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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?

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At a basic level, these kinds of low-dimensional plots where you look at one or two variables at a time can help to give you a sense of what types of relationships you might expect to see, such as linear, non-linear, or periodic relationships, which can steer you toward an appropriate family of models. You wouldn't want to use a linear model to predict data that has highly non-linear relationships, for example, nor would you want to use a monotonic non-linear model to predict a periodic function like a sine wave. Knowing about the general distribution of certain variables can also give you a sense of what statistical assumptions might or might not be met - if a model assumes that data is normally distributed, it might not be appropriate if your histograms suggest otherwise. Statistical analysis can help you determine if the underlying assumptions for certain model classes are or are not met.

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In addition to this answer and given that you were also looking for a resource, I suggest that you read chapter 1 of the book An Introduction to Statistical Learning: with Applications in R, where you can find multiple examples of these plots and explanations of why they can be useful: to understand the relationship between the features and the target variable, which you want to predict.

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