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### Basics of Structured and Unstructured Data in Predictive Analysis

Data contained in databases, documents, e-mails, and other data files for predictive analysis can be categorized either as structured or unstructured data. [more…]

### How to Identify 3 Data Categories in Predictive Analysis

As a result of doing business, companies have gathered masses of data about their business and customers, often referred to as *business intelligence*. Predictive analysis uses this data. To help you develop [more…]

### How to Generate Predictive Analytics with Data and User-Driven Data

There are two ways to go about generating or implementing predictive analytics: purely on the basis of your data (with no prior knowledge of what you’re after) or with a proposed business goal that the [more…]

### How to Find Value in Your Predictive Analysis Data

Any successful journey takes serious preparation. Predictive analytics models are essentially a deep dive into large amounts of data. If the data is not well prepared, the predictive analytics model will [more…]

### How to Categorize Predictive Analysis Models

You have various ways to categorize the models used for predictive analytics. In general, you can sort them out by [more…]

### How to Convert Raw Data into a Predictive Analysis Matrix

Before you can extract groups of similar data items from your dataset for your predictive analysis project, you might need to represent your data in a tabular format known as a [more…]

### How to Use K-means Cluster Algorithms in Predictive Analysis

**K** is an input to the algorithm for predictive analysis; it stands for the number of groupings that the algorithm must extract from a dataset, expressed algebraically as [more…]

### How to Cluster by Nearest Neighbors in Predictive Analysis

Nearest Neighbors is a simple algorithm widely used in predictive analysis to cluster data by assigning an item to a cluster by determining what other items are most similar to it. A typical use of the [more…]

### Basics of User-Based Collaborative Filters in Predictive Analysis

With a user-based approach to collaborative filtering in predictive analysis, the system can calculate similarity between pairs of users by using the cosine similarity formula, a technique much like the [more…]

### How to Visualize Predictive Analysis' Raw Data

A picture is worth a thousand words — especially when you're trying to get a good handle on your predictive analysis data. At the pre-processing step, while you're preparing your data, it's a common practice [more…]

### Basics of Data Clusters in Predictive Analysis

A *dataset* (or data collection) is a set of items in predictive analysis. For instance, a set of documents is a dataset where the data items are documents. A set of social network users’ information [more…]

### How to Utilize Bird Flock Clusters in Predictive Analysis

Imagine birds' flocking behavior as a model for your company's predictive analysis data. Each data item corresponds to a single bird in the flock; an appropriate visual application can show the flock in [more…]

### How to Apply Any Colony Clusters in Predictive Analysis

A natural example of self-organizing group you can apply in predictive analysis behavior is a colony of ants hunting for food. The ants collectively optimize their track so that it always takes the shortest [more…]

### How to Use Predictive Analysis Decision Trees to Predict the Future

A *decision tree* is an approach to predictive analysis that can help you make decisions. Suppose, for example, that you need to decide whether to invest a certain amount of money in one of three business [more…]

### How Support Vector Machine Predictive Analysis Predicts the Future

The *support vector machine* (SVM) is a predictive analysis data-classification algorithm that assigns new data elements to one of labeled categories. SVM is, in most cases, a [more…]

### How Predictive Analysis Neural Networks Work

A complex algorithm used for predictive analysis, the *neural network*, is biologically inspired by the structure of the human brain. A neural network provides a very simple model in comparison to the human [more…]

### How to Use Item-Based Collaborative Filters in Predictive Analysis

One of Amazon’s recommender systems for predictive analysis uses *item-based collaborative filtering* — doling out a huge inventory of products from the company database when a user views a single item on [more…]

### Basics of Major Technological Trends in Predictive Analytics

Traditional predictive analytical techniques can only provide insights on the basis of historical data. Your data — both past and incoming — can provide you with a reliable predictor that can help you [more…]

### How to Use Apache Hadoop for Predictive Analytics

Apache Hadoop is a free, open-source software platform for writing and running applications that process a large amount of data for predictive analytics. It enables a distributed parallel processing of [more…]

### Basics of K-Means and DBSCAN Clustering Models for Predictive Analytics

Unsupervised learning has many challenges for predictive analytics — including not knowing what to expect when you run an algorithm. Each algorithm will produce different results; you’ll never be certain [more…]

### How to Visualize the Clusters in a K-Means Unsupervised Learning Model

The Iris dataset is not easy to graph for predictive analytics in its original form. Therefore you have to reduce the number of dimensions by applying a [more…]

### Basics of R Programming for Predictive Analytics

R is a programming language originally written for statisticians to do statistical analysis, including predictive analytics. It’s open-source software, used extensively in academia to teach such disciplines [more…]

### Basics of Data Types and Structures in R Programming for Predictive Analytics

In R programming for predictive analytics, data *types* are sometimes confused with data *structures*. Each variable in the program memory has a data type. Sure, you can get away with having several variables [more…]

### How to Call a Function in R Programming for Predictive Analytics

Functions are lines of code that do something useful and concrete. Because these operations are often repeated in a predictive analytics project, they are usually saved with a name so you can [more…]

### How to Introduce the Data in R Regression for Predictive Analytics

The dataset you will use in this example is the Auto-MPG dataset, which can be found in the UCI repository. This dataset has 398 observations and 8 attributes plus the label. [more…]