book

Data Science for Marketing Analytics

Name: Data Science for Marketing Analytics
ISBN: 9781789959413

by Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

March 2019

Beginner to intermediate

420 pages

7h 19m

English

Packt Publishing

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Preface
About the BookAbout the AuthorsObjectivesAudienceApproachMinimum Hardware RequirementsSoftware RequirementsConventionsInstallation and SetupInstalling the Code BundleAdditional Resources
Chapter 1
Data Preparation and CleaningIntroductionData Models and Structured DatapandasImporting and Exporting Data With pandas DataFramesViewing and Inspecting Data in DataFramesExercise 1: Importing JSON Files into pandasExercise 2: Identifying Semi-Structured and Unstructured DataStructure of a pandas SeriesData ManipulationSelecting and Filtering in pandasCreating Test DataFrames in PythonAdding and Removing Attributes and ObservationsExercise 3: Creating and Modifying Test DataFramesCombining DataHandling Missing DataExercise 4: Combining DataFrames and Handling Missing ValuesApplying Functions and Operations on DataFramesGrouping DataExercise 5: Applying Data TransformationsActivity 1: Addressing Data SpillingSummary
Chapter 2
Data Exploration and VisualizationIntroductionIdentifying the Right AttributesExercise 6: Exploring the Attributes in Sales DataGenerating Targeted InsightsSelecting and Renaming AttributesTransforming ValuesExercise 7: Targeting Insights for Specific Use CasesReshaping the DataExercise 8: Understanding Stacking and UnstackingPivot TablesVisualizing DataExercise 9: Visualizing Data With pandasVisualization through SeabornVisualization with MatplotlibActivity 2: Analyzing AdvertisementsSummary
Chapter 3
Unsupervised Learning: Customer SegmentationIntroductionCustomer Segmentation MethodsTraditional Segmentation MethodsUnsupervised Learning (Clustering) for Customer SegmentationSimilarity and Data StandardizationDetermining SimilarityStandardizing DataExercise 10: Standardizing Age and Income Data of CustomersCalculating DistanceExercise 11: Calculating Distance Between Three CustomersActivity 3: Loading, Standardizing, and Calculating Distance with a Datasetk-means ClusteringUnderstanding k-means ClusteringExercise 12: k-means Clustering on Income/Age DataHigh-Dimensional DataExercise 13: Dealing with High-Dimensional DataActivity 4: Using k-means Clustering on Customer Behavior DataSummary
Chapter 4
Choosing the Best Segmentation ApproachIntroductionChoosing the Number of ClustersSimple Visual InspectionExercise 14: Choosing the Number of Clusters Based on Visual InspectionThe Elbow Method with Sum of Squared ErrorsExercise 15: Determining the Number of Clusters Using the Elbow MethodActivity 5: Determining Clusters for High-End Clothing Customer Data Using the Elbow Method with the Sum of Squared ErrorsDifferent Methods of ClusteringMean-Shift ClusteringExercise 16: Performing Mean-Shift Clustering to Cluster Datak-modes and k-prototypes ClusteringExercise 17: Clustering Data Using the k-prototypes MethodActivity 6: Using Different Clustering Techniques on Customer Behavior DataEvaluating ClusteringSilhouette ScoreExercise 18: Calculating Silhouette Score to Pick the Best k for k-means and Comparing to the Mean-Shift AlgorithmTrain and Test SplitExercise 19: Using a Train-Test Split to Evaluate Clustering PerformanceActivity 7: Evaluating Clustering on Customer Behavior DataSummary
Chapter 5
Predicting Customer Revenue Using Linear RegressionIntroductionUnderstanding RegressionFeature Engineering for RegressionFeature CreationData CleaningExercise 20: Creating Features for Transaction DataAssessing Features Using Visualizations and CorrelationsExercise 21: Examining Relationships between Predictors and OutcomeActivity 8: Examining Relationships Between Storefront Locations and Features about Their AreaPerforming and Interpreting Linear RegressionExercise 22: Building a Linear Model Predicting Customer SpendActivity 9: Building a Regression Model to Predict Storefront Location RevenueSummary
Chapter 6
Other Regression Techniques and Tools for EvaluationIntroductionEvaluating the Accuracy of a Regression ModelResiduals and ErrorsMean Absolute ErrorRoot Mean Squared ErrorExercise 23: Evaluating Regression Models of Location Revenue Using MAE and RMSEActivity 10: Testing Which Variables are Important for Predicting Responses to a Marketing OfferUsing Regularization for Feature SelectionExercise 24: Using Lasso Regression for Feature SelectionActivity 11: Using Lasso Regression to Choose Features for Predicting Customer SpendTree-Based Regression ModelsRandom ForestsExercise 25: Using Tree-Based Regression Models to Capture Non-Linear TrendsActivity 12: Building the Best Regression Model for Customer Spend Based on Demographic DataSummary
Chapter 7
Supervised Learning: Predicting Customer ChurnIntroductionClassification ProblemsUnderstanding Logistic RegressionRevisiting Linear RegressionLogistic RegressionExercise 26: Plotting the Sigmoid FunctionCost Function for Logistic RegressionAssumptions of Logistic RegressionExercise 27: Loading, Splitting, and Applying Linear and Logistic Regression to DataCreating a Data Science PipelineObtaining the DataExercise 28: Obtaining the DataScrubbing the DataExercise 29: Imputing Missing ValuesExercise 30: Renaming Columns and Changing the Data TypeExploring the DataStatistical OverviewCorrelationExercise 31: Obtaining the Statistical Overview and Correlation PlotVisualizing the DataExercise 32: Performing Exploratory Data Analysis (EDA)Activity 13: Performing OSE of OSEMNModeling the DataFeature SelectionExercise 33: Performing Feature SelectionModel BuildingExercise 34: Building a Logistic Regression ModelInterpreting the DataActivity 14: Performing MN of OSEMNSummary
Chapter 8
Fine-Tuning Classification AlgorithmsIntroductionSupport Vector MachinesIntuition Behind Maximum MarginLinearly Inseparable CasesLinearly Inseparable Cases Using KernelExercise 35: Training an SVM Algorithm Over a DatasetDecision TreesExercise 36: Implementing a Decision Tree Algorithm Over a DatasetImportant Terminology of Decision TreesDecision Tree Algorithm FormulationRandom ForestExercise 37: Implementing a Random Forest Model Over a DatasetActivity 15: Implementing Different Classification AlgorithmsPreprocessing Data for Machine Learning ModelsStandardizationExercise 38: Standardizing DataScalingExercise 39: Scaling Data After Feature SelectionNormalizationExercise 40: Performing Normalization on DataModel EvaluationExercise 41: Implementing Stratified k-foldFine-Tuning of the ModelExercise 42: Fine-Tuning a ModelActivity 16: Tuning and Optimizing the ModelPerformance MetricsPrecisionRecallF1 ScoreExercise 43: Evaluating the Performance Metrics for a ModelROC CurveExercise 44: Plotting the ROC CurveActivity 17: Comparison of the ModelsSummary
Chapter 9
Modeling Customer ChoiceIntroductionUnderstanding Multiclass ClassificationClassifiers in Multiclass ClassificationExercise 45: Implementing a Multiclass Classification Algorithm on a DatasetPerformance MetricsExercise 46: Evaluating Performance Using Multiclass Performance MetricsActivity 18: Performing Multiclass Classification and Evaluating PerformanceClass Imbalanced DataExercise 47: Performing Classification on Imbalanced DataDealing with Class-Imbalanced DataExercise 48: Visualizing Sampling TechniquesExercise 49: Fitting a Random Forest Classifier Using SMOTE and Building the Confusion MatrixActivity 19: Dealing with Imbalanced DataSummary

