A framework for prototyping and benchmarking imputation methods

Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.

In-sensor reservoir computing for language learning via two-dimensional memristors

Simple and automatic data cleaning in one line of code! It performs one-hot encoding, date & time casting to datetime dtype, detects binary columns, safely convert non-numeric columns to numeric dtypes, cleaning dirty/empty values, normalizing values and removing unwanted columns all in one line of code. Get your data ready for model training an…

NLPiper is a package that agglomerates different NLP tools and applies their transformations in the target document.

EEG/MEG data preprocessing and analyses framework

Jupyter Notebook Praktikum Projects. This is repository with data analyst educational projects from Yandex.Praktikum.

Análise de dados sobre cotas de gênero e seu impacto nas eleições e proposições legislativas da Câmara dos Deputados Federais entre 1934 e 2021. Parte do TCC da pós-graduação em Inteligência Artificial e Aprendizado de Máquina na @pucminas

An open-source tool for performing feature selection process in different areas of research

A Python Library for the Generation of Artificial Missing Data

Flask REST API for Recommender System Book App on Android

ENPM808A: Introduction to Machine Learning Final Project

This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

A clean and modular pipeline for preprocessing the Food-101 dataset using both folder-based and CSV-based workflows.

The specProc package is a collection of preprocessing tools for spectroscopy data analysis.

🤖This is an interactive Streamlit web application that predicts the likelihood of multiple diseases(Diabetes Prediction, Heart Disease Prediction, Parkinson's Disease Prediction) using Machine Learning models.

Cleaned and processed HR data using Python for analysis and visualization. Analyzed employee trends and performance using SQL and Python. Built an interactive Power BI dashboard connected to MySQL for dynamic insights.

All the scripts to prepare the Courtois-Neuromod dataset

450h Data Scientist training - Collect and store large amounts of data - Build prediction models in Machine Learning and Deep Learning - Deploy your models in real conditions

Analyzing logistics data to optimize shipment efficiency, reduce delays, and enhance supply chain visibility using Power BI. Insights include top routes, delays, supplier trends, and peak shipments.