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- Big Dating: It's a (Data) Science
To tackle the missing values, we imputed those values from the mean of the existing data in their corresponding columns. Furthermore, the final dataset that ended up being used was subsetted to a smaller number of columns of the raw data. The actual app itself is also hosted on shinyapps. In addition to the missing values, there were also columns where numeric values represented certain categories. As such, they needed to be transposed back to the actual categories values so the visualizations can be more meaningful eg, changing 0 and 1 from gender column to their respective string equivalent of 'Female' and 'Male'.
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If 'Female' was selected, the dashboard will update the visualizations to show aggregated data from female responses in our dataset. On the activities tab, value boxes were placed to rank the top 3 activities from the average scores given by each gender. We can see that females ranked 'Movies', 'Dining' and 'Music' the highest on average giving them respective scores of 8. Males also enjoyed the same activities on average, but ranked 'Music' highest, followed by 'Movies' then 'Dining'.
A horizontal bar chart was also created to provide more details on the average scores given to additional activities. This chart helps to answer the question of what kind of activities to engage in on dates or on the flipside, what kind of activities one might want to avoid on dates. For example, we can see from our average male responses that 'Sports' is ranked 5th place, but for females the same category ranks 5th from last.
Next, we want to visualize the relationships between the frequency of one who engages in outdoor activities vs the frequency of one who goes out on dates. A facetted scatterplot by gender was used to produce this visualization. The pattern is clear in both genders that the more outgoing a person is, the more dates one gets notice that the scale ranges represents categorical values ranging from 1 being the most frequent to 7 being the least frequent. More granular details can be found on the "details" tab with a similar scatter plot, but grouped by careers.
Here, we have selected females who work in the financial industry as an example, and we can get a sense of how outgoing they are and how frequent they go on dates. If we were to categorize the scale by grouping as 'frequent' and as 'not frequent', we can see that business women are quite outgoing the scale does not even go beyond 3.
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However, they do not date very frequently as we can see from the density distribution on the right hand side of the graph. Most responses tends to be when asked about how often they go on dates. Clicking the calculate button will run a predict function of the independent variables against a logistic regression model. From our dataset, we chose the response variable "decision" which is defined as whether or not a participant of the speed dating event has chosen to date a particular prospect. From here, the data was separated by gender and further subsetted to test and training data with a ratio of Upon clicking the calculate button, the user can also observe a spider chart based on their attribute inputs.
Due to the limitations in the dataset, the visualizations in the dashboard can only produce results for 'male' and 'female'. In today's world, it is important to be as inclusive as possible, and it would be great to have an 'other' selection for sexual preference. Additionally, the model created with logistic regression might include bias, as the scores given to the attributes by the speed dating participants are subjective to their own personal preferences.
The problem with this approach is that it takes no account of your attractiveness.
If the people you contact never reply, then these recommendations are of little use. So Zhao and co add another dimension to their recommendation engine. They also analyze the replies you receive and use this to evaluate your attractiveness or unattractiveness. Obviously boys and girls who receive more replies are more attractive. When it takes this into account, it can recommend potential dates who not only match your taste but ones who are more likely to think you attractive and therefore to reply.
Machine Learning from Forbes - "Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile," Zhao says.
The research team's algorithm will eventually "learn" that while a man says he likes tall women, he keeps contacting short women, and will unilaterally change its dating recommendations to him without notice, much in the same way that Netflix's algorithm learns that you're really a closet drama devotee even though you claim to love action and sci-fi. Finally, for more technical details, the full paper can be found here. A - We want to further improve the method with different datasets from either dating or other reciprocal and bipartite social networks, such as job seeking and college admission.
How to effectively integrate users' personal profiles into recommendation to avoid cold start problems without hurting the method's generalizability is also an interesting question we want to address in future research. That all sounds great - good luck with the next steps! Here we directly measure one's influence, i. A - Sentiment analysis is the basis for our new metric. We developed a sentiment classifier using Adaboost specifically for OHCs among cancer survivors. We did not use off-the-shelf word list because sentiment analysis should be specific to the context.
Some words may have different sentiment in this context than usual. For example, the word "positive" may be a bad thing for a cancer survivor if the diagnosis is positive. A - When finding influential users, the amount of contributions one has made matters, but how others react to one's contributions is also extremely valuable, because it is through such reactions inter-personal influence is reflected and thus measured.jpn.dev3.develag.com
Exploring Attributes of Dating Success | NYC Data Science Academy Blog
A - We would like to further investigate the nature of support in OHCs, so that we can build users' behavioral profiles and better design such communities to help their members. Very interesting - look forward to following all of your different research paths in the future! Finally, it is advice time! Q - What does the future of Machine Learning look like?
A - This is a tough question. I don't know the exact answer but I guess ML will develop along two directions. The first would be on the algorithm side--better and more efficient algorithms for big data, as well as machine learning that mimics human intelligence at a deeper level. The second would be on the application side - how to make ML understandable and available to the general public?
A Date with a Data Scientist
Q - Any words of wisdom for Machine Learning students or practitioners starting out? A - I am not sure whether my words are of real wisdom, but I'd say for a beginner, it is certainly important to understand ML algorithms. In other words, one must learn how to answer the question-- "Now we have the data, what can we do with it? This is very valuable in the era of big data. Kang - Thank you so much for your time! Really enjoyed learning more about your research and its application to real-world problems.
Big Dating: It's a (Data) Science
Kang can be found online at his research home page and on twitter. If you enjoyed this interview and want to learn more about what it takes to become a data scientist what skills do I need what type of work is currently being done in the field then check out Data Scientists at Work - a collection of 16 interviews with some the world's most influential and innovative data scientists, who each address all the above and more! You might also enjoy these interviews because you are awesome: Become A Data Scientist Faster. Here are a few highlights: Editor Note - Back to the interview!
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