Built a predictive model estimating residential sale prices from 79 property features, turning a noisy dataset (1,460 homes, many missing values) into a reliable pricing tool. Cleaned and imputed data based on its real meaning, encoded categorical variables, then compared Linear vs Ridge Regression. Used cross-validated grid search to tune regularization, then validated results with residual analysis. Result: Ridge Regression cut prediction error by 43% vs. baseline (RMSE $52,399 → $29,921), giving a defensible, data-backed price estimate instead of a rule-of-thumb figure.