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Versium Analytics Inc

Green Score

published by: Versium Analytics Inc

Versium’s Green Score helps businesses identify customers who have a high likelihood of making more environmentally conscious purchase decisions so they can better target marketing campaigns and optimize lead qualification programs. The higher the Green Score, the more likely the customer or prospect will purchase green product or services.

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Versium Analytics Inc

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.

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Versium Analytics Inc

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.

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Microsoft

Frequently Bought Together API built with Azure Machine Learning

published by: Microsoft

Frequently Bought Together is an API built with Azure Machine Learning that a helps your customers discover items in your catalog that are frequently purchased together. Use your customer purchase history to add "Frequently Bought Together" recommendations to your website and to improve conversion in your digital store.

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Microsoft

Anomaly Detection API built with Azure Machine Learning

published by: Microsoft

Anomaly Detection API is an example built with Microsoft Azure Machine Learning that runs different types of detectors on time series data and gives alerts and anomaly scores. Time series data occur in a wide range of applications, for example, an e-commerce site tracking #checkouts/hour, a publisher tracking #clicks/category, a service might track job queue length, #exceptions over time, etc. The anomaly detection API can help monitor and analyze such time series for anomalies. The API has 2 parameters 1. "data" is input time series in the format: t1=v1;t2=v2;... 2. "params" set to "SpikeDetector.TukeyThresh=3; SpikeDetector.ZscoreThresh=3" which controls sensitivity of spike detectors. The API runs 2 spike/dip detectors, a positive/negative trend detector and a level change detector and returns the anomaly scores at each point in time. Please refer to https://adresultparser.codeplex.com/ which gives sample code showing how to connect to the API and parse the output.

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Microsoft

Binary Classifier API built with Azure Machine Learning

published by: Microsoft

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.

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Microsoft

Cluster Model API built with Azure Machine Learning

published by: Microsoft

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.

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Microsoft

Multivariate Linear Regression API built with Azure Machine Learning

published by: Microsoft

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).

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Microsoft

Lexicon Based Sentiment Analysis API built with Azure Machine Learning

published by: Microsoft

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.

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Microsoft

Binomial Distribution Quantile Calculator API built with Azure Machine Learning

published by: Microsoft

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.

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