- Analyze restaurant data to uncover trends in ratings, cuisine, cost, and geography.
- Identify key factors influencing restaurant performance.
- Create an interactive Power BI dashboard for stakeholders.
- Data Cleaning:
- Handled missing values and outliers.
- Standardized columns and merged country data.
- Exploratory Data Analysis (EDA):
- Analyzed features such as ratings, cuisines, restaurant types, and locations.
- Created Python-based visualizations to uncover key insights.
- Interactive Visualization:
- Designed a Power BI dashboard for exploring trends and presenting findings.
- Cuisine Popularity: Indian and Chinese cuisines dominate across most regions.
- Cost Analysis: Restaurants offering meals under receive higher customer ratings.
- Geographical Insights: Country-specific preferences influence cuisine trends and restaurant density.
- Ratings: Restaurants with higher reviews tend to offer a better cost-value ratio.
#Power BI Dashboard Screenshot
- Python
- Power BI Desktop
- Required Python libraries :
- Install using pip Command
- Pandas
- Numpy
- Matplotlib
-
Python Analysis:
- Navigate to the project directory and execute notebooks in the folder.
- Output data is saved in
data/Zomato_cleaned_data.csv.
-
Power BI Visualization:
- Open the Power BI file in Power BI Desktop.
- Load the cleaned dataset.
-
Explore the Dashboard:
- Interact with visualizations and drill down into data insights.
- Incorporate real-time Zomato API data for dynamic updates.
- Include advanced predictive analytics for restaurant performance forecasting.
- Expand Power BI dashboards with more user-specific filters.
Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request.
For any inquiries or feedback, please reach out at:
[gouravpanchal2015@gmail.com]
Happy Analyzing! 🎉