In this project we use linear regression to predict housing prices in Iowa. The dataset is of the features of houses from 2006-2010.
Enviornment and Tools: Jupyter notebook,pandas, numpy, matplotlib, scikit learn , seaborn
Initailly, dataset is explored and preprocessing techniques like filling missing data, Encoding, log Transformation, etc are applied.
In the project we use 4 regression models: Ordinary least square error, Lasso(L1 regularization) , Ridge(L2 regularization) , ElasticNet. We also use classification models : RandomForest and Support vector machine.
After comparing RMSE of each model , the model with the minimum RMSE is chosen to be the best fit for the dataset.