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

You have to get the data into a form that the algorithm can use to build a predictive analytical model. To do so, you have to take some time to understand the data and to know the structure of the data [more…]

### How to Create a Predictive Analytics Model with R Regression

You want to create a predictive analytics model that you can evaluate by using known outcomes. To do that, we’re going to split our dataset into two sets: one for training the model and one for testing [more…]

### How to Explain the Predictive Analytical Results of R Regression

Once you create an R regression model for predictive analytics, you want to be able to explain the results of the analysis. To see some useful information about the model, type in the following code: [more…]

### How to Outline Testing and Test Data for Predictive Analytics

When your data is ready and you’re about to start building your predictive model for analysis, it’s useful to outline your testing methodology and draft a test plan. Testing should be driven by the business [more…]

### How to Load Data into an SVM Supervised Learning Model

For predictive analytics, you need to load the data for your algorithms to use. Loading the Iris dataset in scikit is as simple as issuing a couple of lines of code because [more…]

### How to Create an Unsupervised Learning Model with DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm used as an alternative to K-means in predictive analytics. It doesn’t require that you input the number [more…]

### Basics of Classification Models for Analytical Predictions

Once you have all the tools and data necessary to start creating a predictive model, the fun begins. In general, creating a learning model for classification tasks will entail the following steps: [more…]

### Why Visualization Matters for Predictive Analytics

Reading rows of spreadsheets, scanning pages and pages of reports, and going through stacks of analytical results generated by predictive models can be painstaking, time-consuming, and — let’s face it [more…]

### How to Run Training Data in an SVM Supervised Learning Model

Before you can feed the Support Vector Machine (SVM) classifier with the data that was loaded for predictive analytics, you must split the full dataset into a training set and test set. [more…]

### How to Visualize the Classifier in an SVM Supervised Learning Model

The Iris dataset is not easy to graph for predictive analytics in its original form because you cannot plot all four coordinates (from the features) of the dataset onto a two-dimensional screen. Therefore [more…]

### How to Run Test Data and Evaluate an SVM Supervised Learning Model

*Supervised learning* is a machine-learning task that learns from predictive analysis data that has been labeled. One way to think about supervised learning is that the labeling of data is done under the [more…]

### How to Create and Run an Unsupervised Learning Model to Make Predictions with K-Means

The K-means algorithm requires one initialization parameter from the user in order to create an instance for predictive analytics. It needs to know how many [more…]

### How to Evaluate an Unsupervised Learning Model with K-Means

After you’ve chosen your number of clusters for predictive analytics and have set up the algorithm to populate the clusters, you have a predictive model. You can make predictions based on new incoming [more…]

### How to New Analytical Predictions with R Regression

To make analytical predictions with new data, you simply use the function with a list of the seven attribute values. The following code does that job: [more…]

### Basics of Variables in R Programming for Predictive Analytics

Because it was designed for statistical analysis, the programming language R can be very useful for predicting outcomes based on a set of data. In order to use R, you first must know how to use variables [more…]

### Basics of Static and Streamed Data in Predictive Analyics

Data in predictive analytics can be identified as streamed, static, or a mix of the two. *Streamed data* changes continuously; examples include the constant stream of Facebook updates, tweets on Twitter, [more…]

### How to Search Your Predictive Analytics Data

To utilize your predictive analytics data you need to know how to find the information you are want to find. There are two main concepts of searching your data in preparation for using it in predictive [more…]

### How to Fund Associations among Predictive Analytics Data Items

The use of predictive analytics as a data-mining tool also seeks to discover hidden relationships among items in your data. These hidden relationships are called [more…]

### How to Introduce Predictive Analytics Data Classifications to Your Business

If your business has yet to use data classification utilized in predictive analytics, maybe it’s time to introduce it as a way to make better management or operating decisions. This process starts with [more…]

### Basics of Predictive Analytics Data-Classifications Process

At a brass-tacks level, predictive analytic data classification consists of two stages: the learning stage and the prediction stage. The learning stage entails training the classification model by running [more…]

### How to Ensemble Methods to Boost Prediction Analytic Accuracy

As in the real world, so with the multiplicity of predictive analytic models: Where there is unity, there is strength. Several models can be combined in different ways to make predictions. You can then [more…]

### How to Utilize Linear Regressions in Predictive Analytics

*Linear regression* is a statistical method that analyzes and finds relationships between two variables. In predictive analytics it can be used to predict a future numerical value of a variable. [more…]

### How to Utilize the Markov Model in Predictive Analytics

The *Markov Model* is a statistical model that can be used in predictive analytics that relies heavily on probability theory. (It’s named after a Russian mathematician whose primary research was in probability [more…]

### How to Develop a Predictive Analysis Model for Specific Decisions

A good model needs a prototype as proof of concept. To build a prototype for your predictive analytics model, start by defining a potential *use case* (a scenario drawn from the typical operations of your [more…]

### How to Define and Test Predictive Analytics Prototypes

An effective way to state your business objectives for predictive analytics clearly is as a bulleted list of user decisions. Then run your prototype to generate predictions and scores for each possible [more…]