{"id":4061,"date":"2023-07-13T20:58:15","date_gmt":"2023-07-13T20:58:15","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8-%e0%a4%ae%e0%a5%87%e0%a4%82-%e0%a4%95%e0%a5%8b%e0%a4%b9%e0%a4%a8%e0%a5%80-%e0%a4%b5%e0%a4%bf%e0%a4%a7%e0%a4%bf\/"},"modified":"2023-07-13T20:58:15","modified_gmt":"2023-07-13T20:58:15","slug":"%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8-%e0%a4%ae%e0%a5%87%e0%a4%82-%e0%a4%95%e0%a5%8b%e0%a4%b9%e0%a4%a8%e0%a5%80-%e0%a4%b5%e0%a4%bf%e0%a4%a7%e0%a4%bf","status":"publish","type":"post","link":"https:\/\/statorials.org\/hi\/%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8-%e0%a4%ae%e0%a5%87%e0%a4%82-%e0%a4%95%e0%a5%8b%e0%a4%b9%e0%a4%a8%e0%a5%80-%e0%a4%b5%e0%a4%bf%e0%a4%a7%e0%a4%bf\/","title":{"rendered":"\u0907\u0937\u094d\u091f\u0924\u092e \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0932\u094d\u092c\u094b \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902"},"content":{"rendered":"

<\/p>\n

\n

\u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917<\/a> \u092e\u0947\u0902 \u0938\u092c\u0938\u0947 \u0906\u092e \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0932\u094d\u0917\u094b\u0930\u093f\u0926\u092e \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0915\u094b \u0915\u0947-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0947<\/strong> \u0930\u0942\u092a \u092e\u0947\u0902 \u091c\u093e\u0928\u093e \u091c\u093e\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n

\u0915\u0947-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0915 \u0910\u0938\u0940 \u0924\u0915\u0928\u0940\u0915 \u0939\u0948 \u091c\u093f\u0938\u092e\u0947\u0902 \u0939\u092e \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0938\u0947 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u094b \u0915\u0947<\/em> \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u092e\u0947\u0902 \u0930\u0916\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n

\u0905\u0902\u0924\u093f\u092e \u0932\u0915\u094d\u0937\u094d\u092f K<\/em> \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u092c\u0928\u093e\u0928\u093e \u0939\u0948 \u091c\u093f\u0938\u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0947 \u092d\u0940\u0924\u0930 \u0905\u0935\u0932\u094b\u0915\u0928 \u090f\u0915-\u0926\u0942\u0938\u0930\u0947 \u0915\u0947 \u0938\u092e\u093e\u0928 \u0939\u094b\u0924\u0947 \u0939\u0948\u0902 \u091c\u092c\u0915\u093f \u0935\u093f\u092d\u093f\u0928\u094d\u0928 \u0938\u092e\u0942\u0939\u094b\u0902 \u092e\u0947\u0902 \u0905\u0935\u0932\u094b\u0915\u0928 \u090f\u0915-\u0926\u0942\u0938\u0930\u0947 \u0938\u0947 \u0915\u093e\u092b\u0940 \u092d\u093f\u0928\u094d\u0928 \u0939\u094b\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n

\u0915\u0947-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0930\u0924\u0947 \u0938\u092e\u092f, \u092a\u0939\u0932\u093e \u0915\u0926\u092e \u0915\u0947<\/em> \u0915\u0947 \u0932\u093f\u090f \u090f\u0915 \u092e\u093e\u0928 \u091a\u0941\u0928\u0928\u093e \u0939\u0948 – \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0940 \u0938\u0902\u0916\u094d\u092f\u093e \u091c\u093f\u0938\u092e\u0947\u0902 \u0939\u092e \u0905\u0935\u0932\u094b\u0915\u0928 \u0930\u0916\u0928\u093e \u091a\u093e\u0939\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n

