This project focuses on analyzing global restaurant data to uncover meaningful insights into customer preferences, pricing trends, and service availability. The dataset includes information such as restaurant names, locations, cuisines, ratings, price ranges, and services offered (e.g., online delivery, table booking).
The goal of this analysis is to help businesses, customers, and food-tech platforms make informed decisions by exploring patterns across restaurants worldwide.
Dataset Source: Kaggle Restaurant Dataset)
The restaurant industry is highly competitive and diverse. By analyzing global restaurant data, we aim to:
- Understand cuisine popularity across regions.
- Identify relationships between pricing, ratings, and service availability.
- Explore geographic patterns in restaurant clusters.
- Generate insights to improve restaurant strategy and customer satisfaction.
- Project Overview
- Data Understanding
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Insights Derived
- Suggestions
- Challenges Faced
- Future Scope
- Final Outcome
- Project Credits
We explored global restaurant data using Python, Pandas, Matplotlib, and Seaborn. Key focus areas included:
- Distribution of cuisines across regions.
- Analysis of ratings vs. pricing.
- Service availability (online delivery, table booking) by price range.
- Geographic clustering of restaurants.
- The dataset includes structured restaurant information such as:
Restaurant Name,Cuisines,City,Country,Price Range,Rating,Has Table booking,Has Online Delivery.
- Some missing values were present in cuisine and rating fields.
- Variables were categorical (e.g., cuisines, city) and numerical (ratings, price range).
- Handled missing values in cuisines and ratings.
- Normalized city and country names for consistency.
- Encoded categorical variables where necessary.
- Removed duplicates and ensured data integrity.
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Cuisine Popularity
- North Indian, Chinese, and Mughai cuisines dominate globally.
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Ratings vs Price Range
- Moderate price range restaurants (2–3) received the highest average ratings.
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Service Availability
- Higher-priced restaurants are more likely to offer table booking.
- Online delivery is more popular among mid-range restaurants.
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Geographic Trends
- Certain countries show high concentration of global cuisines (e.g., India, USA).
- Handling inconsistent cuisine names (e.g., “North Indian”, “North-Indian”).
- Large dataset size slowed processing and visualization.
- Balancing insights across multiple countries with varying sample sizes.
- Build recommendation systems for cuisine/restaurant suggestions.
- Apply clustering algorithms for customer segmentation.
- Develop interactive dashboards using Power BI / Tableau / Plotly Dash.
This project highlighted global restaurant trends, service availability, and cuisine patterns. The insights can guide restaurant owners, customers, and food-tech businesses in making smarter decisions.
Rohit Sharma