1.
Intro to
Python:
Build a
Predictive
Model

2.
Introductions
➔ What's your name?
➔ What brought you here today?
➔ What is your programming experience?

3.
We train developers and
data scientists through
1x1 mentorship and
project-based learning.
Guaranteed.
About Thinkful

4.
Learn
by
Doing
➔ Why is Data Science a thing?
➔ What is Python?
➔ How do we use it with a real
world project?
➔ How do I learn more?

5.
What
is
a
Data
Scientist?

6.
“[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

7.
➔ Joined LinkedIn in 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 the question
➔ Collect the raw data
➔ Process the data
➔ Explore the data
➔ Communicate results
The
Process:
LinkedIn
Example

9.
➔ What questions do 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 data do we need to
answer these questions?
Case:
Collect
the
Data

12.
➔ Connection data (who is who connected to?)
➔ Demographic data (what is the profile of
the connection)
➔ Engagement data (how do they use the site)
Case:
Collect
the
Data

13.
➔ How is the 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 are the meaningful
patterns in the data?
Case:
Explore
the
Data

16.
➔ Triangle closing
➔ Time Overlaps
➔ Geographic Overlaps
Case:
Explore
the Data

17.
➔ How do we communicate this?
➔ To whom?
Case:
Communicate
Findings

18.
➔ Marketing - sell X more ad space, results in X more
impressions per day
➔ Product - build X more features
➔ Development - grow our team by X
➔ Sales - attract X more premium accounts
➔ C-Level - more revenue, 8M - 450M in 10 years
Case:
Communicate
Findings

19.
The
Result

20.
Python for Programming
➔ Great for Data Science
➔ Robotics
➔ Web Development
(Python/Django)
➔ Automation
Let’s
Learn
Python

21.
Let’s
Learn
Python

22.
➔ Our model is 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

23.
Decision
Trees:
Golf?

24.
➔ We’ll be using 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 import tree
➔ 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]])
print prediction
➔ Now we give our inputs, in the same format
➔ Height (cm), Weight (kg)
➔ Print our prediction
Code
Block 4

29.
Our model has a few weaknesses:
➔ Limited inputs
➔ Assumptions
Shortcomings

30.
Ways
to
Learn
Data
Science

31.
➔ Start with Python and Statistics
➔ Personal Program Manager
➔ Unlimited Q&A Sessions
➔ Student Slack Community
➔ bit.ly/freetrial-ds
Thinkful
Two-Week
Free
Trial

32.
The
Student
Experience
Marnie Boyer, Thinkful Graduate
Capstone
Wolfgang Hall, Thinkful Graduate
Capstone

33.
➔ bit.ly/tf-event-feedback
Survey