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Cut through the noise and get real results with a step-by-step approach to data science.

• Cover
• FM
• Copyright
• Table of Contents
• Preface
• Chapter 1: Introduction to Data Science in Python
  • Introduction
  • Application of Data Science
    • What Is Machine Learning?
      • Supervised Learning
      • Unsupervised Learning
      • Reinforcement Learning
  • Overview of Python
    • Types of Variable
      • Numeric Variables
      • Text Variables
      • Python List
      • Python Dictionary
    • Exercise 1.01: Creating a Dictionary That Will Contain Machine Learning Algorithms
  • Python for Data Science
    • The pandas Package
      • DataFrame and Series
      • CSV Files
      • Excel Spreadsheets
      • JSON
    • Exercise 1.02: Loading Data of Different Formats into a pandas DataFrame
  • Scikit-Learn
    • What Is a Model?
      • Model Hyperparameters
      • The sklearn API
    • Exercise 1.03: Predicting Breast Cancer from a Dataset Using sklearn
    • Activity 1.01: Train a Spam Detector Algorithm
  • Summary
• Chapter 2: Regression
  • Introduction
  • Simple Linear Regression
    • The Method of Least Squares
  • Multiple Linear Regression
    • Estimating the Regression Coefficients (β0, β1, β2 and β3)
    • Logarithmic Transformations of Variables
    • Correlation Matrices
  • Conducting Regression Analysis Using Python
    • Exercise 2.01: Loading and Preparing the Data for Analysis
    • The Correlation Coefficient
    • Exercise 2.02: Graphical Investigation of Linear Relationships Using Python
    • Exercise 2.03: Examining a Possible Log-Linear Relationship Using Python
    • The Statsmodels formula API
    • Exercise 2.04: Fitting a Simple Linear Regression Model Using the Statsmodels formula API
    • Analyzing the Model Summary
    • The Model Formula Language
    • Intercept Handling
    • Activity 2.01: Fitting a Log-Linear Model Using the Statsmodels formula API
  • Multiple Regression Analysis
    • Exercise 2.05: Fitting a Multiple Linear Regression Model Using the Statsmodels formula API
  • Assumptions of Regression Analysis
    • Activity 2.02: Fitting a Multiple Log-Linear Regression Model
  • Explaining the Results of Regression Analysis
    • Regression Analysis Checks and Balances
    • The F-test
    • The t-test
  • Summary
• Chapter 3: Binary Classification
  • Introduction
  • Understanding the Business Context
    • Business Discovery
    • Exercise 3.01: Loading and Exploring the Data from the Dataset
    • Testing Business Hypotheses Using Exploratory Data Analysis
    • Visualization for Exploratory Data Analysis
    • Exercise 3.02: Business Hypothesis Testing for Age versus Propensity for a Term Loan
    • Intuitions from the Exploratory Analysis
    • Activity 3.01: Business Hypothesis Testing to Find Employment Status versus Propensity for Term Deposits
  • Feature Engineering
    • Business-Driven Feature Engineering
    • Exercise 3.03: Feature Engineering – Exploration of Individual Features
    • Exercise 3.04: Feature Engineering – Creating New Features from Existing Ones
  • Data-Driven Feature Engineering
    • A Quick Peek at Data Types and a Descriptive Summary
  • Correlation Matrix and Visualization
    • Exercise 3.05: Finding the Correlation in Data to Generate a Correlation Plot Using Bank Data
    • Skewness of Data
    • Histograms
    • Density Plots
    • Other Feature Engineering Methods
    • Summarizing Feature Engineering
    • Building a Binary Classification Model Using the Logistic Regression Function
    • Logistic Regression Demystified
    • Metrics for Evaluating Model Performance
    • Confusion Matrix
    • Accuracy
    • Classification Report
    • Data Preprocessing
    • Exercise 3.06: A Logistic Regression Model for Predicting the Propensity of Term Deposit Purchases in a Bank
    • Activity 3.02: Model Iteration 2 – Logistic Regression Model with Feature Engineered Variables
    • Next Steps
  • Summary
• Chapter 4: Multiclass Classification with RandomForest
  • Introduction
  • Training a Random Forest Classifier
  • Evaluating the Model's Performance
    • Exercise 4.01: Building a Model for Classifying Animal Type and Assessing Its Performance
    • Number of Trees Estimator
    • Exercise 4.02: Tuning n_estimators to Reduce Overfitting
