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ease
features
design
support

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Description

DataPreparator is a complimentary software application aimed at facilitating various aspects of data preparation, also known as data preprocessing, within the realms of data analysis and mining. This tool provides numerous functionalities to help you explore and ready your data before engaging in analysis or mining activities. It encompasses a range of features including data cleaning, discretization, numerical adjustments, scaling, attribute selection, handling missing values, addressing outliers, conducting statistical analyses, visualizations, balancing, sampling, and selecting specific rows, among other essential tasks. The software allows users to access data from various sources such as text files, relational databases, and Excel spreadsheets. It is capable of managing substantial data volumes effectively, as datasets are not retained in computer memory, except for Excel files and the result sets from certain databases that lack data streaming support. As a standalone tool, it operates independently of other applications, boasting a user-friendly graphical interface. Additionally, it enables operator chaining to form sequences of preprocessing transformations and allows for the creation of a model tree specifically for test or execution data, thereby enhancing the overall data preparation process. Ultimately, DataPreparator serves as a versatile and efficient resource for those engaged in data-related tasks.

Description

MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

GitHub
Microsoft 365
Microsoft Excel
Python

Integrations

GitHub
Microsoft 365
Microsoft Excel
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

DataPreparator

Founded

2010

Country

Australia

Website

www.datapreparator.com/features.html

Vendor Details

Company Name

Axel ARONIO DE ROMBLAY

Founded

2017

Website

mlbox.readthedocs.io/en/latest/

Product Features

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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