Appendix
Chapter 1: Data Preparation and CleaningActivity 1: Addressing Data SpillingChapter 2: Data Exploration and VisualizationActivity 2: Analyzing AdvertisementsChapter 3: Unsupervised Learning: Customer SegmentationActivity 3: Loading, Standardizing, and Calculating Distance with a DatasetActivity 4: Using k-means Clustering on Customer Behavior DataChapter 4: Choosing the Best Segmentation ApproachActivity 5: Determining Clusters for High-End Clothing Customer Data Using the Elbow Method with the Sum of Squared ErrorsActivity 6: Using Different Clustering Techniques on Customer Behavior DataActivity 7: Evaluating Clustering on Customer Behavior DataChapter 5: Predicting Customer Revenue Using Linear RegressionActivity 8: Examining Relationships between Storefront Locations and Features about their AreaActivity 9: Building a Regression Model to Predict Storefront Location RevenueChapter 6: Other Regression Techniques and Tools for EvaluationActivity 10: Testing Which Variables are Important for Predicting Responses to a Marketing OfferActivity 11: Using Lasso Regression to Choose Features for Predicting Customer SpendActivity 12: Building the Best Regression Model for Customer Spend Based on Demographic DataChapter 7: Supervised Learning: Predicting Customer ChurnActivity 13: Performing OSE from OSEMNActivity 14: Performing MN of OSEMNChapter 8: Fine-Tuning Classification AlgorithmsActivity 15: Implementing Different Classification AlgorithmsActivity 16: Tuning and Optimizing the ModelActivity 17: Comparison of the ModelsChapter 9: Modeling Customer ChoiceActivity 18: Performing Multiclass Classification and Evaluating PerformanceActivity 19: Dealing with Imbalanced Data

Overview

Data Science for Marketing Analytics introduces you to leveraging state-of-the-art data science techniques to optimize marketing outcomes. You'll learn how to manipulate and analyze data using Python, create customer segments, and apply machine learning algorithms to predict customer behavior. This book provides a comprehensive, hands-on approach to marketing analytics.

What this Book will help me do

Learn to use Python libraries like pandas & Matplotlib for data analysis.
Understand clustering techniques to create meaningful customer segments.
Implement linear regression for predicting customer lifetime value.
Explore classification algorithms to model customer preferences.
Develop skills to build interactive dashboards for marketing reports.

Author(s)

None Blanchard, Nona Behera, and Pranshu Bhatnagar are experienced professionals in data science and marketing analytics, with extensive backgrounds in applying machine learning to real-world business applications. They bring a wealth of knowledge and an approachable teaching style to this book, focusing on practical, industry-relevant applications for learners.

Who is it for?

This book is for developers and marketing professionals looking to advance their analytics skills. It is ideal for individuals with a basic understanding of Python and mathematics who want to explore predictive modeling and segmentation strategies. Readers should have a curiosity for data-driven problem-solving in marketing contexts to benefit most from the content.

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