Name: Restaurant Revenue Prediction Dataset
Creator: Kaggle
License: https://creativecommons.org/licenses/by-sa/4.0/

About this Dataset

Restaurant Revenue Prediction Dataset

This dataset contains information about various restaurants and aims to predict the revenue based on several features. Each row represents a unique restaurant with various attributes that may influence its revenue.

Columns

Name: The name of the restaurant.
Location: The location of the restaurant (e.g., Rural, Downtown).
Cuisine: The type of cuisine offered (e.g., Japanese, Mexican, Italian).
Rating: The average rating of the restaurant.
Seating Capacity: The number of seats available in the restaurant.
Average Meal Price: The average price of a meal at the restaurant.
Marketing Budget: The marketing budget allocated for the restaurant.
Social Media Followers: The number of social media followers.
Chef Experience Years: The number of years of experience of the head chef.
Number of Reviews: The total number of reviews the restaurant has received.
Avg Review Length: The average length of reviews.
Ambience Score: A score representing the ambience of the restaurant.
Service Quality Score: A score representing the quality of service.
Parking Availability: Indicates if parking is available (Yes/No).
Weekend Reservations: The number of reservations made on weekends.
Weekday Reservations: The number of reservations made on weekdays.
Revenue: The total revenue generated by the restaurant.

Objective

The main objective of this dataset is to predict the revenue of a restaurant based on the given features.

Usage

This dataset can be used for regression analysis, machine learning model training, and other predictive analytics tasks to understand and forecast restaurant revenue based on various influencing factors.

Tables

Restaurant Data

@kaggle.anthonytherrien_restaurant_revenue_prediction_dataset.restaurant_data

396.73 kB
8,368 rows
17 columns

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
);