On April 15, 1912, the largest passenger liner ever made at the time collided with an iceberg during her maiden voyage. When the Titanic sank, it killed 1,502 out of 2,224 passengers and crew. The titanic.csv (https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv) file contains data for 887 real Titanic passengers. Each row represents one person. The columns describe different attributes about the person in the ship where the PassengerId column is a unique ID of the passenger, Survived is the number that survived (1) or died (0), Pclass is the passenger's class (that is, first, second, or third), Name is the passenger's name, Sex is the passenger's sex, Age is the passenger's age, Siblings/Spouses Aboard is the number of siblings/spouses aboard the Titanic, Parents/Children Aboard...
Hands-On Exploratory Data Analysis with Python
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Hands-On Exploratory Data Analysis with Python
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Overview of this book
Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization.
You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.
By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.
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Current Title:
Hands-On Exploratory Data Analysis with Python
Table of Contents (17 chapters)
Preface
Section 1: The Fundamentals of EDA
Free Chapter
Exploratory Data Analysis Fundamentals
Visual Aids for EDA
EDA with Personal Email
Data Transformation
Section 2: Descriptive Statistics
Descriptive Statistics
Grouping Datasets
Correlation
Time Series Analysis
Section 3: Model Development and Evaluation
Hypothesis Testing and Regression
Model Development and Evaluation
EDA on Wine Quality Data Analysis
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