Downloaded 28 times
![“[LinkedIn] was like arriving at a conference
reception and realizing you don’t know anyone. So
you just stand in the corner sipping your drink —
and you probably leave early.”
— LinkedIn Manager, June 2006
Example:
LinkedIn
2006](https://image.slidesharecdn.com/tf-itpbapm-180808012823/85/Tf-itpbapm-6-320.jpg)
![X = [[181,80], [177,70], [160,60], [154,54], [166,65],
[190,90], [175,64], [177,70], [159,55], [171,75], [181,85]]
Y = ['male','female','female','female','male','male','male','female',
'male','female','male']
➔ Load in our seed data
➔ X is an array of inputs, each input is itself
an array that contains Height (in cm) and
Weight (in kg)
➔ Y is an array of strings that map to the
inputs in X so we can train the model
Code
Block 2](https://image.slidesharecdn.com/tf-itpbapm-180808012823/85/Tf-itpbapm-26-320.jpg)
![prediction = clf.predict([[183,76]])
print prediction
➔ Now we give our inputs, in the same format
➔ Height (cm), Weight (kg)
➔ Print our prediction
Code
Block 4](https://image.slidesharecdn.com/tf-itpbapm-180808012823/85/Tf-itpbapm-28-320.jpg)
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- 4. Learn by Doing ➔ Why isData Science a thing? ➔ What is Python? ➔ How do we use it with a real world project? ➔ How do I learn more?
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- 6. “[LinkedIn] was likearriving at a conference reception and realizing you don’t know anyone. So you just stand in the corner sipping your drink — and you probably leave early.” — LinkedIn Manager, June 2006 Example: LinkedIn 2006
- 7. ➔ Joined LinkedInin 2006, only 8M users (450M in 2016) ➔ Started experiments to predict people’s networks ➔ Engineers were dismissive: “you can already import your address book” Enter: Data Scientist
- 8. ➔ Frame thequestion ➔ Collect the raw data ➔ Process the data ➔ Explore the data ➔ Communicate results The Process: LinkedIn Example
- 9. ➔ What questionsdo we want to answer? ◆ Who? ◆ What? ◆ When? ◆ Where? ◆ Why? ◆ How? Case: Frame the Question
- 10. ➔ What connections(type and number) lead to higher user engagement? ➔ Which connections do people want to make but are currently limited from making? ➔ How might we predict these types of connections with limited data from the user? Case: Frame the Question
- 11. ➔ What datado we need to answer these questions? Case: Collect the Data
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- 13. ➔ How isthe data “dirty” and how can we clean it? Case: Process the Data
- 14. ➔ User input ➔Redundancies ➔ Feature changes ➔ Data model changes Case: Process the Data
- 15. ➔ What arethe meaningful patterns in the data? Case: Explore the Data
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- 17. ➔ How dowe communicate this? ➔ To whom? Case: Communicate Findings
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- 22. ➔ Our modelis going to be a Decision Tree ➔ Decision Trees predict the most likely outcome based on input ➔ Like a computer building a version of 20 questions The Model
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- 24. ➔ We’ll beusing a Google-hosted Python notebook to build this model called Colaboratory ➔ Go to: Colab.research.google.com ➔ Click New Python 3 Notebook The Notebook
- 25. from sklearn importtree ➔ Import Tree functionality from the SKLearn Python Package ➔ bit.ly/sklearn-python Code Block 1
- 26. X = [[181,80],[177,70], [160,60], [154,54], [166,65], [190,90], [175,64], [177,70], [159,55], [171,75], [181,85]] Y = ['male','female','female','female','male','male','male','female', 'male','female','male'] ➔ Load in our seed data ➔ X is an array of inputs, each input is itself an array that contains Height (in cm) and Weight (in kg) ➔ Y is an array of strings that map to the inputs in X so we can train the model Code Block 2
- 27. clf = tree.DecisionTreeClassifier() clf= clf.fit(X,Y) #print tree.export_graphviz(clf,None) ➔ We create an empty DecisionTreeClassifier and assign it to the variable clf ➔ We fit the decision tree with our X and Y seed data ➔ SKLearn is automatically creating our Decision Tree questions for us (Example: Is height > 177? Yes - Male) ➔ Uncomment the last line and paste the return string into: webgraphviz.com Code Block 3
- 28. prediction = clf.predict([[183,76]]) printprediction ➔ Now we give our inputs, in the same format ➔ Height (cm), Weight (kg) ➔ Print our prediction Code Block 4
- 29. Our model hasa few weaknesses: ➔ Limited inputs ➔ Assumptions Shortcomings
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