Improving Pakistan Weather Forecasting Accuracy with Machine Learning: A Data- Driven Approach

This study investigates the application of machine learning models in forecasting weather patterns, concentrating on the distributions of temperature, humidity, and precipitation in the Pakistan region. The research forecasts weather conditions using a variety of methods, such as logistic regression, decision tree classifiers, random forest classifiers, and support vector classifiers, using data from the Open-Meteo weather API for the years 2020 to 2024. Important factors impacting weather forecasts are identified using feature significance analysis with a Random Forest classifier. The study illustrates the potential of machine learning in weather forecasting using visualizations of temperature, humidity, and rain distributions for the cities of Lahore, Peshawar, and Karachi. It also projects the amount of rain that will fall in each city over the next five years, highlighting the significance of region-specific weather prediction models. The results highlight the usefulness of machine learning, in particular the Random Forest model, in improving the accuracy of weather predictions and provides information for guidance in agricultural planning and disaster management in Pakistan's various climate zones