• G N V S S Eshwar
• Pranav A Mudholkar
• D K Aishwarya

Challenging across the globe, Income segregation in urban classes is a famous case. Research shows that segregation increases with the difference in socioeconomic status but is asymmetric for purchase activity, i.e., the interactive space between a poorer neighbourhood to a more affluent neighbourhood is more significant than vice versa. This project aims at computing the Segregated Interaction using machine learning techniques viz MLPC classification and knn classification. Training a backpropagation MLP Classifier implements an algorithm. The project comprises four modules. The first module stores the Segregation feature set data int the database. The second module deals with creating comma-separated value files of the data stored in the DB. The third module deals with developing python routines to execute the MLPC on the segregation data set, and the final module deals with the knn classification of segregation data. The project uses Python's 'sklearn' module to perform the MLP Classifier and knn analysis. This shows various libraries in Python such as pandas, keras and numpy. The different classification and regression algorithms part of this random forests and boosting techniques are an added advantage. This can be primarily used as python numerical and array operations. We use various variables for the intricate calculations and mapping involved while building and working with the system. It is different from MLP Classifier in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers.

slkearn's mlp classifier, knn classifier modules

"Segregated Interactions In Urban and Online Space", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 2, page no.c61-c63, February-2022, Available :http://www.jetir.org/papers/JETIR2202226.pdf

"Segregated Interactions In Urban and Online Space", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 2, page no. ppc61-c63, February-2022, Available at : http://www.jetir.org/papers/JETIR2202226.pdf