RADseq Data Exploration, Manipulation and Visualization using R
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Updated
Jun 5, 2024 - HTML
RADseq Data Exploration, Manipulation and Visualization using R
Certifiable Outlier-Robust Geometric Perception
Projects of Business Analyst Nanodegree Program
Direct and robust methods for outlier detection in linear regression
[IEEE TKDE 2023] A list of up-to-date papers on streaming tensor decomposition, tensor tracking, dynamic tensor analysis
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.
Localization processes for functional data analysis. Software companion for the paper “Localization processes for functional data analysis” by Elías, A., Jiménez, R., and Yukich, J. (2020)
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
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
Obstructive Sleep Apnea classification with help of numerical data set which having the physical body characteristics with the help of machine learing
Techniques to Explore the Data
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
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.
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.
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.
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.
An Apache Spark (Scala) workflow for outlier detection, using K-means clustering.
In this repository, using the statistical software R, are been analyzed robust techniques to estimate multivariate linear regression in presence of outliers, using the Bootstrap, a simulation method where the construction of sample distribution of given statistics occurring through resampling the same observed sample.
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