Data exploration is one of the most critical steps in data analysis. It is considered the first stage of a data scientist's work, in fact, the whole MLOps team. Data analysts tend to get overwhelmed when they start working with large sets of data, so an effective way to perform this step is by using statistical techniques for small datasets (as well as for large ones).
Data exploration is the approach you’ll need when looking at your data. It helps you gain insight into what your data means. It translates to creating a bunch of tables, visuals, and statistics that will help the team make sense of these never-ending arrays and lists of data.
This technique combines a bunch of manual and automated processes that together ensure the conveyance of the data and can benefit both the team and, in some cases, the corporate clients that you are trying to sell your product to.
Let us dive deeper into this vast world of Data Science with this small and vital topic and the industry secrets and arsenal that will help your and your team’s practices.
Data exploration, as mentioned above, is the most critical step in any data analytics stage of your machine learning pipeline. This step, paired with a competent team, helps map out the inputs and required outputs by putting all the data in the form of infographics in front of you. An open mind of a data analyst opens up various doors or, in some cases, windows to the bigger picture, which may not be the immediate solution but a pair or combinations of different inputs which will, in turn, guide you to the destination.
Data analysts must first understand and develop a comprehensive view of the data before extracting relevant data for further analysis, such as univariate, bivariate, multivariate, and principal components analyses because data is often gathered in large, unstructured volumes from various sources.
Data Exploration has become the staple for many upcoming industries with the sheer amount of data boom and increased numbers of sensors and census. The benefits of data exploration can be applied to any business or industry that deals with or uses data. The software industry, the healthcare industry, and the education sector are all examples of highly prevalent fields of work.
Not only limited to infographics or charts, but data exploration also allows powerful visualization and pattern generation to identify relationships between input variables and structures. One of the critical things that data exploration helps uncover is anomalies and outliers. (To understand more about the difference between these two, head over to Anomaly Detection in Machine Learning)
Let us dive deeper into the aforementioned critical aspects of data exploration in machine learning:
1. Variable Intuition
The basis of any efficient model development is understanding where the numbers are coming from and why they exist. A quick peruse through the columns and base statistics like the average, mean, maximum, and minimum values will give you an excellent intuition of the data and what is expected from the model’s behaviour.
This read also gives a chance to check out the metadata denoted by each field and check out the missing values and how they need to be dealt with.
2. Outlier Detection
Outliers and Anomalies hold great potential for derailing your entire machine learning pipeline and data exploration gives you a chance to get a look at it. Once found, these values can be dealt with efficiently to clean the data better for the model, either by editing, omitting, or ignoring. No matter how these values are dealt with, they need to be dealt with at this stage of development.
3. Pattern Recognition
Any potential model has a potential relationship between different variables; it can be either bivariate or multivariate, which is dictated by the relationships between variables. Visualizations and Statistics generated during the data exploration step help identify such relationships. For example, the relationship between the square footage of a plot vs. the area or average of plot prizes while doing a house price estimation.
Now that we have defined data exploration and perused the importance of this stage in a data analytics pipeline let us look at some of the data exploration techniques and steps.
Steps to data exploration are the baseline or the first step to ensure that the time and money spent on a model training and further development is made on a distribution that makes sense and can produce good results.
There are multiple levels to cross while cleaning and exploring data which can not be ignored. Let us look at the multiple steps involved in data exploration, breaking down each step and why they are important:
1. Variable Identification
Each variable needs to be first identified, whether categorical or continuous. Once that is established, the broader category of the variable needs to be defined. Is it an input variable, supporting, redundant, or output variable?
2. Univariate Analysis
This stage requires the individual analysis of different variables and trying to comprehend these variables to get a feel of the individual spheres of these variables. Univariate Analysis depends on two sub-categories, Continuous and Categorical Variables. Let us look at both of them:
Continuous Variables: In the case of continuous variables, we need to analyze and look at the value spread’s central tendency. This central tendency can be measured with statistical values like mean, median, mode, minimum, maximum, standard deviation, and variance.
Categorical Variables: We will use a frequency table to understand the distribution of each category for categorical variables. Also readable as a percentage of values within each category. It can be measured using the Count and Count% metrics against each category. A histogram can be used as a visual representation.
3. Bivariate Analysis
Bivariate analysis is the step wherein we try to associate or dissociate relationships between two variables at predefined metrics. This analysis comes with three possible combinations depending on the individual variables. Let us look at all three:
Continuous and Continuous: The best way to analyze two continuous variables is to plot them on a scatter plot, which effectively puts the relationship between them in comprehensible form. The relationship identified between them can be either correlated or not correlated, later of which is effectively unusable. The correlated relationship can be either a positive or negative relationship.
Categorical and Categorical: The best way to run an analysis between two categorical variables is by using a two-way table which can put the values against each other in a more comprehensive way and show the count or count% of observations available in each combination of row and column categories.
