Abstract:
This thesis discusses machine learning transformations in environmental contexts using a dataset
that contains information about both historical and current environments. The dataset, which
spans the years 2013 to 2022, includes about 10 different locales. Machine learning algorithms
can execute a change suggestion and a synopsis of the climate technological strategy. The
suggested machine learning approach, using the Random Forest Classifier, shows an accuracy of
83.5%. Build a temporal framework, include historical data, and create models that forecast
seasonal and long-term trends. Predictive modeling of ecological processes to plan a
fragmentation is supported by the integration of climate data and historical background. Using
visual aids and intuition to traverse the intricacies of environmental conditions is the foundation
of this multidisciplinary endeavor.
To decode complicated data and forecast environmental change, our world requires machine
learning.
