COMP1252 MACHINE LEARNING BASED RAINFALL PREDICTION

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This unit delves into the use of ML methods for forecasting precipitation. Predicting when and how much rain will fall is essential in many fields, such as farming, water management, & emergency planning. Machine learning technologies have the potential to improve upon the precision and dependability of rainfall predictions, which have hitherto relied on meteorological models & the examination of historical data. Students will study machine learning algorithms, data preparation, feature selection, & model evaluation as they apply to the problem of predicting rainfall throughout the semester.

The first part of the course with COMP1252 MACHINE LEARNING BASED RAINFALL PREDICTION assignment help teaches students the basics of precipitation forecasting and the difficulties that come with making reliable predictions. Students will gain knowledge of data from remote sensing, weather station observations, and satellite photography as data sources for precipitation forecasting. To learn how to deal with missing data, scale features, & pick appropriate input parameters for machine learning models, they will investigate methods for data preparation and feature engineering.

With our experts COMP1252 MACHINE LEARNING BASED RAINFALL Prediction academic assistance, students can boost their knowledge related to this topic.

Learning Outcomes:

• Acquire an awareness of the methods used in precipitation forecasting and the difficulties encountered in making reliable predictions.
• Use a number of ML techniques, including random forests, decision trees, SVMs, and NNs, to forecast the weather.
• Use proper measurements and analysis methods to assess an ML model's efficacy.
• Improve the precision of rain forecasts by using modern machine learning techniques like ensemble approaches, deep learning, as well as time-series forecasting.

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