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.history/README_20250328223710.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
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README.md

@@ -4,7 +4,9 @@ This study develops a machine learning approach to classify 23,974 air quality m
 
 ### Files
 `data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
 `TOAR-classifier_v2.ipynb` is the notebook contening the code
+
 `requirements.txt` contains all neccessary packages.
 
 ### Run code
\ No newline at end of file