Name: Zomato Bangalore Data (Clean)
Creator: Kaggle
License: http://opendatacommons.org/licenses/dbcl/1.0/

About this Dataset

Zomato Bangalore Data (Clean)

The original Zomato Bengaluru dataset is, let's face it, dirty. There are too many redundancies and too much information which does not make any sense. There are a multitude of URLs for the same restaurant and it's about time we got a cleaner, crisper dataset.
This dataset contains the original data (in the diners.db file) and the new data included in the SQL database and in CSV format too. There are other useful files like user preferences files, Bengaluru POI .geojson files, location density and rent analysis files which can aid geospatial analysis.
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Tables

Bengaluru House Data

@kaggle.saranimje_zomato_data_clean.bengaluru_house_data

168.7 kB
13,320 rows
9 columns

CREATE TABLE bengaluru_house_data (
  "area_type" VARCHAR,
  "availability" VARCHAR,
  "location" VARCHAR,
  "size" VARCHAR,
  "society" VARCHAR,
  "total_sqft" VARCHAR,
  "bath" DOUBLE,
  "balcony" DOUBLE,
  "price" DOUBLE
);

Cuisine Location Rate

@kaggle.saranimje_zomato_data_clean.cuisine_location_rate

4.5 kB
196 rows
3 columns

CREATE TABLE cuisine_location_rate (
  "category" VARCHAR,
  "city" VARCHAR,
  "rating" DOUBLE
);

Cuisines

@kaggle.saranimje_zomato_data_clean.cuisines

83.38 kB
20,934 rows
8 columns

CREATE TABLE cuisines (
  "cuisine1" VARCHAR,
  "cuisine2" VARCHAR,
  "cuisine3" VARCHAR,
  "cuisine4" VARCHAR,
  "cuisine5" VARCHAR,
  "cuisine6" VARCHAR,
  "cuisine7" VARCHAR,
  "cuisine8" VARCHAR
);

Location

@kaggle.saranimje_zomato_data_clean.location

9.85 kB
93 rows
7 columns

CREATE TABLE location (
  "index" BIGINT,
  "location" VARCHAR,
  "density" BIGINT,
  "rent" BIGINT,
  "rate" DOUBLE,
  "latitude" DOUBLE,
  "longitude" DOUBLE
);

New Rent

@kaggle.saranimje_zomato_data_clean.new_rent

26.81 kB
1,306 rows
3 columns

CREATE TABLE new_rent (
  "location" VARCHAR,
  "number" BIGINT,
  "avg_cost" DOUBLE
);

Onlinedeliverydata

@kaggle.saranimje_zomato_data_clean.onlinedeliverydata

57.48 kB
388 rows
55 columns

CREATE TABLE onlinedeliverydata (
  "age" BIGINT,
  "gender" VARCHAR,
  "marital_status" VARCHAR,
  "occupation" VARCHAR,
  "monthly_income" VARCHAR,
  "educational_qualifications" VARCHAR,
  "family_size" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "pin_code" BIGINT,
  "medium_p1" VARCHAR  -- Medium (P1),
  "medium_p2" VARCHAR  -- Medium (P2),
  "meal_p1" VARCHAR  -- Meal(P1),
  "meal_p2" VARCHAR  -- Meal(P2),
  "perference_p1" VARCHAR  -- Perference(P1),
  "perference_p2" VARCHAR  -- Perference(P2),
  "ease_and_convenient" VARCHAR,
  "time_saving" VARCHAR,
  "more_restaurant_choices" VARCHAR,
  "easy_payment_option" VARCHAR,
  "more_offers_and_discount" VARCHAR,
  "good_food_quality" VARCHAR,
  "good_tracking_system" VARCHAR,
  "self_cooking" VARCHAR,
  "health_concern" VARCHAR,
  "late_delivery" VARCHAR,
  "poor_hygiene" VARCHAR,
  "bad_past_experience" VARCHAR,
  "unavailability" VARCHAR,
  "unaffordable" VARCHAR,
  "long_delivery_time" VARCHAR,
  "delay_of_delivery_person_getting_assigned" VARCHAR,
  "delay_of_delivery_person_picking_up_food" VARCHAR,
  "wrong_order_delivered" VARCHAR,
  "missing_item" VARCHAR,
  "order_placed_by_mistake" VARCHAR,
  "influence_of_time" VARCHAR,
  "order_time" VARCHAR,
  "maximum_wait_time" VARCHAR,
  "residence_in_busy_location" VARCHAR,
  "google_maps_accuracy" VARCHAR,
  "good_road_condition" VARCHAR,
  "low_quantity_low_time" VARCHAR,
  "delivery_person_ability" VARCHAR,
  "influence_of_rating" VARCHAR,
  "less_delivery_time" VARCHAR,
  "high_quality_of_package" VARCHAR,
  "number_of_calls" VARCHAR,
  "politeness" VARCHAR,
  "freshness" VARCHAR,
  "temperature" VARCHAR,
  "good_taste" VARCHAR,
  "good_quantity" VARCHAR,
  "output" VARCHAR,
  "reviews" VARCHAR
);

