MACHINE LEARNING VALIDATION OF AI CONTENT MARKETING IMPACT IN THE INDIAN MANUFACTURING SECTOR: A COMPARATIVE ANALYSIS OF REAL AND SYNTHETIC DATA

This research proposes a machine learning validation of AI content marketing's impact on Indian manufacturing competitiveness
through comprehensive real vs synthetic dataset benchmarking. Research Analysis is comprises of 269 real manufacturing
responses (44% MSME, 50% manufacturing sector) using five algorithms for validation: Logistic Regression (61.1%), SVM
(59.3%), Random Forest (59.3%), XGBoost (55.6%), and ensemble (51.9%) reveals designation as the primary satisfaction
predictor (6.7% feature importance). These low satisfaction variance (σ=0.29, μ=4.04/5) limited the real-data which is obtained
responses, so 1000 sample synthetic dataset is used with engineered demographic and the response correlations achieved 95.5%
accuracy (Random Forest), representing 34.4% methodological improvement and confirming ML pipeline robustness. Few
important parameters are considered such as young tech professionals (Below 30 years, Technology/AI Services) and MSME
owners, who exhibit highest AI marketing receptivity (4.6/5 average), while senior postgraduate managers show skepticism (2.8/5
trust scores). These findings provides an evidence that AI marketing adoption enhance MAKE IN INDIA competitiveness, and
particularly among MSME's firms with the results analyzed from responses. This dual-dataset technique establishes a rigorous
approach to find a demographic patterns in large responses