#Clustering restaurant category yelp api software

For example, here is a visualization of all nightlife businesses in Toronto:Īs shown, clicking on a business will reveal its different attributes. This first is a interactive visualization map, which allows for the simple visualization of businesses, either by city, category of business, or both. Segmenting by category was hard to automate, and required the drafting of a list of the most popular business categories based on 'domain knowledge'.Īfter creating categories for each business, it was time to decided on the different features: Location would involve the use of Leaflet and a Google Maps API key to find the Lat/Lon coordinates of businesses from their address.

My hope was to segment businesses by both category and location. After loading the data from Kaggle, the first steps were data 'cleaning' (data comes relatively clean so hard to call it cleaning). I had used some Shiny apps in the past, and this project would be a great way to investigate the usefulness of Shiny dashboards. I decided to learn this tool as a way to apply what I had learned to a new R package. While my Big Data course had extensively covered ggplot and Tidyverse, Shiny was not. However, as Yelp prohibits scraping and the method was quite slow, I settled on the Kaggle data. Since I chose R as my programming language of choice, I ran a Python script using BeautifulSoup/Selenium through R to collect data from Yelp. While this Kaggle dataset was a great resource, finding data on Kaggle was not my intended method of data collection. While the review dataset contained information such as: The business dataset contained information about random business on Yelp in the form: For this project, the specific datasets I was interested in were the business and business review datasets. The Yelp dataset is a publicly available dataset on Kaggle.

Specifically, I was hoping to use the abundant visualization tools associated with both geographic and text data to create a interactive platform for users to better understand possible locations/markets they hoped to enter. As part of my Collecting and Analyzing Big Data course at UCSD, I was tasked with the open ended assignment of finding any dataset and attempting to create and attempt to solve a hypothetical business question, in this case, 'How can analytics be used to assist prospective business owners in planning for challenges to come with entering a new market'. This was my motivation behind creating this Yelp Data Visualization Dashboard. For this reason, a tool that would help prospective business owners anticipate potential issues in these domains can help them better prepare for them. The problems, if not managed adequately, can lead a business to its demise. Questions can arise ranging from managing customers to managing the actual business space, assuming we are discussing a business with an actual location. Once an idea has translated from an idea to a physical space, the complexity of the problems that a business owner now faces increases exponentially. While the hardest part of the small business creation process may in fact come before the market entrance itself, this part brings on many challenges of its own. However, one tool that has been missing is one that could help business owners with their market entrance strategy.

#Clustering restaurant category yelp api software

Access to many software solutions that were once restricted to larger companies have now become more affordable for small business owners. Fore example, sites like Kickstarter and Gofundme help with gathering funds. With the advances of technology/analytics, the availability of tools to assist prospective business owners with the many different aspects of opening a business have greatly increased. a global pandemic) that could cause even successful businesses to fail, there is no doubt that opening a business provides its owners with innumerable challenges. While there are some extraneous circumstances (i.e. Requires active Google Maps API key MotivationĪccording the Bureau of Labor Statistics, only 25% of business manage to stay open for more than 15 years. Visualization of yelp dataset available on Kaggle.