#Data-preprocessing
Showing 41 of 41 repositories tagged #data-preprocessing, ranked by stars
End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
Machine learning with dataframes
Open source project for data preparation for GenAI applications
Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
Machine Learning library for the web and Node.
Easy to use Python library of customized functions for cleaning and analyzing data.
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
Veldra — talk an agent into existence, then watch it grow. A self-hostable, local-first agent platform: describe what you need in plain language and it compiles a working agent tools, MCP, RAG, teams. The more you use it, the better it gets agents learn from your feedback and reshape as you talk.
Jupyter Notebooks and Data Sets for Pandas Library
A day to day plan for this challenge. Covers both theoritical and practical aspects
The Triton backend that allows running GPU-accelerated data pre-processing pipelines implemented in DALI's python API.
Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
A time series signal analysis and classification framework
Accelerating AI Training and Inference from Storage Perspective (Must-read Papers on Storage for AI)
I will update this repository to learn Machine learning with python with statistics content and materials
sciblox - Easier Data Science and Machine Learning
A quantitative study on over 1.25 million tweets about ChatGPT, employed data scrapping, data cleaning, EDA, topic modeling, and sentiment analysis.
“Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components: Data exploration & analysis. Included here: Pandas; NumPy; SciPy; a helping hand from Python's Standard Library.
A Python library for Automated Exploratory Data Analysis, Automated Data Cleaning, and Automated Data Preprocessing For Machine Learning and Natural Language Processing Applications in Python.
Movie Recommendation System: Project using R and Machine learning
Personalized anime recommendations based on collaborative filtering. Discover your next favorite anime!
The objective of this assignment is to extract textual data articles from the given URL and perform text analysis to compute variables that are explained
This is a project based on Data Science Bowl 2017. I did my best to propose a solution for the problem but I am still new to Deep Learning so my solution is not the optimal one but it can definitely be improved with some fine tuning and better resources.
Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset
By Smart Shaped s.r.l. (https://www.smartshaped.com/)
PTRAIL is a state-of-the art parallel computation library for Mobility Data Preprocessing and feature extraction.
This repository contains projects I have worked on for Data Cleaning and Manipulation in Python.
Assignment Solution of PW Skills Data Master Course
AI-driven Personal Goal Assistant: Reinforcement learning-powered software mimics user behavior, interacts with computer inputs, and autonomously achieves goals in finance, social networking, and productivity. Open-source, Python-based RL agent.
Especially useful for preprocessing of datasets like Raman spectra, infrared spectra, UV/Vis spectra, but also HPLC data and many other types of data. pyPreprocessing includes baseline correction, smoothing, filtering, normalization and transformation.
Missing Data Doctor is a diagnostic and treatment toolkit for missing values in machine learning datasets. It profiles missingness patterns, visualizes gaps, applies multiple imputation strategies, and evaluates their impact on model performance. Includes automated plots, metrics, and a full HTML report.
Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..
Numpy and Pandas are one of the most important building blocks of knowledge to get started in the field of Data Science, Analytics, Machine Learning, Business Intelligence, and Business Analytics. This Tutorial Focuses to help the Beginners to learn the core Concepts of Numpy and Pandas and get started with Machine Learning and Data Science.
Data preprocessing is a data mining technique that involves transforming raw data into an understandable format.
🚀 Complete ML Project: Salary Prediction using Linear Regression & Streamlit. 95.6% accuracy, interactive web interface, clean dataset, pre-trained model. Perfect for learning ML, web development, and practical HR applications.
A CLI-based dataset preprocessing tool for machine learning tasks. Features include data exploration, null value handling, one-hot encoding, and feature scaling, and download the modified dataset effortlessly.
🍾 A comprehensive machine learning project using Random Forest algorithm to predict wine quality based on physicochemical properties. Features EDA, model training, hyperparameter tuning, feature importance analysis, and detailed documentation.
This repository provides Python code for converting satellite data into a format suitable for deep learning models. It supports various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs).
This repository consists of cleaning and preprocessing datasets using various python libraries
Study notebooks made for learning machine learning for the Hawk team