\uc5f0\uad00 \uaddc\uce59 \ud559\uc2b5 (Association Rule Learning):<\/strong> \ub370\uc774\ud130 \uac04\uc758 \uc5f0\uad00\uc131\uc744 \ucc3e\uc73c\uba70, \uc2dc\uc7a5 Basket \ubd84\uc11d \ub4f1\uc5d0 \ud65c\uc6a9\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n2. \ube44\uc9c0\ub3c4\ud559\uc2b5\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc0ac\ub840<\/h2>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc5ec\ub7ec \uc608\uc2dc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
2.1 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uae30\ubc95 \ud65c\uc6a9<\/h3>\n
\ud074\ub7ec\uc2a4\ud130\ub9c1 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc720\uc0ac\ud55c \uc8fc\uc2dd\ub4e4\uc744 \uadf8\ub8f9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ud2b9\uc815 \uad70\uc9d1 \ub0b4\uc5d0\uc11c\uc758 \ub3d9\ud5a5\uc744 \ubd84\uc11d\ud558\uace0, \uc2dc\uc7a5 \uac10\uc815\uc774\ub098 \ub3d9\ud5a5\uc5d0 \ub530\ub978 \uacb0\uc815\uc744 \uc9c0\uc6d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('stock_data.csv')\n\n# KMeans \uad70\uc9d1\ud654\nkmeans = KMeans(n_clusters=3)\ndata['Cluster'] = kmeans.fit_predict(data[['Return', 'Volume']])\n\n# \ud50c\ub85c\ud305\nplt.scatter(data['Return'], data['Volume'], c=data['Cluster'], cmap='viridis')\nplt.title('K-Means Clustering')\nplt.xlabel('Return')\nplt.ylabel('Volume')\nplt.show()\n <\/pre>\n2.2 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95 \ud65c\uc6a9<\/h3>\n
PCA\uc640 t-SNE \ub4f1\uc758 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc758 \ud575\uc2ec \ud2b9\uc131\uc744 \uc2dc\uac01\ud654\ud558\uace0, \uadf8\uc5d0 \ub530\ub978 \ud2b8\ub80c\ub4dc \uc608\uce21\uc744 \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\uc740 \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uc9c1\uad00\uc131\uc744 \ub192\uc774\uace0, \ub354 \ub098\uc740 \uc2e4\uc9c8\uc801 \uc778\uc0ac\uc774\ud2b8\ub97c \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.decomposition import PCA\nimport seaborn as sns\n\n# PCA \ucc28\uc6d0 \ucd95\uc18c\npca = PCA(n_components=2)\npca_result = pca.fit_transform(data)\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(8, 6))\nsns.scatterplot(x=pca_result[:, 0], y=pca_result[:, 1], hue=data['Cluster'])\nplt.title('PCA Result')\nplt.xlabel('Principal Component 1')\nplt.ylabel('Principal Component 2')\nplt.show()\n <\/pre>\n2.3 \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uac1c\uc120<\/h3>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc740 \uc27d\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 SILHOUETTE SCORE\uc640 \uac19\uc740 \uba54\ud2b8\ub9ad\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc720\ud6a8\uc131\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud558\uc5ec \ubcf4\ub2e4 \uc815\ubc00\ud55c \uacb0\uacfc\ub97c \uc5bb\ub294 \uac83\ub3c4 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.metrics import silhouette_score\n\n# \uc2e4\ub8e8\uc5e3 \uc810\uc218 \uacc4\uc0b0\nscore = silhouette_score(data[['Return', 'Volume']], data['Cluster'])\nprint(f'Silhouette Score: {score}')\n <\/pre>\n3. \ube44\uc9c0\ub3c4\ud559\uc2b5\uc758 \ub3c4\uc804 \uacfc\uc81c<\/h2>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5\uc758 \uc801\uc6a9\uc740 \uba87 \uac00\uc9c0 \ub3c4\uc804 \uacfc\uc81c\ub97c \ub3d9\ubc18\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \ud488\uc9c8, \ud45c\ubcf8 \ud06c\uae30, \uadf8\ub9ac\uace0 \ud574\uc11d\uc758 \uc8fc\uad00\uc131\uc774 \uadf8 \uc608\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc774 \uae30\uc220\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc801\uc808\ud55c \ub370\uc774\ud130 \ucc98\ub9ac\uc640 \ud574\uc11d \ubc29\ubc95\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \ud488\uc9c8 \ubb38\uc81c<\/h3>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5\uc758 \uc131\ub2a5\uc740 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc5d0 \ud06c\uac8c \uc758\uc874\ud569\ub2c8\ub2e4. \ub178\uc774\uc988\uac00 \ub9ce\uc740 \ub370\uc774\ud130, \uacb0\uce21\uce58\uac00 \uc788\ub294 \ub370\uc774\ud130\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc800\ud558\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n
3.2 \uacb0\uacfc \ud574\uc11d\uc758 \uc8fc\uad00\uc131<\/h3>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5\uc758 \uacb0\uacfc\ub294 \uc885\uc885 \uc8fc\uad00\uc801\uc785\ub2c8\ub2e4. \ud574\uc11d\uc790\uc758 \uc804\ubb38\uc131\uacfc \uacbd\ud5d8\uc5d0 \ub530\ub77c \uc11c\ub85c \ub2e4\ub978 \uacb0\ub860\uc5d0 \ub3c4\ub2ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc810\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \uacfc\uc815\uc5d0\uc11c\ub3c4 \uc911\uc694\ud55c \uc694\uc18c\uc785\ub2c8\ub2e4.<\/p>\n
3.3 \uc801\uc808\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815<\/h3>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5\uc758 \ubaa8\ub378\uc740 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc5d0 \ubbfc\uac10\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud074\ub7ec\uc2a4\ud130\uc758 \uc218 K\ub97c \uacb0\uc815\ud558\ub294 \uac83\uc740 K-means \uc54c\uace0\ub9ac\uc998\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \uc801\uc808\ud55c \uac12\uc744 \ucc3e\uae30 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \uc2e4\ud5d8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
4. \ube44\uc9c0\ub3c4\ud559\uc2b5\uc758 \ubbf8\ub798 \uac00\ub2a5\uc131<\/h2>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5\uc740 \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d\uc758 \ud544\uc218\uc801\uc778 \ub3c4\uad6c\uac00 \ub418\uace0 \uc788\uc73c\uba70, \uadf8 \uac00\ub2a5\uc131\uc740 \ubb34\uad81\ubb34\uc9c4\ud569\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \uae30\ubc95\uacfc \uacb0\ud569\ud558\uc5ec \ub354\uc6b1 \uc815\uad50\ud55c \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0, \uc2dc\uc7a5\uc758 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\ub294 \ub370 \uae30\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uac15\ud654\ud559\uc2b5\uacfc \uac19\uc740 \ub2e4\ub978 \ud559\uc2b5 \ubc29\ubc95\uacfc\uc758 \uc870\ud569\uc744 \ud1b5\ud574 \ub354\uc6b1 \ucd5c\uc801\ud654\ub41c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uacb0\ub860<\/h2>\n
\ube44\uc9c0\ub3c4\ud559\uc2b5\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uc720\uc6a9\ud55c \ud328\ud134\uc744 \ubc1c\uacac\ud558\uace0, \ud6a8\uacfc\uc801\uc778 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub370 \ud575\uc2ec\uc801\uc778 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \ubd84\uc11d\uc744 \ud1b5\ud574 \uc2dc\uc7a5\uc758 \ud2b8\ub80c\ub4dc\ub97c \uc774\ud574\ud558\uace0, \ubbf8\ub798\uc758 \ubcc0\ud654\ub97c \uc608\uce21\ud558\ub294 \ub370 \uc788\uc5b4 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc740 \ub354 \uc774\uc0c1 \uc120\ud0dd\uc774 \uc544\ub2cc \ud544\uc218\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub098\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uae30 \uc704\ud574 \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c\uc640 \ud65c\uc6a9\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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