A practical introduction to data science and machine learning

1. A practical introduction to data science and machine learning Lisa Torlina lisa.torlina@akquinet.de knowhow@akquinet.de

2. Introduction 2.5 million TB of data produced every day of all data has been generated in the last 2 years 90% 150 times!

3. Introduction – John Naisbitt

4. Types of data analytics

5. 1. Descriptive analytics • What happened? → Plots, summary statistics, alerts → Make data human-friendly akquinet fridge: daily energy consumption

6. 1. Descriptive analytics • What happened? → Plots, summary statistics, alerts → Make data human-friendly akquinet fridge: daily energy consumption Mean = 0.52 kWh

9. 2. Diagnostic analytics • Why did it happen?

10. 2. Diagnostic analytics • Why did it happen?

11. 2. Diagnostic analytics • Why did it happen? R2 = 0.90 R2 = 0.93

12. 3. Predictive analytics • What will happen in the future?

13. 3. Predictive analytics • What will happen in the future? Machine learning!

14. 4. Prescriptive analytics • What should we do about it? Time to call a technician?

15. 4 types of data analytics 1. Descriptive – what happened? 2. Diagnostic – why did it happen? 3. Predictive – what will happen in the future? 4. Prescriptive – what should we do about it?

16. 4 types of data analytics 1. Descriptive – what happened? 2. Diagnostic – why did it happen? 3. Predictive – what will happen in the future? 4. Prescriptive – what should we do about it?

17. Predictive modelling & machine learning

18. The problem of prediction Price?House Image Cat? Fridge Energy consumption? Coin Denomination?

19. The problem of prediction • What do we mean by prediction? Information we use to make the prediction Quantity we want to predict y = f(X) X y ?? • In mathematical terms: Features Target variable

20. Types of prediction problem $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c}

23. How do we predict? $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c} ?? ?? ?? ??

24. How do we predict? $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c} ?? ?? ?? Manufacturer

25. How do we predict? $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c} ?? ?? Manufacturer Engineer, physical laws

26. How do we predict? $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c} Real estate agent ?? Manufacturer Engineer, physical laws

27. How do we predict? $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c} Real estate agent ?? Manufacturer Engineer, physical laws Rule based

28. How do we predict? $ … Size, location Pixel values {‘cat’, ‘not cat’} Usage, weather … kWh Diameter, weight {1c, 5c, 10c, 25c} Real estate agent ?? Manufacturer Engineer, physical laws Data!

29. Rules vs data Rule-based Data-based Figures: Abu-Mostafa, Magdon-Ismail, Lin, Learning from data, AMLBook.com (2012)

30. Machine learning Classification Regression

31. Machine learning • Prediction based on data → model learns by looking at examples Classification Regression

32. What can machine learning do? • Image, speech recognition (> 95% accuracy) • Self-driving vehicles • DeepMind defeated Go champion, • Recommendation engines, spam filters, fraud detection, text analysis… Google’s energy efficiency by 15%

33. From data to predictive model: a step-by-step guide

34. From data to predictive model… Formulate the problem Explore the data Clean the data Feature engineering Evaluate model performance Choose and fit model(s)

36. Formulate the problem • What do we want to predict? • What data could we use to do this? X y

39. Formulate the problem • What do we want to predict? • What data could we use to do this? Energy consumption Weekday/holiday Weather E.g. fridge X y

40. Formulate the problem Price E.g. house Size Location No. rooms Condition … • What do we want to predict? • What data could we use to do this? X y

43. Explore the data • Visualization, descriptive analytics 1. Individual variables 2. Relationships between variables

46. Explore the data • Outliers and spurious values

50. Clean the data • Missing entries • Outliers • Spurious values } • Remove? • Impute/correct? • Label and keep?

51. Convert data types Lot Shape Regular Slightly irregular Moderately Irregular Irregular 0 1 2 3 Ordinal encoding

52. Convert data types Neighbourhood 1 North Ames 2 North Ames 3 Gilbert 4 Gilbert 5 Stone Brook 6 Gilbert 7 Gilbert North Ames Gilbert Stone Brook 1 1 0 0 2 1 0 0 3 0 1 0 4 0 1 0 5 0 0 1 6 0 1 0 7 0 1 0 Dummy (one-hot) encoding

54. From data to predictive model… Formulate the problem Explore the data Feature engineering Evaluate model performance Choose and fit model(s) Clean the data

56. Feature engineering • Transform data to optimize learning → Construct new features → Transform features → Select features 2 x1 x2 x3 x4 x5

59. Choose and fit model • Linear models (generalized) • Tree-based models • Neural networks X y Model?? • Support vector machines, k-nearest neighbour, Bayesian networks, …

62. How well did my model do?

65. Quantifying error (regression) Typical choice – Root mean squared error: RMSE = v u u t 1 n nX i=1 (yi ˆyi)2

66. What makes a good model?

67. What makes a good model?

68. What makes a good model? • Must generalize well to unseen data

69. What makes a good model? • Must generalize well to unseen data Overfitting

70. What makes a good model? • Must generalize well to unseen data Underfitting

71. What makes a good model? • Must generalize well to unseen data Just right

72. What makes a good model? Underfit OverfitJust right

73. Evaluating model performance • Cross validation Full dataset

74. Evaluating model performance • Cross validation Training set Model learns from this data

75. Evaluating model performance • Cross validation Training set Model learns from this data Test set Used to evaluate model performance

76. K-fold cross validation Test set RMSE: Train Test 1 0.12 Train Train Test 2 0.14 Train Train Test 3 0.10 Train Test 4 0.11 0.12 Average

80. The akquinet fridge

81. House prices on Kaggle • Prediction error within 1% in most cases • Top 2% of all submissions on Kaggle

82. Summary • Types of data analytics • Machine learning for prediction • From data to predictive model → Data preparation → Modelling and cross validation Cat Data!

83. Thanks for listening!

A practical introduction to data science and machine learning

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