from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
model = LogisticRegression(max_iter=200).fit(X, y)
print(model.predict([X[0]]))

from sklearn.cluster import KMeans
import numpy as np

X = np.array([[1, 10], [2, 20], [100, 9000]])
KMeans(n_clusters=2).fit(X)

from sklearn.decomposition import PCA
import numpy as np

X = np.random.rand(100, 5)
X_pca = PCA(n_components=2).fit_transform(X)
print(X_pca.shape)

from sklearn.manifold import TSNE
import numpy as np

X = np.random.rand(200, 10)
X_emb = TSNE(n_components=2).fit_transform(X)