.

1. Intro to
Python:
Predict
the
Box Office

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. Import
the
Data
bit.ly/boxoffice-notebook

5. Understand
Your
Data

6. Understand
Your
Data

7. data.shape() returns the # of (rows, columns)
Understanding
Your
Data

8. data.describe() returns general statistics
Understanding
Your
Data

9. Cleaning
the
Data

10. Cleansing
Your
Data

11. Cleansing
Your
Data

12. Cleansing
Your
Data

13. Cleansing
Your
Data

14. Finalizing
Your
Data

15. Visualizing
Your
Data

16. Visualizing
Your
Data

17. Visualizing
Your
Data

18. Visualizing
Your
Data

19. Visualizing
Your
Data

20. Correlations
with the
Target
Feature

21. Box
Office
Predictions

22. Box
Office
Predictions

23. Linear
Regression

24. Linear
Regression

25. Error
Metrics

26. Random
Forest

27. Random
Forest

28. Random
Forest

29. Error
Metrics

30. Dropping
Post-Fact
Data

31. Re-Run
Random
Forest
Regressor

32. Prediction

33. Error
Metrics

34. Ways
to
Learn
Data
Science

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

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