To prepare EIS data for machine learning, we employed a multi-step preprocessing approach. Initially, the impedance values were transformed into a complex logarithmic form, X = log[Re(Z))] + i log[Im(Z)]. To make the data distribution more Gaussian-like and facilitate feature extraction, a Power Transformer function was applied, which enhanced the distinction between samples. The normalized data were then subjected to Fourier transformation to emphasize frequency-domain characteristics pertinent to solute composition differentiation. Subsequently, principal component analysis (PCA) was performed to reduce dimensionality, with the top 20 principal components-accounting for 99.99% of variance-selected as input features for the machine learning model.