The power you need to cleanse, filter, sort, reshape, manage and analyze data from CSV files.
MultiReplace is a Notepad++ plugin for advanced multi-string replacements. It supports reusable lists, CSV column targeting, highlighting, and external data lookups. All replacements can be enhanced with scriptable rules, conditional logic, and math.
With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.
👾 package to cleanse complex networks data, extracted from the ml-graph-network-analyser
Movie recommender system based on content using Tfid-idf vectorizer and cosine similarity to calculate the scores and render.com free trail to deploy the results
A notebook aimed at predicting and improving water safety by analyzing contaminants and pollution levels in water sources, enhancing public health and ensuring access to clean drinking water.
Linux CLI tools to compare text files and find nearest neighbours across large directories using TF‑IDF or SimHash, with optional dedup workflows, useful in RAG pipelines to remove duplicate documents that have different MD5/SHA-256/SHA-512 hashes but same/similar contents. C++/C performance.
API Flask for Cleansing Input Text and Tweet File
Notebooks and scripts with data cleansing methods.
CVA highlights shifts in the process mean, making it particularly effective for detecting small changes.
Airbnb Paris - analytics and accommodation price prediction
API ini secara umum berfungsi untuk melakukan cleansing data teks (remove emoji, lowercase, three or more, stemming, tokenization, normalization, remove number, remove punctuation, stopwords) dengan 3 pilihan input output data:
In this challenge my goals are create API for cleansing data and make data analysis report from it. I use dataset from Kaggle: Indonesian Abusive and Hate Speech Twitter Text. It is Multi-Labeled Hate Speech and Abusive Indonesian Twitter Text by Muhammad Okky Ibrohim and Indra Budi (2019).
Data preparation for machine learning involves a. Removing data with blanks b. Picking up only those rows where there X value column has valid values
computational intelligence, university of gdańsk 2019-2020
Self learning projects in Python and R for data cleansing.
Cleaning and Analysing the raw data containing customers details of a grocery store based on their age, gender and products they purchase.
🔍 Streamline document processing with efficient similarity scoring to eliminate duplicates in RAG pipelines using powerful CLI tools.
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