\u0625\u0646 \u062a\u062c\u0645\u064a\u0639 \u0648\u0633\u0627\u0626\u0644 K \u0647\u0648 \u0623\u0633\u0644\u0648\u0628 \u0646\u0636\u0639 \u0641\u064a\u0647 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0625\u062d\u062f\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a K.<\/em><\/span><\/p>\n \u0627\u0644\u0647\u062f\u0641 \u0627\u0644\u0646\u0647\u0627\u0626\u064a \u0647\u0648 \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a K<\/em> \u062d\u064a\u062b \u062a\u0643\u0648\u0646 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u062f\u0627\u062e\u0644 \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u062a\u0634\u0627\u0628\u0647\u0629 \u062a\u0645\u0627\u0645\u064b\u0627 \u0645\u0639 \u0628\u0639\u0636\u0647\u0627 \u0627\u0644\u0628\u0639\u0636 \u0628\u064a\u0646\u0645\u0627 \u062a\u062e\u062a\u0644\u0641 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 \u062a\u0645\u0627\u0645\u064b\u0627 \u0639\u0646 \u0628\u0639\u0636\u0647\u0627 \u0627\u0644\u0628\u0639\u0636.<\/span><\/p>\n \u0645\u0646 \u0627\u0644\u0646\u0627\u062d\u064a\u0629 \u0627\u0644\u0639\u0645\u0644\u064a\u0629\u060c \u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062e\u0637\u0648\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u062a\u0646\u0641\u064a\u0630 \u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u0648\u0633\u0627\u0626\u0644 K:<\/span><\/p>\n 1. \u0627\u062e\u062a\u0631 \u0642\u064a\u0645\u0629 \u0644\u0640 K.<\/em><\/strong><\/span><\/p>\n 2. \u0642\u0645 \u0628\u062a\u062e\u0635\u064a\u0635 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0628\u0634\u0643\u0644 \u0639\u0634\u0648\u0627\u0626\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0623\u0648\u0644\u064a\u0629\u060c \u0645\u0646 1 \u0625\u0644\u0649 K.<\/em><\/strong><\/span><\/p>\n 3. \u0642\u0645 \u0628\u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u0627\u0644\u064a \u062d\u062a\u0649 \u062a\u062a\u0648\u0642\u0641 \u062a\u0639\u064a\u064a\u0646\u0627\u062a \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0639\u0646 \u0627\u0644\u062a\u063a\u064a\u064a\u0631.<\/strong><\/span><\/p>\n \u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0645\u062c\u0645\u0648\u0639\u0627\u062a k-means \u0641\u064a Python \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 KMeans<\/strong> \u0645\u0646 \u0648\u062d\u062f\u0629 sklearn<\/strong> .<\/span><\/p>\n \u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0648\u0641 \u0646\u0642\u0648\u0645 \u0628\u0627\u0633\u062a\u064a\u0631\u0627\u062f \u0643\u0627\u0641\u0629 \u0627\u0644\u0648\u062d\u062f\u0627\u062a \u0627\u0644\u062a\u064a \u0633\u0646\u062d\u062a\u0627\u062c\u0647\u0627 \u0644\u0625\u062c\u0631\u0627\u0621 \u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u0648\u0633\u0627\u0626\u0644 k:<\/span><\/p>\n \u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 DataFrame \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u062b\u0644\u0627\u062b\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u0640 20 \u0644\u0627\u0639\u0628\u064b\u0627 \u0645\u062e\u062a\u0644\u0641\u064b\u0627 \u0644\u0643\u0631\u0629 \u0627\u0644\u0633\u0644\u0629:<\/span><\/p>\n \u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 DataFrame \u0627\u0644\u0628\u0627\u0646\u062f\u0627:<\/span><\/p>\n \u0633\u0646\u0633\u062a\u062e\u062f\u0645 \u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u0648\u0633\u0627\u0626\u0644 k \u0644\u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u062c\u0647\u0627\u062a \u0627\u0644\u0641\u0627\u0639\u0644\u0629 \u0627\u0644\u0645\u0645\u0627\u062b\u0644\u0629 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0647\u0630\u0647 \u0627\u0644\u0645\u0642\u0627\u064a\u064a\u0633 \u0627\u0644\u062b\u0644\u0627\u062b\u0629.