I am solving for a
regression use case using tensorflow's
DNNRegressor. For EDA purpose, I referred to this post and used pandas boxplot to plot my numerical predictors and target variable(here, pid demand) and scatter_matrix for plotting the distributions and here are the results :
predictor_target_boxplot ; features_label_pdf_scatter_matrix
I need help in interpreting these two plots, specifically on these fronts:
- How come the boxplot shows so many points beyond whiskers (~10%), can there be so many outliers in a dataset?
- How do I handle those outliers?
- Based on the second plot (feature, label pdf), should I normalize my features to exhibit Gaussian distribution? If so, why?