Deployed machine learning algorithms to predict prices in a residential market. Determined missingness, imputed data, created ordinal categorical scales, and performed feature engineering. Project involved checking features for normality and Gaussian assumptions, and developing solutions to combat violations (i.e., box cox transformation and robust scaling). Trained an algorithm to predict housing prices using Lasso, Ridge, Gradient Boosting and Support Vector Regressor
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