The aim of this project is look at various ways to accurately predict measurable minerals used in treatment of drinking water. sample drinking water was tested and recorded for 10 minerals. "Calcium.CA" "magnesium.Mg" "Sodium.Na" "Potassium" "Sulfates" "Chlorides" "Nitrates" "Nitrites" "dry.residues" "Bicarbonates" ##Task !developed a classification model to identify ideal mineral composition for water treatment using both WHO standards and data-driven approaches. While WHO guidelines served as the foundation benchmark, the decision tree and random forest models provided refined thresholds tailored to the data set. we shall be comparing the record with WHO standard to determine if those mineral element are present at recommended amount or in excess in our drinking water. we shall train our model to assist us to easily predict the recommended amount of mineral required.

Extends the analysis with non-parametric and semi-parametric methods — Kaplan–Meier estimation, Nelson–Aalen cumulative hazard, formal hypothesis testing, full Cox regression with diagnostics, model refinement for PH violations, and a comprehensive synthesis integrating all three analytical frameworks.

Regression Analysis II Lab

Tempe Pre-Calls / Texts Analysis for DAT 301. This project analyzes outreach results, student locations, majors, campaign interests, and relationships among stage, location, and grouped call results.