Python Tasks & ML | Задачи по питону и машинному обучению

from sklearn.model_selection import train_test_split

X = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)

print(len(X_test))

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6])
plt.title("График")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(df[df['A'] > 1].iloc[0]['B'])

import numpy as np

a = np.array([5, 10, 15, 20])
result = np.where(a > 10)
print(result)

import numpy as np

a = np.array([1, 2, 3, 4, 5])
mask = a % 2 == 0
print(a[mask])

import numpy as np

a = np.array([[1], [2], [3]])
b = np.array([10, 20, 30])
print(a + b)

import numpy as np

a = np.arange(6)
b = a.reshape((2, 3))
b[0][1] = 42
print(a)

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = a.T
b[0][1] = 99
print(a)

from sklearn.manifold import TSNE
import numpy as np

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