Disclosed herein is a method of ranking real estate listings of real estate. Further, the method may include retrieving, using a storage device, real estate listing data associated with a real estate listing of a real estate. Further, the method may include analyzing, using a processing device, the real estate listing data using a machine learning model. Further, the method may include determining, using the processing device, a value for a quantitative variable associated with the real estate listing based on the analyzing. Further, the method may include determining, using the processing device, a quality of the real estate listing based on the value of the quantitative variable. Further, the method may include generating, using the processing device, a listing score for the real estate listing based on the quality. Further, the method may include storing, using the storage device, the listing score.

A broker system infrastructure includes a database warehouse configured to store and provide insurance data for one or more clients, and a data analytics server in communication with the database warehouse. The broker system infrastructure analyzes policy data and member data to define a model configured to identify a relationship therebetween, receives query data including first member data, uses the policy data to generate a quote based on the query data, and applies the query data and the quote to the model to determine whether the quote is inaccurate. If the quote is inaccurate, the broker system infrastructure modifies the policy data and member data to incorporate to the quote and first member data, and analyzes at least the modified policy data and member data to modify the model such that the model is configured to identify a relationship therebetween.

A method and system for repairing data with incongruent or incompatible types that detects anomalies in human resources data, and if anomalies are present in the data, then suggests to a user corrections and synchronizing actions that better match patterns in the data, specifically listing reasons why the data is potentially erroneous and justifies the suggestion based on objective data to aid the user in accepting corrections and synchronizing actions or performing further review and analysis on the data using the method and system.

In one implementation, a system for providing product suggestions to provide to a customer during a checkout process at a retail store includes a checkout configuration that includes a point of sale terminal, a scanner to scan products, a scanning preparation area where products to be scanned are positioned, and a customer-facing device. The system can further include one or more product sensors to capture and transmit product signals. The system can also include a product identification system that is programmed to identify the products contained in the scanning preparation area. The system can further include a product recommendation engine that is configured to identify contents of a customer cart from the products contained in the scanning preparation area; determine product recommendations based, at least in part, on the contents of the customer cart; and transmit the product recommendations to the customer-facing device for display to the user.

A machine learning system for forecasting demand implements an improved process using event streams for predicting the impact that events in the geographic area surrounding an establishment have on demand forecasts for the establishment. A range of distances from the establishment can be determined and divided up into different distances. Event streams can be generated from the events by grouping the events into combinations of event attributes, such as event category and the distance of the event from the establishment. The event streams can be provided as inputs along with demand data for computing demand forecasts that account for increased or decreased traffic patterns in the geographic area surrounding the event. The event streams can be filtered based on the magnitude of the impact the events have on the demand for the products and/or services of the establishment before, during and after the event takes place.

Techniques are described for training machine learning (ML) models using one or more electronic lists of items previously used in campaigns and labeled with an engagement rate corresponding to the list. A vocabulary formed from a union of the one or more lists may then be used to generate at least some items of a target recipient list. An engagement rate for the target recipient list may be inferred for the target recipient list. Natural language processing (NLP) techniques may be also be applied to optimize an engagement rate of a target recipient list and/or select content for the list.

An information processing device calculates a quoted price to be presented to a purchase contemplation person for a vehicle. The information processing device includes a control unit that calculates a demand forecast when the purchase contemplation person uses the vehicle as a car-sharing vehicle, calculates an expected income when the purchase contemplation person uses the vehicle as the car-sharing vehicle, based on the demand forecast, and calculates the quoted price based on a vehicle price of the vehicle and the expected income, to present the quoted price to the purchase contemplation person.

A system of determining an alimentary preparation provider is disclosed. The system comprises a computing device. The computing device is configured to receive input from a user device at a current geographical location. The input comprises an alimentary request and a plurality of request parameters. The computing device generates a plurality of alimentary preparation providers as a function of the plurality of request parameters. A request parameter classifier is generated by the computing device. The computing device classifies, by the request parameter classifier, the plurality of request parameters and the plurality of alimentary preparation providers. The classification outputs an ordered list of request parameters. The computing device assigns the alimentary request to a first alimentary preparation provider as a function of an ordered list of request parameters. A method for determining an alimentary preparation provider is disclosed.

The present application is generally directed to a decentralized identity management system that has one or more processors configured to receive a review from a user regarding an entity, determine one or more locations associated with the entity, determine a location associated with the user; and determine a reputation score for the entity based on the review, a location of the one or more locations, and the location associated with the user. Additionally, the one or more processors are configured to cause the reputation score to be recorded on a distributed ledger.

Methods and systems are provided for geocoding. An example method encompasses receiving, from a delivery data corpus, a delivery data comprising one or more addresses. The method thereafter comprises identifying, one or more tokens from said one or more addresses. Further the method leads to calculating, for each of said one or more tokens, an end token score and a sub-sequence token score. The method thereafter encompasses chunking, said one or more addresses based on said end token score and said sub-sequence token score to generate one or more address chunks. Further the method comprises generating, one or more polygon regions for each generated address chunks based on said chunking. Thereafter the method comprises merging, one or more generated polygon regions for each of the generated address chunks.