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Data Preprocessing with Python for Absolute Beginners
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Table of Contents
Preface
About the Author
Chapter 1: Introduction
1.1. What is Data Preparation?
1.2. Environment Setup
1.2.1. Windows Setup
1.2.2. Mac Setup
1.2.3. Linux Setup
1.3. Python Crash Course
1.3.1. Writing Your First Program
1.3.2. Python Variables and Data Types
1.3.3. Python Operators
1.3.4. Conditional Statements
1.3.5. Iteration Statements
1.3.6. Functions
1.3.7. Objects and Classes
1.4. Different Libraries for Data Preparation
1.4.1. NumPy
1.4.2. Scikit Learn
1.4.3. Matplotlib
1.4.4. Seaborn
1.4.5. Pandas
Exercise 1.1
Exercise 1.2
Chapter 2: Understanding Data Types
2.1. Introduction
2.1.1. What Is a Variable?
2.1.2. Data Types
2.2. Numerical Data
2.2.1. Discrete Data
2.2.2. Continuous Data
2.2.3. Binary Data
2.3. Categorical Data
2.3.1. Ordinal Data
2.3.2. Nominal Data
2.4. Date and Time Data
2.5. Mixed Data Type
2.6. Missing Values
2.6.1. Causes of Missing Data
2.6.2. Disadvantages of Missing Data
2.6.3. Mechanism Behind Missing Values
2.7. Cardinality in Categorical Data
2.8. Probability Distribution
2.9. Outliers
Exercise 2.1
Chapter 3: Handling Missing Data
3.1. Introduction
3.2. Complete Case Analysis
3.3. Handling Missing Numerical Data
3.3.1. Mean or Median Imputation
3.3.2. End of Distribution Imputation
3.3.3. Arbitrary Value Imputation
3.4. Handling Missing Categorical Data
3.4.1. Frequent Category Imputation
3.4.2. Missing Category Imputation
Exercise 3.1
Exercise 3.2
Chapter 4: Encoding Categorical Data
4.1. Introduction
4.2. One Hot Encoding
4.3. Label Encoding
4.4. Frequency Encoding
4.5. Ordinal Encoding
4.6. Mean Encoding
Exercise 4.1
Exercise 4.2
Chapter 5: Data Discretization
5.1. Introduction
5.2. Equal Width Discretization
5.3. Equal Frequency Discretization
5.4. K-Means Discretization
5.5. Decision Tree Discretization
5.6. Custom Discretization
Exercise 5.1
Exercise 5.2
Chapter 6: Outlier Handling
6.1. Introduction
6.2. Outlier Trimming
6.3. Outlier Capping Using IQR
6.4. Outlier Capping Using Mean and Std
6.5. Outlier Capping Using Quantiles
6.6. Outlier Capping using Custom Values
Exercise 6.1
Exercise 6.2
Chapter 7: Feature Scaling
7.1. Introduction
7.2. Standardization
7.3. Min/Max Scaling
7.4. Mean Normalization
7.5. Maximum Absolute Scaling
7.6. Median and Quantile Scaling
7.7. Vector Unit Length Scaling
Exercise 7.1
Exercise 7.2
Chapter 8: Handling Mixed and DateTime Variables
8.1. Introduction
8.2. Handling Mixed Values
8.3. Handling Date Data Type
8.4. Handling Time Data Type
Exercise 8.1
Exercise 8.2
Chapter 9: Handling Imbalanced Datasets
9.1. Introduction
9.2. Imbalanced Dataset
9.3. Down Sampling
9.4. Up Sampling
9.5. SMOTE Up Sampling
Exercise 9.1
Final Project – A Complete Data Preparation Pipeline
1.1. Introduction
1.2. Data Preparation
1.3. Classification Project
1.4. Regression Project
Exercise Solutions
Exercise 2.1
Exercise 3.1
Exercise 3.2
Exercise 4.1
Exercise 4.2
Exercise 5.1
Exercise 5.2
Exercise 6.1
Exercise 6.2
Exercise 7.1
Exercise 7.2
Exercise 8.1
Exercise 8.2
Exercise 9.1
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