Participants in speed dating events rated their dates on attractiveness, sincerity, intelligence, fun, ambition, and shared interests. Data analysis found that attractiveness was the top rated trait but other traits were also important. The model could predict dating outcomes with high accuracy when knowing how much the participant enjoyed the date. Physical attractiveness was overestimated in importance compared to sincerity. The study provided insights but was limited by only examining short interactions and not incorporating factors like lifestyle preferences.

2. Contents
• Introduction
• Speed Dating data key
• Criterion for judgment
• Relevant questions & basis of analysis
• Observations & Interpretations
• Things we may have missed
• Technical accomplishments
• Conclusion
• Sources & References

3. Introduction
• Several researches consistently show that we rank most of the traits as more
important than good looks. However, consciously ranking traits may not reflect the
way we make our real-life dating and mating decisions.
• Basis of experiment: Data was gathered from participants in experimental speed
dating events from 2002-2004 & compiled by Columbia Business School professors.
The attendees would have a 4 minute "first date" with every other participant of
the opposite sex. They were also asked to rate their date on six attributes:
Attractiveness, Sincerity, Intelligence, Fun, Ambition, and Shared Interests &
whether they would like to meet again.
• Rating and deciding. After 4 minutes talking with a stranger, each participant
graded their experience and took a decision, what patterns were hiding in there?
• This project is useful for everyone who wants to have a date or maybe a life long
partner and needs help on deciding whether someone’s attractiveness matches
enough with yours.
• Data analyzed by exploratory analysis.

4. Speed Dating Data Key
(21 waves/rounds for experiment)
iid: unique subject number, group(wave id gender)
id: subject number within wave
Gender: Female=0
• Male=1
idg: subject number within gender, group(id gender)
condtn: 1=limited choice
• 2=extensive choice
• round: number of people that met in wave
• position: station number where met partner
• positin1: station number where started
• order: the number of date that night when met partner
• partner: partner’s id number the night of event
• pid: partner’s iid number

5. Criterion for judgment
• Races of students: Black/African American ; European/Caucasian-American;
Latino/Hispanic American ; Asian/Pacific Islander/Asian-American ; Native
American ; Other
• Waves 6-9: Rate on a scale of 1-10 (1=not at all important, 10=extremely
important):
• Waves 1-5, 10-21: 100 points to distribute among the following attributes:
Attractive +
Sincere +
Intelligent +
Fun +
Ambitious +
Shared Interests +___
• 100
• Graphs drawn to interpret the outcome.

6. Relevant questions & basis of analysis
• What do you look for in the opposite sex ?
• What do you think the opposite sex looks for in a date?
• How do you think you measure up?
• how do you think others perceive you?
• How do you rate your partner?
• In this exploratory analysis we have 8378 individuals with many repetitions.
• In other words, if I am White(race) and I participate to a wave with 10 participants of the opposite gender, I count
as 10 white people.
• Male: 277
• Female: 274
• The real number of participants: 551
• Total data analysed: 5 Mb
• In the following graphs different attributes with abbreviations for them are used such as: attr, sinc, intel,
fun, amb, shar for Attractiveness, Sincerity, Intelligence, Fun, Ambition, and Shared Interests respectively.
• The shared interests which have not been mentioned in the charts are: sports, tvsports, museum, dining,
arts, exercise, hiking, gaming, clubbing, tv, radio, theatre, music, concerts, shopping, movies, yoga.
• NOTE: It is to be remembered that the prediction & analysis of this model has also been given on the basis
of other questions which were asked in the questionnaire & not just on the basis of graphs &
interpretations in this presentation (which have been mentioned in technical accomplishments )

7. Observations

10. Interpretation
• People clearly rated attractiveness in the first place, then realized that
they had to score up to a 100 and focused on very simple multiples of 5
and 10. This has to be taken into account if we want to model on that:
these features are not continuous.
• The participants look for all the categories more or less equally in general,
but having shared interests and being ambitious are generally less
important.
• People generally think that attractiveness is as important as other things
(with some exceptions) and also that everyone else (same gender and
opposite gender) value this aspect much more.

13. Interpretation
• They are all fairly confident.
• We indeed see that the scores they expect and how they measure
themselves are very correlated, in particular on Attractive, Fun, and
Intelligent.
• The questions explored are done right after the session, and weeks after
it. However, no significant change can be observed.

15. • As expected, higher rates lead to positive decisions, with the higher gaps in Attractive, and
'How much do you like them' (coincidentally it is also the same gap). We also notice,
however, that positive feedback can arrive even if the score in some categories is very low,
with the exception of Intelligence, where there is a minimum of 3 to get positive feedback.
• It looks like being Fun is making quite the difference in the decision, while being Sincere looks
not influential.
• Interpretations other than the mentioned charts:
Looking at the correlations:
• It seems that the negative correlation between Attractive and Sincere or Intelligent (which
are always positively correlated) is getting milder when the decision is positive (although the
causal link can easily go in the opposite direction).
• Interesting to see how rating a person as ambitious or attractive is reducing the expectations
of the participants about being liked by them.
• When the decision is negative we can observe a positive correlation between the Like score
and the Fun one that is not observed in cases of positive feedback (a fancy way of saying "I
like you as a friend"?)

