Search Results for "data cleaning" - Page 2
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Showing 52 open source projects for "data cleaning"
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Practical Machine Learning with Python
Master the essential skills needed to recognize and solve problems
Practical Machine Learning with Python is a comprehensive repository built to accompany a project-centered guide for applying machine learning techniques to real-world problems using Python’s mature data science ecosystem. It centralizes example code, datasets, model pipelines, and explanatory notebooks that teach users how to approach problems from data ingestion and cleaning all the way through feature engineering, model selection, evaluation, tuning, and production-ready deployment patterns. The repository emphasizes end-to-end workflows rather than isolated code snippets, showing how to handle common challenges like class imbalance, overfitting, hyperparameter optimization, and interpretability. ... -
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Streamline Analyst
AI agent that streamlines the entire process of data analysis
Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates all the tasks such as data cleaning, preprocessing, and even complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. -
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sketch
AI code-writing assistant that understands data content
...The tool integrates directly into pandas dataframes through an extension, making it easy to use within existing Python workflows without requiring additional IDE plugins. Sketch supports a variety of tasks including data cleaning, feature engineering, visualization, and exploratory analysis, all driven by simple natural language prompts. It also includes advanced capabilities for generating structured outputs and applying transformations directly to datasets, reducing the need for manual coding. -
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text-dedup
All-in-one text de-duplication
text-dedup is a Python library that enables efficient deduplication of large text corpora by using MinHash and other probabilistic techniques to detect near-duplicate content. This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible deduplication strategies, making it ideal for cleaning web-scraped data, language model training datasets, or document archives. -
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paramspider
Mine parameterized URLs from web archives for security testing
...These endpoints are commonly used during reconnaissance because parameters often expose inputs that may be vulnerable to issues like cross-site scripting, SQL injection, or server-side request forgery. ParamSpider automates the process of retrieving archived URLs, cleaning them, and preparing them for fuzzing or further probing. It can process a single domain or multiple domains from a list, making it useful for both targeted testing and large-scale reconnaissance. -
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funNLP
Resources, corpora, and tools for Chinese natural language processing
...The repository is organized into categories such as sentiment analysis, text classification, named entity recognition, knowledge graphs, and various lexicons (e.g. sensitive words, emotion dictionaries, stopwords). It also includes links to academic papers, open-source model implementations, and practical utilities like word segmentation or text cleaning scripts. The project is highly community-oriented, frequently updated with contributions and new resources, and it’s widely used in both academic and applied NLP research. Its value lies in providing not just tools but also curated, domain-specific data, which can be hard to find elsewhere. -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
...Each step in the workflow is described by a Trial Component. There is no relationship between Trial Components such as ordering. Trial Component: A description of a single step in a machine learning workflow. For example data cleaning, feature extraction, model training, model evaluation, etc. -
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tidyverse
Easily install and load packages from the tidyverse
tidyverse is a meta‑package that installs and loads a cohesive suite of R packages designed for data science, sharing underlying design principles, grammar, and data structures. Core components include ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats, and more. It promotes tidy data workflows and consistency across tasks. -
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Cookie AutoDelete
Firefox and Chrome WebExtension that deletes cookies
Firefox and Chrome WebExtension that deletes cookies and other browsing site data as soon as the tab closes, domain changes, browser restarts, or a combination of those events. Control your cookies! This extension is inspired by Self-Destructing Cookies. When a tab closes, any cookies not being used are automatically deleted. Prevent tracking by other cookies and add only the ones you trust. Easily import and export your cookie whitelist. -
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CSSO
CSS minifier with structural optimizations
...CSSO is based on CSSTree to parse CSS into AST, AST traversal and to generate AST back to CSS. All CSSTree API is available behind syntax field extended with compress() method. You may minify CSS step by step. CSSO can use data about how CSS is used in the markup for better compression. File with this data (JSON) can be set using the usage option. -
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Tidy
The granddaddy of HTML tools, with support for modern standards
The granddaddy of HTML tools. Supports modern standards. Thanks to the efforts of HTACG and prominent contributors, HTML Tidy has a whole new heartbeat and a whole new life. Tidy tidies HTML and XML. It can tidy your documents by itself, and developers can easily integrate its features into even more powerful tools. Tidy is a console application for macOS, Linux, Windows, UNIX, and more. It corrects and cleans up HTML and XML documents by fixing markup errors and upgrading legacy code to... -
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R4DS (R for Data Science)
R for data science: a book
...Includes many example datasets, diagrams, code samples, and “hands-on” exercises. Comprehensive coverage of data-science workflow: data import, cleaning, transformation, exploration, modelling etc. Includes topics beyond basics: relational data (joins), date/time, strings, working with missing values, visualizing data, etc. -
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queXC
Web based system for cleaning and classifying open text fields
An Open Source, web based data cleaning and coding system. queXC takes a data file (such as questionnaire data) and cleans the text input fields by spacing and spell checking them. Operators then code text fields using new/existing coding schemes. -
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Parsr
Transforms PDF, Documents and Images into Enriched Structured Data
Parsr is an open-source document parsing tool that converts PDFs, scanned images, and other structured documents into structured, machine-readable data formats. -
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Tashkeela processed
Tashkeela dataset cleaned and normalized.
