book

Data Science for Marketing Analytics - Second Edition

Name: Data Science for Marketing Analytics - Second Edition
ISBN: 9781800560475

by Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali

September 2021

Beginner to intermediate

636 pages

11h 38m

English

Packt Publishing

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Data Science for Marketing Analytics
second edition
Preface
About the BookAbout the AuthorsWho This Book Is ForAbout the ChaptersConventionsCode PresentationMinimum Hardware RequirementsDownloading the Code BundleSetting Up Your EnvironmentInstalling Anaconda on Your SystemLaunching Jupyter NotebookInstalling the ds-marketing Virtual EnvironmentRunning the Code Online Using BinderGet in TouchPlease Leave a Review
1. Data Preparation and Cleaning
IntroductionData Models and Structured DatapandasImporting and Exporting Data with pandas DataFramesViewing and Inspecting Data in DataFramesExercise 1.01: Loading Data Stored in a JSON FileExercise 1.02: Loading Data from Multiple SourcesStructure of a pandas DataFrame and SeriesData ManipulationSelecting and Filtering in pandasCreating DataFrames in PythonAdding and Removing Attributes and ObservationsCombining DataHandling Missing DataExercise 1.03: Combining DataFrames and Handling Missing ValuesApplying Functions and Operations on DataFramesGrouping DataExercise 1.04: Applying Data TransformationsActivity 1.01: Addressing Data SpillingSummary
2. Data Exploration and Visualization
IntroductionIdentifying and Focusing on the Right AttributesThe groupby( ) FunctionThe unique( ) functionThe value_counts( ) functionExercise 2.01: Exploring the Attributes in Sales DataFine Tuning Generated InsightsSelecting and Renaming AttributesReshaping the DataExercise 2.02: Calculating Conversion Ratios for Website Ads. Pivot TablesVisualizing DataExercise 2.03: Visualizing Data With pandasVisualization through SeabornVisualization with MatplotlibActivity 2.01: Analyzing AdvertisementsSummary
3. Unsupervised Learning and Customer Segmentation
IntroductionSegmentationExercise 3.01: Mall Customer Segmentation – Understanding the DataApproaches to SegmentationTraditional Segmentation MethodsExercise 3.02: Traditional Segmentation of Mall CustomersUnsupervised Learning (Clustering) for Customer SegmentationChoosing Relevant Attributes (Segmentation Criteria)Standardizing DataExercise 3.03: Standardizing Customer DataCalculating DistanceExercise 3.04: Calculating the Distance between Customers K-Means ClusteringExercise 3.05: K-Means Clustering on Mall CustomersUnderstanding and Describing the ClustersActivity 3.01: Bank Customer Segmentation for Loan CampaignClustering with High-Dimensional DataExercise 3.06: Dealing with High-Dimensional DataActivity 3.02: Bank Customer Segmentation with Multiple FeaturesSummary
4. Evaluating and Choosing the Best Segmentation Approach
IntroductionChoosing the Number of ClustersExercise 4.01: Data Staging and VisualizationSimple Visual Inspection to Choose the Optimal Number of ClustersExercise 4.02: Choosing the Number of Clusters Based on Visual InspectionThe Elbow Method with Sum of Squared ErrorsExercise 4.03: Determining the Number of Clusters Using the Elbow MethodActivity 4.01: Optimizing a Luxury Clothing Brand's Marketing Campaign Using ClusteringMore Clustering TechniquesMean-Shift ClusteringExercise 4.04: Mean-Shift Clustering on Mall CustomersBenefits and Drawbacks of the Mean-Shift Technique k-modes and k-prototypes ClusteringExercise 4.05: Clustering Data Using the k-prototypes MethodEvaluating ClusteringSilhouette ScoreExercise 4.06: Using Silhouette Score to Pick Optimal Number of ClustersTrain and Test SplitExercise 4.07: Using a Train-Test Split to Evaluate Clustering PerformanceActivity 4.02: Evaluating Clustering on Customer DataThe Role of Business in Cluster EvaluationSummary
5. Predicting Customer Revenue Using Linear Regression
IntroductionRegression ProblemsExercise 5.01: Predicting Sales from Advertising Spend Using Linear RegressionFeature Engineering for RegressionFeature CreationData CleaningExercise 5.02: Creating Features for Customer Revenue PredictionAssessing Features Using Visualizations and CorrelationsExercise 5.03: Examining Relationships between Predictors and the OutcomeActivity 5.01: Examining the Relationship between Store Location and RevenuePerforming and Interpreting Linear RegressionExercise 5.04: Building a Linear Model Predicting Customer SpendActivity 5.02: Predicting Store Revenue Using Linear RegressionSummary
6. More Tools and Techniques for Evaluating Regression Models
IntroductionEvaluating the Accuracy of a Regression ModelResiduals and ErrorsMean Absolute ErrorRoot Mean Squared ErrorExercise 6.01: Evaluating Regression Models of Location Revenue Using the MAE and RMSEActivity 6.01: Finding Important Variables for Predicting Responses to a Marketing OfferUsing Recursive Feature Selection for Feature EliminationExercise 6.02: Using RFE for Feature SelectionActivity 6.02: Using RFE to Choose Features for Predicting Customer SpendTree-Based Regression ModelsRandom ForestsExercise 6.03: Using Tree-Based Regression Models to Capture Non-Linear TrendsActivity 6.03: Building the Best Regression Model for Customer Spend Based on Demographic DataSummary
7. Supervised Learning: Predicting Customer Churn
IntroductionClassification ProblemsUnderstanding Logistic RegressionRevisiting Linear RegressionLogistic RegressionCost Function for Logistic RegressionAssumptions of Logistic RegressionExercise 7.01: Comparing Predictions by Linear and Logistic Regression on the Shill Bidding DatasetCreating a Data Science PipelineChurn Prediction Case StudyObtaining the DataExercise 7.02: Obtaining the DataScrubbing the DataExercise 7.03: Imputing Missing ValuesExercise 7.04: Renaming Columns and Changing the Data TypeExploring the DataExercise 7.05: Obtaining the Statistical Overview and Correlation PlotVisualizing the DataExercise 7.06: Performing Exploratory Data Analysis (EDA)Activity 7.01: Performing the OSE technique from OSEMNModeling the DataFeature SelectionExercise 7.07: Performing Feature SelectionModel BuildingExercise 7.08: Building a Logistic Regression ModelInterpreting the DataActivity 7.02: Performing the MN technique from OSEMNSummary
8. Fine-Tuning Classification Algorithms
IntroductionSupport Vector MachinesIntuition behind Maximum MarginLinearly Inseparable CasesLinearly Inseparable Cases Using the KernelExercise 8.01: Training an SVM Algorithm Over a DatasetDecision TreesExercise 8.02: Implementing a Decision Tree Algorithm over a DatasetImportant Terminology for Decision TreesDecision Tree Algorithm FormulationRandom ForestExercise 8.03: Implementing a Random Forest Model over a DatasetClassical Algorithms – Accuracy ComparedActivity 8.01: Implementing Different Classification AlgorithmsPreprocessing Data for Machine Learning ModelsStandardizationExercise 8.04: Standardizing DataScalingExercise 8.05: Scaling Data After Feature SelectionNormalizationExercise 8.06: Performing Normalization on DataModel EvaluationExercise 8.07: Stratified K-foldFine-Tuning of the ModelExercise 8.08: Fine-Tuning a ModelActivity 8.02: Tuning and Optimizing the ModelPerformance MetricsPrecisionRecallF1 ScoreExercise 8.09: Evaluating the Performance Metrics for a ModelROC CurveExercise 8.10: Plotting the ROC CurveActivity 8.03: Comparison of the ModelsSummary

