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# Find a wide variety of data including demographic, environment, financial, retail and sports. Use this data in your Microsoft Office software, BI tools and your very own custom applications.

**218** Results in:

### Wealth Score

#### published by: Versium Analytics Inc

Versium’s Wealth Score can help companies understand the estimated net worth of their customers and prospects so they can better target their marketing campaigns and optimize their lead qualification programs. The higher the Wealth Score, the more likely the customer or prospect has a high net worth.

### Giving Score

#### published by: Versium Analytics Inc

Versium’s Giving Score helps organizations understand which of their current contributors have a higher propensity to make larger donations and become repeat donors, as well as predicting the propensity of a prospect to donate to a charity or other organization. The higher the Giving Score, the more likely the customer or prospect will donate.

### Frequently Bought Together

#### published by: Azure Machine Learning

Frequently Bought Together is a market basket analysis API built with Azure Machine Learning. It helps your customers discover items in your catalog that are frequently purchased together. Use your purchase history to add recommendations to your website.

### Anomaly Detection

#### published by: Azure Machine Learning

Anomaly Detection API runs different types of detectors on time series data and gives alerts when unusual patterns in the time series are detected.

### Binary Classifier API built with Azure Machine Learning

#### published by: Azure Machine Learning

Binary Classifier API is an example built with Microsoft Azure Machine Learning that fits a logistic regression model to user inputted data and outputs the predicted value for each of the observations in the data. Suppose you have a dataset and would like to predict a binary dependent variable based on the independent variables. ‘Logistic Regression’ is a popular statistical technique used for such predictions. Here the dependent variable is binary or dichotomous and 'p' is the probability of presence of the characteristic of interest. A simple scenario could be where the researcher is trying to predict whether the prospective student is likely to accept the admission offer to a university based on information (GPA in high school, family income, resident state, gender). The predicted outcome is the probability of a prospective student accepting their admission offer.

### Cluster Model API built with Azure Machine Learning

#### published by: Azure Machine Learning

Cluster Model API is an example built with Microsoft Azure Machine Learning that classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of variables. The purpose of cluster analysis is to discover a system of organizing observations, usually people or their characteristics, into groups, where members of the groups share properties in common. This web service uses the K-Means methodology, a commonly used clustering technique, to cluster arbitrary data into groups. This web service takes the data and the number of clusters k as input, and produces predictions of which of the k groups to which each observations belongs.

### Multivariate Linear Regression API built with Azure Machine Learning

#### published by: Azure Machine Learning

Multivariate Linear Regression API is an example built with Microsoft Azure Machine Learning that fits a linear regression model to user inputted data and outputs the predicted value for each of the observations in the data. Suppose you have a dataset and would like to predict a dependent variable y for each individual based on the other independent variables (x1,x2,…,xn). Linear Regression is a popular statistical technique used for such predictions. A simple scenario could be trying to predict the weight of an individual based on their height. A more advanced scenario could be predicting based on additional information for the individual (such as height, gender, and race).

### Lexicon Based Sentiment Analysis API built with Azure Machine Learning

#### published by: Azure Machine Learning

Lexicon Based Sentiment Analysis API is an example built with Microsoft Azure Machine Learning that conducts sentiment analysis on short sentences, such as facebook status, tweets, etc. You can use it to classify text into positive, negative and neutral. A score is also provide to indicate the intensity level of the sentence.

### Binomial Distribution Quantile Calculator API built with Azure Machine Learning

#### published by: Azure Machine Learning

The Binomial Distribution Quantile Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handle binomial distributions. This service is a part of the Binomial distribution suite of services that allows the user to generate a binomial distribution sequence of any length, calculate quantiles out of given probability (this specific service) and calculate probability from a given quantile. Each of the services emit different outputs based on the selected service (see description below). The Binomial Distribution Suite is based on R functions qbinom, rbinom and pbinom that are included in R stats package.

### Survival Analysis API built with Azure Machine Learning

#### published by: Azure Machine Learning

Survival Analysis API is an example built with Microsoft Azure Machine Learning that computes the probability when an event occurs. Under many scenarios, the main outcome under assessment is the time to an event of interest. In other words, the question “when will an event occur” is asked. Some examples include situations where the data describes the elapsed time (days, years, mileage, etc.) until the event of interest (disease relapse, PhD degree received, brake pad failed) occurs. Each instance in the data represents a specific object (a patient, a person, a car, etc.). This web service helps address the question “what is the probability that the event of interest will occur by time n for object x?” By providing a survival analysis model, this web service enables the users to supply data to train the model and test it. The main theme of the experiment is to model the length of the elapsed time until the event of interest occurs.