Readability is crucial in data visualization because it directly impacts the audience's ability to understand and interpret the data correctly. A readable graph communicates the data's message effectively, allows the audience to draw accurate conclusions, and makes the data accessible to a broader audience.

Violin plots are ideal for visualizing the full distribution of a variable including its probability density, quartiles, and outliers. These plots combine a box plot and a density plot, providing a rich, dense summary of the data.

When features in a dataset are highly correlated, they might suffer from a problem known as multicollinearity, which can negatively impact the machine learning model. Multicollinearity can affect the stability and interpretability of the model, and may cause certain algorithms to perform poorly.

The removal of outliers often leads to a reduction in the variability (or variance) of the dataset as outliers are extreme values that increase variability.

A Histogram is preferred over a kernel density plot for discrete data. While kernel density plots can give a smoother representation of data, they are more suitable for continuous data. A histogram's bar-like representation suits the discrete nature of the data.

If the dataset is large and removing outliers would lead to a significant loss of data, binning could be a suitable method. In binning, the outliers are not removed but rather they are replaced with summary statistics like mean, median, etc.

Zero skewness means the distribution is symmetrical, and high kurtosis means the distribution is leptokurtic with a sharp peak and fatter tails. Therefore, the data distribution will be symmetrical but with a potential for outliers. This may affect the results of statistical tests or models that assume normality, as extreme values could have a disproportionate effect on the results.

The logarithmic transformation is a common method used in data transformation to handle outliers. It helps in pulling in high values, which reduces skewness.

Visualizing high-dimensional datasets (those with many variables) can be challenging. However, techniques like parallel coordinates or a scatter plot matrix can help. Parallel coordinates plot each variable on a separate column, and lines connecting the columns represent individual data points. A scatter plot matrix shows all pairwise scatter plots of the variables.

When visualizing real-time data for a live audience, simplicity and clarity are key factors. The audience needs to understand the data quickly as it updates in real time. A clear and straightforward graph type, simple labels, and a thoughtful color scheme can help achieve this.