Ankurrsingh / EDA_Adult_dataset_UCI

Dataset: -www.cs.toronto.edu/~delve/data/adult/desc.html More about Dataset: -www.cs.toronto.edu/~delve/data/adult/desc.html

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

EDA_Adult_dataset_UCI

Dataset: -www.cs.toronto.edu/~delve/data/adult/desc.html More about Dataset: -www.cs.toronto.edu/~delve/data/adult/desc.html

exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods.

Typical graphical techniques used in EDA are: Box plot Histogram Multi-vari chart Run chart Pareto chart Scatter plot Stem-and-leaf plot Parallel coordinates Odds ratio Targeted projection pursuit Glyph-based visualization methods such as PhenoPlot[8] and Chernoff faces Projection methods such as grand tour, guided tour and manual tour Interactive versions of these plots Dimensionality reduction:

Multidimensional scaling Principal component analysis (PCA) Multilinear PCA Nonlinear dimensionality reduction (NLDR) Typical quantitative techniques are:

Median polish Trimean Ordination

About

Dataset: -www.cs.toronto.edu/~delve/data/adult/desc.html More about Dataset: -www.cs.toronto.edu/~delve/data/adult/desc.html


Languages

Language:Jupyter Notebook 98.9%Language:Python 1.1%