A version of the Tashkeela Arabic diacritized text dataset cleaned from the non-Arabic content and the undiacritized text, then divided into training, development, and testing sets. The cleaning process includes removing the XML tags and strange symbols, as well as fixing diacritics errors. After that, the tokenization is performed while focusing on the extraction of the Arabic words. The result is a space-separated tokens file, where the words and the numbers are separated, but not the... -
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DISTOD
Distributed discovery of bidirectional order dependencies
...They can express that, for example, sorting books by publication date in ascending order also sorts them by age in descending order. The knowledge about order relationships is useful for many data management tasks, such as query optimization, data cleaning, or consistency checking. Because the bODs of a specific dataset are usually not explicitly given, they need to be discovered. The discovery of all minimal bODs (in set-based canonical form) is a task with exponential complexity in the number of attributes. -
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cocoNLP
A Chinese information extraction tool
cocoNLP is a lightweight natural-language processing toolkit geared toward practical information extraction from raw text, especially for Chinese and mixed Chinese–English content. Instead of requiring a heavy pipeline, it focuses on quick wins such as extracting names, places, organizations, emails, phone numbers, and dates directly from unstructured sentences. The project blends pattern-based methods with NLP heuristics, giving developers dependable results for real-world texts like chats,... -
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MLBox
MLBox is a powerful Automated Machine Learning python library
MLBox is a powerful Automated Machine Learning python library. Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,...) Prediction with model interpretation. MLBox has been developed and used by many active community members. -
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DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
DS-Take-Home is a repository that provides practical solutions to a series of real-world data science challenges inspired by the book A Collection of Data Science Take-Home Challenges. The project is designed as a learning resource where aspiring data scientists can study how typical industry-style take-home assignments are solved using data analysis and machine learning techniques. Each challenge is implemented in a separate Jupyter notebook that walks through the process of analyzing... -
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OGLDataScienceTool
Opengl tool for data science visualization
Data visualization tool written in LWJGL Compatible with libgdx and other opengl wrappers The project depends on apache poi, and apache commons, for office files support Planned features for next release: * reading json, and other nosql data structures * jdbc connection for creating dataframes * data heatmaps, and additional plots for questions, contact me kumar.santhi1982@hotmail.com more details: http://www.java-gaming.org/topics/ds/41920/view.html http://datascienceforindia.com/ -
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fooltrader
Quant framework for stock
Build a standard data schema, and then implement various connectors to import systems you are familiar with for analysis. fooltrader is a quantitative analysis trading system designed using big data technology, including data capture, cleaning, structuring, calculation, display, backtesting and trading. Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. ... -
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. ... -
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Data Science Specialization
Course materials for the Data Science Specialization on Coursera
...It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow. -
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Clear4Amiga
Joomla! plugin for cleaning HTML code and convert it to HTML 3.2 or 4
Clear4Amiga is plugin intended to clear HTML code generated with Joomla!, to strip codes not compatible with HTML 3.2 or HTML 4.0. Plugin will clear code only for selected Web browsers so data will be altered only for e.g. older or mobile browsers. In combination with compatible template same site and same articles can be CSS formated for modern browsers and converted to HTML 3 for ancient browsers. Plugin sould work with Joomla! 2.5.x (1.6, 1.7), up to version 3.x. -
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Febrl (Freely Extensible Biomedical Record Linkage) does data standardisation (segmentation and cleaning) and probabilistic record linkage ("fuzzy" matching) of one or more files or data sources which do not share a unique record key or identifier.
