About this Dataset

Restaurants & Members & Orders Dataset

This dataset is taken from SQL Server Database, so the files (tables) are related to each other. The dataset includes the category, restaurant information, member information, order information, date, time, location, finance information, and more...

What can you do with this dataset?

Because the files are related, you need to decide how to combine the data and which method you will apply - join(), merge(), append(), concat(), maybe anything else idea... Which columns do you think are not necessary? Are there within the data duplicates or may be missing data? There is a lot of interesting information here.

You can use this data to discover which City and period of the day the members do the most orders?
What is the ratio of meal types in restaurants in each city?
What is the ratio of the orders in cities with the most Italian restaurants?
Which cities have the most vegan meals?
What is the difference in the range price of the hot or cold meal?
What is the correlation between the sex of members and serve_type?

Exploratory Data Analysis
Descriptive Analysis and Visualization
More and more exciting plots

In addition to classical EDA, could you use the dataset and apply a range of machine learning methods, most notably classifier models (logistic regression, SVM, random forest, etc.)?

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Tables

Cities

@kaggle.vainero_restaurants_customers_orders_dataset.cities

2.09 KB
5 rows
2 columns


CREATE TABLE cities (
  "id" BIGINT,
  "city" VARCHAR
);

Meals

@kaggle.vainero_restaurants_customers_orders_dataset.meals

11.27 KB
350 rows
7 columns


CREATE TABLE meals (
  "id" BIGINT,
  "restaurant_id" BIGINT,
  "serve_type_id" BIGINT,
  "meal_type_id" BIGINT,
  "hot_cold" VARCHAR,
  "meal_name" VARCHAR,
  "price" DOUBLE
);

Meal Types

@kaggle.vainero_restaurants_customers_orders_dataset.meal_types

2.08 KB
4 rows
2 columns


CREATE TABLE meal_types (
  "id" BIGINT,
  "meal_type" VARCHAR
);

Members

@kaggle.vainero_restaurants_customers_orders_dataset.members

12.38 KB
200 rows
7 columns


CREATE TABLE members (
  "id" BIGINT,
  "first_name" VARCHAR,
  "surname" VARCHAR,
  "sex" VARCHAR,
  "email" VARCHAR,
  "city_id" BIGINT,
  "monthly_budget" DOUBLE
);

Monthly Member Totals

@kaggle.vainero_restaurants_customers_orders_dataset.monthly_member_totals

47.02 KB
1200 rows
14 columns


CREATE TABLE monthly_member_totals (
  "member_id" BIGINT,
  "first_name" VARCHAR,
  "surname" VARCHAR,
  "sex" VARCHAR,
  "email" VARCHAR,
  "city" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "order_count" BIGINT,
  "meals_count" BIGINT,
  "monthly_budget" DOUBLE,
  "total_expense" DOUBLE,
  "balance" DOUBLE,
  "commission" DOUBLE
);

Orders

@kaggle.vainero_restaurants_customers_orders_dataset.orders

429.86 KB
36000 rows
6 columns


CREATE TABLE orders (
  "id" BIGINT,
  "date" TIMESTAMP,
  "hour" VARCHAR,
  "member_id" BIGINT,
  "restaurant_id" BIGINT,
  "total_order" DOUBLE
);

Order Details

@kaggle.vainero_restaurants_customers_orders_dataset.order_details

751.97 KB
70577 rows
3 columns


CREATE TABLE order_details (
  "id" BIGINT,
  "order_id" BIGINT,
  "meal_id" BIGINT
);

Restaurants

@kaggle.vainero_restaurants_customers_orders_dataset.restaurants

4.6 KB
30 rows
5 columns


CREATE TABLE restaurants (
  "id" BIGINT,
  "restaurant_name" VARCHAR,
  "restaurant_type_id" BIGINT,
  "income_persentage" DOUBLE,
  "city_id" BIGINT
);

Restaurant Types

@kaggle.vainero_restaurants_customers_orders_dataset.restaurant_types

2.16 KB
5 rows
2 columns


CREATE TABLE restaurant_types (
  "id" BIGINT,
  "restaurant_type" VARCHAR
);

Serve Types

@kaggle.vainero_restaurants_customers_orders_dataset.serve_types

2.08 KB
3 rows
2 columns


CREATE TABLE serve_types (
  "id" BIGINT,
  "serve_type" VARCHAR
);