Analysis of Global Car Sales Data

Raport analizy danych o diamentach oraz budowy modelu predykcyjnego mającego na celu oszacowanie ich cen. Raport zawiera przygotowanie, oczyszczenie danych oraz analizę statystyczną w środowisku RStudio. Oceniono dopasowanie modelu, jego dokładność oraz zgodność z założeniami regresji liniowej. Wyniki potwierdziły wysoką skuteczność zastosowanego podejścia oraz możliwość praktycznego wykorzystania modelu do prognozowania wartości diamentów.

This assignment explores **housing price prediction and model interpretability** using the **Ames Housing Dataset** in R. The objective is to build, evaluate, and interpret a multiple linear regression model that predicts residential property sale prices based on structural, quality, and location-related variables. Through a series of analytical questions, the project covers the full regression workflow — from **data loading and preprocessing**, to **model building**, **assumption testing**, **validation**, and **prediction uncertainty**. The analysis applies fundamental statistical theory (BLUE: Best Linear Unbiased Estimator) while integrating practical, real-world reasoning to connect quantitative results with housing market behavior. Each section mirrors an applied data analytics process: - **A–B:** Prepare and fit a predictive model. - **C–D:** Identify key factors and assess statistical significance. - **E–G:** Evaluate model fit, test predictive accuracy, and interpret confidence and prediction intervals. The ultimate goal is to understand **how and why regression works**, not just how to run it — developing intuition for interpreting coefficients, evaluating significance, diagnosing assumptions, and communicating results meaningfully. This assignment demonstrates how data analytics and AI-assisted exploration can enhance evidence-based decision-making in **real estate valuation** and **economic modeling**.

En esta publicación se presenta el desarrollo y los resultados obtenidos al solucionar el caso solicitado por el docente.

Here you will find instructions for building and publishing your own RShiny app from open source iNaturalist data