LLOVE, an interface in many dimensions

As the marginal cost of information continues to decrease with the rise of virtual environments and digital technologies, traditional pen-and-paper graphs are becoming insufficient in daily research routine. Instead, abstract frameworks that manage large datasets, driven by statistical analysis or automated learning methods, are gaining prominence. However, there remain certain scenarios, particularly in exploratory research, where simple visual representations can provide initial momentum and valuable insights during the preliminary phases of studies or when modelling tools are limited. This article introduces a suite of functions developed within the Mathematica environment, designed for straightforward data visualisation and prediction in higher (yet manageable) dimensions. The suite includes simple histograms and correlation analysis, parallel axis plots, pseudo four-dimensional input and singular output visualisation, and iterative optimisation processes that extrapolate relevant novel manifolds for exploration. The suite is tested against three heterogeneous datasets: energy efficiency in residential buildings, abalone age prediction from physical measurements, and the optimisation of hybrid organic-inorganic solar cells.