<\/span><\/p>\n \u0648\u0628\u0639\u062f \u0630\u0644\u0643 \u0633\u0648\u0641 \u0646\u0642\u0648\u0645 \u0628\u0627\u0644\u062e\u0637\u0648\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/span><\/p>\n \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0642\u064a\u0627\u0645 \u0628\u0630\u0644\u0643:<\/span><\/p>\n \u0645\u0644\u0627\u062d\u0638\u0629<\/strong> : \u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0642\u064a\u0627\u0633 \u0628\u062d\u064a\u062b \u064a\u0643\u0648\u0646 \u0644\u0643\u0644 \u0645\u062a\u063a\u064a\u0631 \u0646\u0641\u0633 \u0627\u0644\u0623\u0647\u0645\u064a\u0629 \u0639\u0646\u062f \u0645\u0644\u0627\u0621\u0645\u0629 \u062e\u0648\u0627\u0631\u0632\u0645\u064a\u0629 k-means. \u0648\u0625\u0644\u0627 \u0641\u0625\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0630\u0627\u062a \u0627\u0644\u0646\u0637\u0627\u0642\u0627\u062a \u0627\u0644\u0623\u0648\u0633\u0639 \u0633\u064a\u0643\u0648\u0646 \u0644\u0647\u0627 \u062a\u0623\u062b\u064a\u0631 \u0643\u0628\u064a\u0631 \u0644\u0644\u063a\u0627\u064a\u0629.<\/span><\/p>\n \u0644\u062a\u0646\u0641\u064a\u0630 \u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u0648\u0633\u0627\u0626\u0644 k \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062f\u0627\u0644\u0629 KMeans<\/strong> \u0645\u0646 \u0648\u062d\u062f\u0629 sklearn<\/strong> .<\/span><\/p>\n \u062a\u0633\u062a\u062e\u062f\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u0623\u0633\u0627\u0633\u064a \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n KMeans(init=’\u0639\u0634\u0648\u0627\u0626\u064a’\u060c n_clusters=8\u060c n_init=10\u060c Random_state=None)<\/strong><\/span><\/p>\n \u0630\u0647\u0628:<\/span><\/p>\n \u0627\u0644\u0648\u0633\u064a\u0637 \u0627\u0644\u0623\u0643\u062b\u0631 \u0623\u0647\u0645\u064a\u0629 \u0644\u0647\u0630\u0647 \u0627\u0644\u062f\u0627\u0644\u0629 \u0647\u0648 n_clusters\u060c \u0627\u0644\u0630\u064a \u064a\u062d\u062f\u062f \u0639\u062f\u062f \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u062a\u064a \u0633\u064a\u062a\u0645 \u0648\u0636\u0639 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a\u0647\u0627.<\/span><\/p>\n \u0648\u0645\u0639 \u0630\u0644\u0643\u060c \u0644\u0627 \u0646\u0639\u0631\u0641 \u0645\u0633\u0628\u0642\u064b\u0627 \u0639\u062f\u062f \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0623\u0645\u062b\u0644\u060c \u0644\u0630\u0644\u0643 \u0646\u062d\u062a\u0627\u062c \u0625\u0644\u0649 \u0625\u0646\u0634\u0627\u0621 \u0631\u0633\u0645 \u0628\u064a\u0627\u0646\u064a \u064a\u0639\u0631\u0636 \u0639\u062f\u062f \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0628\u0627\u0644\u0625\u0636\u0627\u0641\u0629 \u0625\u0644\u0649 SSE (\u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0623\u062e\u0637\u0627\u0621 \u0627\u0644\u0645\u0631\u0628\u0639\u0629) \u0644\u0644\u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n \u0639\u0627\u062f\u0629\u060c \u0639\u0646\u062f\u0645\u0627 \u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0647\u0630\u0627 \u0627\u0644\u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u062d\u0628\u0643\u0629\u060c \u0641\u0625\u0646\u0646\u0627 \u0646\u0628\u062d\u062b \u0639\u0646 “\u0627\u0644\u0631\u0643\u0628\u0629” \u062d\u064a\u062b \u064a\u0628\u062f\u0623 \u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0641\u064a “\u0627\u0644\u0627\u0646\u062d\u0646\u0627\u0621” \u0623\u0648 \u0627\u0644\u0627\u0633\u062a\u0642\u0631\u0627\u0631. \u0647\u0630\u0627 \u0647\u0648 \u0628\u0634\u0643\u0644 \u0639\u0627\u0645 \u0627\u0644\u0639\u062f\u062f \u0627\u0644\u0623\u0645\u062b\u0644 \u0644\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a.<\/span><\/p>\n \u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0647\u0630\u0627 \u0627\u0644\u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0645\u062e\u0637\u0637 \u0627\u0644\u0630\u064a \u064a\u0639\u0631\u0636 \u0639\u062f\u062f \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0639\u0644\u0649 \u0627\u0644\u0645\u062d\u0648\u0631 \u0627\u0644\u0633\u064a\u0646\u064a \u0648SSE \u0639\u0644\u0649 \u0627\u0644\u0645\u062d\u0648\u0631 \u0627\u0644\u0635\u0627\u062f\u064a:<\/span> <\/p>\n \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0631\u0633\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u064a\u060c \u064a\u0628\u062f\u0648 \u0623\u0646 \u0647\u0646\u0627\u0643 \u0634\u0628\u0643 \u0623\u0648 “\u0631\u0643\u0628\u0629” \u0639\u0646\u062f k = 3 \u0645\u062c\u0645\u0648\u0639\u0627\u062a<\/strong> .<\/span><\/p>\n \u0644\u0630\u0644\u0643\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 3 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0639\u0646\u062f \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u062e\u0627\u0635 \u0628\u0646\u0627 \u0641\u064a \u0627\u0644\u062e\u0637\u0648\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629.<\/span><\/p>\n \u0645\u0644\u062d\u0648\u0638\u0629<\/strong> : \u0641\u064a \u0627\u0644\u0639\u0627\u0644\u0645 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u060c \u064a\u0648\u0635\u0649 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0632\u064a\u062c \u0645\u0646 \u0647\u0630\u0647 \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629 \u0648\u062e\u0628\u0631\u0629 \u0627\u0644\u0645\u062c\u0627\u0644 \u0644\u0627\u062e\u062a\u064a\u0627\u0631 \u0639\u062f\u062f \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u062a\u064a \u0633\u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627.<\/span><\/p>\n \u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062c\u0645\u064a\u0639 \u0639\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u0645\u062b\u0644\u0649 \u0644\u0640 k<\/em> \u0644\u0640 3:<\/span><\/p>\n \u064a\u0639\u0631\u0636 \u0627\u0644\u062c\u062f\u0648\u0644 \u0627\u0644\u0646\u0627\u062a\u062c \u062a\u0639\u064a\u064a\u0646\u0627\u062a \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0644\u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0641\u064a DataFrame.<\/span><\/p>\n \u0644\u062a\u0633\u0647\u064a\u0644 \u062a\u0641\u0633\u064a\u0631 \u0647\u0630\u0647 \u0627\u0644\u0646\u062a\u0627\u0626\u062c\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0625\u0636\u0627\u0641\u0629 \u0639\u0645\u0648\u062f \u0625\u0644\u0649 DataFrame \u064a\u0648\u0636\u062d \u0645\u0647\u0645\u0629 \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0644\u0643\u0644 \u0644\u0627\u0639\u0628:<\/span><\/p>\n \u064a\u062d\u062a\u0648\u064a \u0639\u0645\u0648\u062f \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629<\/strong> \u0639\u0644\u0649 \u0631\u0642\u0645 \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 (0\u060c 1\u060c \u0623\u0648 2) \u0627\u0644\u0630\u064a \u062a\u0645 \u062a\u0639\u064a\u064a\u0646\u0647 \u0644\u0643\u0644 \u0644\u0627\u0639\u0628.