Restaurant Revenue Prediction Dataset

A dataset about restaurant

@kaggle.anthonytherrien_restaurant_revenue_prediction_dataset

Table List

1 table(387.43 KB)

Restaurant Data

@kaggle.anthonytherrien_restaurant_revenue_prediction_dataset.restaurant_data

387.43 KB
8368 rows
17 columns

name Name	location Location	cuisine Cuisine	rating Rating	seating_capacity Seating Capacity	average_meal_price Average Meal Price	marketing_budget Marketing Budget	social_media_followers Social Media Followers	chef_experience_years Chef Experience Years	number_of_reviews Number Of Reviews	avg_review_length Avg Review Length	ambience_score Ambience Score	service_quality_score Service Quality Score	parking_availability Parking Availability	weekend_reservations Weekend Reservations	weekday_reservations Weekday Reservations	revenue Revenue
Restaurant 0	Rural	Japanese	4	38	73.98	2224	23406	13	185	161.92490574327928	1.3	7	Yes	13	4	638945.52
Restaurant 1	Downtown	Mexican	3.2	76	28.11	4416	42741	8	533	148.7597173739537	2.6	3.4	Yes	48	6	490207.83
Restaurant 2	Rural	Italian	4.7	48	48.29	2796	37285	18	853	56.84918945954191	5.3	6.7	No	27	14	541368.62
Restaurant 3	Rural	Italian	4.4	34	51.55	1167	15214	13	82	205.43326529316235	4.6	2.8	Yes	9	17	404556.8
Restaurant 4	Downtown	Japanese	4.9	88	75.98	3639	40171	9	78	241.68158411313135	8.6	2.1	No	37	26	1491046.35
Restaurant 5	Downtown	Indian	4.4	79	35.69	6787	75378	5	400	247.5444607760481	2	8.5	Yes	27	22	644220.9
Restaurant 6	Rural	Mexican	4.9	49	35.3	2594	32587	11	106	206.159034961204	1.5	5.3	No	12	28	400348.65
Restaurant 7	Suburban	Japanese	4.1	61	73.65	3213	33429	14	377	135.319312410584	1.8	4.6	No	56	23	1014153.65
Restaurant 8	Rural	Japanese	4.2	37	73.75	3437	37102	18	494	55.65241712047435	7.1	9.4	Yes	27	24	635606.1
Restaurant 9	Rural	French	5	32	59.8	2569	28419	18	907	273.9448868142822	6.4	7.4	No	12	19	452920.65


CREATE TABLE restaurant_data (
  "name" VARCHAR,
  "location" VARCHAR,
  "cuisine" VARCHAR,
  "rating" DOUBLE,
  "seating_capacity" BIGINT,
  "average_meal_price" DOUBLE,
  "marketing_budget" BIGINT,
  "social_media_followers" BIGINT,
  "chef_experience_years" BIGINT,
  "number_of_reviews" BIGINT,
  "avg_review_length" DOUBLE,
  "ambience_score" DOUBLE,
  "service_quality_score" DOUBLE,
  "parking_availability" VARCHAR,
  "weekend_reservations" BIGINT,
  "weekday_reservations" BIGINT,
  "revenue" DOUBLE
);