K<\/em> \u0915\u0947 \u0932\u093f\u090f \u092e\u093e\u0928 \u091a\u0941\u0928\u0928\u0947 \u0915\u0947 \u0938\u092c\u0938\u0947 \u0906\u092e \u0924\u0930\u0940\u0915\u094b\u0902 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0915\u094b \u0915\u094b\u0939\u0928\u0940 \u0935\u093f\u0927\u093f<\/strong> \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u091c\u093e\u0928\u093e \u091c\u093e\u0924\u093e \u0939\u0948, \u091c\u093f\u0938\u092e\u0947\u0902 x-\u0905\u0915\u094d\u0937 \u092a\u0930 \u0938\u092e\u0942\u0939\u094b\u0902 \u0915\u0940 \u0938\u0902\u0916\u094d\u092f\u093e \u0914\u0930 y-\u0905\u0915\u094d\u0937 \u092a\u0930 \u0935\u0930\u094d\u0917\u094b\u0902 \u0915\u0947 \u092f\u094b\u0917 \u0915\u0947 \u0938\u093e\u0925 \u090f\u0915 \u092a\u094d\u0932\u0949\u091f \u092c\u0928\u093e\u0928\u093e \u0936\u093e\u092e\u093f\u0932 \u0939\u0948, \u092b\u093f\u0930 \u092a\u0939\u091a\u093e\u0928\u0947\u0902 \u091c\u0939\u093e\u0902 \u0915\u0925\u093e\u0928\u0915 \u092e\u0947\u0902 “\u0918\u0941\u091f\u0928\u093e” \u092f\u093e \u092e\u094b\u0921\u093c \u0926\u093f\u0916\u093e\u0908 \u0926\u0947\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n

\u090f\u0915\u094d\u0938-\u0905\u0915\u094d\u0937 \u092a\u0930 \u0935\u0939 \u092c\u093f\u0902\u0926\u0941 \u091c\u0939\u093e\u0902 “\u0918\u0941\u091f\u0928\u093e” \u0939\u094b\u0924\u093e \u0939\u0948, \u0939\u092e\u0947\u0902 \u0915\u0947-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0932\u094d\u0917\u094b\u0930\u093f\u0926\u092e \u092e\u0947\u0902 \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0940 \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0902\u0916\u094d\u092f\u093e \u092c\u0924\u093e\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n

\u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0909\u0926\u093e\u0939\u0930\u0923 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0932\u094d\u092c\u094b \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902\u0964<\/span><\/p>\n

\u091a\u0930\u0923 1: \u0906\u0935\u0936\u094d\u092f\u0915 \u092e\u0949\u0921\u094d\u092f\u0942\u0932 \u0906\u092f\u093e\u0924 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h2>\n

