{"id":4061,"date":"2023-07-13T20:58:15","date_gmt":"2023-07-13T20:58:15","guid":{"rendered":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%90%e1%80%b6%e1%80%90%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%86%e1%80%85%e1%80%ba%e1%80%94%e1%80%8a%e1%80%ba%e1%80%b8%e1%80%9c%e1%80%99%e1%80%ba\/"},"modified":"2023-07-13T20:58:15","modified_gmt":"2023-07-13T20:58:15","slug":"python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%90%e1%80%b6%e1%80%90%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%86%e1%80%85%e1%80%ba%e1%80%94%e1%80%8a%e1%80%ba%e1%80%b8%e1%80%9c%e1%80%99%e1%80%ba","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%90%e1%80%b6%e1%80%90%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%86%e1%80%85%e1%80%ba%e1%80%94%e1%80%8a%e1%80%ba%e1%80%b8%e1%80%9c%e1%80%99%e1%80%ba\/","title":{"rendered":"\u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1021\u102f\u1015\u103a\u1005\u102f\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u101b\u103e\u102c\u101b\u1014\u103a python \u101b\u103e\u102d elbow method \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

machine learning<\/a> \u1010\u103d\u1004\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038 \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1021\u1015\u103c\u102f\u1036\u101c\u102d\u102f\u1000\u103a \u1021\u101a\u103a\u101c\u1002\u102d\u102f\u101b\u102e\u101e\u1019\u103a\u1019\u103b\u102c\u1038\u1011\u1032\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1001\u103c\u1004\u103a\u1038<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

K \u1006\u102d\u102f\u101e\u100a\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f K<\/em> \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101e\u102d\u102f\u1037 \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1019\u103e \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1011\u102c\u1038\u101b\u103e\u102d\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1006\u102f\u1036\u1038\u1015\u1014\u103a\u1038\u1010\u102d\u102f\u1004\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u101b\u103e\u102d \u101b\u103e\u102f\u1019\u103c\u1004\u103a\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1010\u1030\u100a\u102e\u1015\u103c\u102e\u1038 \u1000\u103d\u1032\u1015\u103c\u102c\u1038\u101e\u1031\u102c\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1000\u103d\u102c\u1001\u103c\u102c\u1038\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 K<\/em> \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038 \u101b\u103e\u102d\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1015\u1011\u1019\u1021\u1006\u1004\u1037\u103a\u1019\u103e\u102c \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101c\u1031\u1037\u101c\u102c\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1011\u100a\u1037\u103a\u101c\u102d\u102f\u101e\u1031\u102c\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a K<\/em> \u1021\u1010\u103d\u1000\u103a\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f\u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

K<\/em> \u1021\u1010\u103d\u1000\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1019\u103b\u102c\u1038\u1011\u1032\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f elbow method<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a \u104a x-axis \u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1021\u1005\u102f\u1021\u1005\u100a\u103a\u1038\u1019\u103b\u102c\u1038\u104f \u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1014\u103e\u1004\u1037\u103a y-\u101d\u1004\u103a\u101b\u102d\u102f\u1038\u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u1015\u1031\u102b\u1004\u103a\u1038\u104f \u1005\u102f\u1005\u102f\u1015\u1031\u102b\u1004\u103a\u1038 \u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a\u1016\u1014\u103a\u1010\u102e\u1038\u1001\u103c\u1004\u103a\u1038 \u1015\u102b\u101d\u1004\u103a\u101e\u100a\u1037\u103a elbow method \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104a \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a \u1001\u103d\u1032\u1001\u103c\u102c\u1038\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u102b\u104b \u201c\u1012\u1030\u1038\u201d \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1021\u101c\u103e\u100a\u1037\u103a\u1010\u1005\u103a\u1001\u102f\u101e\u100a\u103a \u1000\u103d\u1000\u103a\u1000\u103d\u1000\u103a\u1011\u1032\u1010\u103d\u1004\u103a \u1015\u1031\u102b\u103a\u101c\u102c\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u201c\u1012\u1030\u1038\u201d \u1016\u103c\u1005\u103a\u1015\u1031\u102b\u103a\u101c\u102c\u101e\u100a\u1037\u103a x \u101d\u1004\u103a\u101b\u102d\u102f\u1038\u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1021\u1019\u103e\u1010\u103a\u101e\u100a\u103a k-\u1006\u102d\u102f\u101c\u102d\u102f\u101e\u100a\u103a \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1021\u1015\u103c\u102f\u1036\u101c\u102d\u102f\u1000\u103a \u1021\u101a\u103a\u101c\u1002\u102d\u102f\u101b\u102e\u101e\u1019\u103a\u1010\u103d\u1004\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038 \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u1015\u103c\u1031\u102c\u1015\u103c\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1025\u1015\u1019\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a\u1010\u1036\u1010\u1031\u102c\u1004\u103a\u1006\u1005\u103a\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102f\u1036\u1000\u102d\u102f\u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1006\u1004\u1037\u103a 1: \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c module \u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n

