Power Tools

Warning: curl_setopt() [function.curl-setopt]: CURLOPT_FOLLOWLOCATION cannot be activated when in safe_mode or an open_basedir is set in /home/mjwpmag1/public_html/mandwtile.com/wp-content/plugins/affiliate-mage/classes/amazon.class.php on line 1

Warning: curl_setopt() [function.curl-setopt]: CURLOPT_FOLLOWLOCATION cannot be activated when in safe_mode or an open_basedir is set in /home/mjwpmag1/public_html/mandwtile.com/wp-content/plugins/affiliate-mage/classes/cj.class.php on line 1

Predictive Analytics

Definition
Predictive analytics is an area of statistical analysis refers to the extraction of information from data and use it to predict future trends and behaviors. The core of predictive analytics relies on capturing relationships between explanatory variables and predicted variables from past events, and use it to predict future results.
Type
In general, predictive analytics is used in the sense of prediction models, models Scoring prediction and prognosis. However, people are increasingly using the term to describe associated disciplines of analysis, such as modeling descriptive and decision modeling or optimization. These disciplines must also include a rigorous analysis of the data, and are widely used in business for segmentation and decision making, but have different purposes and statistical techniques that underlie the change.
Forecasting models
Forecasting models analyze past performance to assess the probability that a customer is to show a specific behavior in the future to improve the effectiveness of marketing. This category also includes models that subtle patterns of data to satisfy questions about the performance of the client, such as fraud detection models. Predictive models often perform calculations during live transactions, for example, to assess the risk or the possibility of a particular customer or transaction, to guide a decision.
descriptive models
descriptive models to quantify the relationships among the data in a way that is often used to classify customers or potential customers into groups. Unlike predictive models that focus on predicting the behavior of a customer (eg credit risk), descriptive models identify many relationships among different customers or products. descriptive models are not rank order customers by their likelihood of taking a particular action how predictive models do not. models descriptors can be used, for example, to classify customers for your product preferences and stage of life. descriptive devices for use in developing models new models to simulate the large number of individual agents and make predictions.
Decision models
Decision models describe the relationship between all elements a decision known data (including results of predictive models), the decision and expected outcomes of the decision in order to predict the results decisions involving many variables. These models can be used for optimization, maximize results and minimize others. decision models are usually used to develop the logic of the decision or set of trading rules to produce the necessary action for each client or circumstances.
Applications
Although predictive analytics can be used in many applications, are some examples where predictive analysis has demonstrated the positive impact of recent years.
Analytical Relationship Management (CRM)
Analytical Customer Relationship Management is a commercial application of common predictive analysis. Predictive analysis methods applied to customer data to meet the objectives of CRM.
Systems Clinical Decision Support
Experts use predictive analysis in health care especially to determine which patients are at risk of developing certain conditions such as diabetes, asthma, disease hearts and lives of others. In addition, sophisticated clinical decision support incorporating predictive analytics to support decision point health care. A definition work was proposed by Dr. Robert Hayward of the Centre for Health Evidence: "The systems of clinical decision support comments related health knowledge of health to influence health care decisions by physicians to better care. "
Collection of Google Analytics
Each portfolio is a collection outstanding accounts receivable that do not meet their payments on time. The financial institution must make efforts to collect from these customers to recover their claims. Many resources are wasted in the collection of customers that are difficult or impossible to recover. predictive analytics can help optimize the allocation of resources collection by identifying the collection agencies more effective strategies contact, legal actions and other strategies for each customer, increasing significant recovery, while reducing collection costs.
Cross-selling
Business organizations often collect and maintain large numbers data (eg customer records, sales transactions) and exploiting hidden relationships between data can provide a competitive advantage to the organization. For an organization that offers multiple products, an analysis of existing customer behavior can lead to cross-sell products effectively. This leads directly to improved customer profitability and strengthen customer relationships. predictive analytics can help analyze customer spending, usage and behavior of others, and help cross-sell the right product at the right time.
loyalty
