Data Science Projects with Python
Stephen Klosterman更新时间：2021-06-11 13:29:22
最新章节：Chapter 6: Imputation of Missing Data Financial Analysis and Delivery to Client封面
版权页
Preface
About
Chapter 1: Data Exploration and Cleaning
Introduction
Python and the Anaconda Package Management System
Different Types of Data Science Problems
Loading the Case Study Data with Jupyter and pandas
Data Quality Assurance and Exploration
Exploring the Financial History 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
Model Performance Metrics for Binary Classification
Summary
Chapter 3: Details of Logistic Regression and Feature Exploration
Introduction
Examining the Relationships between Features and the Response
Univariate Feature Selection: What It Does and Doesn't Do
Summary
Chapter 4: The Bias-Variance Trade-off
Introduction
Estimating the Coefficients and Intercepts of Logistic Regression
Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
Summary
Chapter 5: Decision Trees and Random Forests
Introduction
Decision trees
Random Forests: Ensembles of Decision Trees
Summary
Chapter 6: Imputation of Missing Data Financial Analysis and Delivery to Client
Introduction
Review of Modeling Results
Dealing with Missing Data: Imputation Strategies
Final Thoughts on Delivering the Predictive Model to the Client
Summary
Appendix
About
Chapter 1: Data Exploration and Cleaning
Chapter 2: Introduction to Scikit-Learn and Model Evaluation
Chapter 3: Details of Logistic Regression and Feature Exploration
Chapter 4: The Bias-Variance Trade-off
Chapter 5: Decision Trees and Random Forests
Chapter 6: Imputation of Missing Data Financial Analysis and Delivery to Client
更新时间：2021-06-11 13:29:22