This project analyzes the performance of Asian Fusion cuisine restaurants using Yelp data. We explore restaurant trends, identify high-rated locations, and analyze competition levels in different cities. The project uses MongoDB, ETL processing, and data visualization to derive key insights.
How is the performance of restaurants that serve Asian cuisine? Is there scope for improvement in any area?
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Data Extraction & Filtering:
- Pulled data from Yelp's MongoDB database.
- Converted JSON data into tabular format.
- Filtered only restaurant-related businesses.
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Identifying Trends:
- Extracted Asian Fusion restaurants.
- Created a pie chart to analyze restaurant distribution.
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Rating Analysis:
- Filtered restaurants with 4-star and above ratings.
- Identified Boston and Orlando as top cities with high-rated Asian Fusion restaurants.
- Created bar charts to compare cities.
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Competition & Demand:
- Analyzed restaurant counts per postal code.
- Found that Boston (02118) has the highest number of Asian Fusion restaurants, indicating high demand but high competition.
- Orlando shows good demand and lower competition, offering an opportunity for new restaurants.
- MongoDB – Data storage and retrieval.
- ETL Processing – JSON to Table transformation.
- KNIME – Workflow automation & data filtering.
- Data Visualization – Pie charts, bar graphs for insights.
Yelp_Project3_Team11.knwf– KNIME workflow for data extraction & transformation.Yelp Data Transformation.png– Process flow of the data pipeline.
- Download and open
Yelp_Project3_Team11.knwfin KNIME. - Connect to the MongoDB dataset and run the workflow.
- Analyze the Asian Fusion restaurant trends and ratings.
- Use the bar charts and pie charts to derive insights.
- Expand analysis to other cuisine types.
- Integrate machine learning models to predict restaurant success based on past trends.
📢 Connect with me: [https://www.linkedin.com/in/balakrishnan-iyer-811436143]
🔗 Project Link: (https://github.com/BalakrishnanIyer/Yelp-Asian-Cuisine-Analysis)