Analysis fundamental opciones binarias demograficos
Templates are sample solutions that demonstrate best practices and provide building blocks to help you implement a solution fast. Each template is designed to solve a specific problem, for a specific vertical or industry. The tasks in each template extend from data preparation and feature engineering to model training and scoring.
Then, feel free to customize the template to fit your own scenario and build a custom solution. Most of the templates come in multiple versions analysis fundamental opciones binarias demograficos both on-premises and cloud platforms for machine learning. Exciting new templates in Azure ML. For download and setup instructions, see How to use the templates. The ability to detect fraudulent transactions is important for online businesses.
To reduce charge-back losses, businesses need to quickly identify transactions that were made using stolen payment instruments or credentials. When fraudulent transactions are discovered, businesses analysis fundamental opciones binarias demograficos take measures to block certain accounts as soon as possible, to prevent further losses. In this scenario, you learn how to use data from online purchase transactions to identify likely fraud.
Fraud detection is solved as a binary classification problem. The methodology used in this template can be easily applied to fraud detection in other domains. This solution uses insurance industry data to predict leads based on demographics, historical analysis fundamental opciones binarias demograficos data, and product-specific details.
Recommendations are also generated to indicate the best channel and time to approach users to influence purchase behavior. The solution creates and compares multiple models. The solution also demonstrates automated data integration and data preparation using stored procedures.
Predecir permanencia en el hospital Predicting length of stay in hospitals. Accurately predicting which patients might require long-term hospitalization is an important part of both care and planning. Administrators need to be able to determine which facilities require more resources, and caregivers want to guarantee that they can meet the needs of patients.
It also includes a set of Power BI reports that you can use to interact with a deployed model. Analyzing and predicting customer churn is important in any industry where the loss of customers to competitors must be managed and prevented: The analysis fundamental opciones binarias demograficos of churn analysis is to identify which customers are likely to churn, and then take appropriate actions to retain such customers and keep their business.
This template formulates the churn problem as a binary classification problem. It uses sample data from two sources, customer demographics and customer transactions, to classify customers as likely or unlikely to churn.
Predictive maintenance aims to increase the efficiency of maintenance tasks by capturing past failures and using that information to predict when or where a device might fail.
The ability to forecast device obsolescence is especially valuable for applications that rely on distributed data or sensors.
New predictive maintenance template. This solution focuses on answering the question, "When will an in-service machine fail? The input data represents simulated sensor analysis fundamental opciones binarias demograficos for aircraft engines.
Regression modelsto predict how much longer an engine will last before it fails. Classification modelsto predict whether an engine is likely to fail.
The binary classification model predicts if analysis fundamental opciones binarias demograficos engine will fail within a certain time frame. The multi-class classification model predicts whether a particular engine might fail, and provides a probable time window of failure. For example, for a given day, you can predict whether any device is likely to fail on the given day, or in some time period following the given day. Demand forecasting is an important problem in various domains including energy, retail, and services.
Accurate demand forecasting helps companies conduct better production planning, resource allocation, and make other important business decisions. In the energy sector, demand forecasting is critical for reducing energy storage cost and balancing supply and demand. The model used for prediction is a random forest regression model based on rxDForesta high-performance machine learning algorithm included in Microsoft R Server.
The solution includes a demand simulator, all the R and T-SQL code needed to train a model, and stored procedures that you can use to generate and report predictions. To download the files included in each template, analysis fundamental opciones binarias demograficos can use GitHub commands, or you can open the link and click Download Zip to save all files to your computer. When downloaded, the solution typically contains these folders: Contains the sample data for each application.
Contains all the R development code you need for the solution. The R code has been optimized so that computations are performed "in-database", by setting the compute context to a SQL Server instance.
The folder also contains a Analysis fundamental opciones binarias demograficos script that you can run to invoke all scripts and create the end-to-end environment. Be sure to edit the script to suit your environment.
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