该项目使用朴素贝叶斯分类器将电子邮件分类为垃圾邮件或火腿。它预处理电子邮件数据，训练模型，并删除关键词以优化性能。还可以对模型进行交叉验证、特征选择和单独评估。
This project classifies emails as spam or ham using a Naive Bayes classifier. It preprocesses email data, trains the model, and removes stopwords to optimize performance. Cross Validation, Feature Selection and individual assessment over model can also be done. (2024-06-26, Jupyter Notebook, 0KB, 下载0次)
python弹性搜索qiuck启动器
python elasticsearch qiuck starter (2024-05-11, Jupyter Notebook, 0KB, 下载0次)
该项目使用NLP技术（停止字、词干分析、计数矢量器）进行语言检测，使用随机森林分类实现~95%的准确性。利用自己的数据进行训练，探索不同的语言！
This project uses NLP techniques (stopwords, stemming, CountVectorizer) for language detection, achieving ~95% accuracy with Random Forest classification. Train on your own data and explore different languages! (2024-04-07, Jupyter Notebook, 0KB, 下载0次)
使用NLP的垃圾邮件分类器。（分类用例）
Spam Classifier Using NLP. (Classification Use Case) (2023-01-09, Jupyter Notebook, 0KB, 下载0次)
本项目涉及自然语言处理的概念，其中通过降低大小写、删除非索引词和将词干分析器算法应用于文本数据，并最终应用矢量化技术，将原始数据转换为有用的文本数据。通过将文本转换为垃圾邮件和非垃圾邮件类别，该模型在处理的输入文本数据上使用支持向量分类器算法获得了更好的准确性。
This project deals with the concepts of Natural Language Processing, where the raw data is transformed into useful text data by lowering the case, removing stopwords, and applying stemmer algorithm to the text data, and finally applied Vectorization techniques. The model has given better accuracy using the Support Vector Classifier algorithm on the processed input text data by converting the text into spam and non-spam categories. (2024-02-19, Jupyter Notebook, 0KB, 下载0次)
使用KNeighbors算法构建简单垃圾邮件分类器，并使用标记化和词干化等概念来删除非索引词。
Build a Simple Spam Classifier using KNeighbors Algorithm and used concept like Tokenization and stemming to remove the stopwords. (2020-12-13, Jupyter Notebook, 0KB, 下载0次)
垃圾邮件脱离模型。#EDA-关键词，数据清理#词干#词袋#朴素贝叶斯分类器
Spam defection model . # EDA - stopwords, data cleaning # Stemming # Bag of words # Naive bayes classifier (2021-03-12, Jupyter Notebook, 0KB, 下载0次)
用于pySEOBNR的pycbc波形生成插件
A pycbc waveform generation plugin for pySEOBNR (2024-02-12, Jupyter Notebook, 0KB, 下载0次)
欢迎来到我们的搜索引擎项目！探索倒排指数、TF-IDF评分和弹性搜索比较。利用伪相关反馈优化MAP。比较文档排名模型并评估分类器。深入代码和结果！
Welcome to our Search Engine project! Explore inverted index, TF-IDF scoring, and Elasticsearch comparison. Optimize MAP with pseudo relevance feedback. Compare document ranking models and evaluate classifiers. Dive into code and results! (2024-01-27, Jupyter Notebook, 0KB, 下载0次)
该项目是一个语义搜索引擎，使用句子转换器构建，用于嵌入最近的蟋蟀数据。
This project is an semantic search engine built using sentence transformers for embedding on recent cricket data. (2023-12-06, Jupyter Notebook, 0KB, 下载0次)
具有HuggingFace句子transformer的图像搜索引擎，
Image Search Engine with HuggingFace Sentence Transformer, (2023-08-31, Jupyter Notebook, 0KB, 下载0次)
CCTV导航器（2019.10.14~11.01）
CCTV Navigator (2019.10.14 ~ 11.01) (2021-08-02, Jupyter Notebook, 80KB, 下载0次)
pycbc波形SEOBNRE，，
pycbc-waveform-SEOBNRE,, (2020-10-05, Jupyter Notebook, 2218KB, 下载0次)
Jupyter笔记本SEO代码段生成器
Jupyter Notebook SEO Snippet Generator (2019-03-28, Jupyter Notebook, 12KB, 下载0次)
关于推特搜索器是一个搜索引擎，它在推特上废弃的推文语料库中搜索文档...
About Twitter Searcher is a search engine which searches documents in the corpus of tweets scrapped from your Twitter homepage. The search engine is the implementation of the Vector Space Model. The tweets are scrapped using selenium and stored in MongoDB as a corpus. MongoDB is a document-based Database System. (2021-10-19, Jupyter Notebook, 529KB, 下载0次)
搜索引擎搜索结果数据剪贴器
SEARCH ENGINE SEARCH RESULTS DATA SCRAPPER (2023-05-22, Jupyter Notebook, 23KB, 下载0次)
垂直搜索引擎和主题分类器
Vertical Search Engine & Subject Classifier (2021-03-31, Jupyter Notebook, 3614KB, 下载0次)
搜索引擎绘图器，，
searchEngineCrawler,, (2023-04-01, Jupyter Notebook, 406KB, 下载0次)
狗的品种分类器和类似的狗搜索引擎。一个Flask应用程序，用于分类狗的类型和查找相似的狗。
Dog s breed Classifier and Similar Dogs search engine. A Flask app to classify Dog s type and Find similar dogs. (2020-07-27, Jupyter Notebook, 90563KB, 下载0次)
职位描述关键词提取器旨在从发布在网站上的任何给定职位描述中提取重要关键词（主题）...
JobDescription-Keywords-Extractor aims to extract important keywords (topics) from any given job description posted online for better search engine optimization (SEO) using Topic Modeling techniques. It can also be used to build a Resume Screening Tool where any given resume would be matched against the topics extracted from JD_Keywords_Extractor. (2019-06-14, Jupyter Notebook, 5708KB, 下载0次)