With the amount of services available in the competition, companies need concentrate efforts on maintaining customer satisfaction continues. In this competitive scenario, loyalty should be rewarded and wear The client must be minimized. Companies tend to respond to customer attrition in a reactive manner, acting only after the customer has initiated the process of termination of service. At this stage, the possibility to change the customer's decision is almost impossible. Application proper analysis can lead to predictive retention strategy more proactive. Through revision frequent use of past service client, service performance, expenses and other behaviors, predictive models can determine the likelihood of customers who wish to cancel the service in the near future. An intervention with lucrative offers can increase the chances of retaining customers. silent drop behavior is a customer to use and gradually is another problem facing many companies. Predictive analysis can also be predicted with accuracy and this behavior before it occurs, so that society can take steps to increase customer activity.
Direct Marketing
Product marketing is constantly faced with the challenge of addressing the increasing number of competing products, various consumer preferences and the variety of methods (channels) available to interact with each consumer. marketing effective is a process of understanding the degree of variability and adaptation of marketing strategy for greater profitability. predictive analytics can help identify customers more likely to respond to a range of marketing in particular. Models can be constructed from data consumers spent buying history and rates Past response for each channel. Additional information on consumer demographic characteristics, geographical and others can be used to make predictions more accurate. Targeting these consumers may cause a significant increase in the rate of response that can lead to a significant reduction in cost per acquisition. Apart from identifying prospects, predictive analytics can also help identify the most effective combination of products and marketing channels to be used to reach a specific consumer.
Fraud Detection
Fraud is a serious problem for many companies and can be of different types. application Incorrect credit, fraudulent transactions, identity theft and false insurance claims are examples of this problem. These problems plague companies across the spectrum and some examples are likely victims of credit card issuers, insurance companies, retailers, manufacturers, suppliers, business enterprises and even service providers. This is an area where it is often a predictive model used to help reduce and eliminate the advertising exposure of a company for fraud.
predictive modeling can also be used to detect financial statement fraud in the business, allowing the accounts to assess the relative risk of a company and increasing background check procedures as required.
The Internal Revenue Service (IRS) United States also used predictive analytics to try to find tax evasion.
Portfolio, product or predicting the level of the economy
Often the object of analysis is not the consumer, but the product portfolio, the company, industry or the economy. For example, a retailer might be interested in forecasting demand inventory management Store. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be solved by techniques using time series prediction (see below).
Subscription
Many companies have to account for exposure to risk because its various services and determine the necessary cost to cover the risk. For example, auto insurance providers to determine accurately the amount of the premium charged to cover every car and driver. A finance company needed to assess the potential of the borrower and its ability to pay before granting a loan. Business Solutions health insurance, predictive analytics can analyze the past few years, given the reimbursement of medical expenses, laboratory, and, pharmacy and other documents where appropriate, to predict how much one is likely to be enrolled in future. Predictive analysis can help in securing these amounts in the provision of disease, by default, bankruptcy, etc. predictive analysis simplifies the process of customer acquisition, prediction of risk behaviors from a client Using the data in the applications. predictive analytics in the form of credit ratings have reduced the time needed to approve loans, especially in the mortgage market, where lending decisions are now made in hours instead of days or weeks. A good predictive analytics can lead to pricing decisions properly, which can help mitigate future risks of default.
Statistical Techniques
The approaches and techniques used for predictive analysis in terms can be grouped into general regression techniques and machine learning techniques.
Regression techniques
The regression models are pillars of predictive analytics. Emphasis is placed on the establishment of a mathematical equation as a model to represent the interactions between different variables account. For location, there are a variety of models that can be applied in the exercise of predictive analytics. Some of them are briefly discussed below.
Model linear regression
The linear regression model examines the relationship between the response or dependent variable and a set of independent or explanatory variables. This relationship is expressed by an equation that predicts the response variable as a linear function parameters. These parameters are adjusted so that an adjustment to the policy is optimized. Much of the effort in the adjustment model focuses on reducing the size of the waste, and ensure that is distributed at random with respect to model predictions.
The goal of regression is to select model parameters to minimize the sum of squared residuals. This is called ordinary least squares that (OLS) estimation and results in best linear unbiased estimates (Blue) parameters if and only if the Gauss-Markov assumptions are met.
