book

The Supervised Learning Workshop - Second Edition

Name: The Supervised Learning Workshop - Second Edition
ISBN: 9781800209046

by Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur, Tiffany Ford, Sukanya Mandal, Ashish Pratik Patil

February 2020

Beginner to intermediate

532 pages

9h 36m

English

Packt Publishing

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The Supervised Learning Workshop
Second Edition
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstallation and SetupInstalling Anaconda on Windows Installing Anaconda on macOSInstalling Anaconda on LinuxInstalling LibrariesAccessing the Code Files
1. Fundamentals
IntroductionWhen to Use Supervised LearningPython Packages and ModulesLoading Data in PandasExercise 1.01: Loading and Summarizing the Titanic DatasetExercise 1.02: Indexing and Selecting DataExercise 1.03: Advanced Indexing and SelectionPandas MethodsExercise 1.04: Using the Aggregate MethodQuantilesLambda FunctionsExercise 1.05: Creating Lambda FunctionsData Quality ConsiderationsManaging Missing DataClass ImbalanceLow Sample SizeActivity 1.01: Implementing Pandas FunctionsSummary
2. Exploratory Data Analysis and Visualization
IntroductionExploratory Data Analysis (EDA)Summary Statistics and Central ValuesExercise 2.01: Summarizing the Statistics of Our DatasetMissing ValuesFinding Missing ValuesExercise 2.02: Visualizing Missing ValuesImputation Strategies for Missing ValuesExercise 2.03: Performing Imputation Using PandasExercise 2.04: Performing Imputation Using Scikit-LearnExercise 2.05: Performing Imputation Using Inferred ValuesActivity 2.01: Summary Statistics and Missing ValuesDistribution of ValuesTarget VariableExercise 2.06: Plotting a Bar ChartCategorical DataExercise 2.07: Identifying Data Types for Categorical VariablesExercise 2.08: Calculating Category Value CountsExercise 2.09: Plotting a Pie ChartContinuous DataSkewnessKurtosisExercise 2.10: Plotting a HistogramExercise 2.11: Computing Skew and KurtosisActivity 2.02: Representing the Distribution of Values VisuallyRelationships within the DataRelationship between Two Continuous VariablesPearson's Coefficient of CorrelationExercise 2.12: Plotting a Scatter PlotExercise 2.13: Plotting a Correlation HeatmapUsing PairplotsExercise 2.14: Implementing a PairplotRelationship between a Continuous and a Categorical VariableExercise 2.15: Plotting a Bar ChartExercise 2.16: Visualizing a Box PlotRelationship Between Two Categorical VariablesExercise 2.17: Plotting a Stacked Bar ChartActivity 2.03: Relationships within the DataSummary
3. Linear Regression
IntroductionRegression and Classification ProblemsThe Machine Learning WorkflowBusiness UnderstandingData UnderstandingData PreparationModelingEvaluationDeploymentExercise 3.01: Plotting Data with a Moving AverageActivity 3.01: Plotting Data with a Moving AverageLinear RegressionLeast Squares MethodThe Scikit-Learn Model APIExercise 3.02: Fitting a Linear Model Using the Least Squares MethodActivity 3.02: Linear Regression Using the Least Squares MethodLinear Regression with Categorical VariablesExercise 3.03: Introducing Dummy VariablesActivity 3.03: Dummy VariablesPolynomial Models with Linear RegressionExercise 3.04: Polynomial Models with Linear RegressionActivity 3.04: Feature Engineering with Linear RegressionGeneric Model TrainingGradient DescentExercise 3.05: Linear Regression with Gradient DescentExercise 3.06: Optimizing Gradient DescentActivity 3.05: Gradient DescentMultiple Linear RegressionExercise 3.07: Multiple Linear RegressionSummary
4. Autoregression
IntroductionAutoregression ModelsExercise 4.01: Creating an Autoregression ModelActivity 4.01: Autoregression Model Based on Periodic DataSummary
5. Classification Techniques
