Abstract:
This study focuses on the crucial task of forecasting population growth, a fundamental
aspect of planning for a nation's future development. Utilizing advanced machine learning
techniques, we aim to predict future population trends by analyzing historical demographic
data. This approach is expected to enhance the process of strategic national planning
significantly. Applying time series analysis to a comprehensive collection of historical
population data, our methodology is able to provide valuable insights. Our team has utilized
a range of machine learning models, such as Facebook's Prophet, LSTM (Long Short-Term
Memory), State Space Model, Holt Winters, and SARIMA (Seasonal Autoregressive
Integrated Moving Average). For processing time series data, each of these algorithms was
carefully chosen based on its unique strengths. By utilizing a combination of these various
models, we can guarantee a comprehensive and efficient population forecasting approach.
The effectiveness of our methods is demonstrated by our encouraging findings.
Highlighting its precision in forecasting, the Prophet algorithm achieved an impressively
low Mean Absolute Percentage Error (MAPE) of just 0.48%. Reinforcing the accuracy of
our approach, the LSTM model recorded a Root Mean Squared Error (RMSE) of
300020.64. Vast and impactful are the potential applications of this research. Crucial
insights for various sectors, including urban development, resource management, and
environmental planning, can be offered by providing more detailed and location-specific
population predictions. Setting a foundation for future applications of machine learning in
generating precise and dependable population predictions, this study makes a significant
contribution to the field of demographic forecasting. Not just academic achievements, these
advancements in forecasting methodologies serve as vital tools for policymakers and
planners. They empower them to make more informed decisions for the betterment of
national and regional development
