CONCLUSION

In Denver (2017), distinct trends in temperature and humidity over different time intervals were observed. Temperature exhibited a clear pattern of increasing from January to July and then gradually decreasing from July to November. Conversely, humidity did not show a significant pattern, although January and May recorded the highest levels, while March saw the lowest. These insights provide valuable information for residents and local authorities to anticipate seasonal weather changes and address potential humidity-related concerns throughout the year, such as adjusting clothing and indoor heating or cooling systems accordingly.

The weather patterns in different cities were further clarified by investigation, which showed distinct trends depending on the location. For example, towns like Las Vegas had more days with clear weather, while other cities, like Seattle and Pittsburgh, had more days with rain and snow, respectively. Businesses, event coordinators, and locals may all benefit greatly from this knowledge by using it to plan trips, engage in outdoor activities, and invest in infrastructure that is appropriate for the weather where they live.

A slight negative correlation between temperature and humidity was discovered while looking for correlations between weather variables. This implies that humidity tends to decrease with rising temperatures, which could affect people's perceptions and experiences of the weather. The complexity and diversity of weather patterns highlight the significance of taking into account a variety of parameters when forecasting and preparing for various weather scenarios, even though no significant connections were found among other variables.

Cities like Detroit, Minneapolis, and Pittsburgh, situated in regions prone to winter weather systems, regularly experience significant snowfall. Conversely, cities like Houston and San Francisco, with warmer temperatures and distinct geographic locations, see less snowfall. Clustering analysis provides valuable insights for efficiently allocating resources and reducing risks associated with snowfall, particularly in areas where snowy weather significantly impacts daily operations and transportation, such as Pittsburgh.

In contrast, cities like Las Vegas, known for their desert climate and clear skies, benefit from understanding the correlation between clear weather and visitor experiences. Businesses and event planners can leverage this knowledge to organize outdoor activities effectively and maximize tourist engagement. Additionally, forecasting clear weather offers insights for organizing outdoor events, arranging travel, and ensuring safer experiences for locals and tourists alike, enhancing overall community well-being and economic prosperity.

The study's overall findings demonstrate the value of predictive modeling in precisely predicting clear weather, which eventually improves community well-being and resistance to weather-related issues. Through the utilization of these insights, individuals and organizations can enhance their decision-making abilities and guarantee safer and more pleasurable experiences for all stakeholders. Communities may improve their quality of life and resistance to weather-related changes by better understanding and preparing for weather-related concerns. This will create safer, more lively, and more sustainable settings for both locals and visitors.