PYTHON-BASED POPULATION FORECASTING FOR DELTA STATE: TRAPEZOIDAL AND INTEGRATION METHOD - Atajeromavwo, Edafe John; Yoro, Rume Elizabeth; Ofuyekpone, Okiemute Dickson; Ukpenusiowho, Daniel; Ugbosu, Charles (FUDMA Journal of Sciences, 2024)
The paper focuses on the development of Population Forecasting of Using Population Exponential Model Python with case Study of Delta State Population Census 2006. The focus is to generate manual estimation using the Population Exponential Model; to compare the result estimation with python library. Also, to formulate of an algorithm for area of population of Delta State; visualize the result of prediction of Population with graph and bar chart to see the trend of prediction population trend of Delta State. Population Area under curve of Integration Method and Trapezoidal Method for Delta State for 20 years is obtained. The performance accuracy of 99.9 percent of the population model of Exponential Model of Python with Trapezoidal and Integration Method. Moreover, the calculated sensitivity of the adopted models are in alignment.