update

skin_cancer_classification.py

@@ -23,14 +23,11 @@ Original file is located at
 | mel   | Melanoma                            | Malignant |
 | akiec | Actinic keratoses / Bowen's disease | Benign    |
 
-## 0. Requirements
+## 1. Requirements and dataset download
 """
 
-# !pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow
-
-"""## 1. Download dataset"""
-
-# !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
+import sys
+IN_COLAB = 'google.colab' in sys.modules
 
 import kagglehub
 
@@ -52,10 +49,14 @@ import seaborn as sns
 
 from PIL import Image
 
+# Source - https://stackoverflow.com/a/53586419
+# Posted by korakot, modified by community. See post 'Timeline' for change history
+# Retrieved 2026-05-21, License - CC BY-SA 4.0
+
 """### Loading dataset"""
 
 # Path to your dataset folder
-dataset_path = "/kaggle/input/skin-cancer-mnist-ham10000"
+dataset_path = path
 
 # Metadata file
 metadata_path = os.path.join(dataset_path, "HAM10000_metadata.csv")
@@ -312,6 +313,8 @@ val_generator = val_datagen.flow_from_dataframe(
 I will compile the model using the Adam optimizer, binary cross-entropy loss (suitable for binary classification), and track accuracy as a metric.
 """
 
+
+
 from tensorflow.keras.optimizers import Adam
 
 model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])