This tutorial shows how to create an interactive side-by-side map visualization of the mean temperature for 2024 and 2100 for the Chaudière-Appalaches region in Quebec using {leaflet} and {leaflet.extras2} R packages.
In this phase of the analysis, we aim to model precipitation patterns in Centre-du-Québec using machine learning techniques, leveraging historical climate and environmental data. We will train an XGBoost models and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.
In this phase of the analysis, we aim to model precipitation patterns in the St. Lawrence Lowlands using machine learning techniques, leveraging historical climate and environmental data. We will compare Random Forest, XGBoost, and Mars models to assess their ability to capture complex relationships and predict precipitation trends. Model performance will be evaluated using cross-validation and regression metrics to determine the most effective approach.
This week we’re exploring historical emissions data from Carbon Majors. They have complied a database of emissions data going back to 1854. In this second part, I’m predicting carbon emission over space and time.
I will list here all the little snipset of code that I look up all the time.