<\/span><\/p>\n \u0627\u0644\u0644\u0627\u0639\u0628\u0648\u0646 \u0627\u0644\u0630\u064a\u0646 \u064a\u0646\u062a\u0645\u0648\u0646 \u0625\u0644\u0649 \u0646\u0641\u0633 \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0644\u062f\u064a\u0647\u0645 \u0642\u064a\u0645 \u0645\u062a\u0634\u0627\u0628\u0647\u0629 \u062a\u0642\u0631\u064a\u0628\u064b\u0627 \u0644\u0623\u0639\u0645\u062f\u0629 \u0627\u0644\u0646\u0642\u0627\u0637<\/strong> \u0648\u0627\u0644\u062a\u0645\u0631\u064a\u0631\u0627\u062a<\/strong> \u0627\u0644\u062d\u0627\u0633\u0645\u0629 \u0648\u0627\u0644\u0645\u0631\u062a\u062f\u0627\u062a<\/strong> .<\/span><\/p>\n \u0645\u0644\u0627\u062d\u0638\u0629<\/strong> : \u064a\u0645\u0643\u0646\u0643 \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0648\u062b\u0627\u0626\u0642 \u0627\u0644\u0643\u0627\u0645\u0644\u0629 \u0644\u0648\u0638\u064a\u0641\u0629 KMeans<\/strong> \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0640 sklearn<\/strong> \u0647\u0646\u0627<\/a> .<\/span><\/p>\n \u062a\u0634\u0631\u062d \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0645\u0647\u0627\u0645 \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a> \u062a\u064f\u0639\u0631\u0641 \u0625\u062d\u062f\u0649 \u062e\u0648\u0627\u0631\u0632\u0645\u064a\u0627\u062a \u0627\u0644\u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u0623\u0643\u062b\u0631 \u0634\u064a\u0648\u0639\u064b\u0627 \u0641\u064a \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0628\u0627\u0633\u0645 \u0627\u0644\u062a\u062c\u0645\u064a\u0639 \u0628\u0648\u0633\u0627\u0626\u0644 k . \u0625\u0646 \u062a\u062c\u0645\u064a\u0639 \u0648\u0633\u0627\u0626\u0644 K \u0647\u0648 \u0623\u0633\u0644\u0648\u0628 \u0646\u0636\u0639 \u0641\u064a\u0647 \u0643\u0644 \u0645\u0644\u0627\u062d\u0638\u0629 \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0625\u062d\u062f\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a K. \u0627\u0644\u0647\u062f\u0641 \u0627\u0644\u0646\u0647\u0627\u0626\u064a \u0647\u0648 \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a K \u062d\u064a\u062b \u062a\u0643\u0648\u0646 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u062f\u0627\u062e\u0644 \u0643\u0644 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u062a\u0634\u0627\u0628\u0647\u0629 \u062a\u0645\u0627\u0645\u064b\u0627 \u0645\u0639 \u0628\u0639\u0636\u0647\u0627 \u0627\u0644\u0628\u0639\u0636 \u0628\u064a\u0646\u0645\u0627 \u062a\u062e\u062a\u0644\u0641 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0627\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\n\n
\n
\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0627\u0633\u062a\u064a\u0631\u0627\u062f \u0627\u0644\u0648\u062d\u062f\u0627\u062a \u0627\u0644\u0636\u0631\u0648\u0631\u064a\u0629<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nfrom<\/span> sklearn. cluster<\/span> import<\/span> KMeans\nfrom<\/span> sklearn. preprocessing<\/span> import<\/span> StandardScaler<\/strong><\/span><\/pre>\n \u0627\u0644\u062e\u0637\u0648\u0629 2: \u0625\u0646\u0634\u0627\u0621 DataFrame<\/strong><\/span><\/h2>\n
\n
