Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Data Cleaning and Exploration with Machine Learning
Contributors
About the author
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
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Section 1 – Data Cleaning and Machine Learning Algorithms
Chapter 1: Examining the Distribution of Features and Targets
    Technical requirements
    Subsetting data
    Generating frequencies for categorical features
    Generating summary statistics for continuous and discrete features
    Identifying extreme values and outliers in univariate analysis
    Using histograms, boxplots, and violin plots to examine the distribution of features
        Using histograms
        Using boxplots
        Using violin plots
    Summary
Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets
    Technical requirements
    Identifying outliers and extreme values in bivariate relationships
    Using scatter plots to view bivariate relationships between continuous features
    Using grouped boxplots to view bivariate relationships between continuous and categorical features
    Using linear regression to identify data points with significant influence
    Using K-nearest neighbors to find outliers
    Using Isolation Forest to find outliers
    Summary
Chapter 3: Identifying and Fixing Missing Values
    Technical requirements
    Identifying missing values
    Cleaning missing values
    Imputing values with regression
    Using KNN imputation
    Using random forest for imputation
    Summary
Section 2 – Preprocessing, Feature Selection, and Sampling
Chapter 4: Encoding, Transforming, and Scaling Features
    Technical requirements
    Creating training datasets and avoiding data leakage
    Removing redundant or unhelpful features
    Encoding categorical features
        One-hot encoding
        Ordinal encoding 
    Encoding categorical features with medium or high cardinality
        Feature hashing
    Using mathematical transformations
    Feature binning
        Equal-width and equal-frequency binning
        K-means binning
    Feature scaling
    Summary
Chapter 5: Feature Selection
    Technical requirements
    Selecting features for classification models
        Mutual information classification for feature selection with a categorical target
        ANOVA F-value for feature selection with a categorical target
    Selecting features for regression models
        F-tests for feature selection with a continuous target
        Mutual information for feature selection with a continuous target
    Using forward and backward feature selection
        Using forward feature selection
        Using backward feature selection
    Using exhaustive feature selection
    Eliminating features recursively in a regression model
    Eliminating features recursively in a classification model
    Using Boruta for feature selection
    Using regularization and other embedded methods
        Using L1 regularization
        Using a random forest classifier
    Using principal component analysis
    Summary
Chapter 6: Preparing for Model Evaluation
    Technical requirements
    Measuring accuracy, sensitivity, specificity, and precision for binary classification
    Examining CAP, ROC, and precision-sensitivity curves for binary classification
        Constructing CAP curves
        Plotting a receiver operating characteristic (ROC) curve
        Plotting precision-sensitivity curves
    Evaluating multiclass models
    Evaluating regression models
    Using K-fold cross-validation
    Preprocessing data with pipelines
    Summary
Section 3 – Modeling Continuous Targets with Supervised Learning
Chapter 7: Linear Regression Models
    Technical requirements
    Key concepts
        Key assumptions of linear regression models
        Linear regression and ordinary least squares
    Linear regression and gradient descent
    Using classical linear regression
        Pre-processing the data for our regression model
        Running and evaluating our linear model
        Improving our model evaluation
    Using lasso regression
        Tuning hyperparameters with grid searches
    Using non-linear regression
    Regression with gradient descent
    Summary
Chapter 8: Support Vector Regression
    Technical requirements
    Key concepts of SVR
        Nonlinear SVR and the kernel trick
    SVR with a linear model
    Using kernels for nonlinear SVR
    Summary
Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression
    Technical requirements
    Key concepts for K-nearest neighbors regression
    K-nearest neighbors regression
    Key concepts for decision tree and random forest regression
        Using random forest regression
    Decision tree and random forest regression
        A decision tree example with interpretation
        Building and interpreting our actual model
        Random forest regression
    Using gradient boosted regression
    Summary
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
Chapter 10: Logistic Regression
    Technical requirements
    Key concepts of logistic regression
        Logistic regression extensions
    Binary classification with logistic regression
        Evaluating a logistic regression model
    Regularization with logistic regression
    Multinomial logistic regression
    Summary
Chapter 11: Decision Trees and Random Forest Classification
    Technical requirements
    Key concepts
        Using random forest for classification
        Using gradient-boosted decision trees
    Decision tree models
    Implementing random forest
    Implementing gradient boosting
    Summary
Chapter 12: K-Nearest Neighbors for Classification
    Technical requirements
    Key concepts of KNN
    KNN for binary classification
    KNN for multiclass classification
        KNN for letter recognition
    Summary
Chapter 13: Support Vector Machine Classification
    Technical requirements
    Key concepts for SVC
        Nonlinear SVM and the kernel trick
        Multiclass classification with SVC
    Linear SVC models
    Nonlinear SVM classification models
    SVMs for multiclass classification
    Summary
Chapter 14: Naïve Bayes Classification
    Technical requirements
    Key concepts
    Naïve Bayes classification models
    Naïve Bayes for text classification
    Summary
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning
Chapter 15: Principal Component Analysis
    Technical requirements
    Key concepts of PCA
    Feature extraction with PCA
    Using kernels with PCA
    Summary
Chapter 16: K-Means and DBSCAN Clustering
    Technical requirements
    The key concepts of k-means and DBSCAN clustering
    Implementing k-means clustering
    Implementing DBSCAN clustering
    Summary
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