  Annual ranking of higher educational institutions (HEIs) is a global
phenomenon and have significant impact on higher education landscape. Most of
the HEIs pay close attention to ranking results and look forward to improving
their ranks. However, maintaining a good rank and ascending in the rankings is
a difficult task because it requires considerable resources, efforts and
performance improvement plan. In this work, firstly, we show how exploratory
data analysis (EDA) using correlation heatmaps and box plots can aid in
understanding the broad trends in the ranking data. Subsequently, we present a
novel idea of classifying the rankings data using Decision Tree (DT) based
algorithms and retrieve decision paths for rank improvement using data
visualization techniques. Using Laplace correction to the probability estimate,
we quantify the amount of certainty attached with different decision paths
obtained from interpretable DT models. The proposed methodology can aid
Universities and HEIs to quantitatively assess the scope of improvement,
adumbrate a fine-grained long-term action plan and prepare a suitable road-map.
