  For several years till date, the major issues in terms of solving for
classification problems are the issues of Imbalanced data. Because majority of
the machine learning algorithms by default assumes all data are balanced, the
algorithms do not take into consideration the distribution of the data sample
class. The results tend to be unsatisfactory and skewed towards the majority
sample class distribution. This implies that the consequences as a result of
using a model built using an Imbalanced data without handling for the Imbalance
in the data could be misleading both in practice and theory. Most researchers
have focused on the application of Synthetic Minority Oversampling Technique
(SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach in handling data
Imbalance independently in their works and have failed to better explain the
algorithms behind these techniques with computed examples. This paper focuses
on both synthetic oversampling techniques and manually computes synthetic data
points to enhance easy comprehension of the algorithms. We analyze the
application of these synthetic oversampling techniques on binary classification
problems with different Imbalanced ratios and sample sizes.