  • Maximum Depth
    • Exercise 4.03: Tuning max_depth to Reduce Overfitting
  • Minimum Sample in Leaf
    • Exercise 4.04: Tuning min_samples_leaf
  • Maximum Features
    • Exercise 4.05: Tuning max_features
    • Activity 4.01: Train a Random Forest Classifier on the ISOLET Dataset
  • Summary
• Chapter 5: Performing Your First Cluster Analysis
  • Introduction
  • Clustering with k-means
    • Exercise 5.01: Performing Your First Clustering Analysis on the ATO Dataset
  • Interpreting k-means Results
    • Exercise 5.02: Clustering Australian Postcodes by Business Income and Expenses
  • Choosing the Number of Clusters
    • Exercise 5.03: Finding the Optimal Number of Clusters
  • Initializing Clusters
    • Exercise 5.04: Using Different Initialization Parameters to Achieve a Suitable Outcome
  • Calculating the Distance to the Centroid
    • Exercise 5.05: Finding the Closest Centroids in Our Dataset
  • Standardizing Data
    • Exercise 5.06: Standardizing the Data from Our Dataset
    • Activity 5.01: Perform Customer Segmentation Analysis in a Bank Using k-means
  • Summary
• Chapter 6: How to Assess Performance
  • Introduction
  • Splitting Data
    • Exercise 6.01: Importing and Splitting Data
  • Assessing Model Performance for Regression Models
    • Data Structures – Vectors and Matrices
      • Scalars
      • Vectors
      • Matrices
    • R2 Score
    • Exercise 6.02: Computing the R2 Score of a Linear Regression Model
    • Mean Absolute Error
    • Exercise 6.03: Computing the MAE of a Model
    • Exercise 6.04: Computing the Mean Absolute Error of a Second Model
      • Other Evaluation Metrics
  • Assessing Model Performance for Classification Models
    • Exercise 6.05: Creating a Classification Model for Computing Evaluation Metrics
  • The Confusion Matrix
    • Exercise 6.06: Generating a Confusion Matrix for the Classification Model
      • More on the Confusion Matrix
    • Precision
    • Exercise 6.07: Computing Precision for the Classification Model
    • Recall
    • Exercise 6.08: Computing Recall for the Classification Model
    • F1 Score
    • Exercise 6.09: Computing the F1 Score for the Classification Model
    • Accuracy
    • Exercise 6.10: Computing Model Accuracy for the Classification Model
    • Logarithmic Loss
    • Exercise 6.11: Computing the Log Loss for the Classification Model
  • Receiver Operating Characteristic Curve
    • Exercise 6.12: Computing and Plotting ROC Curve for a Binary Classification Problem
  • Area Under the ROC Curve
    • Exercise 6.13: Computing the ROC AUC for the Caesarian Dataset
  • Saving and Loading Models
    • Exercise 6.14: Saving and Loading a Model
    • Activity 6.01: Train Three Different Models and Use Evaluation Metrics to Pick the Best Performing Model
  • Summary
• Chapter 7: The Generalization of Machine Learning Models
  • Introduction
  • Overfitting
    • Training on Too Many Features
    • Training for Too Long
  • Underfitting
  • Data
    • The Ratio for Dataset Splits
    • Creating Dataset Splits
    • Exercise 7.01: Importing and Splitting Data
  • Random State
    • Exercise 7.02: Setting a Random State When Splitting Data
  • Cross-Validation
    • KFold
    • Exercise 7.03: Creating a Five-Fold Cross-Validation Dataset
    • Exercise 7.04: Creating a Five-Fold Cross-Validation Dataset Using a Loop for Calls
  • cross_val_score
    • Exercise 7.05: Getting the Scores from Five-Fold Cross-Validation
    • Understanding Estimators That Implement CV
  • LogisticRegressionCV
    • Exercise 7.06: Training a Logistic Regression Model Using Cross-Validation
  • Hyperparameter Tuning with GridSearchCV
    • Decision Trees
    • Exercise 7.07: Using Grid Search with Cross-Validation to Find the Best Parameters for a Model
  • Hyperparameter Tuning with RandomizedSearchCV
    • Exercise 7.08: Using Randomized Search for Hyperparameter Tuning
  • Model Regularization with Lasso Regression
    • Exercise 7.09: Fixing Model Overfitting Using Lasso Regression
  • Ridge Regression
    • Exercise 7.10: Fixing Model Overfitting Using Ridge Regression
    • Activity 7.01: Find an Optimal Model for Predicting the Critical Temperatures of Superconductors
  • Summary
• Chapter 8: Hyperparameter Tuning
  • Introduction
  • What Are Hyperparameters?
