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.

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Date Dimension

published by:

This catalog contains a Date Dimension designed for specific use in Data Warehouses and Self Service Power Query and Power Pivot data models. It contains all typical attributes but also Holidays dates, Seasons and French attributes.

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Ajuntament de Barcelona

Barcelona Basic Addresses

published by: Ajuntament de Barcelona

Barcelona Basic Addresses, offers the addresses and their relationship with the corresponding enumeration district of the Spanish National Institute of Statistics (INE) of all sections and districts of Barcelona city.

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Ajuntament de Barcelona

Barcelona City Blocks

published by: Ajuntament de Barcelona

Barcelona City Blocks offers the information of all urban data of the city blocks in Barcelona.

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Ajuntament de Barcelona

Barcelona Car Registrations in 2009

published by: Ajuntament de Barcelona

Barcelona Car Registrations in 2009 offers all data about the Car Registrations during all 2009 in the city of Barcelona.

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Ajuntament de Barcelona

Barcelona Facilities

published by: Ajuntament de Barcelona

Barcelona Facilities offers a list of facilities in the city of Barcelona.

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Applied Methods Inc

HealthMethods Data Service

published by: Applied Methods Inc

HealthMethods Data Service delivers healthcare cost and utilization data in a spreadsheet format. It was designed to make it easy for individuals to obtain answers to specific questions. HealthMethods Data Service integrates data from several highly regarded healthcare data sources including but not limited to healthcare.gov, data.medicare.gov, and data.cms.gov.

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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.

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Azure Machine Learning

Recommendations

published by: Azure Machine Learning

Recommendations API is an example built with Microsoft Azure Machine Learning that 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.

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Azure Machine Learning

Frequently Bought Together API built with Azure Machine Learning

published by: Azure Machine Learning

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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Azure Machine Learning

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).

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