{"id":28389543,"url":"https://github.com/eins51/restaurantanalytics","last_synced_at":"2025-06-27T17:31:29.357Z","repository":{"id":239505242,"uuid":"799699385","full_name":"Eins51/RestaurantAnalytics","owner":"Eins51","description":"Comprehensive business analytics project using Python and Tableau. 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The aim is to provide insights into customer check-in behaviors, restaurant popularity, market analysis based on geographical and temporal data points, and so on.\n\n## Features\n- Data extraction from Google BigQuery.\n- Data preprocessing including data cleaning and data transformation.\n- Analytical visualizations of check-in data across different cities and price ranges.\n- Implementation of data aggregation and analysis using Python Pandas and visualization using Tableau.\n\n## Installation\nTo set up this project locally, follow these steps:\n\n1. Clone the repository:\n\n   ```\n   git clone https://github.com/Eins51/RestaurantAnalytics.git\n   ```\n\n2. Navigate to the project directory:\n\n   ```\n   cd RestaurantAnalytics\n   ```\n\n3. Install required Python packages:\n\n   ```\n   pip install -r requirements.txt\n   ```\n\n## Usage\n1. Run the Jupyter Notebook for data preprocessing and analysis:\n\n   ```\n   jupyter notebook notebooks/restaurant_data_analysis.ipynb\n   ```\n\n2. Explore the Tableau dashboards for interactive visualizations:\n\n   - Dashboard 1: Market Trends and Spending Insights\n     - Analyze restaurant distribution, spending trends, and high-potential regions.\n     - [Video Demo](https://github.com/Eins51/RestaurantAnalytics/blob/master/tableau/videos/market_trends_and_spending_insights.mp4)\n     - ![Dashboard 1 Video Demo](https://github.com/Eins51/RestaurantAnalytics/blob/master/tableau/videos/market_trends_and_spending_insights.gif)\n   - Dashboard 2: Peak and Off-Peak Customer Behavior\n     - Understand peak dining hours, months, and consumer trends.\n     - [Video Demo](https://github.com/Eins51/RestaurantAnalytics/blob/master/tableau/videos/peak_and_off-Peak_customer_behavior.mp4)\n     - ![Dashboard 2 Video Demo](https://github.com/Eins51/RestaurantAnalytics/blob/master/tableau/videos/peak_and_off-Peak_customer_behavior.gif)\n\n## Data Preprocessing\n\n- **Data Acquisition**: Data was sourced from public Google Cloud Storage buckets and loaded into Google BigQuery.\n- **Data Cleaning**: Removed duplicates, handled missing values, and conducted feature engineering (e.g., geographic classification, temporal data enrichment).\n- **Data Transformation**: Time data was binned for granular analysis, and datasets were simplified for focused analysis.\n\n## Dashboards\n\n### Dashboard 1: Market Trends and Spending Insights\n\n- Key Metrics:\n\n  - Total Restaurants: 8.5 million\n  - Operational Restaurants: 6.8 million\n  - Total Check-ins: 12 billion\n  - Total Reviews: 4.5 billion\n  - Average Rating: 4/5\n\n- Visualizations:\n\n  - Geographic distribution of restaurant activity.\n  - Spending trends by state and city.\n  - Seasonal consumer behavior insights.\n\n- Insights:\n\n  - High-potential regions include Pennsylvania, Florida, and Louisiana.\n  - Seasonal trends show spending peaks in March, May, and July.\n- Link: https://public.tableau.com/app/profile/yi.wang4922/viz/MarketTrendsandSpendingInsights/Overview\n\n### Dashboard 2: Peak and Off-Peak Customer Behavior\n\n- Key Metrics:\n\n  - Peak Months: March, May, July\n  - Off-Peak Months: January, September, November\n  - Peak Hours: 11 PM to 1 AM\n  - Off-Peak Hours: 8 AM to 10 AM\n- Visualizations:\n\n  - Hourly and monthly check-in patterns.\n  - Peak traffic periods by location.\n- Insights:\n\n  - Late-night dining trends in urban areas.\n  - Inventory and staffing optimization based on peak/off-peak patterns.\n- Link: https://public.tableau.com/app/profile/yi.wang4922/viz/RestaurantAnalysisofPeakandOff-PeakTrafficPeriods/TopandBottom\n\n## Acknowledgments\n\nSpecial thanks to:\n\n- The Google Cloud Platform team for providing the data.\n- Tableau for powerful visualization tools.\n- Contributors and collaborators who supported this project.\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feins51%2Frestaurantanalytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feins51%2Frestaurantanalytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feins51%2Frestaurantanalytics/lists"}