Continuous and Categorical: We can make boxplots for each level of a categorical variable to look at the relationship between it and a continuous variable. If there aren't many levels, it won't be possible to show that something is statistically significant.
4. Missing Values
Missing values in a dataset may exist due to various reasons, like sensor malfunction or human error. Such errors, however, can affect the performance of a model by creating a weird bias due to unbalanced values resulting in the wrong bivariate analysis. Let us look at ways to deal with missing values without affecting or increasing our model’s performance:
Row Deletion: In this method, whenever a missing value is detected, the particular row containing that missing value is obliterated, avoiding the consideration of such a row in the model. However, even though the data is better off without a missing value, this method leaves us with a smaller data sample size, which can be a problem when dealing with more minor data.
Mean/Median Imputation: Imputation, contrary to deletion, is a method wherein we fill in the missing values to preserve the sample size of the data. This method aims to replace the missing values with statistically valid ones that can be credited while preserving a particular variable’s general nature and distribution.
Prediction Model: This is one of the more sophisticated methods to deal with missing data, which is achieved by running a more rudimentary model on those particular variables wherein the missing values occur. These models can predict a somewhat rough estimate for the values seeing the pattern set by the other values in the variable and give a better value than just a normal Mean/Median Imputation.
5. Outlier Detection
Outliers are those particular observations that seem to be far and standing out from the general population. These outliers may not necessarily be wrong values but something that comes from some special instances. Maybe the listed house price of a house is way higher than the others due to some celebrity inhabiting it before or cases like these. These outliers tend to increase the error variance of a model and affect the general training of the model.
Outliers can be detected by plotting the whole population on graphs like box-plot, histograms, or scatter plots. Either of whom can give us a general idea of the outliers. As far as removing outliers goes, there are several methods to do so:
Thresholding, the values in the distribution, to just keep the observations that fall between the accepted variance of the model.
The outliers can also impute the same as missing values, wherein we will replace the outliers with the accepted mean or median values.
Suppose the number of outliers in the distribution is significant. In that case, we may have to consider these values, hence requiring a separate model to be trained for these sets of values separate from the normal distribution.
6. Feature Engineering
Feature engineering is the technique of getting more out of the existing data by tweaking or running operations on the existing data. We are not exactly adding new data to the mix but creating the existing distribution is more useful. One good example of this situation and method is the extraction of individual values of years, months, and dates from a more consolidated field like the full date, which can give us a more general and broader analysis of the existing data.
However, dealing with all these steps can be tedious if you do not have the right tools to get the first few steps done easier. There are numerous tools in the form of frameworks and libraries which can be easily integrated into your machine learning pipeline.
Let us take a look at some of the famous and easy ways to do data analysis on your raw data effectively and efficiently:
Matplotlib - was made to look like all of the graphics that MATLAB could do but in a simpler way. Over the years, the library has gotten more and more functions. Not only that, but many libraries and tools for visualizing data are built on top of Matplotlib and have new, interactive, and appealing graphics.
Because of the variety of options available, new users may find it difficult to make decisions or keep track of details. Fortunately, the documentation has all the information we need, including real-life examples, information about the arguments used in each plot, and so on. Some of the features offered by the library are
Pandas Profiling - is an open-sourced Python module coded meticulously for efficient Data Exploration with just a few lines of code. Moreover, the package helps you generate interactive reports that can be easily interpretable by someone oblivious to Data Science in general.
Multiple versions of the package can be integrated into other frameworks like Streamlit. It can act as a rudimentary way to present data. Pandas Profiling provides features:
Grafana - is an Open-Sourced multi-platform analytics and visualization web application equipped with modern visualization techniques for efficient model monitoring.
Some of the features provided by Grafana are:
Seaborn - is one of the many tools that use Matplotlib as a foundation. Make professional-looking charts quickly and easily in Seaborn. It includes convenient, high-level tools for generating informative and visually appealing versions of standard statistical plots.
Some of the features of seaborn are:
D3.js - is an open-source JavaScript library for making interactive and dynamic visualizations for the web. It generates charts and graphs by employing HTML, CSS, and SVG. D3, short for "data-driven documents," was developed by Mike Bostock. It combines visual components with a data-driven approach to manipulate the DOM, making it one of the best tools for data visualization in web analytics.
The Django or Flask web frameworks are available to us. The combination of Python's ease of use and D3's extensive set of plots makes this a great idea. Since D3 is compatible with HTML, CSS, and SVG, Python can serve as the backend system. You can quickly and easily create a dashboard using D3.js and the information you wish to analyze. Let us look at some features of D3.js:
While data visualization can be used in various business functions. It is important to recognize that it is a fundamental component of effective data analysis. The ability to quickly and accurately detect anomalies in a dataset can make or break an entire analysis. We hope this article gives you a better understanding of your data and how you can go about analyzing it.