2018 · 56 citations · 8 references
ComputingComputer ScienceData ScienceFeature EngineeringForecastingMachine LearningEconomic ForecastingForecast EvaluationForecasting MethodsPredictive ModelingTime Series ForecastingTime Series PredictionData ProcessingFeature Selection For ForecastingFeature Vector EngineeringHousing Market ForecastingMachine Learning Forecasting ModelsMachine Learning-based ForecastingMachine Learning-based Price ForecastingPredictive Price Modeling
Based on the data set compiled by D. D. Cock and the competition run by kaggle.com, we propose a house prices prediction algorithm in Ames, lowa by deliberating on data processing, feature engineering and combination forecasting. Our prediction ranks the 35th of the total 2221 results on the public leaderboard of Kaggle.com and the RMSE of predicted results after taking logarithm from all the test data is 0.12019, which shows good performance and small of over-fitting.
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Greedy function approximation: A gradient boosting machine.
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