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

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

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.

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

Techniques to Explore the Data

Predict laptop prices using machine learning. This project leverages multiple linear regression to achieve an 82% prediction precision. Explore the influence of features like brand, specs, and more on laptop prices.

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

Files created to the Identificazione dei Sistemi Incerti project. Implemented Kalman Filter, EKF, UKF and a smoother. The Matlab files contain also the white-noise charaterzation of the signal and the outliers identification.

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.

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