# Pca on iris dataset python

**on iris dataset**. I will use default hyper-parameters for the classifier. from sklearn import

**dataset**s from sklearn.tree import DecisionTreeRegressor from sklearn import I'm.

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**PCA**2D projection of

**Iris dataset**The

**Iris dataset**represents 3 kind of

**Iris**flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (

**PCA**) applied to this data identifies the combination of attributes (principal components, or directions in the feature space) that account for the most variance in.

**dataset**using

**PCA**to visualize it in both 2-D and 3-D. Standardizing the

**Dataset**. Python_Tutorials / Sklearn /

**PCA**/ PCA_Data_Visualization_Iris_Dataset_Blog.ipynb Go to file Go to file T; Go to line L; Copy path Copy permalink; This commit does not belong to any branch on this repository, and may.

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**PCA**of the

**iris**data set in

**Python**. After performing the

**PCA**on your data, you can access the results with the following methods. Scores

**pca**.transform(data) will.

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**PCA**in 3D scatterplots. Note that the 3 red lines highlighting the dimensions. # libraries import pandas as pd import numpy as np from sklearn. decomposition import

**PCA**import matplotlib. pyplot as plt import seaborn as sns # Get the

**iris**

**dataset**sns. set_style ("white") df = sns. load.