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Machine Learning: The Definitive Data Exploration Guide
Author

Revathipathi Namballa(RP)
CloudAngles

What is data exploration?
Data exploration, also known as exploratory data analysis (EDA), is the first step in data analysis, in which data analysts use data visualization and statistical tools to define dataset characteristics, such as size, amount, and precision, in order to better comprehend the nature of the data. Data analysts can gain more insight into the raw data using data exploration techniques, which include both manual analysis and automated data exploration software solutions that visually explore and identify relationships between different data variables, the structure of the dataset, the presence of outliers, and the distribution of data values. Before extracting useful data for further analysis, such as univariate, bivariate, multivariate, and principal components analysis, data analysts must first comprehend and construct a holistic understanding of the data, which is frequently collected in enormous, unstructured volumes from numerous sources. Data exploration is often overlooked in favour of model architecture construction and parameter tuning, but this is a mistake. Say, for instance, that you have created a flawless model. However, even the best model won’t do you any good if the data you feed it is flawed in some way or violates the model’s assumptions. You could spend a lot of time validating your model without ever finding the issue in the dataset if you don’t do any data exploration.
Why is Data Exploration Important?
It is incredibly difficult for data scientists and data analysts to assign meaning to hundreds of rows and columns of data points and communicate that meaning without any visual components since humans interpret visual data better than numerical data. Shapes, sizes, colours, lines, points, and angles are all examples of visual cues used in data exploration, and they help data analysts visualize and define metadata before doing data purification. The first phase in data analysis is data exploration, which helps analysts see patterns and outliers in the data that might otherwise be missed.
Data Exploration in Machine Learning
The quality of a Machine Learning project is directly proportional to the quantity and quality of its training data. Model accuracy would suffer if the data used by machine learning data exploration models are not fully explored before the models are applied to it. It is recommended to perform the following data exploration tasks before constructing a machine learning model.
- Variable identification define each variable and its role in the dataset
- Univariate analysis for continuous variables, build box plots or histograms for each variable independently; for categorical variables, build bar charts to show the frequencies
- Bi-variable analysis — determine the interaction between variables by building visualization tools
- Continuous and Continuous scatter plots
- Categorical and Categorical stacked column chart
- Categorical and Continuous boxplots combined with swarm plots
- Detect and treat missing values
- Detect and treat outliers
The end goal of data exploration in machine learning is to generate insights from the data that can inform the feature engineering and model-building phases that follow. Creating features from raw data, as is done in feature engineering, simplifies and improves the prediction ability of machine learning algorithms.
What are the advantages of data exploration in machine learning?
The use of machine learning for exploratory data analysis assists data scientists in monitoring their data sources and exploring data for large-scale investigations. While human data exploration can be valuable for zeroing in on certain datasets of interest, machine learning provides a much broader lens that can improve your company’s knowledge of patterns and trends.
Additionally, machine learning tools can make your data much simpler to comprehend. By converting data points to data visualization displays such as bar charts and scatter plots, businesses may extract valuable information without spending time evaluating and questioning outcomes. When you begin to study your data using automated data exploration tools, you can gain in-depth insights that lead to more informed judgments. Today’s machine learning solutions include open-source tools with regression capabilities and visualization techniques employing programming languages such as Python for data processing.
Methods of data exploration
The basic objectives of data exploration are to emphasize the characteristics of individual variables and to identify patterns and correlations between variables.
When utilizing machine learning for exploratory data analysis, data scientists begin by defining metrics or variables, doing univariate and bivariate analyses, and treating missing values. Identifying outliers is another essential stage, followed by variable transformation and variable creation. Let’s examine these processes in greater depth.
Identifying variables
To begin, data scientists will identify the factors that change or may change in the future. Then, scientists will determine the data type and variable category.
Univariate and bivariate analysis
Univariate analysis is the process of examining each variable independently with box plots or histograms to determine if it is categorical or continuous. This method can help identify missing data and anomalous values. Next, a bivariate analysis will assist in establishing the association between variables.
Missing values
It is fairly common for datasets to contain missing numbers or data. Identifying these gaps increases the overall precision of your data analysis.
Identifying outliers
The existence of outliers is a prevalent characteristic of data sets. Outliers in data refer to observations that deviate from a sample’s generalized trend. Outliers can significantly distort data and should be identified and corrected prior to deriving insights.
Variable transformation and creation
Sometimes it can be advantageous to change or create new variables. Scaling variables through transformation can improve their display, while variable creation can emphasize new relationships between variables.
Using data exploration, businesses and organizations may extract useful insights from massive datasets. Using machine learning, you can expedite data discovery, making it a faster and more seamless process for your firm.Feature Engineering
Feature engineering is the process of extracting new information from current data by modifying or processing it. We are not technically adding new data to the mix, but it is more useful to create the existing distribution. This circumstance and method are exemplified by the extraction of individual values of years, months, and dates from a more consolidated field such as the complete date, which allows for a more comprehensive and extensive analysis of the existing data.
Top tools for data exploration
If the correct tools are not used for data exploration, then this activity can pretty soon turn tedious and time-consuming.
