This project explores a real estate dataset from Taiwan, applying exploratory data analysis and multiple linear regression models to understand the factors affecting housing prices per square meter. We visualize key relationships through scatterplots, histograms, and boxplots, and evaluate several regression models with increasing complexity. Model performance is assessed using R² and SSE metrics, while we also discuss important statistical assumptions such as normality and homoscedasticity. The analysis concludes with a comparison of the models and the selection of an optimal one for price prediction.

Nuestro estudio busca encontrar patrones ocultos, integrando variables demográficas.

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