This document outlines a data science project to predict Oscar winners using machine learning techniques. It discusses collecting financial and review data on films, cleaning and formatting the data, exploring it for patterns, building a decision tree model, improving the model with a random forest classifier, and using the model to predict 2016 winners. The goal is to walk through the full data science process and how these techniques can be applied to a real-world prediction problem.

1. Predicting the Oscars with Data Science
http://bit.ly/tf-predict-oscars

2. About me
• Jasjit Singh
• Self-taught developer
• Worked in finance & tech
• Co-Founder Hotspot
• Thinkful General Manager

3. About us
Thinkful prepares students for web development &
data science jobs with 1-on-1 mentorship programs

4. What’s your background?
• I have a software background
• I have a math or stats background
• None of the above

5. Learning goals
• “Data Science Process”
• Python’s data science toolkit and methods
• Basic machine learning concepts

6. Data Science Process
• Frame the question.
• Collect the raw data.
• Process the data.
• Explore the data.
• Communicate results.

7. Frame the question
• Who will win the Oscar for Best Picture?

8. Collect the Data
• What kind of data do we need?

9. Collect the Data
• Financial data (Budget, box office…)
• Reviews, ratings and scores.
• Awards and nominations.

10. Process the data
• How’s the data “dirty” and how can we fix it?

11. Process the data
• User input, redundancies, missing data…
• Formatting: adapt the data to meet certain
specifications.
• Cleaning: detecting and correcting
corrupt or inaccurate records.

12. Explore the data
• What are the meaningful patterns in the
data?
• How meaningful is each data point for our
predictions?

13. Communicate the Data

14. Jupyter Notebooks
• One of data scientist’s everyday tools.
• Lets us show our work: can read the story,
follow the process, and run the code
• Find the links in our classroom tool.

15. NumPy
• The fundamental package for scientific
computing with Python.
• Lets us store our data into special multi-
dimensional array objects.
• Many methods for fast operations on arrays.

16. Pandas
• Fundamental high-level building block for
doing practical, real world data analysis in
Python.
• Built on top of NumPy.

17. Scikit-learn
• Python module for machine learning.

18. Working through the code
• The goal is to have you follow along. Towards
the end of the class, you can play around
with the code yourself
• Go to Jupyter notebook (http://bit.ly/tf-
jupyter)
• Open “Stage One.ipynb”

19. Initial imports and loading data with Pandas

20. Understanding your data
• .head(n) method: Returns first n rows.
• .value_counts() method: Returns the counts
of unique values in that column

21. Understanding your data

22. Processing our data: Formatting
• Ratings are in a non-numeric format. We
need to assign each rating a unique integer
so that Python can handle the information.
• We’ll do that with the .ix method

23. Formatting your Data

24. Processing our Data: Cleaning

25. Classification vs Regression
• Regression — Predict values
• Classification — Predict categories.

26. Classification

27. Classification
?

28. For our model we’ll use a decision tree
• It breaks down a dataset into smaller and
smaller subsets.
• The final result is a model with a tree
structure that has:
• Decision nodes: ask a question and have
two or more branches.
• Leaf nodes: represent a classification or
decision.

30. Why a decision tree?
• Decision tree is just one of many algorithms
you can use (e.g. Naive Bayes, SVM, Least
Squares, Logistic, etc)
• We’re using decision trees because it mirrors
human decision making so it’s simpler to
understand and interpret

31. Creating your first Decision Tree
You will use the scikit-learn and numpy
libraries to build your first decision tree. We
will need the following to build a decision tree
• target: A one-dimensional numpy array
containing the target from the train data.
• features: A multidimensional numpy array
containing the features/predictors from the
train data.

32. Creating your first Decision Tree

33. Importances and Score

34. Problem #1: Poor Predictions
• We only got 1 out of 4 movies correct
(Amadeus, 1984)
• Open Stage 2 and lets try and improve our
predictions!

35. Solution: Modify the feature list

36. Run the prediction again

37. Success!?
• We now got 3 out of 4 movies correct
• Our model predicted Inglorious Bastards would win.
Hurt Locker won instead

38. Problem #2: Overfitting
• Resulting model too tied to the training set.
• It doesn’t generalize to new data, which is
the point of prediction.

39. Overfitting, a visualization
In overfitting, a statistical model describes random error or
noise instead of the underlying relationship.

40. Solution: Random Forest Classifier
• Random Forest Classifiers use many
Decision Trees to build a classifier.
• We introduce a bit of randomness.
• Each Tree can give a different answer (a
vote). The final classification is the most
common amongst the Trees.

41. Random Forest Classifier

42. Creating your first Decision Tree

43. Importances and Score

44. Results

45. 1976
Rocky

46. 1984
Amadeus

47. 1996
The English Patient

48. 2009
The Hurt Locker

49. And our prediction for the 2016
Oscar is…

51. We can predict the Oscars

52. Except for 2017 ¯_(ツ)_/¯

53. Next steps for learning
• Google (self-taught)
• Coursera
• Bootcamps

54. 1-on-1 mentorship enables flexibility

55. Next steps for learning

56. Graduate outcomes
Job Titles after GraduationMonths until Employed

57. Special Introductory Offer
• Prep course for 50% off — $250 instead of $500
• Covers math, stats, Python, and data science toolkit
• Option to continue into full data science program
• If you’re interested talk to me after or email me at
jasjit@thinkful.com