Once that the model has been estimated to be interested in whether the explanatory variables belong to the model that is, estimating the contribution of each variable reliable? For do this, we can see the sense Statistical model coefficients can be measured using the t-statistics. This is equivalent to check if the coefficient is significantly different zero. How well the model predicts the dependent variable with the value of the independent variables can be assessed using Statistics R. Measures ability Model prediction of the IE portion of the total variance of the dependent variable is Xplain (representatives) to variation in the independent variables.
discrete choice models
multivariate regression (above) is generally used when response variable is continuous and unlimited in scope. Often the variable response can be continuous, but unobtrusive. Although mathematically it is possible to apply multivariate regression for ordered discrete dependent variables, some assumptions supporting the theory of multivariate linear regression no longer have, and there are other techniques such as discrete choice models, which are better suited to this type of analysis. If the dependent variable is discrete, some of these methods are superior to logistic regression, multinomial logit and probit models. And logistic regression models probit are used when the dependent variable is binary.
Logistic regression
For more details on this topic, see the regression.
In a classification framework, determining the probabilities of the results of observations can be achieved by using a logistic model, which is essentially a method that transforms the information on the binary dependent variable in a continuous and unlimited variable estimates of a multivariate reference model (logistic regression see Allison for more information on the theory of logistic regression).
Wald and likelihood ratio are used to test the statistical significance each coefficient b in the model (similar to the t tests used in the OLS regression, see above). A test to evaluate the goodness of fit of a classification model is the Hosmer and Lemeshow test.
Multinomial logistic regression
An extension of binary logit model where the dependent variable has more than two categories is the multinomial logit model. In this case, the collapse of the data in two categories can not make sense or can lead to loss of wealth of data. The multinomial logit model technique is appropriate in these cases, especially when the categories are not ranked dependent variable (for examples like the colors red, blue, green). Some authors have multinomial regression extended to include feature selection and importance of methods such as multinomial logit random.
Probit regression
Probit models provide an alternative to logistic regression models for categorical dependent variables. Same if the results tend to be similar, the distributions underlying different. Probit models are popular in the social sciences as economics.
A good way to understand the essential difference between the Logit and Probit models, is to assume that there is a latent variable z
We observed no A to Z, but instead and see which takes the value 0 or 1. In the logit model, and we assume that follows a logistic distribution. In the probit model, I guess, and follows a standard normal distribution. Note that in the social sciences (economics, for example) is often used probit to model situations where the observed variable is continuous, but takes values between 0 and 1.
Logit vs Probit
The probit model has been more than the logit model. Seem identical, except that the logistic distribution tends to be a little flat tail. In fact, one reason why the logit model made was that the probit model has been extremely difficult to calculate because it was difficult to calculate integrals. Modern information technology has made this calculation, however, quite simple. The coefficients obtained from the logit and probit model is also very close. However, the odds ratio logit model is easier to interpret.
For practical reasons, only reasons to choose the probit model in the logistic model is:
There is a strong belief that the underlying distribution is normal
The very fact is a binary outcome (for example, bankruptcy or no bankruptcy), but a proportion (as a percentage of the population at different levels of debt).
models Series
series models are used to predict or forecast the future behavior of variables. These models represent the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be taken into account. Therefore standard regression techniques can not be applied to time series data and has developed a methodology to decompose trend, seasonal and cyclical component of the series. Modeling the dynamic path of a variable can improve predictions of predictable series component can be projected into the future.
time series models estimated stochastic differential equations containing components. Two ways of using these models are autoregressive (AR) and moving average (MA) models. Box-Jenkins (1976) developed by George Box GM Jenkins and AR and MA models combined to produce the ARMA (autoregressive moving average) model is the cornerstone of the analysis of stationary time series. ARIMA (autoregressive integrated moving average models) are used to describe other non-stationary time series. Box and Jenkins suggested the differentiation of nonstationary time series to obtain a stationary series in which an ARMA model can be applied. nonstationary time series have a strong and steady does not mean a long term or the variance.
Box and Jenkins proposed a methodology that includes three stages: model identification, estimation and validation. The identification phase is to determine whether the series is stationary or not, and the presence of seasonality by examining plots of the series, autocorrelation functions and autocorrelation party. In the estimation stage, models are estimated using nonlinear estimation procedures or maximum likelihood. Finally, the validation phase is the diagnostic test, as plotting the residuals to detect outliers and fit testing model.
