Python Machine Learning Case Studies Five Case Studies for the Data Scientist

✍ Scribed by Haroon, Danish

• Publisher: Apress
• Year: 2017
• Tongue: English
• Leaves: 216
• Edition: 1st edition
• Category: Library

✦ Synopsis

Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.
Python Machine Learning Case Studiestakes you through the steps to improve business processes and determine the pivotal points that frame strategies. You'll see machine learning techniques that you can use to support your products and services. Moreover you'll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs.
By taking a step-by-step approach to coding in Python you'll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.
What You Will Learn
Gain insights into machine learning concepts


Work on real-world applications of machine learning
Learn concepts of model selection and optimization
Get a hands-on overview of Python from a machine learning point of view



Who This Book Is For
Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.

✦ Table of Contents

Contents at a Glance......Page 4
Contents......Page 5
About the Author......Page 11
About the Technical Reviewer......Page 12
Acknowledgments......Page 13
Introduction......Page 14
Case Study: Cycle Sharing Scheme—Determining Brand Persona......Page 15
Feature Exploration......Page 18
Continuous/Quantitative Variables......Page 20
Ordinal Variables......Page 21
Dependent and Independent Variables......Page 22
Univariate Analysis......Page 23
Multivariate Analysis......Page 28
Cyclic Pattern......Page 32
Trend......Page 33
Mean......Page 34
Geometric Mean......Page 35
Variance......Page 36
Changes in Measure of Center Statistics due to Presence of Constants......Page 37
The Normal Distribution......Page 39
Skewness......Page 40
Outliers......Page 41
Kendall Rank Correlation......Page 48
Spearman Rank Correlation......Page 49
t-Statistics......Page 51
t-Distributions and Sample Size......Page 52
Central Limit Theorem......Page 54
Case Study Findings......Page 55
Business Analytics......Page 56
Elections......Page 57
Case Study: Removing Inconsistencies in Concrete Compressive Strength......Page 58
Interpolation and Extrapolation......Page 61
Linear Regression......Page 62
Least Squares Regression Line of y on x......Page 63
Multiple Regression......Page 64
Stepwise Regression......Page 65
Polynomial Regression......Page 66
Assumptions of Regressions......Page 67
Multicollinearity and Singularity......Page 68
Features’ Exploration......Page 69
Correlation......Page 71
Overfitting and Underfitting......Page 77
Regression Metrics of Evaluation......Page 80
Mean Squared Error......Page 81
Residual......Page 82
Types of Regression......Page 83
Linear Regression......Page 84
Ridge Regression......Page 88
Lasso Regression......Page 92
ElasticNet......Page 94
Gradient Boosting Regression......Page 95
Support Vector Machines......Page 99
Predicting Sales......Page 102
Rate of Inflation......Page 103
Predicting Salary......Page 104
Real Estate Industry......Page 105
Case Study: Predicting Daily Adjusted Closing Rate of Yahoo......Page 108
Feature Exploration......Page 110
Evaluating the Stationary Nature of a Time Series Object......Page 111
Exploratory Data Analysis......Page 112
Dickey-Fuller Test......Page 113
Log Transformation......Page 115
Square Root Transformation......Page 117
Moving Average Smoothing......Page 119
Exponentially Weighted Moving Average......Page 121
Differencing......Page 123
Decomposition......Page 124
Autocorrelation Function......Page 126
Durbin Watson Statistic......Page 127
Modeling a Time Series......Page 128
Deciding Upon the Parameters for Modeling......Page 129
Auto-Regressive Moving Averages......Page 132
Auto-Regressive......Page 133
Moving Average......Page 134
Combined Model......Page 135
Scaling Back the Forecast......Page 136
Unemployment Estimates......Page 140
Stock Market Prediction......Page 141
Case Study: Determination of Short Tail Keywords for Marketing......Page 142
Features’ Exploration......Page 144
Unsupervised Learning......Page 146
Clustering......Page 147
Data Transformation for Modeling......Page 148
k-Means Clustering......Page 150
Elbow Method......Page 151
Variance Explained......Page 152
Bayesian Information Criterion Score......Page 154
Silhouette Score......Page 155
Applying k-Means Clustering for Optimal Number of Clusters......Page 156
Principle Component Analysis......Page 157
Gaussian Mixture Model......Page 164
Bayesian Gaussian Mixture Model......Page 169
Demographic-Based Customer Segmentation......Page 172
Case Study: Ohio Clinic—Meeting Supply and Demand......Page 174
Features’ Exploration......Page 177
Performing Data Wrangling......Page 181
Performing Exploratory Data Analysis......Page 185
Features’ Generation......Page 191
Classification......Page 193
Confusion Matrix......Page 194
Binary Classification: Receiver Operating Characteristic......Page 195
Ensuring Cross-Validation by Splitting the Dataset......Page 197
Decision Tree Classification......Page 198
Kernel Approximation......Page 199
SGD Classifier......Page 200
Bagging......Page 202
Random Forest Classification......Page 203
Gradient Boosting......Page 206
Applications of Classification......Page 208
Insurance......Page 209
Pie chart......Page 210
Histogram......Page 211
Box plot......Page 212
Index......Page 213