, and or likelihood of events and the environmental categories (Ginter et al., 2013, p. 54), such as the judicial/political environment of the Affordable Care Act. In doing so, it will allow management to see the possibilities of how these issues can affect the future of the company. In turn, this allows the management team to build a better strategic plan, so that the healthcare business has longevity in the fast-paced environment. However, one must assess all the information proposed from the scanning, monitoring, and forecasting of the potential threats or opportunities to the healthcare
Once the market research data is compiled, it is then evaluated and upon which recommendations and conclusions about are drawn. This includes how the design of the product would look like, its price, initial niche markets, etc.
Various learning situations may dictate differing learning processes. The three that will be briefly highlighted in this paper are; learning by induction, through the use of decision rules or decision trees; learning by discovery; and learning by taking advice, explanation-based generalization. The concept of multi-strategy learning in order to handle more complex problems will also be examined.
Amazon developed a program to recruit an army of suppliers and convince them it was a trustworthy partner that could help them increase the market for their products. Amazon wheeled out a program called Amazon Chai Cart: mobile tea carts that navigated city streets, serving refreshments to small-business owners while teaching them the virtues
Definition. A tree consists of a (possible empty) set of nodes. If it is not empty, it consists of a distinguished node r called the root and zero or more non-empty subtrees T1, T2, …, Tk such that there is a directed edge from r to each of the roots of T1, T2, …, Tk.Definition. A forest is a collection of trees. You can always create a tree form a forest by creating a new root node and making it the parent of the roots of all of the trees in the forest. Conversely, if you lop off the root of a tree, what is left is a forest.
It has reached the day and age where accurate and real time prediction tools are needed in modern clinics and hospitals. To utilize predictive medicine it is important to use the right trends of data mining methodologies to get accurate results (Paramasivam et al. 2014).
Analytics means using data and performing statistical analysis on it, applying quantitative and predictive models, in order to arrive at a certain decision. Analytics can be the first step in a process or can rather be an intermediate step as well. Analysis can be done using different set of tools that are available in the market or it can done manually using different concept and formulas. Business intelligence firms like Cognos, SAS and BusinessObjects have developed different tools that are readily available in market that assist in analysis and decision making. Analytics is used in order to find solutions to the problems and the solutions provided enables us to be successful and in the business world allow us to compete with our contenders.
Regression analysis is a technique used in statistics for investigating and modeling the relationship between variables (Douglas Montgomery, Peck, &
Business forecasting is the process of studying historical performance for the purpose of using the knowledge gained to project future business conditions so that decisions can be made today that will aid in the achievement of established goals. Forecasting plays a crucial role in today's uncertain global marketplace. Forecasting is traditionally either qualitative or quantitative, with each offering specific advantages and disadvantages.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.