Say we have a problem statement that involves predicting whether a particular earthquake caused a tsunami. How do we decide what model to use? What do we know about the data we have? Nothing! But if we don't know and understand our data, chances are we'll end up building a model that's not very interpretable or reliable. When it comes to data science, it's important to have a thorough understanding of the data we're dealing with, in order to generate features that are highly informative and, consequently, to build accurate and powerful models. To acquire this understanding, we perform an exploratory analysis of the data to see what the data can tell us about the relationships between the features and the target variable (the value that you are trying to predict using the other variables). Getting to know our data will even help us interpret the model we build and identify ways we can improve its accuracy. The approach we take to achieve this is...

Exploratory data analysis (EDA) is defined as a method to analyze datasets and sum up their main characteristics to derive useful conclusions, often with visual methods.

The purpose of EDA is to:

• Discover patterns within a dataset
• Spot anomalies
• Form hypotheses regarding the behavior of data
• Validate assumptions

Everything from basic summary statistics to complex visualizations helps us gain an intuitive understanding of the data itself, which is highly important when it comes to forming new hypotheses about the data and uncovering what parameters affect the target variable. Often, discovering how the target variable varies across a single feature gives us an indication of how important a feature might be, and a variation across a combination of several features helps us to come up with ideas for new informative features to engineer.

Most explorations and visualizations are intended to understand the relationship between...

In order to find out what our data really looks like, we use a technique known as data profiling. This is defined as the process of examining the data available from an existing information source (for example, a database or a file) and collecting statistics or informative summaries about that data. The goal is to make sure that you understand your data well and are able to identify any challenges that the data may pose early on in the project, which is done by summarizing the dataset and assessing its structure, content, and quality.

Data profiling includes collecting descriptive statistics and data types. Common data profile commands include those you have seen previously, including data.describe(), data.head(), and data.tail(). You can also use data.info(), which tells you how many non-null values there are in each column, along with the data type of the values (non-numeric types are represented as object types).

Exercise 2.01: Summarizing...

When there is no value (that is, a null value) recorded for a particular feature in a data point, we say that the data is missing. Having missing values in a real dataset is inevitable; no dataset is ever perfect. However, it is important to understand why the data is missing, and whether there is a factor that has affected the loss of data. Appreciating and recognizing this allows us to handle the remaining data in an appropriate manner. For example, if the data is missing randomly, then it's highly likely that the remaining data is still representative of the population. However, if the missing data is not random in nature and we assume that it is, it could bias our analysis and subsequent modeling.

Let's look at the common reasons (or mechanisms) for missing data:

• Missing Completely at Random (MCAR): Values in a dataset are said to be MCAR if there is no correlation whatsoever between the value missing and any other recorded variable or external...

In this section, we'll look at how individual variables behave—what kind of values they take, what the distribution across those values is, and how those distributions can be represented visually.

Target Variable

The target variable can either have values that are continuous (in the case of a regression problem) or discrete (as in the case of a classification problem). The problem statement we're looking at in this chapter involves predicting whether an earthquake caused a tsunami, that is, the flag_tsunami variable, which takes on two discrete values only—making it a classification problem.

One way of visualizing how many earthquakes resulted in tsunamis and how many didn't involves the use of a bar chart, where each bar represents a single discrete value of the variable, and the height of the bars is equal to the count of the data points having the corresponding discrete value. This gives us a good comparison of the absolute...

There are two reasons why it is important to find relationships between variables in the data:

• Establishing which features are potentially important can be deemed essential, since finding ones that have a strong relationship with the target variable will aid in the feature selection process.
• Finding relationships between different features themselves can be useful since variables in the dataset are usually never completely independent of every other variable and this can affect our modeling in a number of ways.

Now, there are a number of ways in which we can visualize these relationships, and this really depends on the types of variable we are trying to find the relationship between, and how many we are considering as part of the equation or comparison.

Relationship between Two Continuous Variables

Establishing a relationship between two continuous variables is basically seeing how one varies as the value of the other is increased...

In this chapter, we started by talking about why data exploration is an important part of the modeling process and how it can help in not only preprocessing the dataset for the modeling process but also help us engineer informative features and improve model accuracy. This chapter focused on not only gaining a basic overview of the dataset and its features but also gaining insights by creating visualizations that combine several features. We looked at how to find the summary statistics of a dataset using core functionality from pandas. We looked at how to find missing values and talked about why they're important while learning how to use the Missingno library to analyze them and the pandas and scikit-learn libraries to impute the missing values. Then, we looked at how to study the univariate distributions of variables in the dataset and visualize them for both categorical and continuous variables using bar charts, pie charts, and histograms. Lastly, we learned how to...