In recent years time series models have become more sophisticated and treat conditional heteroskedasticity model models such as the Arc models (autoregressive conditional heteroskedasticity) and GARCH (generalized autoregressive conditional heteroskedasticity) frequently used in financial time series. Over time series models are also used to understand the interrelationships between economic variables represented by systems of equations using VAR (VAR) and structural VAR models.
Analysis of survival or duration
Survival analysis is another name for the analysis of events. These techniques have been developed primarily in medical and biological sciences, but also are widely used in the social sciences such as economics, engineering, and in (the reliability and lack of time for analysis).
Censorship and lack of normalcy that are characteristic to generate survival data difficult to analyze the data using traditional statistical models like multiple linear regression. The normal distribution is a symmetrical distribution, making positive and negative values, but the duration of their nature, can not be negative normal and therefore can not be assumed when the length survival data /. Hence the normality assumption of regression models is violated.
The assumption is that if the data are not censored, it would be representative of the population of interest. In survival analysis, censored observations arise when the dependent variable of interest is the time of a terminal event and the duration of the study is limited in time.
An important concept in survival analysis is the rate of risk. The risk index is defined as the probability the event occurring at time t conditional on surviving to time t. Another related concept is the role of risk the survival rate can be defined as the probability survival at time t.
Most models try to model the hazard rate by choosing underlying distribution based on the shape of the function risk. A distribution whose hazard function slopes upward is said to be positive duration dependence, showing a decreased risk of negative dependence, while the constant term risk is a process without memory in general is characterized by an exponential distribution. Some of the options for the distribution of survival models are: F, gamma, Weibull, lognormal, etc. normal inverse, exponential All these distributions is a non-negative random variable.
duration models may be parametric, nonparametric or semiparametric. Some models, the usual model of Kaplan-Meier proportional hazards Cox (nonparametric).
trees classification and regression
Classification and regression trees (CART) is a nonparametric technique that produces either classification or regression trees, as the dependent variable is categorical or digital, respectively.
The trees are formed by a set of rules based on the values of certain variables in modeling data set
The rules are chosen according to how they are divided according to the values of the variables can differ based on the observations of the variable dependent
Once a standard is selected and divided into two one node, the same logic applies to each node of Hild (ie, is a recursive procedure)
The division CART stops when detecting any additional gain can be done, or stop pre-established rules are met
Every tree branch ends in a terminal node
Each observation falls into a terminal node and exactly
Each terminal node is the only defined by a set of rules
A very popular method for predictive analysis Leo Breiman's random forests or derivative versions of this technique as multinomial logit random.
splines multivariate adaptive regression
regression splines multivariate adjustment (MARS) is a nonparametric technique that builds flexible models by fitting piecewise linear regression.
An important concept associated with regression splines is that of a node. Nudo is where the local regression model gives way to another and therefore the point of intersection between two splines.
In splines Multivariate regression and adaptive functions are the basic tool used nodes in the generalization of research. The basic functions are a set of functions that are used to represent the information contained in one or more variables. Multivariable adaptive regression splines and model it is almost always the basic functions pairs.
Multivariate adaptive regression spline approach overfits deliberately design, and prunes, then go to the optimum model. The algorithm is very intense and in practice, we are required to specify an upper limit on the number of basis functions.
Machine learning techniques
machine learning, a branch of artificial intelligence was used to develop techniques for computers to learn. Currently, it covers a number of advanced methods statistical regression and classification is applicable in a wide range of fields including medical diagnostics, detection of credit card fraud, the face and voice recognition and analysis of the stock market. In some applications, it is sufficient to directly predict the dependent variable, without focusing on the relationship between the underlying variables. In other cases, the underlying relationships can be very complex and mathematically to unknown dependencies. For these cases, learning techniques machine to emulate human cognition and learning from training examples to predict future events.
A brief analysis of some of these methods commonly used for predictive analysis is given below. A detailed study of machine learning is in Mitchell (1997).
Neural Networks
Neural nonlinear techniques networks are models that are capable of sophisticated modeling complex functions. Can be applied to problems of prediction, classification or control of a wide range of areas such as finance, psychology / Cognitive Neuroscience, Medical, engineering and physics.
