EDA makes the least assumptions about data. While CDA and Predictive Modeling typically require some assumptions about the data's distribution or the relationships between variables, EDA is a more open-ended exploration of the data's structure and patterns.

You would prefer a heatmap over a scatter plot when visualizing the correlation between multiple variables. A heatmap can visualize any number of variables at once and is particularly effective when the dataset contains many variables.

Improper handling of missing data can adversely affect the bias-variance tradeoff of a model. This can lead to issues such as overfitting or underfitting, which impact the model's ability to generalize to unseen data.

The effect of standardization on a dataset is that it changes the mean to 0 and standard deviation to 1. After standardization, the dataset will have properties of a standard normal distribution with mean=0 and standard deviation=1.

You would choose standardization over Min-Max scaling when the algorithm requires features to be on the same scale and the data is normally distributed. Standardization does not bound values to a specific range like Min-Max scaling, which can be useful for algorithms that do not require input features to be within a certain range.

Standardization, also known as Z-score normalization, is a scaling technique that subtracts the mean and divides by the standard deviation. It is often used in machine learning as it can handle features that are measured in different units by bringing them to a comparable scale. It also doesn't bound values to a specific range.

One major advantage of wrapper methods over filter methods for feature selection is that they can capture the interaction between input features. Unlike filter methods that evaluate each feature independently, wrapper methods consider the subset of features and can thus capture interactions among features.

Nominal data is a type of qualitative data that can be sorted into non-numerical categories, with no order or priority.

A Box plot, also known as a whisker plot, displays a summary of the set of data values including minimum, first quartile (25th percentile), median, third quartile (75th percentile), and maximum. Outliers are also often indicated in box plots through the use of markers.

Kurtosis is a statistical measure used to describe the distribution's tails and sharpness. It measures the degree of peakedness or flatness in a distribution, or in simple terms, the 'tailedness' of the distribution.

In pairplot, the diagonals often show the frequency distribution of the individual variables. This provides an understanding of the distribution of individual variables in addition to their relationships with other variables.

Points that are far away from the general grouping in a scatter plot may indicate the presence of outliers.