Predictive Analysis of Organized Retail Penetration in India: A Study Using Multivariate Regression Techniques.

This study investigates the influence of various economic and demographic factors on India’s organized retail sector using 1997 to 2018 time series data. Due to data constraints from early organized retailing and the exclusion of the Covid – 19 period, the research aimed to develop a multivariate forecasting model for organized retail sales. Independent variables included population, median age, total personal disposable income, household consumption, employment, infrastructure investment, mall space and internet/smartphone users. Employing linear, polynomial, Ridge and Lasso regressions, the study addressed stationarity and multicollinearity.  The Augmented Dickey Fuller (ADF) test identified employment, infrastructure investment and growing mall culture as key predictors. Final regression analysis, performed in Python addressed model performance and accuracy through comparative analysis.