Using TSNE to Visualize the Iris Dataset in 3D Using Python Machine Learning

Frist Load Iris dataset from sklearn and plot it into 2D:


2D Visualization

#import plotly express
import plotly.express as px
#Read Iris Dataset
df = px.data.iris()
#Select the features
features = ["sepal_width", "sepal_length", "petal_width", "petal_length"]
#plot scatter plot
fig = px.scatter_matrix(df, dimensions=features, color="species")
fig.show()

Output:


3D Visualization

#import libraries
from sklearn.manifold import TSNE
import plotly.express as px
#Read Iris Dataset
df = px.data.iris()
#Select the features
features = df.loc[:, :'petal_width']
#Fit into the model
tsne = TSNE(n_components=3, random_state=0)
projections = tsne.fit_transform(features, )
#Visualize
fig = px.scatter_3d(
    projections, x=0, y=1, z=2,
    color=df.species, labels={'color': 'species'}
)
fig.update_traces(marker_size=8)
fig.show()

Output:














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