update 2

skin_cancer_classification.py

@@ -29,6 +29,10 @@ Original file is located at
 import sys
 IN_COLAB = 'google.colab' in sys.modules
 
+# f IN_COLAB:
+#  !pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow
+#  !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
+
 import kagglehub
 
 # Download latest version
@@ -180,6 +184,18 @@ df['target'] = df['dx'].map(benign_malignant_dict)
 # Preview
 print(df[['dx', 'target']].head())
 
+from sklearn.model_selection import train_test_split
+
+train_df, val_df = train_test_split(
+    df,
+    test_size=0.2,
+    stratify=df['target'],
+    random_state=42
+)
+
+print("Train size:", len(train_df))
+print("Validation size:", len(val_df))
+
 # Convert 'target' column to string type for ImageDataGenerator
 train_df['target'] = train_df['target'].astype(str)
 val_df['target'] = val_df['target'].astype(str)
@@ -198,21 +214,10 @@ plt.show()
 
 print(df['target'].value_counts())
 
-"""### Train / Validation split"""
+"""### Train / Validation split
 
-from sklearn.model_selection import train_test_split
+### Class weight (class imbalance)
-
+"""
-train_df, val_df = train_test_split(
-    df,
-    test_size=0.2,
-    stratify=df['target'],
-    random_state=42
-)
-
-print("Train size:", len(train_df))
-print("Validation size:", len(val_df))
-
-"""### Class weight (class imbalance)"""
 
 from sklearn.utils.class_weight import compute_class_weight