9. Multiclass Classification Algorithms
IntroductionUnderstanding Multiclass ClassificationClassifiers in Multiclass ClassificationExercise 9.01: Implementing a Multiclass Classification Algorithm on a DatasetPerformance MetricsExercise 9.02: Evaluating Performance Using Multiclass Performance MetricsActivity 9.01: Performing Multiclass Classification and Evaluating PerformanceClass-Imbalanced DataExercise 9.03: Performing Classification on Imbalanced DataDealing with Class-Imbalanced DataExercise 9.04: Fixing the Imbalance of a Dataset Using SMOTEActivity 9.02: Dealing with Imbalanced Data Using scikit-learnSummary
Appendix
1. Data Preparation and CleaningActivity 1.01: Addressing Data Spilling2. Data Exploration and VisualizationActivity 2.01: Analyzing Advertisements3. Unsupervised Learning and Customer SegmentationActivity 3.01: Bank Customer Segmentation for Loan CampaignActivity 3.02: Bank Customer Segmentation with Multiple Features4. Evaluating and Choosing the Best Segmentation ApproachActivity 4.01: Optimizing a Luxury Clothing Brand's Marketing Campaign Using ClusteringActivity 4.02: Evaluating Clustering on Customer Data5. Predicting Customer Revenue Using Linear RegressionActivity 5.01: Examining the Relationship between Store Location and RevenueActivity 5.02: Predicting Store Revenue Using Linear Regression6. More Tools and Techniques for Evaluating Regression ModelsActivity 6.01: Finding Important Variables for Predicting Responses to a Marketing OfferActivity 6.02: Using RFE to Choose Features for Predicting Customer SpendActivity 6.03: Building the Best Regression Model for Customer Spend Based on Demographic Data7. Supervised Learning: Predicting Customer ChurnActivity 7.01: Performing the OSE technique from OSEMNActivity 7.02: Performing the MN technique from OSEMN8. Fine-Tuning Classification AlgorithmsActivity 8.01: Implementing Different Classification AlgorithmsActivity 8.02: Tuning and Optimizing the ModelActivity 8.03: Comparison of the Models9. Multiclass Classification AlgorithmsActivity 9.01: Performing Multiclass Classification and Evaluating PerformanceActivity 9.02: Dealing with Imbalanced Data Using scikit-learn
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Content preview from Data Science for Marketing Analytics - Second Edition

Appendix

1. Data Preparation and Cleaning

Activity 1.01: Addressing Data Spilling

Solution:

Import the pandas and copy libraries using the following commands:
import pandas as pd
import copy
Create a new DataFrame, sales, and use the read_csv function to read the sales.csv file into it:
sales = pd.read_csv("sales.csv")
Note
Make sure you change the path (emboldened) to the CSV file based on its location on your system. If you're running the Jupyter notebook from the same directory where the CSV file is stored, you can run the preceding code without any modification.
Now, examine whether your data is properly loaded by checking the first five rows in the DataFrame. Do this using the head() command:
sales.head()
You should get the following output: ...

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