摘要
The purpose of this investigation was to use Python to model global city temperatures for 400+ cities for many decades. The process used a compilation of secondary data to find my renowned sources and use different regression models to plot temperatures. Climate change is an impending crisis for our Earth, and modeling its changes using Machine Learning will be crucial to understanding the next steps to combat it. With this model, researchers can understand which area is most harshly affected by climate change leading to prioritization and solutions. They can also figure out the next sustainable solutions based on climate needs. By using KNeighbors and other regressors, we can see an increase in temperature worldwide. Although there is some error, which is inevitable, this is mitigated through several measures. This paper provides a simple yet critical understanding of how our global temperatures will increase, based on the last 200+ years.
The purpose of this investigation was to use Python to model global city temperatures for 400+ cities for many decades. The process used a compilation of secondary data to find my renowned sources and use different regression models to plot temperatures. Climate change is an impending crisis for our Earth, and modeling its changes using Machine Learning will be crucial to understanding the next steps to combat it. With this model, researchers can understand which area is most harshly affected by climate change leading to prioritization and solutions. They can also figure out the next sustainable solutions based on climate needs. By using KNeighbors and other regressors, we can see an increase in temperature worldwide. Although there is some error, which is inevitable, this is mitigated through several measures. This paper provides a simple yet critical understanding of how our global temperatures will increase, based on the last 200+ years.
作者
Rishi Manjure
Rishi Manjure(California Institute of Technology, Pasadena, USA)
