Predict the Oscars with Data Science
- 1. Predicting the Oscars with data science March 2017
- 2. Data Science Process • Frame the question. • Collect the raw data. • Process the data. • Explore the data. • Communicate results.
- 3. Frame the question • Who will win the Oscar for Best Picture?
- 4. Collect the Data • What kind of data do we need? • Financial data (Budget, box office…) • Reviews, ratings and scores. • Awards and nominations.
- 5. Process the data • How’s the data “dirty” and how can we fix it? • User input, redundancies, missing data… • Formatting: adapt the data to meet certain specifications. • Cleaning: detecting and correcting corrupt or inaccurate records.
- 6. Explore the data • What are the meaningful patterns in the data? • How meaningful is each data point for our predictions?
- 7. Communicate results • Tell story at the right technical level for each audience • Make sure to focus on Whats In It For You (WIIFY!) • Be objective, don’t lie with statistics • Be visual! Show, don’t just tell
- 8. Goals • Introduction to a data scientist's tools and methods: • Jupyter notebooks, numpy, pandas, sklearn… • Overview of basic machine learning concepts: • Data formatting and cleaning, Decision trees, Overfitting, Random Forests…
- 9. Jupyter Notebooks • One of data scientist’s everyday tools. • Find the link in our classroom tool: • (bit.ly/atl-oscars) • Contains cells with code. They have already been executed for you.
- 10. NumPy • The fundamental package for scientific computing with Python. • Provides powerful multi-dimensional array objects. • Many methods for fast operations on arrays.
- 11. Pandas • Fundamental high-level building block for doing practical, real world data analysis in Python. • Built on top of NumPy. • Offers data structures and operations for manipulating numerical tables and time series.
- 12. Scikit-learn • Python module for machine learning. • Provides a large menu of libraries for scientific computation, such as integration, interpolation, signal processing, linear algebra, statistics, etc.
- 13. Initial imports and loading data with Pandas
- 14. Understanding your data • .head(n) method: Returns first n rows. • .value_counts() method: Returns the counts of unique values in the DataFrame.
- 16. Formatting your Data • Rate values in a non-numeric format. Thus, we will need to assign each rate a unique integer so that Python can handle the information. • With the .ix method you create a subset of rows and assign a value to a certain variable of that subset of observations.
- 18. Decision Trees • 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.
- 20. Classification vs Regression • Classification — Predict categories. • Identifying group membership. • Regression — Predict values. • Involves estimating or predicting a response.
- 21. Classification
- 22. Classification ?
- 23. 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.
- 24. Creating your first Decision Tree
- 25. Importances and Score • .feature_importances_ attribute: tells us how important the features are for the final result. • .score() method: returns the mean accuracy of our fitting.
- 27. Predicting
- 28. Overfitting • Resulting model too tied to the training set. • It doesn’t generalize to new data, which is the point of prediction.
- 29. 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.
- 32. Predicting with Random Forest Classifiers
- 33. Results
- 34. 1976 Rocky
- 35. 1984 Amadeus
- 38. And the Oscar goes to…
- 39. La La Land!!
- 42. The End Nothing happened after that. Right?? RIGHT??
- 43. We can predict the Oscars Except for 2017 ¯_( )_/¯
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- 48. Data Science Bootcamp Syllabus: Python Toolkit, Statistics & Probability, Experimentation, Machine Learning, Communicating Data, Algorithms and Big Data
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