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Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk through a few of the highlights and show how to use the new scatter and line plot functions for quickly creating very useful visualizations of data.
Comprehensive, detailed and interactive plots for STRUCTURE and ADMIXTURE population analysis
The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.
My R scripts, primarily R plotting scripts + some genomics software including 16S rRNA metataxnomics and RNAseq
This repository is my collection of various projects involving Data Visualization of various datasets using Python
The Mental Health in Tech Dashboard visualizes a dataset consisting of survey questions and responses about various aspects of the mental health of tech workers.
Higher Diploma in Science in Computing (Data Analytics) - Programme Module: Fundamentals of Data Analysis (COMP08050)
Videogame Sales Visualizations_____________________________________________ #Python #SQLITE #database #matplotlib #numpy #Tableau #barplots #seaborn #DataAnalysis
Appling R coding on the medical data from a given file data.csv, which is a dataset of a patient demographic containing standard information regarding individuals from a variety of ancestral lines.
This repository contains my AVETTI Commerce internship's reports and predicting accuracy of the models.
Some elements of data visualization with R
Create barplots or boxplots with significant level annotations.
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.
Some initial exercises on Matplotlib
This notebook will show you how to perform exploratory data analysis on the Big Mart Sales dataset
EDA and Machine Learning Model Training for the Student Performance Data
An IMDB Movies Shiny Dashboard
Visualized time series data using a line chart, bar chart, and box plots
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.