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Developing Kaggle Notebooks

You're reading from  Developing Kaggle Notebooks

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781805128519
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Gabriel Preda Gabriel Preda
Profile icon Gabriel Preda

Table of Contents (14) Chapters

Preface 1. Introducing Kaggle and Its Basic Functions 2. Getting Ready for Your Kaggle Environment 3. Starting Our Travel – Surviving the Titanic Disaster 4. Take a Break and Have a Beer or Coffee in London 5. Get Back to Work and Optimize Microloans for Developing Countries 6. Can You Predict Bee Subspecies? 7. Text Analysis Is All You Need 8. Analyzing Acoustic Signals to Predict the Next Simulated Earthquake 9. Can You Find Out Which Movie Is a Deepfake? 10. Unleash the Power of Generative AI with Kaggle Models 11. Closing Our Journey: How to Stay Relevant and on Top 12. Other Books You May Enjoy
13. Index

Metadata exploration

We start by importing the utility functions and classes from the utility scripts for data quality, plot utils, video utils, and face object detection. The following code block shows what we import from the utility scripts:

from data_quality_stats import missing_data, unique_values, most_frequent_values
from plot_style_utils import set_color_map, plot_count
from video_utils import display_image_from_video, display_images_from_video_list, play_video
from face_object_detection import CascadeObjectDetector, FaceObjectDetector
from face_detection_mtcnn import MTCNNFaceDetector

After we load the data files (the train and test samples), we are ready to start our analysis. The following code block checks the types of files in TRAIN_SAMPLE_FOLDER:

train_list = list(os.listdir(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER)))
ext_dict = []
for file in train_list:
    file_ext = file.split('.')[1]
    if (file_ext not in ext_dict):
        ext_dict...
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