simple but efficient kernel regression and anomaly detection algorithms

Certifiable Outlier-Robust Geometric Perception

RADseq Data Exploration, Manipulation and Visualization using R

Direct and robust methods for outlier detection in linear regression

Projects of Business Analyst Nanodegree Program

🇵🇸 PalTaqdeer is an AI-Driven Student Success Forecaster. Was developed for Hackathon Google Launchpad, data analysis techniques, Linear regression model, and Flask for the web 🇵🇸

[IEEE TKDE 2023] A list of up-to-date papers on streaming tensor decomposition, tensor tracking, dynamic tensor analysis

Toolkit to assist life science researchers in detecting outliers

Obstructive Sleep Apnea classification with help of numerical data set which having the physical body characteristics with the help of machine learing

Pharmaceutical drug performance analysis using matplotlib

This repository contains my learning path of python for data-science essential training(part-1). Here, I have included chapter-wise topics and my practice problems. Also, feel free to checkout for better understanding.

A tool for simple data analysis. A rip-off of R's dlookr package (https://github.com/choonghyunryu/dlookr)

Python package with a class that allows pipeline-like specification and execution of regression workflows.

1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation

Techniques to Explore the Data

[APSIPA ASC 2022] "Robust Online Tucker Dictionary Learning from Multidimensional Data Streams". In Proc. 14th APSIPA Annual Summit and Conference, 2022.

An Apache Spark (Scala) workflow for outlier detection, using K-means clustering.

The ConfidenceEllipse package provides functions for computing the coordinate points of confidence ellipses and ellipsoids for a given bivariate and trivariate dataset, at user-defined confidence level.

The dataset is about past loans. The loan_train.csv data set includes details of 346 customers whose loans are already paid off or defaulted.