This project performs an end-to-end Exploratory Data Analysis (EDA) on restaurant data to uncover patterns in customer preferences, restaurant performance, and cuisine trends. The goal is to derive meaningful insights that can help food businesses understand what drives ratings, pricing, and popularity.
- Understand the structure and quality of restaurant data.
- Perform detailed data cleaning and preprocessing.
- Explore how ratings vary by location, cuisine, and cost.
- Identify trends in online delivery and pricing strategies.
- Visualize customer engagement patterns through votes and reviews.
- Data Cleaning: Handle missing values and inconsistent records.
- Data Transformation: Encode categorical data and standardize numeric fields.
- Exploratory Analysis: Examine distribution and correlation between variables.
- Visualization: Use advanced plots to illustrate restaurant and cuisine trends.
Shows how customer engagement (votes) relates to overall ratings.
Distribution of restaurants offering online delivery.
Displays average ratings across restaurants and cuisines.
Visualizes the distribution of restaurant costs for two people.
Highlights extreme values in customer votes.
Shows which cities have higher restaurant pricing trends.
Explores how ratings are categorized visually by color codes.
Tracks pricing trends over time using moving averages.
- Most restaurants fall in the mid-rating range (3.0–4.0), indicating generally satisfactory but not exceptional experiences.
- Moderately priced restaurants tend to receive higher ratings, showing that value-for-money strongly influences customer perception.
- Votes and ratings have a strong positive correlation — restaurants with higher engagement are typically better rated.
- Online delivery availability impacts satisfaction; restaurants offering it tend to have slightly lower ratings, possibly due to delivery quality issues.
- Table booking is associated with higher ratings, reflecting the importance of structured service and customer experience.
- High-cost restaurants are concentrated in metro cities like New Delhi, Jakarta, and Colombo, highlighting market segmentation and luxury dining trends.
- Top cuisines include North Indian, Chinese, and Fast Food, while niche cuisines often show lower ratings.
- Tier 2 cities have more low-rated restaurants, suggesting a gap and opportunity for quality-driven expansion.
- Python 3
- pandas, NumPy — Data manipulation and preprocessing
- matplotlib, seaborn — Data visualization
- Environment: Jupyter Notebook
Follow these steps to set up the project locally and reproduce the analysis:
1. Clone the Repository:
git clone https://github.com/indu-explores-data/Restaurant-Data-Analysis.git
2. Navigate to the Project Directory:
cd Restaurant-Data-Analysis
3. Create and Activate a Virtual Environment (Recommended):
python -m venv venv
Windows:
venv\Scripts\activate
Mac/Linux:
source venv/bin/activate
4. Install Required Libraries:
pip install pandas numpy matplotlib seaborn jupyter
5. Launch Jupyter Notebook:
jupyter notebook
6. Open Restaurant Data Analysis.ipynb and run all cells to reproduce the analysis.
- Open Restaurant Data Analysis.ipynb in Jupyter Notebook.
- Run all cells sequentially to view insights and visualizations.
- Results and images are available inside the images/ folder.
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