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

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