this project is to develop a machine learning model capable of predicting weather conditions based on historical weather data. The dataset contains various meteorological features, including temperature, humidity, wind speed, and other relevant parameters. The goal is to train and evaluate a model that can provide accurate forecasts for future weather conditions, thereby assisting in decision-making processes that depend on weather predictions.
The task is a supervised learning problem, where features such as temperature, humidity, and wind speed are analyzed for their relationships with the target weather variables. The dataset must be preprocessed through steps like handling missing values, feature scaling, and data splitting to prepare it for modeling. Different regression algorithms are applied, and their performance is compared using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score to select the best model. Finally, the model’s predictions are visualized against the true values to assess accuracy and reliability.
