  Gaining a deeper understanding of weather and being able to predict its
future conduct have always been considered important endeavors for the growth
of our society. This research paper explores the advancements in understanding
and predicting nature's behavior, particularly in the context of weather
forecasting, through the application of machine learning algorithms. By
leveraging the power of machine learning, data mining, and data analysis
techniques, significant progress has been made in this field. This study
focuses on analyzing the contributions of various machine learning algorithms
in predicting precipitation and temperature patterns using a 20-year dataset
from a single weather station in Dhaka city. Algorithms such as Gradient
Boosting, AdaBoosting, Artificial Neural Network, Stacking Random Forest,
Stacking Neural Network, and Stacking KNN are evaluated and compared based on
their performance metrics, including Confusion matrix measurements. The
findings highlight remarkable achievements and provide valuable insights into
their performances and features correlation.