\u0938\u092c\u0938\u0947 \u092a\u0939\u0932\u0947, \u0939\u092e \u0909\u0928 \u0938\u092d\u0940 \u092e\u0949\u0921\u094d\u092f\u0942\u0932 \u0915\u094b \u0906\u092f\u093e\u0924 \u0915\u0930\u0947\u0902\u0917\u0947 \u091c\u093f\u0928\u0915\u0940 \u0939\u092e\u0947\u0902 k-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0906\u0935\u0936\u094d\u092f\u0915\u0924\u093e \u0939\u094b\u0917\u0940:<\/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
 \u091a\u0930\u0923 2: \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u092c\u0928\u093e\u090f\u0902<\/strong><\/span><\/h2>\n
 \u0907\u0938\u0915\u0947 \u092c\u093e\u0926, \u0939\u092e \u090f\u0915 \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u092c\u0928\u093e\u090f\u0902\u0917\u0947 \u091c\u093f\u0938\u092e\u0947\u0902 20 \u0905\u0932\u0917-\u0905\u0932\u0917 \u092c\u093e\u0938\u094d\u0915\u0947\u091f\u092c\u0949\u0932 \u0916\u093f\u0932\u093e\u0921\u093c\u093f\u092f\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0924\u0940\u0928 \u0935\u0947\u0930\u093f\u090f\u092c\u0932 \u0939\u094b\u0902\u0917\u0947:<\/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
 \u091a\u0930\u0923 3: \u0938\u092e\u0942\u0939\u094b\u0902 \u0915\u0940 \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0902\u0916\u094d\u092f\u093e \u091c\u094d\u091e\u093e\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u094b\u0939\u0928\u0940 \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h2>\n
 \u092e\u093e\u0928 \u0932\u0940\u091c\u093f\u090f \u0915\u093f \u0939\u092e \u0907\u0928 \u0924\u0940\u0928 \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0915\u0947 \u0906\u0927\u093e\u0930 \u092a\u0930 \u0938\u092e\u093e\u0928 \u0905\u092d\u093f\u0928\u0947\u0924\u093e\u0913\u0902 \u0915\u094b \u090f\u0915 \u0938\u093e\u0925 \u0938\u092e\u0942\u0939\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f k-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e \u091a\u093e\u0939\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 k-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e sklearn<\/strong> \u092e\u0949\u0921\u094d\u092f\u0942\u0932 \u0938\u0947 KMeans<\/a> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u0907\u0938 \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0938\u092c\u0938\u0947 \u092e\u0939\u0924\u094d\u0935\u092a\u0942\u0930\u094d\u0923 \u0924\u0930\u094d\u0915 n_clusters<\/strong> \u0939\u0948, \u091c\u094b \u0928\u093f\u0930\u094d\u0926\u093f\u0937\u094d\u091f \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u093f\u0924\u0928\u0947 \u0938\u092e\u0942\u0939\u094b\u0902 \u092e\u0947\u0902 \u0905\u0935\u0932\u094b\u0915\u0928 \u0930\u0916\u0928\u093e \u0939\u0948\u0964<\/span><\/p>\n
 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u094b\u0902 \u0915\u0940 \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0902\u0916\u094d\u092f\u093e \u0928\u093f\u0930\u094d\u0927\u093e\u0930\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e \u090f\u0915 \u0917\u094d\u0930\u093e\u092b\u093c \u092c\u0928\u093e\u090f\u0902\u0917\u0947 \u091c\u094b \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u094b\u0902 \u0915\u0940 \u0938\u0902\u0916\u094d\u092f\u093e \u0915\u0947 \u0938\u093e\u0925-\u0938\u093e\u0925 \u092e\u0949\u0921\u0932 \u0915\u0947 \u090f\u0938\u090f\u0938\u0908 (\u0935\u0930\u094d\u0917 \u0924\u094d\u0930\u0941\u091f\u093f\u092f\u094b\u0902 \u0915\u093e \u092f\u094b\u0917) \u0915\u094b \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n
