wine dApp: Wine Recommendation and Data Analysis Web App

Posted on May 11, 2019

Project GitHub | LinkedIn:   Niki   Moritz   Hao-Wei   Matthew   Oren

The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


Don't wind up with the wrong wine at the wrong time: unwind with the world's best!

winedApp provides insights into prices, ratings, descriptions, and geographic distribution of the world's most esteemed wines. Novice or connoisseur, consumer or seller, this app will meet your oenophile needs.

In the Wine Explorer, you can enter your location, varietal, aroma, taste, vintage, and price range preferences, and retrieve information on compatible wines.

The Global Insights feature offers map visualizations of international wine trends.

Graphs and Charts provides additional lenses into relationships amongst countries of origin, varietals, prices per bottle, and ratings.

The data was sourced from ca. 36,000 wine reviews on the WineEnthusiast site. In this dataset, 145 varietals from 36 countries and 23 US states are represented. 

Further information was extracted from the Wikipedia list of grape varieties

General Trends 

  1. Wine prices (per bottle) and points awarded do not show a strong positive correlation (for certain countries, there is even a negative correlation). 
  2. The US, Italy, and France are by far the most represented in the dataset. 
  3.  The most represented varietals are Chardonnay (10, 996 entries) and Cabernet Sauvignon (9,058 entries). 
  4. Most wines fall within the range of $4-50 per bottle, but the distribution is right-skewed. The full price range is $4-2,013. 
  5. Varietals vary considerably with respect to point and price ranges, as well as country distribution. 
  6. There is a statistically significant difference between average prices per bottle for red vs. white wines ($42.47 and $30.51, respectively, with p << 0.05). 
  7. There is also a statistically significant difference regarding average point values for reds vs. whites (88.43 and 88.29, respectively, with p << 0.05, on an 80-100 point scale). 

Future Work

Features will be added and refined on a continuous basis. Any suggestions are welcome.

Current objectives include: 

  1. Testing the app on a larger dataset; 
  2. Enabling the user to determine if a given wine is available in their local area; 
  3. Building a price predictor. 

Technical Details 

Web scraping was completed using the CRAN rvest package.  The list of descriptive keywords featured in the Wine Explorer menu item was generated using the nltk (Natural Language Toolkit) in Python. All other content was produced via the R Shiny Dashboard library and associated data visualization packages. To view the app code, please visit this Github repository

External Links

App || Project Github || Author's LinkedIn 

About Author

Alexander Sigman

WIth a unique background in music composition + technology, cognitive science, and data science and extensive experience in machine learning R & D and software engineering, Alex Sigman has a passion for adding value to data, gaining actionable...
View all posts by Alexander Sigman >

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