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You are a data scientist working on a regression model to predict housing prices in a large metropolitan area. The dataset contains many features, including location, square footage, number of bedrooms, and amenities. After initial testing, you notice that some features have very high variance, leading to overfitting. To address this, you are considering applying regularization to your model. You need to choose between L1 (Lasso) and L2 (Ridge) regularization. Given the goal of reducing overfitting while also simplifying the model by eliminating less important features, which regularization method should you choose and why?