Predicting the decision for the provision of municipal services using data mining approaches

Every year in certain areas of a city, the population tends to grow, causing a parallel growth in need for services. These needs can be new schools, hospitals, public facilities, road expansions, public parks, etc. These needs are handled by the municipal authorities in those cities, who are representatives of the government charged with carrying out such responsibilities. In this paper, the municipal authority focused on is AACM. We investigate how to improve the needs evaluation process in AACM to streamline the decision-making process. We present models for how these demands/needs can be evaluated to determine whether they will be chosen for implementation and, if not, the reasons for their rejection. The prediction model uses four different classification techniques (DT, SVM, KNN and NB) and proposes the best technique based on accuracy. Finally, we identify the challenges faced during the pre-processing stage and present our recommendations to the AACM for future data gathering techniques.