About the dataset

Note: If you are Looking for Swiggy Dataset : https://www.kaggle.com/datasets/abhijitdahatonde/swiggy-restuarant-dataset/data

Powerbi PBIX files are here : https://github.com/slidescope/restaurants-analysis

This dataset appears to be related to restaurant information and includes the following columns:

Key Columns and Their Descriptions:

1. RestaurantID: A unique identifier for each restaurant.
2. CountryCode: Numeric code representing the country (e.g., “1” could signify a specific country like India).
3. City: The city where the restaurant is located.
4. Locality: A more localized area or neighborhood within the city.
5. LocalityVerbose: Detailed description of the locality, including the city name.
6. Longitude & Latitude: Geographic coordinates of the restaurant.
7. Cuisines: The primary type of cuisine served (e.g., “North Indian”).
8. Currency: Currency used at the restaurant (e.g., “Indian Rupees”).
9. Has_Table_booking: Indicates whether the restaurant offers table bookings (e.g., “Yes” or “No”).
10. Has_Online_delivery: Indicates if the restaurant provides online delivery.
11. Is_delivering_now: Shows whether the restaurant is currently delivering food.
12. Switch_to_order_menu: Indicates if the restaurant has an option to switch to an order menu.
13. Price_range: Categorizes the price level of the restaurant, likely on a scale (e.g., 1 = Low, 5 = High).
14. Votes: Number of customer votes the restaurant has received.
15. Average_Cost_for_two: The average cost of a meal for two people, in the local currency.
16. Rating: A numerical rating of the restaurant, possibly on a scale (e.g., 1–5).
17. Datekey_Opening: The date when the restaurant was opened (formatted as YYYY_M_D).
18. Cuisines 1 to 8: Additional columns to indicate the variety of cuisines offered by the restaurant, if more than one.

Observations:

• The dataset provides a mix of geographical, operational, and customer feedback data.
• It can be used to analyze restaurant distribution, pricing strategies, customer preferences, and trends over time.
• The data is structured and well-suited for tasks like geographic mapping, cost analysis, and rating-based filtering.

Dataset Link : https://github.com/slidescope/data/blob/master/restaurant_data_cl.xlsx