violinplot

an extensive Qt5 Plotter framework (including a feature-richt plotter widget, a speed-optimized, but limited variant and a LaTeX equation renderer!), written fully in C/C++ and without external dependencies

Development version of vioplot R package (CRAN maintainer)

In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

Chart.js Box Plots and Violin Plot Charts

A simple workaround to produce violin plot with highcharts.js

A development version of the vioplot R package. This has been migrated to "vioplot" as version 0.3.

Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot

Employed hyper-parameter tuning (Gridsearch CV) and ensemble methods (Voting Classifier) to combine the results of the best models. Data Cleaning and Exploration using Pandas. Stratified Cross Validation to model and validate the training data

A simple example on creating violin plots using Seaborn library in Python

Visualization using Matplotlib and Seaborn

📓 Visualization and training a basic ML model on the Iris dataset

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.

Exploratory Data Analysis on Haberman Dataset

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.

The objective of this work is to investigate factors affecting borrower rate and loan amount.

Tidy Tuesday: Bench press results by month (men), 1990-2019

The project explores methods like groupby, melt of pandas and infer information from data of patients having cardiovascular disease and healthy individuals.

Python EDA and Visualization Using , Matplotlib, Seaborn,Plotly and Bokeh. Map visualization using Folium