Predicting Weather Patterns Using Machine Learning: A Comparative Study of Random Forest, LSTM, and Linear Regression

Accurate weather prediction is a fundamental challenge in meteorological science with significant implications for agriculture, disaster management, aviation, and daily life. Traditional numerical weather prediction methods, while effective, often lack the adaptability required for complex and rapidly evolving atmospheric conditions. This paper presents a machine learning-based approach to predict weather patterns using three distinct algorithms: Random Forest Classifier, Long Short-Term Memory (LSTM) Neural Networks, and Linear Regression. A synthetic dataset of 2,000 records was generated, incorporating meteorological parameters including temperature, humidity, wind speed, atmospheric pressure, and seasonal variation. The Random Forest model achieved a classification accuracy of 99%, while the Logistic Regression model achieved 79%. Linear Regression was applied for continuous temperature forecasting, achieving a Root Mean Square Error (RMSE) of less than 1°C. The system was deployed as a Python-Flask web application enabling real-time weather prediction. Experimental results demonstrate that ensemble methods, particularly Random Forest, outperform traditional regression techniques for weather classification tasks. The proposed system offers a scalable, interpretable, and computationally efficient solution for short-term weather forecasting.