Restaurant-Ratings-Analysis
Restaurant Ratings Analysis using Microsoft Power BI
Last updated Jul 4, 2026
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README
Restaurant Ratings Analysis
Table of Content
Case Study
Restaurant ratings in Mexico by real consumers from 2012, including additional information about each restaurant and their cuisines, and each consumer and their preferences.Dataset Description
Our data set consists of the following observations which include:Consumers
- Consumer_ID - Unique identifier for each consumer
- City - City where the consumer lives
- State - State where the consumer lives
- Country - Country where the consumer lives
- Latitude - Latitude where the consumer lives
- Longitude - Longitude where the consumer lives
- Smoker - Whether the consumer smokes or not
- Drink_Level - Whether the consumer is an abstemious, casual, or social drinker
- Transportation_Method - Whether the consumer transports on foot, by public transport, or by car
- Marital_Status - The consumer's marital status (single or married)
- Children - Whether the consumer has dependent/independent children or kids
- Age - The consumer's age
- Occupation - The consumer's occupation (student, employed, or unemployed)
- Budget - The consumer's budget (low, medium, high)
Consumer_Preferences
- Consumer_ID - Unique identifier for each consumer
- Preferred_Cuisine - Types of food the consumer prefers
Ratings
- Consumer_ID - Unique identifier for each consumer
- Restaurant_ID - Unique identifier for each restaurant
- Overall_Rating - The overall rating by the consumer for the restaurant (0=Unsatisfactory, 1=Satisfactory, 2=Highly Satisfactory)
- Food_Rating - The food's rating by the consumer for the restaurant (0=Unsatisfactory, 1=Satisfactory, 2=Highly Satisfactory)
- Service_Rating - The service rating by the consumer for the restaurant (0=Unsatisfactory, 1=Satisfactory, 2=Highly Satisfactory)
Restaurants
- Restaurant_ID - Unique identifier for each restaurant
- Name - The restaurant's name
- City - The restaurant's city
- State - The restaurant's state
- Country - The restaurant's country
- Zip_Code - The restaurant's zip code
- Latitude - The restaurant's latitude
- Longitude - The restaurant's longitude
- Alcohol_Service - Whether the restaurant serves no alcohol, wine & beer, or a full bar
- Smoking_Allowed - Whether any smoking is allowed, including in the bar or in smoking sections
- Price - The restaurant's price (low, medium, high)
- Franchise - Whether the restaurant is a franchise
- Area - Whether the restaurant is in an open or closed area
- Parking - Whether the restaurant offers any sort of parking (none, yes, public, valet)
Restaurant_Cuisines
- Restaurant_ID - Unique identifier for each restaurant
- Cuisine - Types of food the restaurant serves
ER Diagram
Data Cleaning
Steps to import data as a folder
- Get data -> More -> All -> Folder -> Connect -> Path leading to the folder dataset -> Click ok
- Click on transform data -> Duplicate the file -> Click on Binary to expand the dataset (Repeat the set for the no of datasets)
- Calculated Fields
Age Group
AgeGroup =
SWITCH(
TRUE(),
consumers[Age] <= 18, "Children and Adolescents",
consumers[Age] <= 30, "Young Adults",
consumers[Age] <= 45, "Adults",
consumers[Age] <= 60, "Middle-aged Adults",
"Seniors"
)
Service Rating Category
ServiceRatingCategory = SWITCH(
TRUE(),
ratings[Service_Rating] = 0, "Unsatisfactory",
ratings[Service_Rating] = 1, "Satisfactory",
"Highly Satisfactory"
)
Overall Rating Category
OverallRatingCategory = SWITCH(
TRUE(),
ratings[Overall_Rating] = 0, "Unsatisfactory",
ratings[Overall_Rating] = 1, "Satisfactory",
"Highly Satisfactory"
)
Food Rating Category
FoodRatingCategory = SWITCH(
TRUE(),
ratings[Food_Rating] = 0, "Unsatisfactory",
ratings[Food_Rating] = 1, "Satisfactory",
"Highly Satisfactory"
)
Data Analysis
Local Insights:
- What is the distribution of consumers by city and state?
- How does the age distribution of consumers vary by state?
- What percentage of consumers are smokers or non-smokers in each city?
- How common is parking availability at restaurants in different cities?
Dining Insights:
- How does the availability of parking correlate with restaurant price levels?
- What is the distribution of restaurants by state?
- How do restaurant franchises compare to non-franchises in terms of consumer ratings?
- What are consumers' preferred cuisines based on their demographic profiles?
Hospitality Insights:
- How does the type of alcohol service offered vary by restaurants in each city?
- What transportation methods are most commonly used by consumers?
- How does the presence of alcohol service influence consumer ratings?
- What percentage of restaurants allow smoking in each state?
Behavior Insights:
- What are the common occupations of consumers in different state?
- How does the drink level (abstemious, casual, social) vary across different states?
- How does marital status correlate with smoking or drinking habits?
- Is there a relationship between consumers' occupations and their budget levels?
Review Insights:
- What are the top 5 restaurants by food rating?
- What are the top 5 restaurants by service rating?
- What are the top 5 restaurants by overall rating?
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