Analytics

What is data exploration in Machine Learning (ML)?

By Brynne Henn - July 28, 2021

Today it’s easier than ever for businesses to collect and store data about every part of their operation. However, the challenge facing business leaders is understanding the implications and opportunities hidden within that data quickly.

Data exploration is a vital process in data science. Analysts investigate a dataset to illuminate specific patterns or characteristics to help companies or organizations understand insights and implement new policies.

While data exploration doesn’t necessarily reveal every minute detail, it helps form a broader picture of specific trends or areas to study. Using manual methods and automated tools, users explore data to determine which model or algorithm is best for subsequent steps in data analysis.

Manual data exploration techniques can help users identify specific areas of interest, which is workable yet falls short of deeper investigation. This is where machine learning can take your data analysis to the next level.

Machine learning algorithms or automated exploration software can easily identify relationships between various data variables and dataset structures to determine whether outliers exist, and create data values that can highlight patterns or points of interest.

Both data exploration and machine learning can identify notable patterns and help draw conclusions from datasets. But machine learning allows users to extract information in large databases quickly and with little room for error.

With more data available than ever before, many companies are faced with an abundance of data but not enough resources to analyze and process it. This is where machine learning comes in.

What are the advantages of data exploration in machine learning?

Using machine learning for exploratory data analysis helps data scientists monitor their data sources and explore data for large analyses. While manual data exploration can be useful for homing in on specific datasets of interest, machine learning offers a much wider lens, offering actionable insights that can transform your company’s understanding of patterns and trends.

Machine learning software can also make your data far easier to digest. By taking data points and exporting them to data visualization displays such as bar charts or scatter plots, companies can extract meaningful information at a glance without spending time interpreting and questioning results.

When you begin to explore your data with automated data exploration tools, you can come away with in-depth insights that lead to better decisions. Today’s machine learning solutions include open-source tools with regression capabilities and visualization methods using programming languages such as Python for data preparation.

Data exploration through machine learning

Data exploration has two primary goals: To highlight traits of single variables, and reveal patterns and relationships between variables.

When using machine learning for data exploration, data scientists start by identifying metrics or variables, running an univariate analysis and bivariate analysis, and conducting a missing values treatment.

Another key step includes identifying outliers, and finally, variable transformation and variable creation. Let’s review these steps in more detail:

Identifying variables

To get started, data scientists will identify the factors that change or could potentially change. Then, scientists will identify the data type and category of the variables.

Univariate and bivariate analysis

Each variable is then explored individually with box plots or histograms to determine whether it is categorical or continuous, a process known as the univariate analysis. This process can also highlight missing data and outlier values. Next, a bivariate analysis will help determine the relationship between variables.

Missing values

It’s not uncommon for datasets to have missing values or missing data. Identifying gaps in information improves the overall accuracy of your data analysis.

Identifying outliers

Another common element in datasets is the presence of outliers. Outliers in data refer to observations that are divergent from a generalized pattern in a data sample. Outliers can skew data considerably, and should be highlighted and addressed before extracting insights.

Variable transformation and creation

Occasionally it can be helpful to transform or create new variables. Transforming can help scale variables for better visualization, while variable creation can highlight new relationships between variables.

Businesses and organizations can use data exploration to help gain actionable insights from large datasets. You can accelerate data exploration with machine learning, making it a far quicker and more seamless process for your organization.

Sisu and data exploration go hand-in-hand

While some companies may be reluctant to hand over data exploration to machine learning models, the truth is that automated data exploration is the key to data processing that can be transformative for an organization. Obtaining insights and understanding your company’s data is vital, and machine learning can help.

Automation can help you avoid bottlenecks in your data analytics, a key problem for companies who have too much data and too few resources to comb through it all. Sisu is designed to help analyze large amounts of data, allowing your organization to understand trends and implement new agendas or policies.

To get started on smarter and faster data exploration, schedule a demo with Sisu today to unlock the potential of your data.


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