ML With Restaurant Inspections Data, Alaska, USA.
2000-2019. There are 45 664 records: date, violation, score, GPS and etc.
@kaggle.danilzyryanov_restaurant_and_food_inspections_alaska_usa
2000-2019. There are 45 664 records: date, violation, score, GPS and etc.
@kaggle.danilzyryanov_restaurant_and_food_inspections_alaska_usa
Restaurant inspection by officials in Alaska, USA. From 03 / 01 / 2000 to 04 / 29 / 2019
What are the reasons, what is result score, address, coordinates and other data.
The source is the local government.
What are the patterns of violations?
Which restaurants are most often with a low/high score? Location or other reasons?
What about ML with this data?
CREATE TABLE restaurant_and_food_inspection_alaska (
"business_id" VARCHAR,
"business_name" VARCHAR,
"business_address" VARCHAR,
"business_city" VARCHAR,
"business_state" VARCHAR,
"business_postal_code" BIGINT,
"business_phone_number" DOUBLE,
"inspection_date" TIMESTAMP,
"inspection_score" DOUBLE,
"inspection_description" VARCHAR,
"inspection_type" VARCHAR,
"violation_description" VARCHAR,
"violation_code" VARCHAR,
"business_location" VARCHAR,
"geo" VARCHAR,
"latitude" VARCHAR,
"longitude" VARCHAR
);Anyone who has the link will be able to view this.