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Multi-input prediction models are gradually finding their places in the arena of social and economic sciences to assess, locate and address the complicated socio-economic issues arising around the globe. These models treat the problems as the output aroused from a complex interaction between a range of variables linked with physical, socio-cultural, economic as well as ambient political systems. The discussion on dropout from the education system belongs to the core of the educational researchers. The researchers within this domain are attempting to develop the ‘tools and techniques’ for efficiently demarcating the space with a given degree of susceptibility. The scope is to drop out and examine the internal functions of the interactive variables associated with the process. In the present study, we try to apply the fuzzy logic in mapping the spatial variation of the susceptibility of school dropout in the district of Purulia, a backwards district in India regarding achieved level of human development. The training datasets for building the fuzzy model based on the available secondary data from different reports published by the Government and a range of primary data collected through a socio-economic survey. The model output is an index, namely the Index of Susceptibility of School Drop Out (ISDO) which reflects the levels of susceptibility to school dropout at different parts of the study area. The proposed model should allow the success within the larger social and economic system

School dropout susceptibility mapping with fuzzy logic – a study in the District of Purulia, India