This project uses SQL to analyze Yelp data, exploring correlations between star ratings and user interactions ("useful," "funny," "cool" votes). Key objectives include profiling data, analyzing interactions, and deriving insights from Yelp’s business data.
This analysis investigates how Yelp star ratings correlate with user engagement. SQL queries were applied to identify trends and explore factors affecting customer satisfaction and business performance.
- Data Profiling: Assess table structures, record counts, unique identifiers, and null values.
- Correlation Analysis: Examine if higher ratings link to more user interactions.
- Insights Discovery: Explore location, review count, and star ratings to understand customer engagement.
- Star Ratings & Engagement: Higher ratings correspond with more user interactions, especially for "useful" votes.
- Top-Performing Locations: Cities like Las Vegas and Phoenix see the highest engagement, likely due to tourism.
- Open vs. Closed: Open businesses have slightly higher ratings and more reviews, indicating stronger customer interest.
- Most Engaged: 4.0-star businesses receive the most "useful" votes, with Delmonico Steakhouse as a standout.
- Location Impact: Suburban businesses generally rate higher than those downtown.
- Sentiment Analysis: Analyze keywords in reviews to understand customer sentiment.
- Enhanced Location Data: Integrate demographic data to explore neighborhood influence on ratings.
- SQL Queries: SQL code samples for data profiling and analysis.
- Insights: Documented findings highlighting Yelp data patterns.