https://github.com/shaadclt/zomato-dataset-analysis
This project involves the analysis of the Zomato dataset for restaurants in Bengaluru city. The dataset provides information about various restaurants, including their ratings, cuisines, costs, and more. Through this analysis, we aim to gain insights into the restaurant landscape in Bengaluru and explore factors that influence ratings.
https://github.com/shaadclt/zomato-dataset-analysis
seaborn
Last synced: 7 months ago
JSON representation
This project involves the analysis of the Zomato dataset for restaurants in Bengaluru city. The dataset provides information about various restaurants, including their ratings, cuisines, costs, and more. Through this analysis, we aim to gain insights into the restaurant landscape in Bengaluru and explore factors that influence ratings.
- Host: GitHub
- URL: https://github.com/shaadclt/zomato-dataset-analysis
- Owner: shaadclt
- Created: 2022-10-06T11:17:31.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-08T07:26:25.000Z (over 2 years ago)
- Last Synced: 2025-02-02T09:41:20.703Z (9 months ago)
- Topics: seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 499 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Zomato Dataset Analysis of Bengaluru City
This project involves the analysis of the Zomato dataset for restaurants in Bengaluru city. The dataset provides information about various restaurants, including their ratings, cuisines, costs, and more. Through this analysis, we aim to gain insights into the restaurant landscape in Bengaluru and explore factors that influence ratings and popularity.
## Dataset
The Zomato dataset used for this analysis includes information about restaurants in Bengaluru, such as their names, cuisines, average cost for two, ratings, and more.
## Prerequisites
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
## Getting Started
To get started, follow the steps below:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Zomato-Dataset-Analysis.git
```
2. Change into the project directory:
```bash
cd Zomato-Dataset-Analysis
```
3. Install the required dependencies:
4. Run Jupyter Notebook:
```bash
jupyter notebook
```
5. Open the `Zomato Dataset Analysis.ipynb` notebook in Jupyter.
6. Run the notebook cells to load the dataset, perform the analysis, and generate visualizations.
## Analysis Overview
The notebook provides a step-by-step guide to analyze the Zomato dataset for Bengaluru city. The analysis includes the following tasks:
- Loading and understanding the dataset
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA) to gain insights into the restaurant landscape
- Visualizing restaurant attributes such as cuisine, cost, and ratings using plots and charts
- Analyzing factors that influence restaurant ratings and popularity
- Drawing conclusions and recommendations based on the analysis results
## Results and Insights
Throughout the analysis, various visualizations such as bar plots, scatter plots, and heatmaps are used to showcase the findings. These insights may include popular cuisines in Bengaluru, the relationship between ratings and cost, or any other interesting observations. Feel free to refer to the notebook for detailed results and interpretations.
## Customization
You can customize the analysis to suit your specific requirements. For example, you can focus on specific aspects of the dataset, explore additional variables, create new visualizations using Matplotlib and Seaborn, or apply advanced statistical techniques to uncover deeper insights.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
## Acknowledgments
- This analysis is inspired by the desire to understand the restaurant landscape in Bengaluru city and explore factors that contribute to ratings and popularity.
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.