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https://github.com/shivabajelan/perth-restaurant-explorer-mapping-insights-analysing-trends
In this project, we looked at Yelp data about restaurants and bars in Perth and performed exploratory data analysis to determine relationships between some different variables. An interactive map is also created giving user the chance to choose their desired criteria from a list.
https://github.com/shivabajelan/perth-restaurant-explorer-mapping-insights-analysing-trends
api etl folium-maps ipywidgets pandas sqlite sqlite-database
Last synced: 3 days ago
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In this project, we looked at Yelp data about restaurants and bars in Perth and performed exploratory data analysis to determine relationships between some different variables. An interactive map is also created giving user the chance to choose their desired criteria from a list.
- Host: GitHub
- URL: https://github.com/shivabajelan/perth-restaurant-explorer-mapping-insights-analysing-trends
- Owner: Shivabajelan
- License: mit
- Created: 2024-03-10T08:59:21.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-08-11T03:26:06.000Z (3 months ago)
- Last Synced: 2024-08-11T05:09:37.035Z (3 months ago)
- Topics: api, etl, folium-maps, ipywidgets, pandas, sqlite, sqlite-database
- Language: Jupyter Notebook
- Homepage:
- Size: 7.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Project 3
### Perth Restaurant Explorer: Mapping Insights & Analysing Trends
### Introduction
Imagine it is a special day, and you want to go out for dinner. We have examined 532 different restaurants in Perth and their surroundings and prepared an interactive map showing where they are, their cuisines, ratings, price levels, and the information you need to know to play your special day out.
We plan to examine the restaurants of Perth and its suburbs (mainly around the city) and prepare a dashboard showing a map of where they are, what kind of cuisines they have, their rating, their price level, and much more information you need to know. We plan to make choosing your favourite cuisine easy for you and help to create a good experience out of your special day.
In the other part of this project, we looked at Yelp data about restaurants and bars in Perth and performed exploratory data analysis to determine relationships between some different variables.
### Research Questions to Answer
We posed the following questions:
1. How does the distance of a business from the General Post Office (GPO) as a reference point correlate with its rating and review count?
2. How do different price levels correlate with the number of reviews? How does price level impact customer review counts and ratings distribution within the restaurant and bar industry?
3. Does the category of restaurants correlate with the number of reviews?
### Data Source
We obtained our data set from Yelp, using its publicly available API restaurants data set. Yelp must first approve a person's application to download data before they can do that. The data are public and are free for educational purposes such as our project. Anrie Santos arranged the downloading of our data set.
https://www.yelp.com
The data were messy, with some columns containing lists of dictionaries, and data not uniformly entered. Shiva Bajelan did most of the cleaning with some help from Sean Whitehead.
Jeremy Hooper started work on creating the interactive map before the data set was cleaned. He did some cleaning by hand to ensure that this dataset was ready for mapping. Thus the data used for the interactive map has some minor differences to the dataset used in the other analyses.
### Ethical Considerations in this Project
When conducting a data analysis project on restaurants in your hometown, it's important to consider several ethical considerations carefully. Privacy and anonymity are paramount; ensuring that personal data, potentially linked to restaurant owners or patrons, is private and anonymous protects individuals. Accuracy in representing restaurant ratings, pricing, and locations is also crucial, as inaccuracies can unfairly affect a restaurant's reputation. Additionally, consider the origins of your data and whether the restaurants consented to their information being utilised, which respects their rights and intentions.
The potential impact on small businesses is another significant concern. It's important to approach the analysis with sensitivity to how it might influence local, especially family-owned, establishments. This involves maintaining a balance in portraying diverse types of restaurants without bias towards certain cuisines, pricing, or locations, and ensuring a fair representation of the community's dining options. Also, the accessibility of your visualisations is critical, ensuring they can be understood by a broad audience, including those with disabilities.
### Workflow for Exploratory Data Analysis part of the project
1. Raw data extracted from the Yelp website using their public API
2. Cleaned Data and made it ready for visualisation and analysis using Pandas and some cleaned by hand for the interactive maps section.
3. The clean data was transferred and stored in the SQLite database for future use in the project.
### Navigating the Repository
We created four notebooks for this project:
1. API_data_extraction.ipynb is used for extracting data from the Yelp Website,
2. data_cleaning.ipynb for cleaning the raw data,
3. database_operation_and_visualisation.ipynb for housing our clean data in SQLite database and performing EDA analysis,
4. Yelp_Restaurants.ipynb for creating an interactive map for users to explore their desired place from different places to eat in Perth.
5. Detailed analysis is provided inside the database_operation_and_visualisation.ipynb
6. The raw data, clean CSV, and Excel data by using Pandas are stored in the data directory
7. Clean data for interactive map visualisation is located in the Interactive_Map directory
### Storage of data online
The two data sets are stored online in SQLite databases. I have commented out the original data loading code from a CSV file, and also the original loading to SQLite database.
### New Libraries
We have used the following libraries to produce out plots that haven't been used in our course todate:
1. folium - Folium is a powerful Python library that helps you create several types of Leaflet maps. By default, Folium creates a map in a separate HTML file. Since Folium results are interactive, this library is very useful for dashboard building. You can also create inline Jupyter maps in Folium.
2. ipywidgets - ipywidgets is a powerful Python Library that provides a list of widgets quite common in web apps and dashboards like dropdowns, checkboxes, radio buttons, buttons, and many more.
## Visualisations
### Interactive Map
This interactive map shows all of the 532 restaurants in our data set. At the top left it shows the list of layers that can be toggled on and off. Fifteen of the layers are the cuisine categories, while four are price points are two are rating levels.
In addition, a popup is displayed that shows the restaurant's name, activities, cuisine category, Price point, Rating, number of ratings, and contact details.
This interactive map shows the Chinese Restaurants located in Perth, Northbridge and some other areas.
This interactive map shows the restaurants with the $$$ price point.
This interactive map shows the restaurants with ratings greater than 4.0.
### Other visualisations
Other visualisations are described in the Jupyter files and the PowerPoint presentation.
# License
This project is licensed under the MIT License.