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Predictive Analytics Tools : The approaches and techniques to conduct predictive analytics can be classified in to regression techniques and machine learning techniques. Predictive analytics deals with extracting the information from raw data and using these data to predict trends and behavior patterns for future. It has now become feasible to collect, analyze, and mine massive amounts of both structured and unstructured data with faster CPU, cheap memory, MPP architectures and the new technologies such as In memory data base, Hadoop, MapReduce, and text analytics. This helps to uncover hidden pattern and provide new insights.
Predictive Analytics Tools - Analytical Tools and Techniques
Regression techniques is the statistical process for estimating the relationships among variables
Linear regression model is the statistical approach to model the relationship between a dependent variable y and one or more explanatory variable X.
Discrete choice models describe, explain, and predict choices between two or more discrete alternatives.
Logistic regression predicts the outcome of a categorical dependent variable example: a class label based on one or more predictor variables example: features.
Multinomial logistic regression is a regression model. This model generalizes the MLG by allowing more than two discrete outcomes.
Probit regression is a regression model where the dependent variable can only take two values, for example married or not married.
Logit versus pro bit are both sigmoid functions with a domain between 0 and 1. These are inverses of the cumulative distribution function (CDF) of a probability distribution. The logit is quantile function of the logistic distribution, and the probit is that of the normal distribution.
Time series models is a sequence of data points, measured typically at successive points in time spaced at uniform time intervals.
Survival or duration analysis deals with the analysis of time to event.
Classification and regression trees are predictive models. These maps the observations about an item to the conclusions about the item's target value.
Multivariate adaptive regression splines are an extension of linear models. This models the non-linearities and interactions between different variables.
Machine learning techniques
Neural networks are systems of interconnected neurons that can compute values from inputs by feeding information through the network.
Multilayer Perceptron (MLP) are artificial neural network model. This model maps sets of input data in to a set of appropriate outputs.
Radial basis functions is a real-valued function whose value depends only on the distance from the origin.
Support vector machines are learning models. There are associated learning algorithms which analyze and recognize patterns in data.
Naïve Bayes are probabilistic classifier based on applying Bayes' theorem with strong independence assumptions.
k-nearest neighbours are non-parametric method for classification and regression. This predicts objects values or class memberships based on the k closest examples in the feature space.
Geospatial predictive modelling are occurrences of events being modeled are limited in distribution. Occurrences of events are neither uniform nor random in distribution.
Predictive Analytics Tools
Advanced skills and expertise were earlier required to use predictive analytics tools in business because of the complexities involved in using the statistical models and tools. Advanced skills were also required to understand the derived results. With the advancement in technology, modern predictive analytics tools are no longer restricted to advanced users. Business users can use the modern user friendly predictive analytics tools marketed by proprietary software companies and freeware companies to predict their business. The modern tools comes with less mathematical complexity and user-friendly graphic interfaces . Modern predictive analytic tools also provide business users simple charts, graphs, and scores which help the business users to understand the likelihood of possible outcomes.
There are numerous tools available in the market that help with the execution of predictive analytics.
Predictive modeling is the process of creating, testing and validating a model to best predict the probability of an outcome using a number of modeling methods from machine learning, artificial intelligence, and statistics.
1.Predictive Analytic Tools : Open source and freeware
R Software Environment, Dataiku, Orange Data mining, RapidMiner, Anaconda, KNIME, DMWay, HP Haven Predictive Analytics, GraphLab Create, Lavastorm Analytics Engine, Actian Vector Express, Scikit-learn, Microsoft R, H2O.ai, Weka Data Mining, Apache Spark, Octave, Tanagra, PredictionIO, Apache Mahout, LIBLINEAR, Vowpal Wabbit, NumPy, and SciPy are the Top Free Predictive Analytics Software.
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