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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.
Chart.js Box Plots and Violin Plot Charts
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.
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.
Two Data Visualization about the relationship between PROMs scores after surgery and weather conditions on the day of compilation. Made with the matplotlib library.
A development version of the vioplot R package. This has been migrated to "vioplot" as version 0.3.
A simple example on creating violin plots using Seaborn library in Python
:blue_book: Ejemplos de gráficos con R
The objective of this work is to investigate factors affecting borrower rate and loan amount.
Python EDA and Visualization Using , Matplotlib, Seaborn,Plotly and Bokeh. Map visualization using Folium
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
EDA - Pre processing | Feature Engineering
:notebook: Visualization and training a basic ML model on the Iris dataset
Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot
Visualization using Matplotlib and Seaborn
Tidy Tuesday: Bench press results by month (men), 1990-2019
Exploratory Data Analysis on Haberman Dataset
Scripts that I've used during grad school for data collection, analysis, visualization, cleaning, wrangling, etc., for classes, project reports, and manuscripts.
The project explores methods like groupby, melt of pandas and infer information from data of patients having cardiovascular disease and healthy individuals.
This repository contains the file for the task that was done as part of my internship in The Sparks Foundation with specialization - Data Science & Business Analytics.
Used libraries and functions as follows:
Interactive dashboard app for Airbnb data developed using Python and Dash