https://github.com/eins51/restaurantanalytics
Comprehensive business analytics project using Python and Tableau. Features include data visualization, interactive dashboards, and data-driven insights for restaurant performance and consumer behavior.
https://github.com/eins51/restaurantanalytics
business-analytics data-visualization python-dashboard restaurant-analysis tableau
Last synced: 6 months ago
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Comprehensive business analytics project using Python and Tableau. Features include data visualization, interactive dashboards, and data-driven insights for restaurant performance and consumer behavior.
- Host: GitHub
- URL: https://github.com/eins51/restaurantanalytics
- Owner: Eins51
- Created: 2024-05-12T22:41:49.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-01-14T07:37:29.000Z (12 months ago)
- Last Synced: 2025-05-31T10:59:29.930Z (7 months ago)
- Topics: business-analytics, data-visualization, python-dashboard, restaurant-analysis, tableau
- Language: Jupyter Notebook
- Homepage: https://public.tableau.com/app/profile/yi.wang4922/viz/MarketTrendsandSpendingInsights/Overview
- Size: 13.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Restaurant Data Analytics
## Project Overview
This repository contains code and datasets for analyzing restaurant data sourced from Google BigQuery. 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.
## Features
- Data extraction from Google BigQuery.
- Data preprocessing including data cleaning and data transformation.
- Analytical visualizations of check-in data across different cities and price ranges.
- Implementation of data aggregation and analysis using Python Pandas and visualization using Tableau.
## Installation
To set up this project locally, follow these steps:
1. Clone the repository:
```
git clone https://github.com/Eins51/RestaurantAnalytics.git
```
2. Navigate to the project directory:
```
cd RestaurantAnalytics
```
3. Install required Python packages:
```
pip install -r requirements.txt
```
## Usage
1. Run the Jupyter Notebook for data preprocessing and analysis:
```
jupyter notebook notebooks/restaurant_data_analysis.ipynb
```
2. Explore the Tableau dashboards for interactive visualizations:
- Dashboard 1: Market Trends and Spending Insights
- Analyze restaurant distribution, spending trends, and high-potential regions.
- [Video Demo](https://github.com/Eins51/RestaurantAnalytics/blob/master/tableau/videos/market_trends_and_spending_insights.mp4)
- 
- Dashboard 2: Peak and Off-Peak Customer Behavior
- Understand peak dining hours, months, and consumer trends.
- [Video Demo](https://github.com/Eins51/RestaurantAnalytics/blob/master/tableau/videos/peak_and_off-Peak_customer_behavior.mp4)
- 
## Data Preprocessing
- **Data Acquisition**: Data was sourced from public Google Cloud Storage buckets and loaded into Google BigQuery.
- **Data Cleaning**: Removed duplicates, handled missing values, and conducted feature engineering (e.g., geographic classification, temporal data enrichment).
- **Data Transformation**: Time data was binned for granular analysis, and datasets were simplified for focused analysis.
## Dashboards
### Dashboard 1: Market Trends and Spending Insights
- Key Metrics:
- Total Restaurants: 8.5 million
- Operational Restaurants: 6.8 million
- Total Check-ins: 12 billion
- Total Reviews: 4.5 billion
- Average Rating: 4/5
- Visualizations:
- Geographic distribution of restaurant activity.
- Spending trends by state and city.
- Seasonal consumer behavior insights.
- Insights:
- High-potential regions include Pennsylvania, Florida, and Louisiana.
- Seasonal trends show spending peaks in March, May, and July.
- Link: https://public.tableau.com/app/profile/yi.wang4922/viz/MarketTrendsandSpendingInsights/Overview
### Dashboard 2: Peak and Off-Peak Customer Behavior
- Key Metrics:
- Peak Months: March, May, July
- Off-Peak Months: January, September, November
- Peak Hours: 11 PM to 1 AM
- Off-Peak Hours: 8 AM to 10 AM
- Visualizations:
- Hourly and monthly check-in patterns.
- Peak traffic periods by location.
- Insights:
- Late-night dining trends in urban areas.
- Inventory and staffing optimization based on peak/off-peak patterns.
- Link: https://public.tableau.com/app/profile/yi.wang4922/viz/RestaurantAnalysisofPeakandOff-PeakTrafficPeriods/TopandBottom
## Acknowledgments
Special thanks to:
- The Google Cloud Platform team for providing the data.
- Tableau for powerful visualization tools.
- Contributors and collaborators who supported this project.