16. Things we may have missed
• These events attracted some segments of the population more than others, making this sample most likely not
representative of the population.
• Physical Attractiveness Is Less Important Than We Think: We don’t necessarily want partners who are extremely attractive—
we just want partners who are attractive enough. In other researches a lack of attractiveness was associated with negative
qualities, but only a moderate level of attractiveness was necessary to make one's associations positive. Or, potential mates
do not need to be exceptionally attractive, only moderately so.
Most of us, consciously or not, view a moderate level of physical attractiveness as a “necessity,” while a higher level of may
be a “luxury.” When we say that physical attractiveness is not important to us, we are likely referring to the luxury
of exceptional attractiveness and not the necessity of a minimum level of attractiveness.
• What is initially desired may later be just a basic need & therefore it may fail when it comes to long term dating/mating.
• More attractive people tend to perceive fewer others as physically attractive while less attractive individuals may
consider a broader range of others appealing. Looking for someone who shares a similar level of physical attractiveness to
your own can enhance your long-term relationship success. But no matter our personal level of attractiveness, or our
partner's, as we get to know, like, and respect each other more, our attraction naturally grows and deepens. The longer we
know each other, the less important physical attractiveness becomes to beginning and maintaining a long-term relationship
• The data also misses important factors such as self-perception, and lifestyle information as key attributes in deciding a
date.
• It is more difficult to predict what an art lover (or movies, or clubbing) will decide after 4 minutes of date, while it is very
possible, for example, for a tv lover.
Future scope of research: In particular, incorporating uncertainty and learning, which are especially relevant in the longer
run, is an important next step for theory. More ambitiously, we hope to develop models that incorporate strategic behaviour
into dating decisions.

17. Technical Accomplishments
• It looks like the model is able to predict well whether or not a person will say yes at the end of a speed date if it
knows everything about that person (including and especially how much is enjoying the date). If we ignore the
votes, a strong indicator of how much the participant is enjoying the date and a very strong predictor of the
outcome, the accuracy drops to 70%.
• Interesting to see little importance given to the gender, indicating the model learns fairly equally the decisions of
Males and Females, and that having met someone that we liked a lot or having met the partner before are equally
unimportant. On the other hand, how well the night is going is the stronger predictor together with how good the
date is.
• Moreover, the model learns better if it doesn't consider the field of study, the dating habits, and, more
surprisingly, how much the participant likes the person with respect to the previous dates and their race.
• The model is way too pessimistic in predicting a yes. In particular, by knowing information about the partner only,
if the participant is Female, it is most likely to have a false negative than a false positive. If the participant is male,
instead, it looks is missing equally in both ways, even though it makes more mistakes.
In other words, if we are this heartless machine we make more mistakes if we have a male in front of us and we
are not sure about what kind of mistake we made. On the other hand, with a female is most likely that we
said No when we should have predicted Yes.
• Another pattern that could be found in the mistakes of this model is about people we found averagely
attractive that led to many false negatives.
• The model is overestimating the importance of attractiveness by being more inclined that it should and falling
into a relatively large number of false positives when this attribute is high and of false negatives for low values
of attr. On the other hand, the model is underestimating the importance of sincerity by getting relatively more
false negatives for high values of this attribute.

18. Conclusion
• Participants were fairly confident before the sessions but, in rating their
expectations after every date, they looked not so confident if they liked the
partner, especially if they were attractive.
• How much the participant found their partners attractive (or other key attributes)
is way more important of shared interest, race, and field of study.
• Not knowing how much the date is appreciated (with respect to the key attributes
above) makes a machine guess the outcome of the date with significantly less
accuracy. But the more you know about the partner, the better (in terms of
accuracy of the prediction)
• Even a machine thinks that people will say yes if they find their date very attractive
and no if they don't more than it actually happens.
• On the other side, it ignores how people actually like very sincere dates.
• On a typical date, these models are very negative and picky.
• Physical attractiveness may serve as a gatekeeper directing us toward partners
who are healthy, age appropriate, and able to reproduce

19. Sources & References
• https://www.kaggle.com/lucabasa/the-data-science-book-
of-love#Introduction
• https://www.psychologytoday.com/us/blog/dating-and-
mating/201701/why-physical-attraction-matters-and-
when-it-might-not
• https://www.kaggle.com/minjeongk/the-most-important-
attribute-for-men-and-women/data
• https://faculty.chicagobooth.edu/emir.kamenica/document
s/racialpreferences.pdf
• https://faculty.chicagobooth.edu/emir.kamenica/document
s/genderDifferences.pdf
• Book: Exploring the Dimensions of Human Sexuality
• By Jerrold Greenberg, Clint Bruess, Sarah Conklin