weather-prediction
Weather-Prediction-Using-Machine-Learning
URL:https://vidhyasivanpandi.github.io/weather-prediction/
Introduction:
Weather prediction is a crucial aspect of daily life, affecting everything from agriculture to transportation to emergency preparedness. While traditional forecasting methods have come a long way, there is still much room for improvement in accuracy and efficiency. In recent years, machine learning (ML) has emerged as a promising approach to weather prediction. By leveraging the vast amounts of data collected from weather stations around the world, ML models can analyze complex patterns and relationships to make more accurate and timely predictions.
In this research paper, we explore the application of ML to weather prediction. Specifically, we focus on the use of supervised learning algorithms, including decision trees, logistic regression, and k-nearest neighbors, to predict weather conditions based on historical data. We use a dataset containing daily weather measurements from multiple weather stations in a particular region and train our ML models on this data to predict future weather conditions.
Our research aims to address several key questions, including: What types of ML algorithms are best suited for weather prediction? How does the size and quality of the training dataset affect the accuracy of the predictions? What features or variables have the most significant impact on weather prediction accuracy? By answering these questions, we hope to shed light on the potential of ML for weather prediction and provide insights into best practices for using these technologies in real-world applications.
Overall, this research has the potential to make a significant contribution to the field of weather prediction and inform future research in this area. By demonstrating the effectiveness of ML for predicting weather conditions, we hope to inspire further exploration and innovation in this important domain.
Conclusion
Here, we will use a typical model from the number of models built above to test the results. We will choose a model built with Decision Tree with variable month that stores month information extracted from date variable, with parameter max_depth = 4. This model has an accuracy of 0.8387.
