Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

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Copyright
Table of Contents
Preface
Chapter 1: Data Exploration and Cleaning
	Introduction
	Python and the Anaconda Package Management System
		Indexing and the Slice Operator
		Exercise 1: Examining Anaconda and Getting Familiar with Python
	Different Types of Data Science Problems
	Loading the Case Study Data with Jupyter and pandas
		Exercise 2: Loading the Case Study Data in a Jupyter Notebook
		Getting Familiar with Data and Performing Data Cleaning
		The Business Problem
		Data Exploration Steps
		Exercise 3: Verifying Basic Data Integrity
		Boolean Masks
		Exercise 4: Continuing Verification of Data Integrity
		Exercise 5: Exploring and Cleaning the Data
	Data Quality Assurance and Exploration
		Exercise 6: Exploring the Credit Limit and Demographic Features
		Deep Dive: Categorical Features
		Exercise 7: Implementing OHE for a Categorical Feature
	Exploring the Financial History Features in the Dataset
		Activity 1: Exploring Remaining Financial Features in the Dataset
	Summary
Chapter 2: Introduction to Scikit-Learn and Model Evaluation
	Introduction
	Exploring the Response Variable and Concluding the Initial Exploration
	Introduction to Scikit-Learn
		Generating Synthetic Data
		Data for a Linear Regression
		Exercise 8: Linear Regression in Scikit-Learn
	Model Performance Metrics for Binary Classification
		Splitting the Data: Training and Testing sets
		Classification Accuracy
		True Positive Rate, False Positive Rate, and Confusion Matrix
		Exercise 9: Calculating the True and False Positive and Negative Rates and Confusion Matrix in Python
		Discovering Predicted Probabilities: How Does Logistic Regression Make Predictions?
		Exercise 10: Obtaining Predicted Probabilities from a Trained Logistic Regression Model
		The Receiver Operating Characteristic (ROC) Curve
		Precision
		Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve
	Summary
Chapter 3: Details of Logistic Regression and Feature Exploration
	Introduction
	Examining the Relationships between Features and the Response
		Pearson Correlation
		F-test
		Exercise 11: F-test and Univariate Feature Selection
		Finer Points of the F-test: Equivalence to t-test for Two Classes and Cautions
		Hypotheses and Next Steps
		Exercise 12: Visualizing the Relationship between Features and Response
	Univariate Feature Selection: What It Does and Doesn't Do
		Understanding Logistic Regression with function Syntax in Python and the Sigmoid Function
		Exercise 13: Plotting the Sigmoid Function
		Scope of Functions
		Why is Logistic Regression Considered a Linear Model?
		Exercise 14: Examining the Appropriateness of Features for Logistic Regression
		From Logistic Regression Coefficients to Predictions Using the Sigmoid
		Exercise 15: Linear Decision Boundary of Logistic Regression
		Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients
	Summary
Chapter 4: The Bias-Variance Trade-off
	Introduction
	Estimating the Coefficients and Intercepts of Logistic Regression
		Gradient Descent to Find Optimal Parameter Values
		Exercise 16: Using Gradient Descent to Minimize a Cost Function
		Assumptions of Logistic Regression
		The Motivation for Regularization: The Bias-Variance Trade-off
		Exercise 17: Generating and Modeling Synthetic Classification Data
		Lasso (L1) and Ridge (L2) Regularization
	Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
		Exercise 18: Reducing Overfitting on the Synthetic Data Classification Problem
		Options for Logistic Regression in Scikit-Learn
		Scaling Data, Pipelines, and Interaction Features in Scikit-Learn
		Activity 4: Cross-Validation and Feature Engineering with the Case Study Data
	Summary
Chapter 5: Decision Trees and Random Forests
	Introduction
	Decision trees
		The Terminology of Decision Trees and Connections to Machine Learning
		Exercise 19: A Decision Tree in scikit-learn
		Training Decision Trees: Node Impurity
		Features Used for the First splits: Connections to Univariate Feature Selection and Interactions
		Training Decision Trees: A Greedy Algorithm
		Training Decision Trees: Different Stopping Criteria
		Using Decision Trees: Advantages and Predicted Probabilities
		A More Convenient Approach to Cross-Validation
		Exercise 20: Finding Optimal Hyperparameters for a Decision Tree
	Random Forests: Ensembles of Decision Trees
		Random Forest: Predictions and Interpretability
		Exercise 21: Fitting a Random Forest
		Checkerboard Graph
		Activity 5: Cross-Validation Grid Search with Random Forest
	Summary
Chapter 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client
	Introduction
	Review of Modeling Results
	Dealing with Missing Data: Imputation Strategies
		Preparing Samples with Missing Data
		Exercise 22: Cleaning the Dataset
		Exercise 23: Mode and Random Imputation of PAY_1
		A Predictive Model for PAY_1
		Exercise 24: Building a Multiclass Classification Model for Imputation
		Using the Imputation Model and Comparing it to Other Methods
		Confirming Model Performance on the Unseen Test Set
		Financial Analysis
		Financial Conversation with the Client
		Exercise 25: Characterizing Costs and Savings
		Activity 6: Deriving Financial Insights
	Final Thoughts on Delivering the Predictive Model to the Client
	Summary
Appendix
Index