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### How Predictive Analytics Increases Returns on Investments (ROI)

Predictive analytics can help you increase return on investment (ROI) through targeted marketing campaigns, improved risk assessment and management, reducing operational costs, and making actionable decisions [more…]

### How Predictive Analytics Increases Business Confidence

Predictive analytics enables business to make smarter decisions, some of which take place in real time. It allows businesses to improve all aspects of decision-making — including confidence in decisions [more…]

### How Predictive Analytics Is Utilized to Make Informed Decisions

Predictive analytics, properly developed and applied, turns your data into key insights, and enables you to take action by making informed decisions about many areas of your business — based on extensive [more…]

### How to Utilize Apache Mahout for Predictive Analytics

An open-source tool that is uniquely useful in predictive analytics is Apache Mahout. This machine-learning library includes large-scale versions of the clustering, classification, collaborative filtering [more…]

### How Predictive Analytics Adds Business Value

In an increasingly competitive environment, organizations always need ways to become more competitive. Predictive analytics found its way into organizations as one such tool. Using technology in the form [more…]

### Basics of Content-based Predictive Analytics Filters

*Content-based* predictive analytics recommender systems mostly match *features* (tagged keywords) among similar items and the user’s profile to make recommendations. When a user purchases an item that has [more…]

### Basics of Uplift Predictive Analytics Models

So how do you know that the customer you targeted using predictive analytics wouldn’t have purchased anyway? To clarify this question, you can restate it in a couple different ways: [more…]

### How to Load the Data in an R Classification Predictive Analytics Model

The dataset we analyze to make a prediction on is the Seeds dataset, which can be found at the UCI machine-learning repository. This dataset has 210 observations and 7 attributes plus the label. The label [more…]

### How to Prepare the Data in an R Classification Predictive Analytics Model

In order to run a predictive analysis, you have to get the data into a form that the algorithm can use to build a model. To do that, you have to take some time to understand the data and to know its structure [more…]

### How to Create an R Classification Predictive Analytics Model

You want to create a predictive analytics model that you can evaluate using known outcomes. To do that, split the seeds datasetinto two sets: one for training the model and one for testing the model. A [more…]

### How to Explain the Results of an R Classification Predictive Analytics Model

Another task in predictive analytics is to classify new data by predicting what class a target item of data belongs to, given a set of independent variables. You can, for example, classify a customer by [more…]

### The Limitations of the Data in Predictive Analytics

As with many aspects of any business system, data is a human creation — so it’s apt to have some limits on its usability when you first obtain it. Here’s an overview of some limitations you’re likely to [more…]

### How to Deal with Outliers Caused by Outside Forces

Be sure you check carefully for outliers *before* they influence your predictive analysis. Outliers can distort both the data and data analysis. For example, any statistical analysis done with data that [more…]

### How to Deal with Outliers Caused by Errors in the System

When you rely on technology or instrumentation to conduct a predictive analytics task, a glitch here or there can cause these instruments to register extreme or unusual values. If sensors register observational [more…]

### How to Decide whether to Keep Outliers in Predictive Analytics

Deciding to include outliers in the analysis — or to exclude them — will have implications for your predictive analytics model. Keeping outliers as part of the data in your analysis may lead to a model [more…]

### How to Use Data Smoothing in Predictive Analytics

*Data smoothing* in predictive analytics is, essentially, trying to find the “signal” in the “noise” by discarding data points that are considered “noisy”. The idea is to sharpen the patterns in the data [more…]

### How to Use Curve Fitting in Predictive Analytics

*Curve fitting* is a process used in predictive analytics in which the goal is to create a curve that depicts the mathematical function that best fits the actual [more…]

### How to Use Assumptions Appropriately in Predictive Analytics

In spite of everything you’ve been told about assumptions causing trouble, a few assumptions remain at the core of any predictive analytics model. Those assumptions show up in the variables selected and [more…]

### How to Use Supervised Analytics to Train Predictive Models

In *supervised analytics,* both input and preferred output are part of the training data. The predictive analytics model is presented with the correct results as part of its learning process. Such supervised [more…]

### The Problem with Relying on Only One Predictive Analysis

As you probably guessed, predictive analytics is not a one-size-fits-all activity — nor are its results once-and-for-all. For the technique to work correctly, you have to apply it again and again over [more…]

### How to Score Your Analytical Predictions Accurately

When analyzing the quality of a predictive model, you’ll want to measure its accuracy. The more accurate a forecast the model makes, the more useful it is to the business, which is an indication of its [more…]

### How to Address Problems in Predictive Analytics

Predictive modeling is gaining popularity as a tool for managing many aspects of business. Ensuring that data analysis is done right will boost confidence in the models employed — which, in turn, can generate [more…]

### Seeing What You Need to Know When Getting Started in Data Science

Traditionally, *big data* is the term for data that has incredible volume, velocity, and variety. Traditional database technologies aren’t capable of handling big data — more innovative data-engineered solutions [more…]

### Looking at the Basics of Statistics, Machine Learning, and Mathematical Methods in Data Science

If statistics has been described as the science of deriving insights from data, then what’s the difference between a statistician and a data scientist? Good question! While many tasks in data science require [more…]

### Using Visualization Techniques to Communicate Data Science Insights

All of the information and insight in the world is useless if it can’t be communicated. If data scientists cannot clearly communicate their findings to others, potentially valuable data insights may remain [more…]