Data Mining with Python (Chapman & Hall/CRC The Python Series)

Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge. The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study. This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work. Cover Half Title Series Page Title Page Copyright Page Dedication Contents List of Figures Foreword Preface Author Bios SECTION I: Data Wrangling CHAPTER 1: Data Collection 1.1. COLLECT DATA FROM FILES 1.1.1. Tutorial – Collect Data from Files 1.1.2. Documentation 1.2. COLLECT DATA FROM THE WEB 1.2.1. Tutorial – Collect Data from Web 1.2.2. Case Study – Collect Weather Data from Web 1.3. COLLECT DATA FROM SQL DATABASES 1.3.1. Tutorial – Collect Data from SQLite 1.3.2. Case Study – Collect Shopping Data from SQLite 1.4. COLLECT DATA THROUGH APIS 1.4.1. Tutorial – Collect Data from Yahoo CHAPTER 2: Data Integration 2.1. DATA INTEGRATION 2.1.1. Tutorial – Data Integration 2.1.2. Case Study – Data Science Salary CHAPTER 3: Data Statistics 3.1. DESCRIPTIVE DATA ANALYSIS 3.1.1. Tutorial – Statistical Understanding 3.1.2. Case Study – Statistical Understanding of YouTube and Spotify CHAPTER 4: Data Visualization 4.1. DATA VISUALIZATION WITH PANDAS 4.1.1. Tutorial – Data Visualization with Pandas 4.2. DATA VISUALIZATION WITH MATPLOTLIB 4.2.1. Tutorial – Data Visualization with Matplotlib 4.3. DATA VISUALIZATION WITH SEABORN 4.3.1. Tutorial – Data Visualization with Seaborn CHAPTER 5: Data Preprocessing 5.1. DEALING WITH MISSING VALUES 5.1.1. Tutorial – Handling Missing Values 5.2. DEALING WITH OUTLIERS 5.2.1. Tutorial – Detect Outliers Using IQR 5.2.2. Tutorial – Detect Outliers Using Statistics 5.3. DATA REDUCTION 5.3.1. Tutorial – Dimension Elimination 5.3.2. Tutorial – Sampling 5.4. DATA DISCRETIZATION AND SCALING 5.4.1. Tutorial – Data Discretization 5.4.2. Tutorial – Data Scaling 5.5. DATA WAREHOUSE 5.5.1. Tutorial – Data Cube 5.5.2. Tutorial – Pivot Table SECTION II: Data Analysis CHAPTER 6: Classification 6.1. NEAREST NEIGHBOR CLASSIFIERS 6.1.1. Tutorial – Iris Binary Classification Using KNN 6.1.2. Tutorial – Iris Multiclass Classification Using KNN 6.1.3. Tutorial – Iris Binary Classification Using RNN 6.1.4. Tutorial – Iris Multiclass Classification Using RNN 6.1.5. Case Study – Breast Cancer Classification Using Nearest Neighbor Classifiers 6.2. DECISION TREE CLASSIFIERS 6.2.1. Tutorial – Iris Binary Classification Using Decision Tree 6.2.2. Tutorial – Iris Multiclass Classification Using Decision Tree 6.2.3. Case Study – Breast Cancer Classification Using Decision Tree 6.3. SUPPORT VECTOR MACHINE CLASSIFIERS 6.3.1. Tutorial – Iris Binary Classification Using SVM 6.3.2. Tutorial – Iris Multiclass Classification Using SVM 6.3.3. Case Study – Breast Cancer Classification Using SVM 6.4. NAIVE BAYES CLASSIFIERS 6.4.1. Tutorial – Iris Binary Classification Using Naive Bayes 6.4.2. Tutorial – Iris Multiclass Classification Using Naive Bayes 6.4.3. Case Study – Breast Cancer Classification Using Naive Bayes 6.5. LOGISTIC REGRESSION CLASSIFIERS 6.5.1. Tutorial – Iris Binary Classification Using Logistic Regression 6.5.2. Tutorial – Iris Multiclass Classification Using Logistic Regression 6.5.3. Case Study – Breast Cancer Classification Using Logistic Regression 6.6. CLASSIFICATION METHODS’ COMPARISON 6.6.1. Case Study – Wine Classification Using Multiple Classifiers CHAPTER 7: Regression 7.1. SIMPLE REGRESSION 7.1.1. Tutorial – California Housing Price 7.1.2. Tutorial – California Housing Price 7.2. MULTIPLE REGRESSION 7.2.1. Tutorial – California Housing Price 7.3. REGULARIZATION 7.3.1. Tutorial – Regularization 7.3.2. Case Study – California Housing Price 7.4. CROSS-VALIDATION 7.4.1. Tutorial – Cross-Validation 7.4.2. Case Study – California Housing Price 7.5. ENSEMBLE METHODS 7.5.1. Tutorial – Iris Binary Classification Using Random Forests 7.5.2. Tutorial – Iris Multi Classification Using Random Forests 7.5.3. Case Study – California Housing Price 7.6. REGRESSION METHODS’ COMPARISON 7.6.1. Case Study – Diabetes CHAPTER 8: Clustering 8.1. PARTITION CLUSTERING 8.1.1. Tutorial 8.1.2. Case Study 8.2. HIERARCHICAL CLUSTERING 8.2.1. Tutorial 8.2.2. Case Study 8.3. DENSITY-BASED CLUSTERING 8.3.1. Tutorial 8.3.2. Case Study 8.4. GRID-BASED CLUSTERING 8.4.1. Tutorial 8.4.2. Case Study 8.5. PRINCIPAL COMPONENT ANALYSIS 8.5.1. Tutorial 8.5.2. Case Study 8.6. CLUSTERING METHODS’ COMPARISON 8.6.1. Case Study CHAPTER 9: Frequent Patterns 9.1. FREQUENT ITEMSET AND ASSOCIATION RULES 9.1.1. Tutorial – Finding Frequent Itemset 9.1.2. Tutorial – Detecting Association Rules 9.2. APRIORI AND FP-GROWTH ALGORITHMS 9.2.1. Tutorial – Apriori Algorithm 9.2.2. Tutorial – FP-Growth Algorithm 9.2.3. Case Study – Online Retail CHAPTER 10: Outlier Detection 10.1. OUTLIER DETECTION 10.1.1. Tutorial 10.1.2. Case Study Index