#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [18, np.nan, 19, 14, 14, 11, 20, 28, 30, 31,\n 35, 33, 29, 25, 25, 27, 29, 30, 19, 23],\n ' assists<\/span> ': [3, 3, 4, 5, 4, 7, 8, 7, 6, 9, 12, 14,\n np.nan, 9, 4, 3, 4, 12, 15, 11],\n ' rebounds<\/span> ': [15, 14, 14, 10, 8, 14, 13, 9, 5, 4,\n 11, 6, 5, 5, 3, 8, 12, 7, 6, 5]})\n\n#view first five rows of DataFrame\n<\/span>print<\/span> ( df.head<\/span> ())\n\n points assists rebounds\n0 18.0 3.0 15\n1 NaN 3.0 14\n2 19.0 4.0 14\n3 14.0 5.0 10\n4 14.0 4.0 8\n<\/strong><\/span><\/pre>\n \u0627\u0644\u062e\u0637\u0648\u0629 3: \u062a\u0646\u0638\u064a\u0641 \u0648\u0625\u0639\u062f\u0627\u062f DataFrame<\/strong><\/span><\/h2>\n
\n
#drop rows with NA values in any columns\n<\/span>df = df. dropna<\/span> ()\n\n#create scaled DataFrame where each variable has mean of 0 and standard dev of 1\n<\/span>scaled_df = StandardScaler(). fit_transform<\/span> (df)\n\n#view first five rows of scaled DataFrame<\/span>\nprint<\/span> (scaled_df[:5])\n\n[[-0.86660275 -1.22683918 1.72722524]\n [-0.72081911 -0.96077767 1.45687694]\n [-1.44973731 -0.69471616 0.37548375]\n [-1.44973731 -0.96077767 -0.16521285]\n [-1.88708823 -0.16259314 1.45687694]]<\/strong><\/pre>\n \u0627\u0644\u062e\u0637\u0648\u0629 4: \u0627\u0628\u062d\u062b \u0639\u0646 \u0627\u0644\u0639\u062f\u062f \u0627\u0644\u0623\u0645\u062b\u0644 \u0644\u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a<\/strong><\/span><\/h2>\n
\n
#initialize kmeans parameters\n<\/span>kmeans_kwargs = {\n\" init<\/span> \": \" random<\/span> \",\n\" n_init<\/span> \": 10,\n\" random_state<\/span> \": 1,\n}\n\n#create list to hold SSE values for each k\n<\/span>sse = []\nfor<\/span> k in<\/span> range(1, 11):\n kmeans = KMeans(n_clusters=k, **<\/span> kmeans_kwargs)\n kmeans. fit<\/span> (scaled_df)\n sse. append<\/span> (kmeans.inertia_)\n\n#visualize results\n<\/span>plt. plot<\/span> (range(1, 11), sse)\nplt. xticks<\/span> (range(1, 11))\nplt. xlabel<\/span> (\" Number of Clusters<\/span> \")\nplt. ylabel<\/span> (\u201c SSE<\/span> \u201d)\nplt. show<\/span> ()<\/strong> <\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/kmmoyenne1.jpg\")
<\/p>\n \u0627\u0644\u062e\u0637\u0648\u0629 5: \u0625\u062c\u0631\u0627\u0621 \u062a\u062c\u0645\u064a\u0639 K-Means \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 Optimal K<\/em><\/strong><\/span><\/h2>\n
#instantiate the k-means class, using optimal number of clusters\n<\/span>kmeans = KMeans(init=\" random<\/span> \", n_clusters= 3<\/span> , n_init= 10<\/span> , random_state= 1<\/span> )\n\n#fit k-means algorithm to data\n<\/span>kmeans. fit<\/span> (scaled_df)\n\n#view cluster assignments for each observation\n<\/span>kmeans. labels_\n\n<\/span>array([1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0]) \n<\/span><\/span><\/strong><\/pre>\n #append cluster assingments to original DataFrame\n<\/span>df[' cluster<\/span> '] = kmeans. labels_<\/span>\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n<\/span><\/span>points assists rebounds cluster\n0 18.0 3.0 15 1\n2 19.0 4.0 14 1\n3 14.0 5.0 10 1\n4 14.0 4.0 8 1\n5 11.0 7.0 14 1\n6 20.0 8.0 13 1\n7 28.0 7.0 9 2\n8 30.0 6.0 5 2\n9 31.0 9.0 4 0\n10 35.0 12.0 11 0\n11 33.0 14.0 6 0\n13 25.0 9.0 5 0\n14 25.0 4.0 3 2\n15 27.0 3.0 8 2\n16 29.0 4.0 12 2\n17 30.0 12.0 7 0\n18 19.0 15.0 6 0\n19 23.0 11.0 5 0\n<\/strong><\/pre>\n \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h2>\n
\u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a>
\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 K-Fold \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"