    • Difference between Hyperparameters and Statistical Model Parameters
    • Setting Hyperparameters
    • A Note on Defaults
  • Finding the Best Hyperparameterization
    • Exercise 8.01: Manual Hyperparameter Tuning for a k-NN Classifier
    • Advantages and Disadvantages of a Manual Search
  • Tuning Using Grid Search
    • Simple Demonstration of the Grid Search Strategy
  • GridSearchCV
    • Tuning using GridSearchCV
      • Support Vector Machine (SVM) Classifiers
    • Exercise 8.02: Grid Search Hyperparameter Tuning for an SVM
    • Advantages and Disadvantages of Grid Search
  • Random Search
    • Random Variables and Their Distributions
    • Simple Demonstration of the Random Search Process
    • Tuning Using RandomizedSearchCV
    • Exercise 8.03: Random Search Hyperparameter Tuning for a Random Forest Classifier
    • Advantages and Disadvantages of a Random Search
    • Activity 8.01: Is the Mushroom Poisonous?
  • Summary
• Chapter 9: Interpreting a Machine Learning Model
  • Introduction
  • Linear Model Coefficients
    • Exercise 9.01: Extracting the Linear Regression Coefficient
  • RandomForest Variable Importance
    • Exercise 9.02: Extracting RandomForest Feature Importance
  • Variable Importance via Permutation
    • Exercise 9.03: Extracting Feature Importance via Permutation
  • Partial Dependence Plots
    • Exercise 9.04: Plotting Partial Dependence
  • Local Interpretation with LIME
    • Exercise 9.05: Local Interpretation with LIME
    • Activity 9.01: Train and Analyze a Network Intrusion Detection Model
  • Summary
• Chapter 10: Analyzing a Dataset
  • Introduction
  • Exploring Your Data
  • Analyzing Your Dataset
    • Exercise 10.01: Exploring the Ames Housing Dataset with Descriptive Statistics
  • Analyzing the Content of a Categorical Variable
    • Exercise 10.02: Analyzing the Categorical Variables from the Ames Housing Dataset
  • Summarizing Numerical Variables
    • Exercise 10.03: Analyzing Numerical Variables from the Ames Housing Dataset
  • Visualizing Your Data
    • How to use the Altair API
    • Histogram for Numerical Variables
    • Bar Chart for Categorical Variables
  • Boxplots
    • Exercise 10.04: Visualizing the Ames Housing Dataset with Altair
    • Activity 10.01: Analyzing Churn Data Using Visual Data Analysis Techniques
  • Summary
• Chapter 11: Data Preparation
  • Introduction
  • Handling Row Duplication
    • Exercise 11.01: Handling Duplicates in a Breast Cancer Dataset
  • Converting Data Types
    • Exercise 11.02: Converting Data Types for the Ames Housing Dataset
  • Handling Incorrect Values
    • Exercise 11.03: Fixing Incorrect Values in the State Column
  • Handling Missing Values
    • Exercise 11.04: Fixing Missing Values for the Horse Colic Dataset
    • Activity 11.01: Preparing the Speed Dating Dataset
  • Summary
• Chapter 12: Feature Engineering
  • Introduction
  • Merging Datasets
    • The left join
      • The right join
    • Exercise 12.01: Merging the ATO Dataset with the Postcode Data
  • Binning Variables
    • Exercise 12.02: Binning the YearBuilt variable from the AMES Housing dataset
  • Manipulating Dates
    • Exercise 12.03: Date Manipulation on Financial Services Consumer Complaints
  • Performing Data Aggregation
    • Exercise 12.04: Feature Engineering Using Data Aggregation on the AMES Housing Dataset
    • Activity 12.01: Feature Engineering on a Financial Dataset
  • Summary
• Chapter 13: Imbalanced Datasets
  • Introduction
  • Understanding the Business Context
    • Exercise 13.01: Benchmarking the Logistic Regression Model on the Dataset
    • Analysis of the Result
  • Challenges of Imbalanced Datasets
  • Strategies for Dealing with Imbalanced Datasets
    • Collecting More Data
    • Resampling Data
    • Exercise 13.02: Implementing Random Undersampling and Classification on Our Banking Dataset to Find the Optimal Result
    • Analysis
  • Generating Synthetic Samples
    • Implementation of SMOTE and MSMOTE
    • Exercise 13.03: Implementing SMOTE on Our Banking Dataset to Find the Optimal Result
