In this following practice, we will attempt to predict house prices in Iowa based on the House Prices Dataset on Kaggle: <https://www.kaggle.com/c/house-prices-advanced-regression-techniques>.
The dataset presents different features of the houses that variably change the predictions. The data will require some work for cleaning and preprocessing to be ready for modeling. First, we will need to load the packages required for preprocessing and modeling phases.
The steps of this practice will involve: 1 - Cleaning the data from null values using domain knowledge or practical judgements. 2 - Removing outliers from train data so they don't affect our predictions. 3 - Feature Engineering: this will involve transforming variables, creating new variables, and eliminating variables in favor of our predictions. 4 - Adjusting skewness for our numeric variables 5 - Using our transformed data with different practices and apply different evaluation models to predict the final price of each home