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About Weather Forecasting Basepaper
This document discusses using machine learning models to predict weather more accurately than traditional methods. It evaluates models like random forest, decision trees, support vector machines, KNN, AdaBoost, gradient boosting and XGBoost on weather data over 21 years. The models are trained on parameters like temperature, dew point, humidity, pressure, visibility and wind speed to predict weather events. The machine learning process involves preprocessing data, encoding categorical variables, feature selection, scaling and splitting data for training and testing models. The accuracy and F1 scores of the different models are then compared to determine the best approach for smart weather prediction using machine learning.
Detailed Information
| Author: | ['srihemabiccavolu'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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