This repository contains data analysis tasks performed on a restaurant dataset . The goal is to uncover trends and insights related to restaurant ratings, cuisine preferences, locations, and chain performance.
- Analyzed the distribution of restaurant ratings.
- Identified the most frequent rating range.
- Calculated average votes received per restaurant.
- Discovered most common cuisine combinations.
- Checked if certain cuisine combos are linked with higher ratings.
- Plotted restaurants by location using latitude and longitude.
- Identified patterns or clusters based on geography.
- Identified restaurant chains in the dataset.
- Compared popularity and ratings of different chains.
- Python
- Pandas
- Seaborn & Matplotlib
- Plotly (for maps)
- Jupyter Notebook
- Ratings tend to cluster in the 3.0–4.5 range.
- Some cuisine pairs like "North Indian & Chinese" are very common and well-rated.
- Urban clusters show dense restaurant populations.
- Chains often show consistent but not necessarily top ratings.
Restaurant-Analysis/
│
├── Task1_Ratings_Analysis.ipynb
├── Task2_Cuisine_Combination.ipynb
├── Task3_Geographic_Analysis.ipynb
├── Task4_Restaurant_Chains.ipynb
├── dataset/
│ └── [restaurant_data.csv]
├── images/
│ ├── rating_distribution.gif
│ ├── cuisine_heatmap.gif
│ └── geo_plot.gif
└── README.md
Clone this repo and install required libraries:
git clone https://github.com/your-username/Restaurant-Analysis.git
cd Restaurant-Analysis
pip install pandas matplotlib seaborn plotlyThen, open the notebooks using Jupyter or VS Code.
Shows how different cuisine types trend over time in terms of popularity.
Animated view of how restaurant ratings are distributed across samples.
Dynamic visualization of restaurant locations across a mapped region.
Prashant Joshi – Data Enthusiast
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