This project focuses on analyzing Zomato Bangalore restaurant data to uncover insights about restaurant ratings, pricing, locations, and customer preferences. The goal is to understand food industry trends in Bangalore using data analysis techniques and visualizations.
This project demonstrates practical skills in data cleaning, exploratory data analysis (EDA), and data visualization using Python.
- Python
- Pandas – data manipulation
- NumPy – numerical operations
- Matplotlib & Seaborn – data visualization
- plotly - interactive data visualization
- warnings - Silences warnings to keep output clean during execution
- Google Colab – cloud-based notebook environment
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Dataset: Zomato Bangalore Restaurants Dataset
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Contains information about:
- url
- address
- Restaurant names
- online_order
- book_table
- rate
- votes
- phone
- location
- rest_type
- dish_liked
- cuisines
- approx_cost(for two people)
- reviews_list
- menu_item
- listed_in(type)
- listed_in(city)
Dataset provided by instructor for academic project work. Used strictly for educational and analytical purposes.
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Data loading and inspection
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Handling missing and duplicate values
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Feature understanding and transformation
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Exploratory Data Analysis (EDA)
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Visualization of:
- Ratings distribution
- Avg rating of restaurant
- Relationship between votes and ratings
- Popular restaurant types
- Location-wise restaurant density
- Impact of online ordering and offline
- Cost of two people
- Delivery vs dine-out preference among restaurants
- Most popular dishes
- Voting trends for online ordering restaurants
- Most restaurants fall within the mid-rating range, indicating competitive quality across the city.
- Certain locations in Bangalore have a high concentration of restaurants, showing strong commercial activity.
- Quick-bites and Casual Dining restaurants dominate the market.
- North Indian, Chinese, and South Indian cuisines are the most widely offered.
- The majority of restaurants do not offer table booking.
- Online ordering is widely available across restaurants.
- Quick-bites restaurants are more popular compared to other restaurant types.
The notebook includes multiple visualizations to better understand:
- Customer preferences across restaurant types, cuisines, and ordering modes
- Pricing trends and cost distribution
- Rating distributions and voting patterns, including location-based trends
This analysis provides meaningful insights into Bangalore’s restaurant landscape. It can help:
- Restaurant owners understand customer behavior
- Analysts explore pricing and rating trends
- Businesses make data-driven decisions
Kirti Gupta
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