I've been learning Python machine-learning using this project report and the guy who wrote it begins by visualizing his data using various statistical analysis methods: histograms, density plots, box plots, scatter plots, etc.
The problem is that he doesn't explain what this is for. The only detail he provides is that "univariate plots help to understand each attribute" and "multivariate plots help to understand the relationships between attributes."
What would be the reason behind using these plots for ML development? Do they help you to determine which algorithm(s) you should try? If so, how? Can anyone explain the main points or maybe point me to a resource that will help?