Wine Not?

Ariani Herrera
Posted on Jul 29, 2018

As a wine lover, I wanted to explore wine data from Kaggle to provide consumers the ability to make data driven decisions when purchasing wine. Data set link: https://www.kaggle.com/zynicide/wine-reviews

The data set I chose is composed of 130,000 wine twitter reviews. The dataset provides the name of the wine, points the wine receives, price of the wine, country, region, and province of the wines. I took a random subset of 15,000 wine reviews from the Kaggle data set to then perform my analysis.

According to https://www.winespectator.com/display/show/id/scoring-scale,  wine ratings are not based on price and rated as:

50-59 wines are undrinkable and not recommended,
60-69 wines are flawed and not recommended but drinkable,
70-79 wines are flawed and taste average,
80-84 wines are ‘above average’ to ‘good’,
85-90 wines are ‘good’ to ‘very good’,
90-94 wines are ‘superior’ to ‘exceptional’
95-100 wines are benchmark examples or ‘classic’.

When walking into the wine store, wines are normally categorized by the country of origin. I wanted to demonstrate with a bubble chart, average price versus average points of wines by country. The size of the bubbles represent the amount of wine reviews per country received.

I wanted to demonstrate the dataset is skewed towards American wines accounting for 45.5% of all the reviews , followed by French wines accounting for 14% of all wine reviews, then Italian wines reflecting 13.4% of of the wine reviews.

In the first chart you can see there is a correlation between price and points, the lower the points the cheaper the wine. In this data set the lowest rating reviewed was 80.  The outliers are interesting and can allow the consumer to view other options of wine in the same rating range as the US, France, and Italy but on average less expensive.  A consumer does not need to sacrifice points they can simply purchase wine from another country they may not traditionally purchase. Canadian wine anyone?

The data did not provide the year the wine was made, I had to extract that information from the title of the wine. The vintage year is a very important component about wine. The grape harvest is  extremely dependent on climate and weather. Most wines are meant to be enjoyed right away or shelf life of 4-5 years, only 1% of wines are meant for consumption over 5 years.

I thought it would be interesting to see how wine prices change depending on the year the wine was made. The graph portrays average price per year. The older the wine the more expensive it is, it is costly to maintain older wines as they must avoid exposure to light, and stored in 65-70 degrees.  In more recent years the data demonstrates that 2001, & 2016 on average were cheaper than the immediate years after. There are various reasons,  winemakers changes their methods, climate affects the grapes  greatly, grapes my not become ripe enough, too much rain dilutes the grape juice.

I wanted the viewer to see the a histogram on the distribution of points, most wine falls in this data set falls in the category of 88 to 90 points.  There are not many wines that reach 100 points as a rating as this is extremely difficult to get.

Typically when a wine expert reviews wine they are trained to taste flavors the average consumers typically cannot detect.  My word cloud demonstrates the words most frequently used to describe wines. The user can also filter by blend, country, and region. The user can view the words used to describe their favorite wines, in turn then search for other wines that may have similar flavors.

I lastly wanted to make this application practical in order for the user to find the wine they seek. The interactive data set allows users to search wine by country, province, region, variety, price range, and points range. The user is able to search by words used to describe the wines.

I hope you find your perfect wine!

Link to my shiny app: https://github.com/ariani86/WineShinyApp.git

 

 

About Author

Ariani Herrera

Ariani Herrera

Demonstrated passion for learning and developing solutions to complex business problems. Skilled in Sales, Business Development, Finance, Alternative Investments, and Equities. Strong analytical professional with a Bachelor’s Degree in Applied Mathematics from Columbia University in the City of...
View all posts by Ariani Herrera >

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws 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 Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R 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 Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp