pythonbeginner
Pca Analysis
Data science technique: pca-analysis
pythonPress ⌘/Ctrl + Shift + C to copy
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
X, _ = load_iris(return_X_y=True)
pca = PCA(n_components=2, random_state=42)
X2 = pca.fit_transform(X)
print(X2.shape)
print(pca.explained_variance_ratio_)Use Cases
- machine learning
- data analysis
Tags
Related Snippets
Similar patterns you can reuse in the same workflow.
pythonbeginner
Polars Dataframe
Data science technique: polars-dataframe
Best for: machine learning
#data#machine-learning
pythonintermediate
Dask Distributed
Data science technique: dask-distributed
Best for: machine learning
#data#machine-learning
pythonadvanced
Vaex Big Data
Data science technique: vaex-big-data
Best for: machine learning
#data#machine-learning
pythonbeginner
Modin Parallel
Data science technique: modin-parallel
Best for: machine learning
#data#machine-learning