My client needed a reliable model to predict house prices in King County, USA, to assist in investment decisions and property valuation. The goal was to identify which property features most influenced pricing and to build a model that could generalize well to unseen data. One of the main challenges was the presence of multicollinearity among numeric variables and some outliers in the price and square footage distributions. I addressed this by conducting a thorough Exploratory Data Analysis (EDA), applying correlation analysis to reduce redundant predictors, and transforming skewed variables. I developed two models: Multiple Linear Regression for interpretability and Random Forest for prediction accuracy. The Random Forest model provided superior performance with a significantly lower RMSE. This project showcased my ability to clean data, engineer features, compare models, and explain results clearly using R and relevant packages.

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Marginación del estado de Puebla a partir de datos de CONAPO 2020