Book Image

Data Science Projects with Python

By : Stephen Klosterman
Book Image

Data Science Projects with Python

By: Stephen Klosterman

Overview of this book

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

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Table of Contents (9 chapters)
Data Science Projects with Python
Data Science Projects with Python
Preface
Preface
Free Chapter
1
Data Exploration and Cleaning
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
2
Introduction toScikit-Learn and Model Evaluation
Introduction toScikit-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
3
Details of Logistic Regression and Feature Exploration
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
4
The Bias-Variance Trade-off
The Bias-Variance Trade-off
Introduction
Estimating the Coefficients and Intercepts of Logistic Regression
Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
Summary
5
Decision Trees and Random Forests
Decision Trees and Random Forests
Introduction
Decision trees
Random Forests: Ensembles of Decision Trees
Summary
6
Imputation of Missing Data, Financial Analysis, and Delivery to Client
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
Appendix
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

Examining the Relationships between Features and the Response

In order to make accurate predictions of the response variable, good features are necessary. We need features that are clearly linked to the response variable in some way. Thus far, we've examined the relationship between a couple features and the response variable, either by calculating a groupby/mean of the response variable, or by trying models directly, which is another way to make this kind of exploration. However, we have not yet made a systematic exploration of how all the features relate to the response variable. We will do that now and capitalize on all the hard work we put in when we were exploring the features and making sure the data quality was good.

A popular way of getting a quick look at how all the features relate to the response variable, as well as how the features are related to each other, is by using a correlation plot. We will first create the correlation plot for the case study data, then discuss how to...

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