This research presents a technical framework for predicting demographic information—such as age, gender, or social status—using Multinomial Logistic Regression. Based on the principles of data mining and statistical learning, Md. Obaidur Rahaman demonstrates how demographic characteristics can be accurately classified from large-scale datasets. The study specifically evaluates the capability of the multinomial model where output variables can exist in more than two categories. By analyzing population or user behavior through various data points, this model serves as a vital tool for targeted marketing, sociological analysis, and government policy formulation. The findings prove that with proper feature selection and algorithm optimization, the logistic regression model is capable of providing a high degree of accuracy in demographic prediction. This work offers an effective mathematical guideline for the application of modern data science and artificial intelligence in understanding population dynamics and enhancing predictive analytics within the Bangladeshi context and beyond.

Keywords: Multinomial Logistic Regression, Demographic Prediction, Data Mining, Predictive Analytics, Machine Learning, Computer Science, AUB.