Preliminary Data Exploration

Studio di un modello di regressione lineare multipla per prevedere il peso dei neonati.

This project presents a comprehensive hydrochemical data analysis aimed at understanding the geochemical characteristics of water samples. The dataset includes major cations and anions, along with supporting physicochemical parameters like pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), and hardness. The objectives of the analysis are: To explore the distribution and correlation of major ions in the water samples. To perform Principal Component Analysis (PCA) to identify dominant hydrogeochemical processes. To convert ionic concentrations from mg/L to meq/L for geochemical classification. To visualize water types using ternary diagrams and Piper diagrams. To evaluate relationships among water quality parameters using correlation matrices and pairwise plots. This analysis supports water resource assessments by identifying hydrochemical facies, revealing potential contamination sources, and interpreting natural versus anthropogenic influences on groundwater quality.