Predictive Analytics For Dummies

Predictive Analytics For Dummies

Language: English

Pages: 408

ISBN: 1119267005

Format: PDF / Kindle (mobi) / ePub


Predictive Analytics For Dummies, 2e will help the you understand the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. You will learn how to incorporate algorithms through discovering data models, identifying similarities and relationships in your data, and how to predict the future through data classification. You will develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get stakeholder buy-in. The author will also address "soft" issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies.

Coverage will include:

Real-world tips for creating business value

Common use cases to help you get started

Details on modeling, k-means clustering, and more

How you can predict the future with classification

Information on structuring your data

Methods for testing models

Hands-on guides to software installation

Tips on outlining business goals and approaches

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automation of this decision-making, backed by thorough testing and refined by feedback from operational deployment, allows for greater consistency of those decisions that require precision. The effect is to avoid the subjective analysis or emotional attachment that can often lead to biased decisions. Deriving actionable information from the use of analytics promotes faster response to rapidly changing business environments and external conditions. This process empowers your business with the

activity, behaviors, and patterns among employees as a first line of defense against fraud. Outlier detection algorithms are in use as tools of fraud detection. The thinking is that fraud is unusual, and won’t happen as commonly as normal transactions. Thus the algorithm looks for events that fall outside regular patterns to detect fraud. Here are some examples: Credit card companies have historically used predictive analytics to review credit applications and determine creditworthiness based

include both, while trying to keep a wide spectrum of readers engaged. The focus of this book will be developing a roadmap for implementing predictive analytics within your organization. Its intended audience is the larger community of business managers, business analysts, data scientists, and information technology professionals. Maybe you are a business manager and you have heard the buzz about predictive analytics. Maybe you've been working with data mining and you want to add predictive

of items without examining each one. The complexity becomes exponential when the dataset is large, diverse, and relatively incoherent — which is why clustering algorithms exist: Computers do that type of work best. A similar, common, and practical example is the application of data clustering to e-mail messages, dividing a dataset of e-mails into spam and non-spam clusters. An ideal spam-detection tool would need to first divide a dataset (all past e-mails) into two types (groups): spam and

slate to work with. Paste the following code in the prompt and observe the output: >>> from sklearn.datasets import load_iris >>> iris = load_iris() After running those two statements, you should not see any messages from the interpreter. The variable iris should contain all the data from the iris.csv file. Create an instance of DBSCAN. Type the following code into the interpreter: >>> from sklearn.cluster import DBSCAN >>> dbscan = DBSCAN(random_state=111) The first line of code imports the

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