Exploratory Data Analytics to Study the Impact of Parameters to Enhance the Population of House Sparrow (Passer Domesticus).

Abstract

To achieve a sustainable conservation or adaptation strategy, it is necessary to understand the impacts of habitat and weather on particular species. Hence the study was focused to analyze the parameters that influence the population of house sparrow in various locations of Madurai Dist. Furthermore, its present and future suitable habitats have been predicted using computational tools. Statistical analysis and correlation were performed with data gathered through random sampling. Using correlation analysis, the association between various parameters was studied. Hierarchical clustering of data was performed by a Kendall Correlation coefficient to identify the suitable habitat and weather. Subsequently the Selection of the major parameters of a study was determined using Principal Component Analysis approach. The study highlights the significance of utilizing data mining and computational analysis to precisely understand the influence of various geographical parameters on the distribution and survival of the house sparrow population in an area. Based on the results obtained, abundance and distribution of house sparrow were closely related to the area of habitat in which house sparrows were found. The preference of particular habitat can be briefly explained using Kendall Correlation Matrix and cluster analysis. Using the Principal Component Analysis (PCA) technique, the population density of the house sparrow was studied. Taking these computational analyses into account will provide a new perspective on predicting the species distribution in the specific area thereby conserving it.