Matplotlib
Matplotlib was created to emulate MATLAB’s supporting graphics in a simplified manner. Multiple functionalities have been added to the library over the years. In addition to this, other visualization libraries and tools are built on top of Matplotlib and include innovative, dynamic, and appealing visuals.
It can be a little challenging to choose or even recall items when dealing with this package because of its flexibility. There can be more than one answer to an issue, so you shouldn’t feel overwhelmed. Let’s see some of the advantages of using this tool.
- Fast and efficient, built on NumPy and SciPy
- You have complete control over your graph and plot, allowing you to make several modifications to improve the readability of your visualizations.
- It is an open-source library with a sizable community and cross-platform support.
- Multiple charts and graphs of superior quality
Scikit Learn
David Cournapeau developed Scikit learn as a Google Summer Code project. In 2010, FIRCA elevated the library to the next level by releasing a beta version. Scikit learn has progressed significantly and is now the most useful robust library. It is constructed in Python using NumPy, SciPy, and Matplotlib.
It provides a broad variety of effective tools for data cleansing, curation, modeling, etc. without focusing on a single area of any data science projects. It has tools for
- Classification
- Regression
- Clustering
- Dimensionality Reduction
- Model Selection
- Preprocessing
Some of its advantages include
- Open source.
- Strong community for support.
- Data exploration tools with the best performance are accessible for usage.
- Various platforms can be integrated with Scikit Learn’s products by using its APIs.
- Provides a pipeline utility for automating machine learning processes.
- It is simple to use, a complete package, and only depends on a few libraries.
Plotly
Plotly develops online data visualization and analysis solutions. It provides visualizations and analytics tools for platforms and frameworks such as Python, R, and MATLAB. It includes plotly.js, an open-source JS library for making graphs and data visualizations. To allow Python to utilize its utilities, plotly.py was created on top of it.
It provides over 40 distinct chart formats to accommodate statistical, financial, geographical, scientific, and 3D use cases. It employs D3.js, HTML, and CSS, which facilitates the incorporation of interactive features such as zoom-in and zoom-out or mouse hover.
If you want your charts to be interactive, attractive, and readable, plotly is your solution.
Some of the advantages are
- You can build interactive plots with JavaScript without its knowledge.
- Plotly lets you share the plots publicly without even sharing your code.
- Simple syntax, almost for all plots it uses the same sequence of parameters.
- You don’t need any technical knowledge to use plotly, you can use the GUI to create visuals.
- Provides 3D plots with multiple interactive tools.
Seaborn
Matplotlib is the foundation for other tools, including Seaborn. You may build visually appealing charts with minimal effort using Seaborn. It provides advanced functionality for standard statistical charts to make them useful and appealing.
It is tightly integrated with pandas and accepts inputs in the format of pandas data structures. Seaborn has not reimplemented any plots but has modified the Matplotlib routines so that we can utilize the plots with minimal parameters.
Axis-level plotting is a feature of Seaborn that enables the direct use of categorized graphs. These plots, such as histplot() and lineplot(), are self-contained and a straight substitute for Matplotlib, albeit they provide some customization, such as the automated addition of axis labels and legends.
Some of the advantages are
- It is simple to modify plots.
- The default method is far more aesthetically pleasing than Matplotlib.
- Facet and regression plots are built-in, unlike Matplotlib’s facet and regression plots. One function can generate a regression line, confidence interval, and scatter plot for regression.
- Seaborn is more compatible with the pandas data structure than matplotlib.
Pandas
One of the most widely used Python packages for data analysis and manipulation. It began as a tool for conducting quantitative analysis on financial data. It is therefore widely used in time series applications.
The majority of data scientists and analysts deal with tabular data formats such as.csv,.xls, etc. Pandas provides SQL-like commands that facilitate data loading, processing, and analysis. It supports both series and data frame data structures. Both data structures can store distinct types of data. Series is a one-dimensional indexed array, whereas a data frame is a two-dimensional data structure — table format — that is commonly used when working with real-world data.
Some of the advantages are
- Intelligible presentation of data
- Comprehensive format compatibility
- A comprehensive range of functionality, including the SQL format for joining, merging, and filtering data.
- Effective at managing huge datasets.
- Supports standard visualization plots and graphs.
Conclusion :
While data visualization is applicable to numerous business operations, it is crucial to acknowledge that it is a vital aspect of efficient data analysis. The capacity to spot anomalies in a dataset quickly and precisely can make or break an entire analysis.
While some organizations may be hesitant to delegate data exploration to machine learning models, automated data exploration is the foundation of data processing for an enterprise, and this can be a revolutionary approach. Understanding and gaining insights from your company’s data is crucial, and machine learning may help.
Automation can help you avoid obstacles in your data analytics, a major issue for businesses with too much data and insufficient resources to analyze it. CloudAngles is meant to aid in the analysis of vast amounts of data, enabling your organization to recognize trends and implement new policies and agendas.
To unlock the potential of your data and get started with smarter and faster data exploration, arrange a demonstration with CloudAngles today.
We hope this post has helped you better understand your data and how to analyze it.