Prediction of the species present at a location is useful for understanding biodiversity and for the purpose of conservation. The objective of the GeoLifeCLEF 2019 Challenge is to build a species recommendation system based on location and Environmental Variables (EVs). In this paper, we discuss different approaches to predict the most probable species based on location and EV values, using Machine Learning. We first developed purely spatial models which took only the spatial coordinates as inputs. We then built models that took both the spatial coordinates and EV values as inputs. For our runs, we mainly used Artificial Neural Networks and the XGBoost framework. Our team achieved a maximum Top30 score of 0.1342 in the test phase, with an XGBoost-based model. Copyright © 2019 for this paper by its authors.