This study was conducted to assist a client planning to open a new restaurant.
By analyzing the Zomato dataset, the project provides insights into customer preferences, restaurant performance, and optimal business decisions.
The main goal is to answer three key business questions:
- What type of restaurant is most preferred to open?
- Which location would be most suitable?
- What kind of services should the restaurant provide?
- Source: Kaggle (Zomato Restaurants Data)
- Type: CSV
- Features: Restaurant type, location, ratings, cost, and services
| Library | Purpose |
|---|---|
| NumPy | Numerical operations |
| Pandas | Data cleaning and manipulation |
| Matplotlib | Data visualization |
| Seaborn | Advanced plotting and aesthetics |
- Dropped irrelevant columns
- Renamed columns for clarity
- Removed duplicate entries
- Handled null values systematically
- Changed data types where necessary
- Clustered categorical values
- Cleaned inconsistent text entries
Used visual techniques to interpret restaurant data effectively:
- Countplot – frequency of restaurant types
- Boxplot – comparison of ratings and prices
- Barplot – popular cities, cuisines, and service categories
- Popular restaurant types preferred by customers
- High-demand locations suitable for new openings
- Preferred services like delivery, dine-in, or reservations
- Relationship between price range and ratings
- Real-world application of data preprocessing
- Advanced data visualization for business insights
- Understanding of customer and market trends through analytics