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Azure Machine Learning

Forecasting - AutoRegressive Integrated Moving Average (ARIMA) API built with Azure Machine Learning

published by: Azure Machine Learning

The Forecasting - AutoRegressive Integrated Moving Average (ARIMA) API is an example built with Microsoft Azure Machine Learning that fits an ARIMA model to user inputted data and outputs predicted forecasted value for future dates. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends and seasonality to predict future trends. This web service implements Autoregressive Integrated Moving Average (ARIMA) to produce predictions based on the historical data provided by the user.

Data
Azure Machine Learning

Normal Distribution Generator API built with Azure Machine Learning

published by: Azure Machine Learning

The Normal Distribution Generator API is an example built with Microsoft Azure Machine Learning that helps generate and handle normal distributions. This service is a part of the Normal distribution suite of services that allows the user to generate a normal distribution sequence of any length (this specific service), calculate quantiles out of given probability and calculate probability from a given quantile. Each of the services emit different outputs based on the selected service. The Normal Distribution Suite is based on R functions qnorm, rnorm and pnorm that are included in R stats package.

Data
Azure Machine Learning

Difference in Proportions Test API built with Azure Machine Learning

published by: Azure Machine Learning

Difference in Proportions Test API is an example built with Microsoft Azure Machine Learning that conducts a hypothesis test based on user inputted data and outputs the test results. Are two proportions statistically different? Suppose a user wants to compare two movies to determine if one movie has a significantly higher proportion of ‘likes’ when compared to the other. This web service conducts a hypothesis test of the difference in two proportions based on user input of number of successes and total number of trials for the 2 comparison groups. A scenario would be where this web service could be called from within a movie comparison app, telling the user based on movie ratings whether one of the movies is really ‘liked’ more often that the other.

Data
Azure Machine Learning

Forecasting - ETS + STL API built with Azure Machine Learning

published by: Azure Machine Learning

Forecasting - ETS + STL API is an example built with Microsoft Azure Machine Learning that fits a ETS + STL model to user inputted data and outputs the forecasting value for observations in the data. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends and seasonality to predict future trends. This web service implements Seasonal Trend decomposition (STL) and Exponential Smoothing model (ETS) to produce predictions based on the historical data provided by the user.

Data
Azure Machine Learning

Normal Distribution Quantile Calculator API built with Azure Machine Learning

published by: Azure Machine Learning

The Normal Distribution Quantile Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handle normal distributions. This service is a part of the Normal distribution suite of services that allows the user to generate a normal 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. The Normal Distribution Suite is based on R functions qnorm, rnorm and pnorm that are included in R stats package.

Data
Azure Machine Learning

Forecasting - Exponential Smoothing (ETS) API built with Azure Machine Learning

published by: Azure Machine Learning

Forecasting - Exponential Smoothing (ETS) API is an example built with Microsoft Azure Machine Learning that fits an Exponential Smoothing model to user inputted data and outputs the forecasting value for observations in the data. Will the demand for a specific product increase this year? Can I predict my product sales for the Christmas season, so that I can effectively plan my inventory? Forecasting models are apt to address such questions. Given the past data, these models examine hidden trends to predict future trends. This web service implements Exponential Smoothing model (ETS) to produce predictions based on the historical data provided by the user.

Data
Azure Machine Learning

Normal Distribution Probability Calculator API built with Azure Machine Learning

published by: Azure Machine Learning

The Normal Distribution Probability Calculator API is an example built with Microsoft Azure Machine Learning that helps generate and handling normal distributions. This service is a part of the Normal distribution suite of services that allows the user to generate a normal distribution sequence of any length, calculate quantiles out of given probability and calculate probability from a given quantile (this specific service). Each of the services emit different outputs based on the selected service. The Normal Distribution Suite is based on R functions qnorm, rnorm and pnorm that are included in R stats package.

Data
Azure Machine Learning

Recommendations

published by: Azure Machine Learning

Recommendations API by Azure Machine Learning helps your customer discover items in your catalog. Customer activity on your website is used to recommend items and to improve conversion in your digital or physical store.

Data
dnb

Contact Details

published by: dnb

Boost Response with the Best Contact Information Available. With D&B's Contacts Details, professionals can generate a list of US professional contact records based on criteria that matches their business needs. Create a foundation for your marketing database and cross-sell campaigns with basic contact details (including D-U-N-S Number). Power your targeted multichannel and email campaigns with email addresses and/or alternate telephone numbers only available through this product. D&B's Contact Details provides the capability to enhance and manage contacts by incorporating the unique Principal Identification Number. (Available ONLY through D&B's Company Contacts product)

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United States Department of Agriculture

USDA ARMS

published by: United States Department of Agriculture

USDA's primary source of information on the financial condition, production practices, and resource use of America's farm businesses and the economic well-being of America's farm households.

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