Data Analysis | How to Win Over the Blind Date

Avatar
Posted on Aug 16, 2019

 

In the era of online dating, I still appreciate the romance brought by a blind date. Two people who have not previously met each other, and they travel from different parts of the city to the same place. 

That is the reason why I like reading the column"The Undateables" on Timeout New york. "The Undateables" picks up two strangers to go on a blind date together. After a romantic dinner (or not), some awkward or hit-off moments, each couple gives a rating to the blind date.

I began wondering: What makes a date well, and what makes it go wrong? These are interesting questions to think. 

Therefore, I started my analysis to figure the answers out.

Data Collection

I scraped the following information on each page of "The Undateables": The professions and living neighborhood of the couple, their ratings for each other, the restaurant they dated and its zip code.

The data I collected is from 2018 to 2019.

Feature Engineering

1) Professions and Wage: I searched the average wage in NYC for each profession on glassdoor, and create a column of "wage" based on the information.

2) The neighborhood, Distance, and Minute Commute: Based on the neighborhood, I have encoded them with their distance to the center of Newyork (Grand Central Station). Moreover, with the help of google map, I have calculated the minute's commute by subway between where they live.

3) Wage/Distance: This is one variable I create for trying to see the correlation between these two variables.

General Analysis

In the left graph, the right to the left means the data from 2018 to 2019. In the right graph, the bar chart means the proportion of high ratings (ratings range from 1-5, high ratings mean 4-5.) 

Wage/Distance Analysis

The X-axis means wage/distance/1000. The Y-axis means rating from 1-5. 

The rectangular in the left means high ratings concentrate in this part. Also, the perfect range is from 0-0.82. When wage/distance/1000 is from 0-0.82, that means the couple can have more possibilities in high ratings.

The three red points in the circle are outlined as outliers are also intriguing. Even if their scores for wage and distance are not perfect, they still have high ratings. 

The three cases are: 

 

couple are from different wage levels and unrelated industries, the dating can still be successful because of:

1. On-time or not mad at someone's being late

"She arrived later. That's not a problem for me."

"We got there at the same time and sat at the bar."

2. Being cute but also reserved is the right attitude.

"Physically, he was my type, but he seemed a bit reserved at first."

"But this was like a slow, building chemistry."

3. Talk is much better than not. Have as many good conversations as possible.

"I think I was kind of rambling, but he was actually really good at steering the conversation. "

 

Geography

I have also made one map for showing the distribution of restaurant where blind dates with high rating happened.

 

And here are the top restaurants where high rating dates happened.

This article is the first episode about the data analysis on a blind date, and I will post the second one soon on building a machine learning model for how to make the successful blind date.

Hope this article can be helpful for single birds in New York City. Happy Dating!

 

About Author

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