Use Cases

Bob, a data analyst, aims to predict house prices across various regions in New York, California, and Illinois. He sourced a house price dataset from Redfin covering these states. Utilizing the dataset’s attributes, Bob trained a random forest classifier to determine if house prices in a particular zipcode are classified as low or high. However, his classifier achieved an accuracy of just 69%. To enhance the classifier’s performance, he’s now looking to incorporate additional attributes into the original house price dataset.

Imagine you are an IT manager for an organization which possesses a lot of tabular datasets. You want to share the datasets among all users in the organziation for productivity. To this end, all users must be able to find the needed datasets easily and precisely. One option is thus to let users ask a natural language (NL) question and the system then returns the datasets needed.

Imagine you are a school counselor in Chicago, to help parents and students choose which schools to go to, you want to find a table that contains information about every public school in Chicago, including the school name, its curriculum/degree type (ex: IB, General Education etc), and its latest rating based on Chicago’s School Quality Rating Policy (SQRP).