diff --git a/.history/README_20250329004405.md b/.history/README_20250329004405.md
new file mode 100644
index 0000000000000000000000000000000000000000..ece686415aaa85cb4e56b39b9799fc84cd5c6ad8
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+++ b/.history/README_20250329004405.md
@@ -0,0 +1,35 @@
+## 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 containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+clone the project by running the following
+`git clone 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.
+
diff --git a/.history/README_20250329004519.md b/.history/README_20250329004519.md
new file mode 100644
index 0000000000000000000000000000000000000000..1566ce9efed7a0da0515d234f22f2e7802397e7d
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+++ b/.history/README_20250329004519.md
@@ -0,0 +1,35 @@
+## 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 containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+`git clone 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.
+
diff --git a/README.md b/README.md
index a18dd296c5807edc381bf47e32172609327eb39f..1566ce9efed7a0da0515d234f22f2e7802397e7d 100644
--- a/README.md
+++ b/README.md
@@ -3,22 +3,21 @@ This study develops a machine learning approach to classify 23,974 air quality m
 <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.
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
 
-`figures` contains all figures
+### Run the Code
 
-`TOAR-classifier_v2.ipynb` is the notebook contening the code
+**Note:** This has been tested on Ubuntu 24.04.
 
-`requirements.txt` contains all neccessary packages.
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
 
-### 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
+3. clone the project by running the following
 `git clone 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`
diff --git a/TOAR-classifier_v2.ipynb b/TOAR-classifier_v2.ipynb
index 9341b977537c2f01565ca0d4ee21633f261d1cbf..fc6b9bd626c556c6e4cb042e9bb3435925546760 100755
--- a/TOAR-classifier_v2.ipynb
+++ b/TOAR-classifier_v2.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 58,
    "id": "8666a040",
    "metadata": {
     "tags": []