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Cafe and Restaurant Management in Istanbul and Economical Background
Last Updated on July 4, 2022 by Editorial Team
Author(s): Gencay I.
Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.
Clustering Algorithm
Introduction
Istanbul is the largest city in Turkey and the country’s economic, cultural and historic center. The city straddles the Bosphorus Strait, and lies in both Europe and Asia, with a population of over 15 million residents, comprising 19% of the population of Turkey. Istanbul is the most populous in Europe and the world’s fifteenth-largest city.
Opening cafes in metropolitans have been a favorite investment tool of investors for too long years now.
Likewise, this business sector has been mostly chosen by investors, but not all these investment story ends well.
Content Table
· Introduction
∘ Business Problem
∘ Interests
· Getting and Cleaning Data
· Exploratory Data Analysis
· Clustering
· Conclusion
∘ References
Business Problem
“Especially, during the pandemic period(2019–2020–2021), 25% of cafes have closed due to economic reasons in Istanbul, Turkey,” said Kaya Demirer who is the president of Tourism Restaurant Investors and Gastronomy Enterprises Associations.
Even though the pandemic effect is the main reason for this problem, even before the pandemic, in some neighbors, it is common to see the owners of shops has been changed 4–5 times per year.
‘One of every two restaurants opened in Turkey closed in its first year’ said Korhan Erçin who is the sales director of Yemeksepeti.
Yemeksepeti is an online food delivery company and currently operates in 70 cities in Turkey and Cyprus, with more than 35,000 member restaurants, 18 million users, and 520,000 daily orders.
In my opinion, especially when taking into account development in Data Science tools and their application, it will be beneficial for future investors to observe cafe’s and restaurants' data from the perspective
of Data scientists.
Interests
As a formal resident of Istanbul, I always have had an interest in Cafe and Restaurant administration.
Firstly, residents of Istanbuls usually meet in a Cafe at least 1–2 times a week, there have existed too many restaurants and cafes in too many categories such as Sea-food Restaurants, Bakery, Turkish Food restaurants, Steak Houses, etc.
As a Data Scientist, I always wonder, in which neighborhood, what kind of Cafes or Restaurants can be successful, and what their distributions according to neighborhoods.
Getting and Cleaning Data
Neighborhoods and their Longitudes and Lattitudes data have been founded on GitHub [2] in CSV format.
A list of Districts and their economical aspects such as household income per month and year have been found in Wikipedia [3] and have been scraped from HTML to pandas Dataframe.
There are several problems that have been confronted while merging two data sets but they have been solved.
One of the too many problems that I have been confronted with is, that in the annual income and monthly income columns, two currencies(the dollar and Turkish lira) have been written in the same column, it is mandatory to separate them.
Exploratory Data Analysis
After mining and cleaning data, Visualization of Istanbul's neighborhood data will be better observed.
I use the folium library to do so.
There are 39 separate neighborhoods that exist.
It is important to visualize economical data too according to neighborhoods.
Clustering
Conclusion
As a result, investors who want to open cafes or restaurants in Istanbul can investigate this data to broaden their minds.
References
2. Istanbul Neighborhoods Longitudes and Latitudes
3. List of Districts of Istanbul
4. Korhan Erçin -Sales director of Yemeksepeti
Cafe and Restaurant Management in Istanbul and Economical Background was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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