IntroductionOrdinary Least Squares as a ClassifierExercise 5.01: Ordinary Least Squares as a ClassifierLogistic RegressionExercise 5.02: Logistic Regression as a Classifier – Binary ClassifierExercise 5.03: Logistic Regression – Multiclass ClassifierActivity 5.01: Ordinary Least Squares Classifier – Binary ClassifierSelect K Best Feature SelectionExercise 5.04: Breast Cancer Diagnosis Classification Using Logistic RegressionClassification Using K-Nearest NeighborsExercise 5.05: KNN ClassificationExercise 5.06: Visualizing KNN BoundariesActivity 5.02: KNN Multiclass ClassifierClassification Using Decision TreesExercise 5.07: ID3 ClassificationClassification and Regression TreeExercise 5.08: Breast Cancer Diagnosis Classification Using a CART Decision TreeActivity 5.03: Binary Classification Using a CART Decision TreeArtificial Neural NetworksExercise 5.09: Neural Networks – Multiclass ClassifierActivity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural NetworksSummary
6. Ensemble Modeling
IntroductionOne-Hot EncodingExercise 6.01: Importing Modules and Preparing the DatasetOverfitting and UnderfittingUnderfittingOverfittingOvercoming the Problem of Underfitting and OverfittingBaggingBootstrappingExercise 6.02: Using the Bagging ClassifierRandom ForestExercise 6.03: Building the Ensemble Model Using Random ForestBoostingAdaptive BoostingExercise 6.04: Implementing Adaptive BoostingGradient BoostingExercise 6.05: Implementing GradientBoostingClassifier to Build an Ensemble ModelStackingExercise 6.06: Building a Stacked ModelActivity 6.01: Stacking with Standalone and Ensemble AlgorithmsSummary
7. Model Evaluation
IntroductionImporting the Modules and Preparing Our DatasetEvaluation MetricsRegression MetricsExercise 7.01: Calculating Regression MetricsClassification MetricsNumerical MetricsCurve PlotsExercise 7.02: Calculating Classification MetricsSplitting a DatasetHold-Out DataK-Fold Cross-ValidationSamplingExercise 7.03: Performing K-Fold Cross-Validation with Stratified SamplingPerformance Improvement TacticsVariation in Train and Test ErrorsLearning CurveValidation CurveHyperparameter TuningExercise 7.04: Hyperparameter Tuning with Random SearchFeature ImportanceExercise 7.05: Feature Importance Using Random ForestActivity 7.01: Final Test ProjectSummary
Appendix
1. FundamentalsActivity 1.01: Implementing Pandas Functions2. Exploratory Data Analysis and VisualizationActivity 2.01: Summary Statistics and Missing ValuesActivity 2.02: Representing the Distribution of Values VisuallyActivity 2.03: Relationships within the Data3. Linear RegressionActivity 3.01: Plotting Data with a Moving AverageActivity 3.02: Linear Regression Using the Least Squares MethodActivity 3.03: Dummy VariablesActivity 3.04: Feature Engineering with Linear RegressionActivity 3.05: Gradient Descent4. AutoregressionActivity 4.01: Autoregression Model Based on Periodic Data5. Classification TechniquesActivity 5.01: Ordinary Least Squares Classifier – Binary ClassifierActivity 5.02: KNN Multiclass ClassifierActivity 5.03: Binary Classification Using a CART Decision TreeActivity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural Networks6. Ensemble ModelingActivity 6.01: Stacking with Standalone and Ensemble Algorithms7. Model EvaluationActivity 7.01: Final Test Project

Content preview from The Supervised Learning Workshop - Second Edition

Appendix

1. Fundamentals

Activity 1.01: Implementing Pandas Functions

Open a new Jupyter notebook.
Use pandas to load the Titanic dataset:
import pandas as pd
df = pd.read_csv(r'../Datasets/titanic.csv')
Use the head function on the dataset as follows:
# Have a look at the first 5 sample of the data
df.head()
The output will be as follows:
Figure 1.26: First five rows
Use the describe function as follows:
df.describe(include='all')
The output will be as follows:
Figure 1.27: Output of describe()
We do not need the Unnamed: 0 column. We can remove the ...

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