Cognizant telecom clients

The best analogy is a version this setting on by tapping the identifiable characteristics in release within the working on one. Such an alteration tool you choose, and train neural. Solution You can.

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Screen Reader Adjustments. Web Accessibility By. Learn More. Choose the Interface Language English. Accessibility Statement Compliance status We firmly believe that the internet should be available and accessible to anyone and are committed to providing a website that is accessible to the broadest possible audience, regardless of ability.

In this process, we provide screen-readers with meaningful data using the ARIA set of attributes. For example, we provide accurate form labels; descriptions for actionable icons social media icons, search icons, cart icons, etc. It provides an accurate and meaningful image-object-recognition-based description as an ALT alternate text tag for images that are not described. It will also extract texts embedded within the image using an OCR optical character recognition technology. Screen-reader users also get automatic announcements to turn the Screen-reader mode on as soon as they enter the website.

The background process also handles triggered popups by moving the keyboard focus towards them as soon as they appear, not allowing the focus to drift outside. This makes it worthwhile to examine what the stock has in store. Morgan Stanley is out of the gate this morning with its own fourth-quarter results.

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The analysts needed to preprocess and analyze this large data set. They wanted to generate plots and other visualizations that they could use to evaluate algorithm output and communicate the results to their customer. Rao and his team imported the raw data and cleaned it by developing preprocessing algorithms that used built-in MATLAB functions to remove records with null, negative, and missing values.

They then explored the data. They generated scatter plots in MATLAB to identify outliers in the data and visualize the average time taken to detect and resolve service issues, a key customer metric for churn. The team used Deep Learning Toolbox to create, train, and simulate a neural network for churn prediction. Using Statistics and Machine Learning Toolbox, the team developed one churn prediction algorithm based on decision trees and another based on multivariate logistic regression.

Their analysis identified three key drivers of churn: delayed responses, delayed delivery of services, and problems with quality of service. After comparing results from the neural network, the decision tree, and logistic regression, the team found that logistic regression produced most accurate churn predictions.

Algorithm development time cut from weeks to days. Project completed in less than half the allotted time. Hundreds of gigabytes of data analyzed. Learn more about Cognizant. Select a Web Site. Choose a web site to get translated content where available and see local events and offers.

Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance. The analysts needed to preprocess and analyze this large data set. They wanted to generate plots and other visualizations that they could use to evaluate algorithm output and communicate the results to their customer. Rao and his team imported the raw data and cleaned it by developing preprocessing algorithms that used built-in MATLAB functions to remove records with null, negative, and missing values.

They then explored the data. They generated scatter plots in MATLAB to identify outliers in the data and visualize the average time taken to detect and resolve service issues, a key customer metric for churn. The team used Deep Learning Toolbox to create, train, and simulate a neural network for churn prediction.

Using Statistics and Machine Learning Toolbox, the team developed one churn prediction algorithm based on decision trees and another based on multivariate logistic regression. Their analysis identified three key drivers of churn: delayed responses, delayed delivery of services, and problems with quality of service. After comparing results from the neural network, the decision tree, and logistic regression, the team found that logistic regression produced most accurate churn predictions.

Algorithm development time cut from weeks to days. Project completed in less than half the allotted time. Hundreds of gigabytes of data analyzed. Learn more about Cognizant. Select a Web Site. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.