Compute Natural Breaks in Python (Fisher-Jenks algorithm)

A toolset to test data classification engines that generates mock data in various file formats, sizes and data profiles.

Python Data Loss Prevention (DLP) SDK - Nightfall Developer Platform

📊 数据挖掘常用算法：关联分析Apriori算法，数据分类决策树算法，数据聚类K-means算法

BinGuru is an open-source Typescript package to bin/classify data using 18 established binning methods, including a new method, resiliency.

Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data (Nature Communications, 2023)

Scan directories, exports, and backups for sensitive data (like PII and API keys) with Nightfall's data loss prevention (DLP) APIs. Discover what lives at-rest in your data silos.

Two differrent approach to predict Churn customers and finding out important variables that drives churn

Visual Knowledge Discovery tools for interactively visualizing, exploring, and identifying complex n-D data patterns in multivariate CSV data, to visualize machine learning classifier models.

ELT (Extract, Load, Transform) process of accelerometer/gyroscope events with Apache Spark (w/ Structured Streaming) and TimescaleDB

Discover ROPAC, a novel rule-based classifier we proposed. Here, you'll find the code, data, and original paper detailing this data classification algorithm.

Neural Network Deep learning specialization course offered via Coursera

Command Line Data Visualizer

Given the name of a property or attribute like 'BrandName' or 'AmountReceived', try to predict a data type like String, Boolean, Integer...

Nepali Data Classifiers

This project classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.

Data classification defines and categorizes data according to its type, sensitivity, and value

Классы для статистической обработки данных.

Library for one-dimensional data classification and simple statistics in Rust

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.