\u1026\u1038\u1005\u103d\u102c\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u1019\u100a\u1037\u103a module \u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u1019\u100a\u103a-<\/span><\/p>\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
 \u1021\u1006\u1004\u1037\u103a 2: DataFrame \u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1018\u1010\u103a\u1005\u1000\u1010\u103a\u1018\u1031\u102c\u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038 20 \u1021\u1010\u103d\u1000\u103a variable \u101e\u102f\u1036\u1038\u1001\u102f\u1015\u102b \u1040 \u1004\u103a\u101e\u1031\u102c DataFrame \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\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#drop rows with NA values in any columns\ndf = df. dropna<\/span> ()<\/span>\n\n#create scaled DataFrame where each variable has mean of 0 and standard dev of 1\nscaled_df = StandardScaler(). fit_transform<\/span> (df)\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u1021\u1006\u1004\u1037\u103a 3- \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u104f \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f\u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a \u1010\u1036\u1010\u1031\u102c\u1004\u103a\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1024\u1019\u1000\u103a\u1011\u101b\u1005\u103a\u101e\u102f\u1036\u1038\u1001\u102f\u1021\u1015\u1031\u102b\u103a\u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1021\u101c\u102c\u1038\u1010\u1030\u101e\u101b\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u102f\u1015\u103a\u1005\u102f\u1016\u103d\u1032\u1037\u101b\u1014\u103a k-Means \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101c\u102d\u102f\u101e\u100a\u103a\u1006\u102d\u102f\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n
 Python \u1010\u103d\u1004\u103a k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a sklearn<\/strong> module \u1019\u103e KMeans<\/a> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u101b\u1031\u1038\u1021\u1000\u103c\u102e\u1038\u1006\u102f\u1036\u1038 \u1021\u1004\u103c\u1004\u103a\u1038\u1021\u1001\u102f\u1036\u1019\u103e\u102c n_clusters<\/strong> \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104a \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u100a\u103a\u1019\u103b\u103e\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u1031\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u104f SSE (\u1014\u103e\u1005\u103a\u1011\u1015\u103a\u1000\u102d\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038\u1019\u103b\u102c\u1038) \u1000\u102d\u102f\u1015\u103c\u101e\u1019\u100a\u1037\u103a \u1002\u101b\u1015\u103a\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1015\u1031\u102b\u1004\u103a\u1038\u101c\u1012\u103a\u101e\u100a\u103a \u201c\u1000\u103d\u1031\u1038\u101e\u100a\u103a\u201d \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1010\u100a\u103a\u1004\u103c\u102d\u1019\u103a\u1005\u1015\u103c\u102f\u101e\u100a\u1037\u103a \u201c\u1012\u1030\u1038\u201d \u1000\u102d\u102f \u101b\u103e\u102c\u1016\u103d\u1031\u1015\u102b\u1019\u100a\u103a\u104b \u1024\u1021\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u1000\u102d\u102f\u101a\u103a\u1005\u102c\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1000\u102f\u1012\u103a\u101e\u100a\u103a x-axis \u1014\u103e\u1004\u1037\u103a y-axis \u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1021\u1005\u102f\u1021\u1005\u100a\u103a\u1038\u1019\u103b\u102c\u1038\u104f \u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u1037\u103a \u1024\u1000\u103d\u1000\u103a\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span> <\/p>\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
\"\"<\/p>\n
 \u1024\u1002\u101b\u1015\u103a\u1010\u103d\u1004\u103a\u104a k = 3 \u1015\u103c\u103d\u1010\u103a<\/strong> \u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1021\u1000\u103d\u1031\u1038\u1021\u1000\u1031\u102c\u1000\u103a \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a “ \u1012\u1030\u1038”  \u101b\u103e\u102d\u1014\u1031\u101e\u100a\u103a\u1000\u102d\u102f \u1010\u103d\u1031\u1037\u101b\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a\u104a \u1014\u1031\u102c\u1000\u103a\u1010\u1006\u1004\u1037\u103a\u1010\u103d\u1004\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f k-Means \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a \u1021\u1005\u102f\u1021\u1016\u103d\u1032\u1037 3 \u1001\u102f\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 4- Optimal K<\/em> \u1016\u103c\u1004\u1037\u103a K-Means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a k<\/em> -3 \u1021\u1010\u103d\u1000\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1015\u1031\u102b\u103a\u1010\u103d\u1004\u103a k-\u1006\u102d\u102f\u101c\u102d\u102f\u101b\u1004\u103a\u1038 \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\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
