Analysis of the Impact of Economic and Demographic Factors on Women’s FMCG Perspective and Behavior Using Machine Learning Algorithms

Leveraging Machine Learning (ML) techniques, the analytical study aims to unravel the underlying drivers influencing women’s perspectives & buying behavior. The ML investigation is to meticulously analyze the impact of economic & demographic variables on women’s buying behavior with FMCG products. A comprehensive primary survey dataset was collected. It was preprocessed before analysis to address any outliers or inconsistencies. Employed feature selection techniques such as Principal Component Analysis (PCA), Explained Variance and Cumulative Variance are used to identify the most relevant economic and demographic variables. ML algorithms such as regression analysis, decision tree, and clustering methods were employed for analysis of the dataset to uncover patterns, correlations and predictive insights. The performance of ML models was evaluated by using Accuracy, Precision, Recall, F1 score, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) & confusion matrix. Through in-depth analysis & interpretation, we elucidate the key economic & demographic factors influencing Women’s behavior of FMCG products. The study reveals economic influences, like income level & spending habits; demographic dynamics, like education level, age, & lifestyle preferences; regional variances; & emerging trends, like product preferences, geographic location, and product access. This ML investigation helps shed light on complex dynamics shaping women’s perspectives & behaviors to empower stakeholders to make informed decisions that drive positive outcomes.