Discover, try and buy data & applications built on the Microsoft Azure Platform

944 Results

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
Dun & Bradstreet

Contact Details

published by: Dun & Bradstreet

With D&B Company Contacts, professionals can generate a list of US contact records based on criteria that matches their business needs.

Data
nasscropsacpe

USDA ARMS

published by: nasscropsacpe

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.

Data
nasscropsacpe

USDA NASS QuickStats

published by: nasscropsacpe

The National Agricultural Statistics Service (NASS) offers Quick Stats, an on-line database containing official published aggregate estimates related to U.S. agricultural production.

Data
Primal

Gun Control vs. Gun Rights: Opinions in Media

published by: Primal

Public opinion of Gun Control laws and policies are influenced by changes in public opinion. To understand the opinions we provide a meta-analysis of gun control opinions in media. By monitoring online media sources, we identify if the pro Gun Control proponents or opponents carry the loudest voice. We sift through the latest news and blog articles to identify swings in opinion, and how recent events changes the public discourse. Use Gun Control Opinions in Media to understand the social narrative and changes in opinion.

Data
Allmapdata from Mapmechanics

Postcode Sector Drive Time Matrix, GB

published by: Allmapdata from Mapmechanics

Off peak drive-times and distances between every postcode sector (e.g. RG40 1) to every other postcode sector in Great Britain. Each sector appears as both destination and origin for fast querying and simple incorporation into your own application.

Data
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