 \u101b\u101c\u1012\u103a\u1007\u101a\u102c\u1038\u101e\u100a\u103a DataFrame \u101b\u103e\u102d \u1000\u103c\u100a\u1037\u103a\u101b\u103e\u102f\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1021\u1010\u103d\u1000\u103a \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1010\u102c\u101d\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1024\u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1013\u102d\u1015\u1039\u1015\u102c\u101a\u103a\u1016\u103d\u1004\u1037\u103a\u1006\u102d\u102f\u101b\u1014\u103a \u1015\u102d\u102f\u1019\u102d\u102f\u101c\u103d\u101a\u103a\u1000\u1030\u1005\u1031\u101b\u1014\u103a\u104a \u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038\u1010\u1005\u103a\u1026\u1038\u1005\u102e\u104f \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u102c\u101d\u1014\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u1037\u103a DataFrame \u1010\u103d\u1004\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1011\u100a\u1037\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\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
 \u1021\u1005\u102f\u1021\u101d\u1031\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a \u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038\u1010\u1005\u103a\u1026\u1038\u1005\u102e\u1019\u103e \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1021\u1005\u102f\u1014\u1036\u1015\u102b\u1010\u103a (0\u104a 1 \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a 2) \u1015\u102b\u101b\u103e\u102d\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1010\u1030\u100a\u102e\u101e\u1031\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103e \u1015\u102d\u102f\u1004\u103a\u1006\u102d\u102f\u1004\u103a\u101e\u100a\u1037\u103a \u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038<\/strong> \u104a \u1000\u1030\u100a\u102e\u1015\u1031\u1038\u1001\u103c\u1004\u103a\u1038<\/strong> \u1014\u103e\u1004\u1037\u103a \u1015\u103c\u1014\u103a\u101c\u103e\u1014\u103a\u1001\u103c\u1004\u103a\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103c\u1031\u1010\u1030\u100a\u102e\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u101b\u103e\u102d\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a<\/strong> – sklearn<\/strong> \u104f KMeans<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1005\u102c\u101b\u103d\u1000\u103a\u1005\u102c\u1010\u1019\u103a\u1038\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u101e\u1031\u102c \u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u1031\u102c \u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 Python \u1010\u103d\u1004\u103a linear regression \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038<\/a>
 Python \u1010\u103d\u1004\u103a Logistic Regression \u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a>
 Python \u1010\u103d\u1004\u103a K-Fold \u1021\u1015\u103c\u1014\u103a\u1021\u101c\u103e\u1014\u103a validation \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
machine learning \u1010\u103d\u1004\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038 \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1021\u1015\u103c\u102f\u1036\u101c\u102d\u102f\u1000\u103a \u1021\u101a\u103a\u101c\u1002\u102d\u102f\u101b\u102e\u101e\u1019\u103a\u1019\u103b\u102c\u1038\u1011\u1032\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1001\u103c\u1004\u103a\u1038 \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b K \u1006\u102d\u102f\u101e\u100a\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f K \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101e\u102d\u102f\u1037 \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1019\u103e \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1011\u102c\u1038\u101b\u103e\u102d\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1021\u1006\u102f\u1036\u1038\u1015\u1014\u103a\u1038\u1010\u102d\u102f\u1004\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u101b\u103e\u102d \u101b\u103e\u102f\u1019\u103c\u1004\u103a\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1010\u1030\u100a\u102e\u1015\u103c\u102e\u1038 \u1000\u103d\u1032\u1015\u103c\u102c\u1038\u101e\u1031\u102c\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1000\u103d\u102c\u1001\u103c\u102c\u1038\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 K \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038 \u101b\u103e\u102d\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1015\u1011\u1019\u1021\u1006\u1004\u1037\u103a\u1019\u103e\u102c 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