The Gamma distribution is suitable for modeling datasets with the following
characteristics:

1 Positive Values: The data should be continuous and strictly positive, as the Gamma distribution is defined for positive real numbers.
2 Skewness: The distribution is skewed to the right, making it appropriate for datasets with a similar skewness.
3 Shape and Scale Parameters: It is defined by two parameters, shape (k) and
scale (θ), allowing flexibility in modeling various types of data
distributions.
4 Variance: The variance is proportional to the square of the mean, which can
be useful for datasets where variability increases with the mean.
5 Exponential Family: It is part of the exponential family, making it suitable for certain types of statistical modeling and inference.

These characteristics make the Gamma distribution a good fit for modeling
waiting times, rainfall amounts, and other similar datasets.