There are 1 repository under data-cleaning-pipeline topic.
🤖 An automated machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers). Python 3.6 required.
Learn2Clean: Optimizing the Sequence of Tasks for Data Preparation and Cleaning
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.
Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.
A Python library for day to day data analysis and machine learning. This aims to make data building, cleaning and machine learning much much faster. A library of extension and helper modules for Python's data analysis and machine learning libraries.
Create a machine learning pipeline, that categorizes disaster events.
Automating the data preprocessing pipeline
The dataset I wrangled (and analysed and visualized) is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog.
excel, markdown, csv, sql 数据源批量/单独格式互相转换
This is replicable exploratory data analysis of Peru SINADEF database (death index) of covid-19 related cases.
Data ETL for machine learning with dockerizing, including data crawling, data transforming/cleaning, and saving data to s3
Inconsistent company names demo
scrape e-commerce site products information
This data analysis and visualization project aimed at presenting the work of OBA-Floripa NGO to authorities and the general population. The idea is to claim the need for continued funding resources, given the positive impact of the organization's activities on public health issues.
I learnt data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.
A tool that automatically detects and corrects errors in location data and imputes missing values for location-dependent data, such as region name.