https://github.com/somjit101/data_science-eda

A collection of useful implementations to perform EDA on a new dataset in order to understand preliminary patterns in the dataset and gain a high-level grasp of the dataset using plots and visualizations.
https://github.com/somjit101/data_science-eda

boxplots contour-plots distribution eda histogram iris-dataset plots qqplot seaborn-plots statistical-analysis violin-plots

Last synced: 11 months ago
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A collection of useful implementations to perform EDA on a new dataset in order to understand preliminary patterns in the dataset and gain a high-level grasp of the dataset using plots and visualizations.

• Host: GitHub
• URL: https://github.com/somjit101/data_science-eda
• Owner: somjit101
• Created: 2021-06-07T15:54:41.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-07T19:47:47.000Z (over 4 years ago)
• Last Synced: 2025-01-16T18:26:33.544Z (about 1 year ago)
• Topics: boxplots, contour-plots, distribution, eda, histogram, iris-dataset, plots, qqplot, seaborn-plots, statistical-analysis, violin-plots
• Language: Jupyter Notebook
• Homepage:
• Size: 786 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Exploratory Data Analysis on Python

A collection of useful implementations to perform EDA on a new dataset in order to understand preliminary patterns in the dataset and gain a high-level grasp of the dataset using plots and visualizations

**Python Version : 3.6.8**

Virtual Environment : Anaconda 4.8.2

## Topics for Reference

- Exploratory Data Analysis

  1. QQplot

  2. BoxCoxTransform

  

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