The following are some use-cases for Zia AutoML:
- Recommendation Engines: An e-commerce service uses Zia AutoML to predict and suggest recommendations for products that a user might be interested in. The service constructs an efficient recommendation engine by collecting explicit and implicit data from the user’s browsing and purchase history, and using AutoML to analyze and discover patterns in the datasets.
- Dynamic Pricing: A ride service hailing mobile application uses AutoML to determine the price for a trip dynamically. The AutoML model predicts the right price for a trip, consistent with the incentive given to the driver, customer satisfaction, and profitability based on various factors such as the time of the day, location, weather, customer demand, cab availability, and more.
- Sales Forecasting: A pharmaceutical company uses Zia AutoML in a web application designed to be used internally by the company’s sales team. The sales analysts use the application to analyze previous sales and revenue data, evaluate sales patterns, and predict trends in their upcoming proposals to formulate sales forecast and plan strategies. They create and train several models in AutoML, using datasets of various sample sizes in their application.
Some other examples where Zia AutoML can be implemented are:
- A job portal application for a HR service that predicts the suitability of a candidate for a particular job position based on their educational qualifications and previous work experience.
- An election forecasting application that predicts the election results based on previous election performances, results of opinion polls and surveys, user activities on social media, and more.
- Advertisement personalization on a website based on the users’ interests.
- Fraud detection and prevention in banking and finance applications.
Last Updated 2023-05-19 12:11:43 +0530 +0530
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