gonzoDavid Gonzalez
@datagonzowww.linkedin.com/in/datagonzo
● Lots of data science stuff● Lots of kids● Lots of pets● Playing outdoors
ML + JSWhy?
● Smarter validation?● Dynamic/personalize routing,
content, etc.?● Both A & B?● Automation of key points of
friction in your UI?
LabelA category, data point, or phenomenon you want to teach a machine to learn to identify reliably
Explore Statistical and visual explorations of your dataset
Analyze Process of learning how to predict a trend or label from a dataset
Predict Categorizing, identifying, or forecasting a trend from new data
LEAP into Data Science
Label Text
positive Happy Birthday!
negative Get bent!
positive Hell Yeah!
negative #%*@ YOU!
Sentiment
Label Sales1/1/2016 $170.001/2/2016 $141.001/3/2016 $160.001/4/2016 $139.001/5/2016 $200.001/6/2016 $193.001/7/2016 $163.001/8/2016 $200.00
ForecastLabel Image
cat
dog
IdentifyLabel Value Timenormal 1.4 142223600normal 1.5 142223605normal 1.6 142223610anomaly 1.9 142223615anomaly 2.1 142223620anomaly 1.9 142223625
Anomaly
userAction Figures Clothing
Digital Downloads Books
Customer 1 0 1 0 1Customer 2 0 0 1 1Customer 3 0 1 1 1Customer 4 1 1 1 1Customer 5 1 0 1 0Customer 6 1 0 0 0
Recommendation
Label Last loginAvg Time on Page
Age of account
Red 1/1/2016 0:00:00 100 130
Yellow 4/2/2016 0:00:00 200 360
Green 4/3/2016 0:00:00 250 489
Recommendation
Label Text
positive Happy Birthday!
negative Get bent!
positive Hell Yeah!
negative #%*@ YOU!
positive Have a great day!
Sentiment
Label Sales1/1/2016 $170.001/2/2016 $141.001/3/2016 $160.001/4/2016 $139.001/5/2016 $200.001/6/2016 $193.001/7/2016 $163.001/8/2016 $200.001/9/2016 $189.00
ForecastLabel Image
cat
dog
cat
IdentifyLabel Value Timenormal 1.4 142223600normal 1.5 142223605normal 1.6 142223610anomaly 1.9 142223615anomaly 2.1 142223620anomaly 1.9 142223625normal 1.8 142223630
Anomaly
userAction Figures Clothing
Digital Downloads Books
Customer 1 0 1 0 1Customer 2 0 0 1 1Customer 3 0 1 1 1Customer 4 1 1 1 1Customer 5 1 0 1 0Customer 6 1 0 0 0Customer 7 0 1 1 1
Recommendation
Label Last loginAvg Time on Page
Age of account
Red 1/1/2016 0:00:00 100 130
Yellow 4/2/2016 0:00:00 200 360
Green 4/3/2016 0:00:00 250 489
Red 2/3/2016 0:00:00 140 90
RecommendationNew data Prediction
LabelDataset: Banking Marketing Data Set
https://archive.ics.uci.edu/ml/datasets/Bank+Marketing
Label:
Has the client subscribed a term deposit?
age job ... contact euribor3m nr_employed y
44 blue-collar cellular 4.963 5228.1 0
53 technician cellular 4.021 5195.8 0
28 management cellular 0.729 4991.6 1
39 services cellular 1.405 5099.1 0
55 retired cellular 0.869 5076.2 1
30 management cellular 4.961 5228.1 0
37 blue-collar cellular 1.327 5099.1 0
39 blue-collar cellular 1.313 5099.1 0
36 admin. cellular 1.266 5076.2 1
27 blue-collar cellular 1.41 5099.1 0
34 housemaid telephone 4.864 5191 0
41 management cellular 4.964 5228.1 0
Predictw/ JavaScript SDK
Documentation:http://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/MachineLearning.html#predict-property
Links
● LEAP into Data Science (blog post)● How to create Citizen Data Scientists● LEAP Specification● Vega & Vega Online Editor● Voyager (Explore your data)● AWS Machine Learning JavaScript
SDK docs● Example prediction w/ javascript● Commentable version of these slides● Experimental single-step app
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