ProjectsJuly 1, 2024Restaurant Review Data Dive
This project analyzes restaurant reviews across multiple states to uncover the key drivers of customer satisfaction. Using sentiment analysis and predictive modeling, the analysis identifies patterns in customer feedback that correlate with ratings and business success.
- Sentiment Analysis: Extract and quantify customer sentiment from text reviews
- Predictive Modeling: Build models to predict restaurant ratings based on review content
- Pattern Recognition: Identify common themes and factors that drive customer satisfaction
- Geographic Analysis: Compare review patterns across different states and regions
- Data Collection: Gathered restaurant reviews from multiple platforms and regions
- Text Processing: Applied NLP techniques for sentiment extraction and topic modeling
- Feature Engineering: Created features from review text, timing, and metadata
- Model Development: Built and compared multiple predictive models
- Python: Primary programming language for data analysis
- NLP Libraries: NLTK, spaCy for text processing and sentiment analysis
- Machine Learning: Scikit-learn for predictive modeling
- Visualization: Matplotlib, Seaborn for data visualization
The analysis revealed specific keywords and phrases that strongly correlate with positive and negative ratings, providing actionable insights for restaurant owners to improve customer satisfaction. Related projects
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