Neural networks are used when the exact nature the relationship between inputs and outputs are not known. A key feature of neural networks is that they learn the relationship between input and output through training. There are two types of training neural networks used by different networks, supervised and unsupervised with supervised learning is the most common.
Examples training techniques of the neural network is backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some unsupervised network architectures multilayer perceptrons, Kohonen networks, Hopfield networks, etc.
radial basis functions
A radial basis function (RBF) is a function that has built a distance criterion of a center. These functions can be used very efficiently for interpolation and smoothing of the data. radial basis functions were applied in the field of neural networks which are used as a replacement for the sigmoid transfer function. These networks have three layers, input layer, hidden layer with RBF nonlinearity and a linear output layer. The most popular choice for the nonlinearity is that of Gauss. RBF networks have the advantage of not being locked in as minimum local networks such as feed-forward multilayer perceptron.
support vector machines
Support Vector Machines (SVM) are used to detect and exploitation of complex patterns in data by grouping, sorting and classification data. They learn that machines are used to perform binary classification and regression estimates. Generally used kernel methods to apply linear classification techniques to non-linear classification problems. There are a number of types linear SVM, etc polynomial, sigmoid
Nave Bayes
Nave Bayes rules based on Bayesian conditional probability is used for classification tasks from the scene. Nave Bayes assumes the predictors are statistically independent is an effective screening tool that is easy to interpret. It is best used when facing with the problem of dimensionality Urse said when the number of predictors is very high.
k nearest neighbors
The nearest neighbor algorithm (KNN) belongs class methods of statistical pattern recognition. The method requires no a priori assumptions about the distribution of the sample model is drawn. This is a training set of positive and negative values. A new sample is classified by calculating the distance in the case of the formation of nearest neighbor. The sign of this will determine the classification of the sample. In the classifier, k-nearest neighbor, the k nearest points are considered and the sign of the majority used to classify the sample. The performance of KNN algorithm is influenced by three main factors: (1) the distance measure used to locate the neighbors Nearby, (2) the decision rule used to obtain a rating closer to the K-neighbors, and (3) the number of neighbors used to classify the new sample. It can be shown that, unlike other methods, this method is universally asymptotically converge, namely: the size of the training set, if the observations are iid, independent of the distribution from which the sample is drawn, the class will meet the predicted class assignment that minimizes the classification error. View Devroy et al.
Geospatial Predictive Modeling
Conceptually, spatial predictive modeling based on the principle that the occurrences of events that are modeled are limited distribution. The occurrence of events are not uniform or random distribution factors are the space environment (infrastructure, socio-cultural, topographical etc) that limit the influence and the places where events occur. Geospatial prediction models attempts to describe the limitations and the influences of historical space the correlation of events links geographic sites with environmental factors represented by these limitations Otha Pruning and influences. Geospatial Predictive Modeling is a analysis process events through a filter to make statements geographical probability of occurrence of cases or emergency.
Tools
There are many commercially available tools that contribute to the implementation of predictive analytics. These range from those who need very little sophisticated user of the designed professional expert. The difference between these tools is often at the level of customization and weighing data allowed.
In an attempt to provide a standard language for the expression of forecasting models, the Predictive Model Markup Language (PMML) has been proposed. As an XML-based language provides a way for different tools define predictive models and share these applications between PMML compliant. PMML 4.0 was released in June 2009.
See also
Pattern recognition
Data mining
Odds algorithm
References
Devroye, L. Gyrfi, G. Lugosi (1996). A probabilistic theory of pattern recognition. New York: Springer-Verlag.
John R. Davies, Stephen V. Coggeshall, Roger D. Jones and Daniel Schutzer, "intelligent safety systems", Freedman, S. Roy, Flein, Robert A., and Lederman, Jess, editors (1995). Artificial Intelligence financial markets. Chicago: Irwin. ISBN 1-55738-811-3.
Agresti, Alan (2002). Categorical Data Analysis. Hoboken: John Wiley and Sons. ISBN 0-471-36093-7.
Enders, Walter (2004). Applied Econometrics Time Series. Hoboken: John Wiley and Sons. ISBN 052183919X.
Greene, William (2000). Econometric analysis. London: Prentice Hall. ISBN 0-13-013297-7.
Mitchell, Tom (1997). Machine learning. New York: McGraw-Hill. ISBN 0-07-042807-7.
John Tukey (1977). Exploratory Data Analysis. New York: Addison-Wesley. ISBN 0.
Guidre, Mathieu, Howard N, Argamón Sh (2009). Analysis rich language Counterterrrorism. Berlin, London, New York: Springer-Verlag. 978-3-642-01140-5.
v = # http://books.google.fr/books?id=d0l4RKuWWQAC&pg=PT122&dq=mathieu+guidère onepage Mathieu & q = & f = false Guidera
References
List of Predictive Analytics with user comments
Categories: Models statistical | Business intelligence | Insurance About the Author