Poi

@kaggle.saranimje_zomato_data_clean.poi

4.17 kB
38 rows
3 columns

CREATE TABLE poi (
  "place" VARCHAR,
  "poi_lat" DOUBLE,
  "poi_long" DOUBLE
);

Rate Cost

@kaggle.saranimje_zomato_data_clean.rate_cost

8.32 kB
258 rows
5 columns

CREATE TABLE rate_cost (
  "rowid" BIGINT,
  "cost" BIGINT,
  "avg_rate" DOUBLE,
  "listed_type" VARCHAR,
  "sum_votes" BIGINT  -- SUM(votes)
);

Zomato Clean

@kaggle.saranimje_zomato_data_clean.zomato_clean

822.54 kB
20,946 rows
14 columns

CREATE TABLE zomato_clean (
  "index" BIGINT,
  "name" VARCHAR,
  "address" VARCHAR,
  "location" VARCHAR,
  "listed_type" VARCHAR,
  "votes" BIGINT,
  "num_rate" DOUBLE,
  "online_order" VARCHAR,
  "book_table" VARCHAR,
  "cuisines" VARCHAR,
  "rest_type" VARCHAR,
  "cost" DOUBLE,
  "bool_online_order" BIGINT,
  "bool_book_table" BIGINT
);

Zomato Test

@kaggle.saranimje_zomato_data_clean.zomato_test

301 kB
4,189 rows
17 columns

CREATE TABLE zomato_test (
  "index" BIGINT,
  "name" VARCHAR,
  "address" VARCHAR,
  "location" VARCHAR,
  "listed_type" VARCHAR,
  "votes" BIGINT,
  "num_rate" DOUBLE,
  "online_order" VARCHAR,
  "book_table" VARCHAR,
  "cuisines" VARCHAR,
  "rest_type" VARCHAR,
  "cost" DOUBLE,
  "bool_online_order" BIGINT,
  "bool_book_table" BIGINT,
  "prediction" DOUBLE,
  "predictions" DOUBLE,
  "predictions_tuned" DOUBLE
);

Zomato Train

@kaggle.saranimje_zomato_data_clean.zomato_train

763.44 kB
16,757 rows
14 columns

CREATE TABLE zomato_train (
  "index" BIGINT,
  "name" VARCHAR,
  "address" VARCHAR,
  "location" VARCHAR,
  "listed_type" VARCHAR,
  "votes" BIGINT,
  "num_rate" DOUBLE,
  "online_order" VARCHAR,
  "book_table" VARCHAR,
  "cuisines" VARCHAR,
  "rest_type" VARCHAR,
  "cost" DOUBLE,
  "bool_online_order" BIGINT,
  "bool_book_table" BIGINT
);