Dit is een samenvatting van het boek "Data Science for Business" dat werd gebruikt bij het vak "Business Intelligence". Daarnaast bevat het ook lesnotities van de colleges van Len Lemeire. Door enkel deze samenvatting te studeren ben ik met 19/20 geslaagd.
Machine Learning (Data Mining) - Samenvatting (slides en handboek)
Full Summary of Chapters and Lecture Slides Data Science for Business
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Universiteit Gent (UGent)
Bachelor Handelswetenschappen
Business Intelligence (F710377)
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Business Intelligence
0 Introduction.......................................................................................................................... 4
0.1 Belang data voor bedrijven ......................................................................................................... 4
0.2 Belang data voor studenten ........................................................................................................ 5
1 Data-analytical thinking ........................................................................................................ 5
1.1 Waarom? ..................................................................................................................................... 5
1.2 Voorbeelden ................................................................................................................................ 6
1.3 Data analytisch denken ............................................................................................................... 6
1.4 Data Mining & data science......................................................................................................... 6
2 Business problems and data science solutions ....................................................................... 7
2.1 Data mining tasks ........................................................................................................................ 7
2.2 Supervised vs unsupervised methods ......................................................................................... 8
2.3 Data mining ................................................................................................................................. 9
2.4 Implicaties voor het managen van het Data Science team ....................................................... 12
2.5 Andere analyse technieken en technologieën .......................................................................... 12
3 Introduction to predictive modelling ................................................................................... 14
3.1 Terminologie.............................................................................................................................. 14
3.2 Supervised segmentation .......................................................................................................... 15
3.3 Selecting informative attributes ................................................................................................ 16
3.4 Supervised segmentation with Tree-Structured models .......................................................... 19
3.5 Visualizing segmentations ......................................................................................................... 22
3.6 Probability estimation ............................................................................................................... 23
3.7 Samenvatting............................................................................................................................. 24
4 Fitting a model to data ........................................................................................................ 25
4.1 Linear discriminant functions .................................................................................................... 25
4.2 Optimizing an objective function .............................................................................................. 26
4.3 Support vector machines .......................................................................................................... 27
4.4 Regression via mathematical functions..................................................................................... 28
4.5 Classification: Scoring and ranking ............................................................................................ 29
4.6 Class probability estimation & Logistic regression .................................................................... 29
4.7 Logistic regression vs tree induction ......................................................................................... 32
4.8 Wat als de data niet-lineair is? .................................................................................................. 33
5 Overfitting and its avoidance............................................................................................... 34
5.1 Overfitting & Generalisatie ....................................................................................................... 34
5.2 Overfitting herkennen ............................................................................................................... 34
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5.3 Waarom is overfitting slecht ..................................................................................................... 38
5.4 Voorkomen van overfitting ....................................................................................................... 39
6 Similarity, Neighbors & Clusters .......................................................................................... 43
6.1 Similarity & distance .................................................................................................................. 43
6.2 Nearest neighbors ..................................................................................................................... 45
6.3 Geometrische interpretatie, overfitting en complexity control................................................ 47
6.4 Problemen met nearest neightbor methode ............................................................................ 48
6.5 Technische details uitgelegd ..................................................................................................... 49
6.6 Clustering as similarity-based segmentation ............................................................................ 52
6.7 Clustering results ....................................................................................................................... 55
6.8 Wat hebben we tot nu toe gezien ............................................................................................. 56
7 What is a good model? ........................................................................................................ 57
7.1 Evaluating classifiers.................................................................................................................. 57
7.2 Generalizing beyond classification ............................................................................................ 59
7.3 Expected value framework ........................................................................................................ 59
7.4 Baseline performance ............................................................................................................... 63
8 Visualizing model performance ........................................................................................... 64
8.1 Ranking instead of classifying .................................................................................................... 64
8.2 Profit curves .............................................................................................................................. 65
8.3 ROC Graphs & curves ................................................................................................................ 66
8.4 Cumulative Response en lift curve ............................................................................................ 69
8.5 Voorbeeld churn ........................................................................................................................ 70
9 Evidence and Probabilities .................................................................................................. 73
9.1 Combining Evidence Probabilistically ........................................................................................ 73
9.2 Bayes’ Rule ................................................................................................................................ 75
9.3 Evidence lift ............................................................................................................................... 77
10 Representing and Mining Tekst ........................................................................................ 78
10.1 Tekst .......................................................................................................................................... 78
10.2 Terminologie (geleend uit IR = information retrieval) .............................................................. 79
10.3 Bag of words .............................................................................................................................. 79
10.4 Beyond bag of words ................................................................................................................. 82
10.5 Voorbeeld: Mining News Stories for stock price movement .................................................... 83
11 Decision Analytic Thinking ll: Toward Analytical Engineering ............................................ 86
11.1 Case: Geldinzameling vereniging............................................................................................... 86
11.2 Case: Churn................................................................................................................................ 88
12 Other Data Science Tasks and Techniques ........................................................................ 89
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12.1 Co-occurrences & associations.................................................................................................. 89
12.2 Profiling ..................................................................................................................................... 91
12.3 Link prediction ........................................................................................................................... 92
12.4 Data reduction & latent information ........................................................................................ 92
12.5 Bias, variance & ensemble methods ......................................................................................... 93
12.6 Causal Explanation .................................................................................................................... 94
13 Data science and Business Strategy .................................................................................. 95
13.1 Competitief voordeel ................................................................................................................ 95
13.2 Data science management ........................................................................................................ 96
13.3 Aantrekken & behouden van data scientists ............................................................................ 97
13.4 Kleine bedrijven ......................................................................................................................... 97
13.5 Data science maturity ................................................................................................................ 97
13.6 Data mining voorstellen evalueren ........................................................................................... 97
14 Conclusie ........................................................................................................................ 98
14.1 Fundamentele concepten van data science .............................................................................. 98
14.2 Fundamentele concepten in een case....................................................................................... 98
14.3 Andere manier van denken aan een businessprobleem ........................................................... 99
14.4 Wat data niet kan doen ............................................................................................................. 99
14.5 Ethiek & privacy ......................................................................................................................... 99
14.6 Cloud sourcing ........................................................................................................................... 99
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0 Introduction
Belang data voor bedrijven
Jaarlijks:
• Verdubbeld de hoeveelheid data
• Daalt de kost om data bij te houden
Big Data: Een brede verzameling aan data van verschillende bronnen
Maslow’s Hierarchy of Big Data:
• Data verzamelen, dit geeft ons informatie, hier vervolgens kennis uit halen
o Met wijsheid omgaan met deze data
Data warehouse vs data lake
• Data warehouse:
o Data wordt verwerkt in één schema voordat ze in het warehouse bijgehouden
worden
▪ Opmerking: Data is nooit beschikbaar in de vorm dat je ze nodig hebt
o De analyse gebeurt met de “cleansed” data
o ETL: Extract transform load
• Data lake:
o Data wordt “raw” en ongestructureerd bijgehouden in data lake
o Data wordt pas geselecteerd en georganiseerd wanneer dit nodig is
Figuur 1: Data warehouse vs data lake
Data in bedrijven: er is data aanwezig, dit wordt omgezet in inzichten waarop men reageert
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