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Practical application of machine learning- regression models to predict house prices
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With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges to predict the final price of each home.
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Used two different approaches to predict the house prices.
One method using ColumnTransformer, making a pipeline, Using GridSearchCv for hyper parameter tuning.
Second method using standardization, normalisation, XGBoost, SVR, Random forest regressor.
- Python Version: 3.7
- Packages: pandas, numpy, sklearn, matplotlib, seaborn, Xgboost.