Data Science and Analytics with Python

397 Pages·2017·20.8 MB·English

by  Jesus Rogel-Salazar

DATA SCIENCE
ANALYTICS
AND
PYTHON
WITH
Jesús Rogel-Salazar
CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2017 by Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business
Version Date: 20170517
International Standard Book Number-13: 978-1-498-74209-2 (Hardback)
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
http://www.crcpress.com
Contents
1
1 Trials and Tribulations of a Data Scientist
2
1.1 Data? Science? Data Science!
1.1.1 So, What Is Data Science? 3
7
1.2 The Data Scientist: A Modern Jackalope
1.2.1 Characteristics of a Data Scientist and a Data Science Team 12
17
1.3 Data Science Tools
1.3.1 Open Source Tools 20
22
1.4 From Data to Insight: the Data Science Workflow
1.4.1 Identify the Question 24
1.4.2 Acquire Data 25
1.4.3 Data Munging 25
1.4.4 Modelling and Evaluation 26
1.4.5 Representation and Interaction 26
1.4.6 Data Science: an Iterative Process 27
28
1.5 Summary
31
2 Python: For Something Completely Different
33
2.1 Why Python? Why not?!
2.1.1 To Shell or not To Shell 36
2.1.2 iPython/Jupyter Notebook 39
40
2.2 Firsts Slithers with Python
2.2.1 Basic Types 40
2.2.2 Numbers 41
2.2.3 Strings 41
2.2.4 Complex Numbers 43
2.2.5 Lists 44
2.2.6 Tuples 49
2.2.7 Dictionaries 52
54
2.3 Control Flow
2.3.1 if... elif... else 55
2.3.2 while 56
2.3.3 for 57
2.3.4 try... except 58
2.3.5 Functions 61
2.3.6 Scripts and Modules 65
68
2.4 Computation and Data Manipulation
2.4.1 Matrix Manipulations and Linear Algebra 69
2.4.2 NumPy Arrays and Matrices 71
2.4.3 Indexing and Slicing 74
76
2.5 Pandas to the Rescue
81
2.6 Plotting and Visualising: Matplotlib
83
2.7 Summary
3 The Machine that Goes “Ping”: Machine Learning and Pattern
87
Recognition
87
3.1 Recognising Patterns
90
3.2 Artificial Intelligence and Machine Learning
92
3.3 Data is Good, but other Things are also Needed
94
3.4 Learning, Predicting and Classifying
98
3.5 Machine Learning and Data Science
100
3.6 Feature Selection
102
3.7 Bias, Variance and Regularisation: A Balancing Act
105
3.8 Some Useful Measures: Distance and Similarity
110
3.9 Beware the Curse of Dimensionality
116
3.10 Scikit-Learn is our Friend
119
3.11 Training and Testing
124
3.12 Cross-Validation
3.12.1 k-fold Cross-Validation 125
128
3.13 Summary
131
4 The Relationship Conundrum: Regression
131
4.1 Relationships between Variables: Regression
136
4.2 Multivariate Linear Regression
138
4.3 Ordinary Least Squares
4.3.1 The Maths Way 139
144
4.4 Brain and Body: Regression with One Variable
4.4.1 Regression with Scikit-learn 153
155
4.5 Logarithmic Transformation
160
4.6 Making the Task Easier: Standardisation and Scaling
4.6.1 Normalisation or Unit Scaling 161
4.6.2 z-Score Scaling 162
164
4.7 Polynomial Regression
4.7.1 Multivariate Regression 169
170
4.8 Variance-Bias Trade-Off
172
4.9 Shrinkage: LASSO and Ridge
179
4.10 Summary
181
5 Jackalopes and Hares: Clustering
182
5.1 Clustering
183
5.2 Clustering with k-means
5.2.1 Cluster Validation 186
5.2.2 k-means in Action 189
193
5.3 Summary
195
6 Unicorns and Horses: Classification
196
6.1 Classification
6.1.1 Confusion Matrices 198
6.1.2 ROC and AUC 202
205
6.2 Classification with KNN
6.2.1 KNN in Action 206
211
6.3 Classification with Logistic Regression
6.3.1 Logistic Regression Interpretation 216
6.3.2 Logistic Regression in Action 218
226
6.4 Classification with Naïve Bayes
6.4.1 Naïve Bayes Classifier 232
6.4.2 Naïve Bayes in Action 233
238
6.5 Summary
7 Decisions, Decisions: Hierarchical Clustering, Decision Trees and
241
Ensemble Techniques
242
7.1 Hierarchical Clustering
7.1.1 Hierarchical Clustering in Action 245
249
7.2 Decision Trees
7.2.1 Decision Trees in Action 256
265
7.3 Ensemble Techniques
7.3.1 Bagging 271
7.3.2 Boosting 272
7.3.3 Random Forests 274
7.3.4 Stacking and Blending 276
277
7.4 Ensemble Techniques in Action
282
7.5 Summary
285
8 Less is More: Dimensionality Reduction
286
8.1 Dimensionality Reduction
291
8.2 Principal Component Analysis
8.2.1 PCA in Action 295
8.2.2 PCA in the Iris Dataset 300
304
8.3 Singular Value Decomposition
8.3.1 SVD in Action 306
310
8.4 Recommendation Systems
8.4.1 Content-Based Filtering in Action 312
8.4.2 Collaborative Filtering in Action 316
323
8.5 Summary
327
9 Kernel Tricks up the Sleeve: Support Vector Machines
328
9.1 Support Vector Machines and Kernel Methods
9.1.1 Support Vector Machines 331
9.1.2 The Kernel Trick 340
9.1.3 SVM in Action: Regression 343
9.1.4 SVM in Action: Classification 347
353
9.2 Summary
355
Pipelines in Scikit-Learn
361
Bibliography
369
Index
Figures
1.1 Asimplifieddiagramoftheskillsneededindata
scienceandtheirrelationship. 8
1.2 Jackalopesaremythicalanimalsresemblingajackrabbit
withantlers. 10
1.3 Thevariousstepsinvolvedinthedatascience
workflow. 23
2.1 Aplotgeneratedbymatplotlib. 84
3.1 MeasuringthedistancebetweenpointsAand
B. 107
3.2 Thecurseofdimensionality. Tendatainstancesplaced
inspacesofincreaseddimensionality,from1dimension
to3. Sparsityincreaseswiththenumberof
dimensions. 112
3.3 Volumeofahypersphereasafunctionofthe
dimensionality N. Asthenumberofdimensions
increases,thevolumeofthehyperspheretendsto
zero. 115
3.4 Adatasetissplitintotrainingandtestingsets. The
trainingsetisusedinthemodellingphaseandthe
testingsetisheldforvalidatingthemodel. 122