• File size measurements show the distribution of size of files.
• Files are classified in four categories based on their size (lines of code): 1-100 (very small files), 101-200 (small files), 201-500 (medium size files), 501-1000 (long files), 1001+(very long files).
• It is a good practice to keep files small. Long files may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.

• There are 15 files with 7,612 lines of code.
• 0 very long files (0 lines of code)
• 8 long files (5,459 lines of code)
• 5 medium size files (1,942 lines of codeclsfd_ftr_w_mp_ins)
• 1 small files (196 lines of code)
• 1 very small files (15 lines of code)

There are 14 files with lines longer than 120 characters. In total, there are 460 long lines.

File	# lines	# units	# long lines
translation_finetuning.ipynb in notebooks/generative_ai/finetuning	734	-	81
product_attributes_from_image.ipynb in notebooks/generative_ai/content_generation	805	-	68
public_datasets.ipynb in public_datasets	441	-	66
customer_price_index.ipynb in notebooks/sampling/monte_carlo	645	-	49
description_from_video.ipynb in notebooks/generative_ai/content_generation	681	-	46
wine_quality_classification_mlr.ipynb in notebooks/classification/logistic_regression	865	-	34
sentiment_analysis_movie_reviews.ipynb in notebooks/generative_ai/sentiment_analysis	560	-	23
housing_prices_prediction.ipynb in notebooks/regression/decision_tree_regression	623	-	21
delta_quickstart.ipynb in notebooks/quickstart/delta_format	196	-	16
predictive_maintenance.ipynb in notebooks/classification/linear_support_vector_machine	546	-	15
metastore_spark_quickstart.ipynb in notebooks/quickstart/dataproc_metastore	247	-	11
bike_trip_duration_prediction.ipynb in notebooks/regression/random_forest_regression	500	-	11
sms_spam_filtering.ipynb in notebooks/classification/multilayer_perceptron_classifier	389	-	10
iceberg_quickstart.ipynb in notebooks/quickstart/iceberg	365	-	9