Data Study on Best Neighborhoods for Happy Hour

Posted on Nov 16, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


The purpose of this data study was born out of a personal desire to see which neighborhood had best access to happy hour. I was not happy with the option available to me to do such a search, and quite frankly I was frustrated. Given the tools I picked up in this part of the bootcamp I thought it would be a good way to flex my knowledge of webscraping and to expand on ideas I had gotten from speaking with my other fellows regarding their own ideas for projects.


The first thing that comes to mind when people are looking for places regarding food are typically Google and Yelp. Google's algorithm attempts to suggest locations and will recalculate points of interest based on location. In contrast, Yelp's design is based on listing much like the outdated Yellow Pages of old while only providing a small mini-map to view all the postings.

One method seeks to take the decision making out of a user's hands, and the other used a dated form of business listing with limited spatial information.


Tools Use
CartoDB allows developers to use data to create custom maps
Scrapy in Python a special toolkit that enables the scraping of a series of web pages
MongoDB allows for scalability of storing information in case there is more data to be collected for future work
ggmap in R to convert addresses to geospatial data

Data on The Map

note: I opted to use less filter features for the sake of cleanliness and instead chose only price range and neighborhood selection as clickable filters for my map (please click the map for to see and use the actual app)

Data Study on Best Neighborhoods for Happy Hour

Link to the Happy Hour Map

Future Work: Though I initially only intended this map to assist me make decisions about which neighborhoods were close to happy hour locations, it could also be expanded to assist in rental searches and even real estate development decision making. As we delve into machine learning I wonder if, given points of interest and their geospatial location, I could make a webapp that would allow a potential renter to give a training set of desirable locations and then to give a set of potential rental houses and allow the computer to assist in the decision making process.

About Author

Frederick Cheung

Hi my name is Fred. Although my educational background is an M.S. in Medical Science, my professional experience is with Small Business management, operations and sustainable business practices. I’ve recently completed a Data Science program working with languages...
View all posts by Frederick Cheung >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI