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

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

🇵🇸 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 🇵🇸

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

No description provided.

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

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

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.

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.

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.

👩🏻‍🚀 13-DataMining: Clear, beginner-friendly explanations and hands-on resources on Principal Component Analysis (PCA) and Isolation Forest for Outlier Detection — designed to make unsupervised learning approachable for everyone. ✠💚✠

📊 Explore data mining with this guide on Principal Component Analysis (PCA) and Isolation Forest for effective dimensionality reduction and anomaly detection.

Exercises on Timeseries Decompositions, Monte Carlo Simulations, and Outlier Detection

This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset

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.

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection

Rowwise outliers detection is the most common action most spectroscopists/chemometricians take to deal with discordant reading. However, an alternative method such as MacroPCA enables to account for cellwise outliers in spectroscopic analysis.

Consider only the below columns and prepare a prediction model for predicting Price. Corolla<-Corolla[c("Price","Age_08_04","KM","HP","cc","Doors","Gears","Quarterly_Tax","Weight")]

Prediction of Miles per gallon (MPG) Using Cars Dataset

This project focuses on analyzing app data from the Google Play Store to derive insights and identify patterns that can help app developers, marketers, and users make informed decisions. The dataset includes information about various app attributes like ratings, reviews, installs, size, category, content rating, and more.