By Vijay Kotu
Put Predictive Analytics into motion Learn the fundamentals of Predictive research and information Mining via a simple to appreciate conceptual framework and instantly perform the thoughts realized utilizing the open resource RapidMiner device. even if you're fresh to information Mining or engaged on your 10th venture, this booklet will assist you learn information, discover hidden styles and relationships to assist vital judgements and predictions. information Mining has develop into an important software for any firm that collects, shops and approaches info as a part of its operations. This e-book is perfect for company clients, facts analysts, enterprise analysts, company intelligence and knowledge warehousing execs and for somebody who desires to examine facts Mining. You’ll manage to: 1. achieve the mandatory wisdom of other info mining options, that you can choose the appropriate method for a given information challenge and create a normal objective analytics method. 2. wake up and working quick with greater than dozen generic strong algorithms for predictive analytics utilizing sensible use situations. three. enforce an easy step by step strategy for predicting an consequence or gaining knowledge of hidden relationships from the information utilizing RapidMiner, an open resource GUI dependent info mining tool
Predictive analytics and knowledge Mining thoughts lined: Exploratory information research, Visualization, choice bushes, Rule induction, k-Nearest acquaintances, Naïve Bayesian, man made Neural Networks, aid Vector machines, Ensemble types, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, organization research utilizing Apriori and FP progress, K-Means clustering, Density dependent clustering, Self Organizing Maps, textual content Mining, Time sequence forecasting, Anomaly detection and have choice. Implementation documents could be downloaded from the booklet spouse website at www.LearnPredictiveAnalytics.com
- Demystifies information mining thoughts with effortless to appreciate language
- Shows the best way to wake up and operating quick with 20 general strong concepts for predictive analysis
- Explains the method of utilizing open resource RapidMiner tools
- Discusses an easy five step technique for enforcing algorithms that may be used for appearing predictive analytics
- Includes functional use situations and examples
Read or Download Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner PDF
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Extra info for Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner
Example text
However, we can convert data from one data type to another using a type conversion process, but this may be accompanied with possible loss of information. For example, credit scores expressed in poor, average, good, and excellent categories can be converted to either 1, 2, 3, and 4 or average underlying numerical scores like 400, 500, 600, and 700 (scores here are just an example). In this type conversion, there is no loss of information. However, conversion from numeric credit score to categories (poor, average, good, and excellent) does incur some loss of information.
As a data mining practitioner, all we need to be concerned with is having an overview of the algorithm. We want to know how it works and determine what parameters need to be configured based on our understanding of the business and data. Data mining models can be classified into the following categories: classification, regression, association analysis, clustering, and outlier or anomaly detection. Each category has a few dozen different algorithms; each takes a slightly different approach to solve the problem at hand.
Attributes can be numeric, categorical, date-time, text, or Boolean data types. In this example, credit score and interest rate are numeric attribute. n A label (class label or output or prediction or target or response) is the special attribute that needs to be predicted based on all input attributes. 1, interest rate is the output variable. n Identifiers are special attributes that are used for locating or providing context to individual records. For example, common attributes like Names, account numbers, employee ID are identifier attributes.