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

Introduction

The first chapter got you started with some basic Python, and then progressed to equipping you with tools for data exploration. Specifically, we performed operations such as loading the dataset and verifying data integrity, and we performed our first exploratory analysis on our case study dataset.

In this chapter, we finish our exploration of the data by examining the response variable. After we've concluded that the data is of high quality and makes sense, we will be ready to move forward with the practical concerns of developing machine learning models. We will take our first steps with scikit-learn, one of the most popular machine learning packages available in the Python language. Before learning the details of how mathematical models work in the next chapter, here we'll start to get comfortable with the syntax for using them in scikit-learn.

We will also learn some common techniques for how to answer the question, "Is this model good or not?" There are many possible ways...

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