{"id":258044,"date":"2026-07-13T16:00:44","date_gmt":"2026-07-13T13:00:44","guid":{"rendered":"https:\/\/1kitap1.com\/en\/data-mining-competition-practices-kele-xu-1\/"},"modified":"2026-07-13T16:00:44","modified_gmt":"2026-07-13T13:00:44","slug":"data-mining-competition-practices-kele-xu-1","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/data-mining-competition-practices-kele-xu-1\/","title":{"rendered":"Data Mining Competition Practices – Kele Xu (1)"},"content":{"rendered":"
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4.2 Text word count statistics chart 4.1.2 High-Frequency Word Statistics High-frequency word statistics refer to counting the words that appear frequently in a corpus. This is usually done to understand the characteristics of the corpus or to prepare for subsequent information extraction and specific processing tasks. The following code displays high-frequency words in the text in the form of a word cloud, where the more frequently a word appears, the larger its the font in the word cloud effect. import pandas as pd import matplotlib.pyplot as plt from wordcloud import WordCloud train_df = pd.read_csv(‘..\/input\/feedback-prize- effectiveness\/train.csv’) texts_list = train_df[‘text’].to_list() texts = ‘ ‘.join(texts_list) wordcloud = WordCloud(max_font_size=50, max_words=100, width=500, height=500, background_color=”white”).generate(texts) plt.imshow(wordcloud, interpolation=”bilinear”) plt.axis(“off”) The visualization result is shown in Fig.<\/p>\n

4.3. Fig. 4.3 High-frequency word statistics via word cloud 4.2 Data Preprocessing Text data preprocessing is primarily aimed at eliminating minor errors in the text or standardizing the text format to make it more \u201creadable,\u201d thereby facilitating model understanding and improving the model’s performance. 1. Spelling correction Spelling correction refers to correcting input errors or common word usage errors in the text. Since there is often a certain proportion of spelling errors in the text, which can mislead the model, ignoring these errors may affect the text processing results.<\/p>\n

For these subtle yet common errors, open-source tools like Neuspell can be used for batch correction. An example of usage is as follows. import neuspell from neuspell import BertChecker import pandas as pd df_train = pd.read_csv(‘train.csv’) checker = BertChecker() checker.from_pretrained() # Download the model weight files from Google Cloud and load them df_train[‘corrected_text’] = checker.correct_strings(df_train.text.values) # Perform error correction 2. Text cleaning Text cleaning usually refers to using regular expressions and BeautifulSoup (a Python library that can extract data from HTML or XML files) to remove special symbols, emoji, URLs, HTML tags, and other text that is unhelpful or even negatively impacts the task.<\/p>\n

Text cleaning can reduce the interference of abnormal text on modeling results. However, in some tasks, special symbols are key to improving results, such as newline characters for text segmentation tasks and exclamation marks for sentiment classification tasks, so it is necessary to decide whether to clean a certain type of text based on the specific situation. Below is a code example of a general cleaning method. import re from bs4 import BeautifulSoup def text_cleaning(text): ”’ Perform the following operations in order: (1) Remove embedded URLs. (2) Remove HTML tags.<\/p>\n

(3) Remove emojis. (4) Remove special symbols such as &, #, etc. (5) Remove extra spaces.<\/p>\n

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Jointly published with Tsinghua University Press The print edition is not for sale in Mainland China. Customers from Mainland China please order the print book from: Tsinghua University Press. ISBN of Co-Publisher\u2019s edition: 978-7-302-65846-7 Translation from the Chinese Simplified language edition: \u201c\u6570\u636e\u6316\u6398\u7ade\u8d5b\u5b9e\u6218\uff1a\u65b9\u6cd5\u4e0e\u6848\u4f8b\u201d by Kele Xu, \u00a9 Tsinghua University Press 2024. Published by Tsinghua University Press. All Rights Reserved. The original submitted manuscript has been translated into English. The translation was done using artificial intelligence. A subsequent revision was performed by the author(s) to further refine the work and to ensure that the translation is appropriate concerning content and scientific correctness.<\/p>\n

It may, however, read stylistically different from a conventional translation. \u00a9 Tsinghua University Press 2026 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.<\/p>\n

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.<\/p>\n

The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01\/04 Gateway East, Singapore 189721, Singapore OceanofPDF.com Preface Objectives of This Book This book aims to provide readers with a clear implementation process for data mining competition solutions and explain the key details within.<\/p>\n

In addition to providing the necessary theoretical knowledge, it also offers plug-and-play code. By reading this book, readers will understand how to design a solution for a data mining competition, clarify various details and specific implementation methods within the solution, and learn how to continuously refine and optimize the solution. The book also provides specific practical cases to help readers master and strengthen the aforementioned content. Data mining competitions offer datasets that are close to real-world scenarios, making this book a great choice for those who wish to learn data mining techniques through practical experience.<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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