  The purpose of this research report is to present the our learning curve and
the exposure to the Machine Learning life cycle, with the use of a Kaggle
binary classification data set and taking to explore various techniques from
pre-processing to the final optimization and model evaluation, also we
highlight on the data imbalance issue and we discuss the different methods of
handling that imbalance on the data level by over-sampling and under sampling
not only to reach a balanced class representation but to improve the overall
performance. This work also opens some gaps for future work.