    • Exercise 13.04: Implementing MSMOTE on Our Banking Dataset to Find the Optimal Result
    • Applying Balancing Techniques on a Telecom Dataset
    • Activity 13.01: Finding the Best Balancing Technique by Fitting a Classifier on the Telecom Churn Dataset
  • Summary
• Chapter 14: Dimensionality Reduction
  • Introduction
    • Business Context
    • Exercise 14.01: Loading and Cleaning the Dataset
  • Creating a High-Dimensional Dataset
    • Activity 14.01: Fitting a Logistic Regression Model on a High‑Dimensional Dataset
  • Strategies for Addressing High-Dimensional Datasets
    • Backward Feature Elimination (Recursive Feature Elimination)
    • Exercise 14.02: Dimensionality Reduction Using Backward Feature Elimination
    • Forward Feature Selection
    • Exercise 14.03: Dimensionality Reduction Using Forward Feature Selection
    • Principal Component Analysis (PCA)
    • Exercise 14.04: Dimensionality Reduction Using PCA
    • Independent Component Analysis (ICA)
    • Exercise 14.05: Dimensionality Reduction Using Independent Component Analysis
    • Factor Analysis
    • Exercise 14.06: Dimensionality Reduction Using Factor Analysis
  • Comparing Different Dimensionality Reduction Techniques
    • Activity 14.02: Comparison of Dimensionality Reduction Techniques on the Enhanced Ads Dataset
  • Summary
• Chapter 15: Ensemble Learning
  • Introduction
  • Ensemble Learning
    • Variance
    • Bias
    • Business Context
    • Exercise 15.01: Loading, Exploring, and Cleaning the Data
    • Activity 15.01: Fitting a Logistic Regression Model on Credit Card Data
  • Simple Methods for Ensemble Learning
    • Averaging
    • Exercise 15.02: Ensemble Model Using the Averaging Technique
    • Weighted Averaging
    • Exercise 15.03: Ensemble Model Using the Weighted Averaging Technique
      • Iteration 2 with Different Weights
      • Max Voting
    • Exercise 15.04: Ensemble Model Using Max Voting
    • Advanced Techniques for Ensemble Learning
      • Bagging
    • Exercise 15.05: Ensemble Learning Using Bagging
    • Boosting
    • Exercise 15.06: Ensemble Learning Using Boosting
    • Stacking
    • Exercise 15.07: Ensemble Learning Using Stacking
    • Activity 15.02: Comparison of Advanced Ensemble Techniques
  • Summary
• Chapter 16: Machine Learning Pipelines
  • Introduction
  • Pipelines
    • Business Context
    • Exercise 16.01: Preparing the Dataset to Implement Pipelines
  • Automating ML Workflows Using Pipeline
    • Automating Data Preprocessing Using Pipelines
    • Exercise 16.02: Applying Pipelines for Feature Extraction to the Dataset
  • ML Pipeline with Processing and Dimensionality Reduction
    • Exercise 16.03: Adding Dimensionality Reduction to the Feature Extraction Pipeline
  • ML Pipeline for Modeling and Prediction
    • Exercise 16.04: Modeling and Predictions Using ML Pipelines
  • ML Pipeline for Spot-Checking Multiple Models
    • Exercise 16.05: Spot-Checking Models Using ML Pipelines
  • ML Pipelines for Identifying the Best Parameters for a Model
    • Cross-Validation
    • Grid Search
    • Exercise 16.06: Grid Search and Cross-Validation with ML Pipelines
  • Applying Pipelines to a Dataset
    • Activity 16.01: Complete ML Workflow in a Pipeline
  • Summary
• Chapter 17: Automated Feature Engineering
  • Introduction
  • Feature Engineering
    • Automating Feature Engineering Using Feature Tools
    • Business Context
    • Domain Story for the Problem Statement
    • Featuretools – Creating Entities and Relationships
    • Exercise 17.01: Defining Entities and Establishing Relationships
    • Feature Engineering – Basic Operations
    • Featuretools – Automated Feature Engineering
    • Exercise 17.02: Creating New Features Using Deep Feature Synthesis
    • Exercise 17.03: Classification Model after Automated Feature Generation
  • Featuretools on a New Dataset
    • Activity 17.01: Building a Classification Model with Features that have been Generated Using Featuretools
  • Summary
• Index