outliers-detection

thierrygosselin/radiator

RADseq Data Exploration, Manipulation and Visualization using R

jbytecode/LinRegOutliers

Direct and robust methods for outlier detection in linear regression

sondosaabed/PalTaqdeer

🇵🇸 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 🇵🇸

ManarAlharbi/Business_Analyst_Nanodegree_Projects

Projects of Business Analyst Nanodegree Program

rares9301/anomaly-detection

simple but efficient kernel regression and anomaly detection algorithms

thanhtbt/tensor_tracking_survey

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

rupeshsure/Obstructive-Sleep-Apnea-Project

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

cmartell5/pharma-analysis

Pharmaceutical drug performance analysis using matplotlib

madhurima-nath/regression_and_predictions

AlexLietard/otpsy

Toolkit to assist life science researchers in detecting outliers

alinasahoo/python-data-science-essentials-1

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.

jeppbautista/pydatalook

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

abhijha3011/Feature-Engineering-Techniques

Techniques to Explore the Data

ChristianGoueguel/Cellwise-Outliers-Detection-in-Optical-Emission-Spectroscopy

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.

ChristianGoueguel/ConfidenceEllipse

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.

DrSara9888/Machaine-Learning-Big-Data

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

k-papadakis/datadriven-labs

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

manabil/Applied_Machine_Learning

👨‍💻 Learn how to implement a model of machine learning to solve a real problem

MoinDalvs/Assignment_Multi_linear_regression_2

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")]

MoinDalvs/Learn_Multi_Linear_Regression

Prediction of Miles per gallon (MPG) Using Cars Dataset

MuhammadUsmanTipu/Classification-IBM-Project

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.

ShreyaPatil1199/Laptop-Price-Predictor

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.

thanhtbt/ROTDL

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

y656/Weather-data-clustering

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

Bilpapster/cluster-them-out

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

paogam1997/Kalman-NL-Filters

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.

razamehar/Statistical-Analysis-on-the-Boston-Housing-data

R-based statistical analysis of Boston Housing Data. Explored feature scales, computed descriptive stats, visualized data, and identified outliers (e.g., higher crime rates in specific areas). Examined variable relationships, calculated correlation coefficients, and presented findings via cross-classifications.

scrab017/RarPG

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

sergiosaez6/outliers-analysis-r

Outliers Analysis project done as part of MSc Artificial Intelligence Research

Sharanya-Hegde/Credit-EDA-Assignment

This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.

tezam84/EDA_Happiness_report_2019

This is an Exploratory Data Analysis (EDA) in 12 Steps with an easy going dataset for beginners. The goal is to understand the correlation between variables step by step. For advance practionners you can use the profiling package in Python

ashishyadav24092000/EDA_on_HousePrice

In this repository I have performed Exploratory data analysis on the dataset famously known as House Price Prediction.

ashishyadav24092000/Exploratory_data_analysis3

In this repository I have performed Exploratory Data Analysis on the dataset student_performance.csv. In which i have tried to detect outliers,missing values,relationship among features and across features,Categorical data and continuous/numerical data.

ZlatanSU87/DataCleaningProject

Демонстрация применения различных методов очистки данных

ZofiaQlt/sales_analysis

🎯 Database optimization and sales performance analysis for a fine wine company seeking to improve their data management practices and data maturity level - use of Python and JupyterLab (Business insights, Data collection, Cleaning, EDA, and Data Visualization)