更新时间：2021-07-02 14:22:37
coverpage
Title Page
Copyright and Credits
Hands-On Predictive Analytics with Python
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
The Predictive Analytics Process
Technical requirements
What is predictive analytics?
Reviewing important concepts of predictive analytics
The predictive analytics process
Problem understanding and definition
Data collection and preparation
Dataset understanding using EDA
Model building
Model evaluation
Communication and/or deployment
CRISP-DM and other approaches
A quick tour of Python's data science stack
Anaconda
Jupyter
NumPy
A mini NumPy tutorial
SciPy
pandas
Matplotlib
Seaborn
Scikit-learn
TensorFlow and Keras
Dash
Summary
Further reading
Problem Understanding and Data Preparation
Understanding the business problem and proposing a solution
Context is everything
Define what is going to be predicted
Make explicit the data that will be required
Think about access to the data
Proposing a solution
Define your methodology
Define key metrics of model performance
Define the deliverables of the project
Practical project – diamond prices
Diamond prices – problem understanding and definition
Getting more context
Diamond prices – proposing a solution at a high level
Goal
Methodology
Metrics for the model
Deliverables for the project
Diamond prices – data collection and preparation
Dealing with missing values
Practical project – credit card default
Credit card default – problem understanding and definition
Credit card default – proposing a solution
Deliverables of the project
Credit card default – data collection and preparation
Credit card default – numerical features
Encoding categorical features
Low variance features
Near collinearity
One-hot encoding with pandas
A brief introduction to feature engineering
Dataset Understanding – Exploratory Data Analysis
What is EDA?
Univariate EDA
Univariate EDA for numerical features
Univariate EDA for categorical features
Bivariate EDA
Two numerical features
Scatter plots
The Pearson correlation coefficient
Two categorical features
Cross tables
Barplots for two categorical variables
One numerical feature and one categorical feature
Introduction to graphical multivariate EDA
