A correlation heatmap may reveal that two variables move together, but the next research question often requires deeper analysis. You may need to determine whether measurements can be classified into defined groups, whether selected variables relate to a continuous outcome, or whether an ordered dataset contains a trend worth investigating.
Wensura brings those analytical stages into a battery-focused research workspace. You can upload structured datasets, examine their underlying relationships, apply automated machine learning, investigate changes over time, and export the analysis for technical review.
Start With the Structure of Your Dataset
Exploratory data analysis (EDA) provides an initial view of a dataset before model development. It can expose distributions, missing values, possible outliers, and patterns that deserve closer inspection.
Wensura’s Data Sandbox includes EDA, correlation matrices, principal component analysis, raincloud plots, and three-dimensional scatter visualizations. These functions give you several ways to inspect battery or electrochemical data before selecting a classification, regression, or forecasting question.
A useful analysis starts with a defined target rather than a collection of disconnected charts. Your question may concern material groups, measured performance values, process conditions, or observations recorded across time.
Examine Variable Relationships With Correlation Matrices
A correlation matrix summarizes how numerical variables change in relation to one another. Battery R&D datasets may include composition values, processing conditions, test settings, electrochemical measurements, or other structured observations.
Wensura includes correlation-matrix analysis within its Data Sandbox. The output can identify variable pairs that deserve additional statistical review, feature selection, or experimental investigation.
Correlation remains an exploratory signal rather than proof of causation. A strong coefficient may reflect a direct relationship, a shared experimental condition, an indirect dependency, or the way the dataset was constructed.
The matrix therefore provides a practical starting point for further work. You can compare the relationship with domain knowledge, examine the underlying observations, and decide whether the variables belong in a later model.
Investigate Multivariable Structure With PCA
A battery dataset may contain several correlated features that make direct interpretation difficult. Principal component analysis (PCA) transforms the original variables into components that represent major sources of variation within the data.
Wensura includes PCA alongside its other Data Sandbox functions. You can use it to examine clustering, separation, and dominant variation across a multidimensional dataset before moving into supervised modeling.
PCA may show that observations occupy distinct regions of the feature space or that a limited number of components account for much of the variation. Those components remain mathematical combinations of the original variables, so their scientific meaning still depends on experimental design and battery-domain knowledge.
Apply Automated Machine Learning to Defined Targets
Automated machine learning (AutoML) automates portions of the model-development process. It can reduce the manual work involved in comparing suitable algorithms and configuring candidate models, while leaving target selection, data preparation, and scientific interpretation with the research team.
Wensura Pro includes AutoML for classification and regression. Classification examines outcomes represented by defined categories, while regression examines continuous numerical target values.
A classification task might investigate whether observations can be assigned to established material, process, or result groups based on the supplied features. A regression task might examine how selected variables relate to a continuous measured outcome within the uploaded dataset.
Model quality still depends on the information provided. Target definition, missing-data treatment, measurement quality, feature selection, sample size, and class balance may all affect the results.
Wensura lets you compare AutoML outputs with the patterns already visible through EDA, correlation matrices, and PCA. That connected workflow makes it easier to see whether the variables highlighted during exploration also contribute useful information to a defined predictive task.
Explore Trends With Time-Series Forecasting
Some battery R&D datasets contain observations recorded in a meaningful sequence. Repeated measurements, process-monitoring values, and other ordered data may raise questions about trends or how a variable could behave beyond the observed period.
Time-series forecasting uses past observations to estimate future values, with patterns such as trend and seasonality influencing the choice of method. Data order therefore carries information that ordinary cross-sectional analysis may not preserve.
Wensura Pro includes unlimited time-series forecasting. You can apply the function to an appropriate sequential dataset and examine the resulting projection alongside the original observations.
The output remains a model-based estimate under the conditions represented in the data. Changes in experimental procedures, materials, equipment, sampling intervals, or operating conditions may weaken the relationship between historical observations and later results.
AI-generated analyses, summaries, and recommendations require independent verification. Wensura’s terms also prohibit presenting AI output as an independently verified scientific finding without proper disclosure.
Build One Connected Analytical Workflow
The available methods become more useful when they answer different parts of the same research question. A collection of polished figures may look comprehensive while leaving the underlying decision unresolved.
You might begin with EDA to inspect data quality and distributions. A correlation matrix can identify relationships worth examining, while PCA can reveal broader structure across multiple variables.
AutoML can then address a defined classification or regression target. Time-series forecasting becomes appropriate when the dataset contains a valid sequence and the question concerns change across time.
Every dataset will not require the entire sequence. Some projects may stop after exploratory analysis, while others may support model development after suitable preparation and review.
Keep the Analysis Connected to Battery Context
Wensura combines dataset analysis with an AI Copilot that works with dataset and knowledge-base context. Its Data Foundry also supports proprietary PDF and dataset uploads within an isolated research environment.
That setup keeps your research question, supporting documents, structured data, and analytical outputs within one workspace. You can examine a dataset while retaining access to relevant battery-specific context rather than treating the numbers as an unrelated spreadsheet exercise.
Battery scientists and research engineers still determine whether each variable is physically meaningful. They also assess whether the model reflects the experimental system and whether the result warrants additional analysis or laboratory work.
Export Results for Technical Review
Wensura Pro includes PDF report export. A report can carry selected analytical outputs into discussions with project leaders, collaborators, or other technical reviewers.
Useful documentation should preserve the dataset, target question, analytical method, assumptions, and limitations needed to interpret the results. Keeping that information with the figures reduces the risk of an isolated chart or forecast being treated as stronger evidence than the analysis supports.
The export also creates a record of the work completed before another model or experiment begins. Your team can compare the report with existing methods and decide which findings deserve further investigation.
Frequently Asked Questions
What does AutoML do in Wensura?
Automated machine learning in Wensura applies classification or regression workflows to uploaded datasets. Wensura Pro combines AutoML with exploratory tools so you can compare model outputs with patterns identified through EDA, correlation matrices, and PCA.
Can Wensura examine relationships between variables?
Yes, Wensura includes correlation matrices, PCA, and additional exploratory functions for structured datasets. Wensura provides several views of the data so you can identify relationships and multivariable patterns for further statistical or experimental review.
What questions can classification and regression address in Wensura?
Classification in Wensura addresses targets represented by defined categories, while regression addresses continuous numerical outcomes. Wensura’s AutoML functions still require a suitable target, prepared data, and independent assessment of the resulting model.
Does Wensura guarantee accurate time-series forecasts?
No. Wensura provides model-generated forecasts that require independent verification against suitable scientific evidence and experimental work. Its terms provide no guarantee that AI-generated analyses or recommendations are accurate, complete, or suitable for a particular purpose.
Can Wensura export dataset-analysis results?
Yes, PDF report export is included with Wensura Pro. Wensura gives you a practical way to move selected analytical outputs into your established technical-review and documentation process.
Test One Suitable Dataset With Wensura
Wensura combines EDA, correlation matrices, PCA, AutoML, time-series forecasting, and PDF reporting in one battery-focused workflow. You can move beyond basic visualization and investigate a defined classification, regression, variable-relationship, or time-series question without treating model output as experimental proof.
Start Wensura’s 14-day free trial with one suitable structured dataset and one specific analytical question. Compare the findings with your existing methods, verify each output independently, and assess where Wensura can contribute to your battery R&D process.
