Household Consumption Expenditure based on Fuzzy Decision-Making

In recent scenario, the household consumption expenditure decision-making is a complex process influenced by various factors such as socioeconomic characteristics, lifestyle, and environment concerns. However, the existing models faced difficulties in handling large datasets and it has assumed equal spending distribution among household members which led to inaccurate results. Hence, this research proposes Fuzzy Decision Making (FDM) to improve accuracy of prediction with the ability to handle diverse datasets. Initially, the input data is collected from National Sample Survey Organization (NSSO) Consumer Expenditure Survey dataset. Then, input data undergoes preprocessing with K-Nearest Neighbor (KNN) imputation and min-max normalization to handle missing values as well as to rescale the data into significant range. Further, Density Based Spatial Clustering of Applications with Noise (DBSCAN) is employed to identify the spending patterns to assist in effective house hold expenditure prediction. At last, the proposed FDM is employed to household expenditure prediction through fuzzy logic by representing the patterns in fuzzy sets. From the simulations, the proposed FDM attained better outcomes when compared to existing Long Short-Term Memory (LSTM) in terms of coefficient (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) with 0.49, 0.67, and 0.71 respectively.