creat requirements file

.history/README_20250328232554.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.
+
+`figures` 
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+
+Install if needed python3, most Linux system already have python install
+
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328232619.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.
+
+`figures` contains all figures
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+
+Install if needed python3, most Linux system already have python install
+
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

README.md

@@ -5,6 +5,8 @@ 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.
 
+`figures` contains all figures
+
 `TOAR-classifier_v2.ipynb` is the notebook contening the code
 
 `requirements.txt` contains all neccessary packages.