This project, carried out by a team of 5 students, focuses on the extraction and analysis of meteorological data from stations across Galicia. We leverage the AEMET API to gather detailed climatic data from various stations across the four provinces of Galicia.
- Data Extraction: Identify and extract indicators from all meteorological stations in Galicia. Collect data on temperature, precipitation, wind speed, and atmospheric pressure for specific periods between 2017 and 2022.
- Data Storage: Store the extracted data in a CSV file for further analysis.
- Data Visualization: Create four distinct graphs to interpret and understand the gathered data.
- Documentation: Source code commented and credited to the authors in the group.
In this segment, we analyze the dataset previously generated, employing various Machine Learning techniques. The goal is to develop and evaluate predictive models with cross-validation to better understand the climatic patterns of Galicia.
- Data Preparation: Modify the dataset to categorize rainy days.
- Exploratory Analysis: Initial inspection of the data to identify trends and patterns.
- Predictive Modeling: Implementation of various models, including Logistic Regression, KNN, Decision Trees, SVM, and Naive Bayes.
- Model Comparison: Evaluation and comparison of the models on training and validation sets.