I am China Computer Parts writer, reports some information about fire starter gel , meat flavor injector.

Irwin Industrial Tool 53227 Hex Head Multi-Spline Extractor Set, 25-Piece $48.50 Designed to remove broken screws, studs, pipes, and alemite fittings Hex head for use with sockets and flat wrenches Fine knurled multi-spline grips a larger cross section vs. standard extractors Made of tough C4150 alloy steel for longer life Comes in a blow molded case1/8” thru 7/8” extractors…

IRWIN 53228 15 piece Multi Spline Extractor Set $33.11 Designed to remove broken studs, bolts, socket screws and fittings for high torque applications Aggressive left hand spiral design for extra gripping power Includes plastic case Includes sizes: 1/8″ thru 9/16″….

Irwin 53226 10 Piece 1/8-Inch to 13/32-Inch Hex Head Multi-Spline Screw and Bolt Extractor Assortment in Plastic Case $19.99 Ideal for removal of broken studs, pipes, screws and alemite fittings. Steel construction. Includes: 1/8, 5/32, 3/16, 7/32, 1/4, 9/32, 5/16, 11/32, 3/8 and 13/32in. Case. Extractor Material: Steel, Removal Of: Studs, Pipes, Screws, and Fittings, Compatible Tool: Ratchet Wrench, Extractor Type: Multispline, Extractor (qty.): 10-Assorted, Storage Case Included: Yes…

…

Specialty Products Company 41000 Deluxe Wheel Lock Removal Kit $106.39 This set safely removes and replaces GM,Ford and Chrysler wire wheel hubcap locks on 1978 and up vehicles. Variable drive keys will also fit Corvette,McGard hubcap locks and aftermarket wheel lug locks. Kit comes in durable plastic carrying case with complete instructions. Replacement parts available – #40008 – Punch #40010 – Ford and Chrysler lock cylinder #40020 – GM spline hubcap locks & triang…

Hans n Multi – Spline Screw Extract r Set – 10 Pc. $31.54 Designed to remove broken srews, studs, pipes, and alemite fittings Hex head for use with sockets and flat wrenches Fine knurled multi – spline grips a larger cross section vs. standard extractors Made of tough C4150 alloy steel for longer life Comes in a blow molded case 1/8″ thru 13/32″ extractors…