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https://github.com/gajendrasharma-github/exploratory-data-analysis

This Repository contains all EDA projects
https://github.com/gajendrasharma-github/exploratory-data-analysis

data-visualization eda exploratory-data-analysis

Last synced: 1 day ago
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This Repository contains all EDA projects

Host: GitHub
URL: https://github.com/gajendrasharma-github/exploratory-data-analysis
Owner: gajendrasharma-github
Created: 2024-08-16T12:30:48.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-08-19T17:38:42.000Z (3 months ago)
Last Synced: 2024-08-20T15:20:19.749Z (3 months ago)
Topics: data-visualization, eda, exploratory-data-analysis
Language: Jupyter Notebook
Homepage:
Size: 8.87 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# Guide to all the projects

**1. Exploring Big Mart Sales** [Link](https://github.com/gajendrasharma-github/Exploratory-Data-Analysis/blob/main/EDA_Big_Mart_Sales.ipynb)

**2. Exploring Student's Grade Dataset** [Link](https://github.com/gajendrasharma-github/EDA-Exploring-Data-to-uncover-hidden-Patterns/blob/main/Students%20Grade%20EDA.ipynb)

**3. Exploring MPG Dataset** [Link](https://github.com/gajendrasharma-github/EDA-Exploring-Data-to-uncover-hidden-Patterns/blob/main/EDA%20-%20MPG%20Dataset.ipynb)

**4. Exploring Iris Dataset** [Link](https://github.com/gajendrasharma-github/EDA-Exploring-Data-to-uncover-hidden-Patterns/blob/main/EDA-Iris%20Dataset.ipynb)

**5. Exploring Penguin Dataset** [Link](https://github.com/gajendrasharma-github/EDA-Exploring-Data-to-uncover-hidden-Patterns/blob/main/Penguin%20EDA.ipynb)

# EDA-Exploring-Data-to-uncover-hidden-Patterns

![image](https://github.com/user-attachments/assets/98421ad5-f3b1-4c93-8534-a91d70c6cdcb)

Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods.The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.