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.
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.
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
To get started, follow the steps below:
- Clone the repository:
git clone https://github.com/shaadclt/Zomato-Dataset-Analysis.git- Change into the project directory:
cd Zomato-Dataset-Analysis-
Install the required dependencies:
-
Run Jupyter Notebook:
jupyter notebook-
Open the
Zomato Dataset Analysis.ipynbnotebook in Jupyter. -
Run the notebook cells to load the dataset, perform the analysis, and generate visualizations.
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
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.
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.
This project is licensed under the MIT License. See the LICENSE file for more information.
- This analysis is inspired by the desire to understand the restaurant landscape in Bengaluru city and explore factors that contribute to ratings and popularity.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.