 \u092b\u093f\u0930 \u0939\u092e \u090f\u0915 “\u0918\u0941\u091f\u0928\u0947” \u0915\u0940 \u0924\u0932\u093e\u0936 \u0915\u0930\u0947\u0902\u0917\u0947 \u091c\u0939\u093e\u0902 \u0935\u0930\u094d\u0917\u094b\u0902 \u0915\u093e \u092f\u094b\u0917 “\u091d\u0941\u0915\u0928\u093e” \u092f\u093e \u0938\u094d\u0925\u093f\u0930 \u0939\u094b\u0928\u093e \u0936\u0941\u0930\u0942 \u0939\u094b \u091c\u093e\u0924\u093e \u0939\u0948\u0964 \u092f\u0939 \u092c\u093f\u0902\u0926\u0941 \u0938\u092e\u0942\u0939\u094b\u0902 \u0915\u0940 \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0902\u0916\u094d\u092f\u093e \u0915\u093e \u092a\u094d\u0930\u0924\u093f\u0928\u093f\u0927\u093f\u0924\u094d\u0935 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0907\u0938 \u092a\u094d\u0930\u0915\u093e\u0930 \u0915\u093e \u092a\u094d\u0932\u0949\u091f \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u092f\u093e \u091c\u093e\u090f \u091c\u094b x-\u0905\u0915\u094d\u0937 \u092a\u0930 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0940 \u0938\u0902\u0916\u094d\u092f\u093e \u0914\u0930 y-\u0905\u0915\u094d\u0937 \u092a\u0930 SSE \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948:<\/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
 \u0907\u0938 \u0917\u094d\u0930\u093e\u092b\u093c \u092e\u0947\u0902, \u0910\u0938\u093e \u092a\u094d\u0930\u0924\u0940\u0924 \u0939\u094b\u0924\u093e \u0939\u0948 \u0915\u093f k = 3 \u0938\u092e\u0942\u0939\u094b\u0902<\/strong> \u092a\u0930 \u090f\u0915 \u0915\u093f\u0902\u0915 \u092f\u093e “\u0918\u0941\u091f\u0928\u093e” \u0939\u0948\u0964<\/span><\/p>\n
 \u0907\u0938\u0932\u093f\u090f, \u0905\u0917\u0932\u0947 \u091a\u0930\u0923 \u092e\u0947\u0902 \u0905\u092a\u0928\u0947 k-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0924\u0947 \u0938\u092e\u092f \u0939\u092e 3 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/span><\/p>\n
 \u091a\u0930\u0923 4: \u0911\u092a\u094d\u091f\u093f\u092e\u0932 K<\/em> \u0915\u0947 \u0938\u093e\u0925 K-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h2>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f 3 \u092e\u0947\u0902 \u0938\u0947 k<\/em> \u0915\u0947 \u0932\u093f\u090f \u0907\u0937\u094d\u091f\u0924\u092e \u092e\u093e\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0915\u0947 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092a\u0930 k-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/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
 \u092a\u0930\u093f\u0923\u093e\u092e\u0940 \u0924\u093e\u0932\u093f\u0915\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u0947 \u0932\u093f\u090f \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0905\u0938\u093e\u0907\u0928\u092e\u0947\u0902\u091f \u0926\u093f\u0916\u093e\u0924\u0940 \u0939\u0948\u0964<\/span><\/p>\n
 \u0907\u0928 \u092a\u0930\u093f\u0923\u093e\u092e\u094b\u0902 \u0915\u0940 \u0935\u094d\u092f\u093e\u0916\u094d\u092f\u093e \u0915\u0930\u0928\u093e \u0906\u0938\u093e\u0928 \u092c\u0928\u093e\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u092e\u0947\u0902 \u090f\u0915 \u0915\u0949\u0932\u092e \u091c\u094b\u0921\u093c \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u091c\u094b \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0916\u093f\u0932\u093e\u0921\u093c\u0940 \u0915\u0947 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0905\u0938\u093e\u0907\u0928\u092e\u0947\u0902\u091f \u0915\u094b \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948:<\/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
 \u0915\u094d\u0932\u0938\u094d\u091f\u0930<\/strong> \u0915\u0949\u0932\u092e \u092e\u0947\u0902 \u090f\u0915 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0928\u0902\u092c\u0930 (0, 1, \u092f\u093e 2) \u0939\u094b\u0924\u093e \u0939\u0948 \u091c\u093f\u0938\u0947 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0916\u093f\u0932\u093e\u0921\u093c\u0940 \u0915\u094b \u0938\u094c\u0902\u092a\u093e \u0917\u092f\u093e \u0939\u0948\u0964<\/span><\/p>\n
 \u090f\u0915 \u0939\u0940 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0938\u0947 \u0938\u0902\u092c\u0902\u0927\u093f\u0924 \u0916\u093f\u0932\u093e\u0921\u093c\u093f\u092f\u094b\u0902 \u0915\u0947 \u092a\u093e\u0938 \u0905\u0902\u0915<\/strong> , \u0938\u0939\u093e\u092f\u0924\u093e<\/strong> \u0914\u0930 \u0930\u093f\u092c\u093e\u0909\u0902\u0921<\/strong> \u0915\u0949\u0932\u092e \u0915\u0947 \u0932\u093f\u090f \u0932\u0917\u092d\u0917 \u0938\u092e\u093e\u0928 \u092e\u0942\u0932\u094d\u092f \u0939\u094b\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u0928\u094b\u091f<\/strong> : \u0906\u092a sklearn<\/strong> \u0915\u0947 KMeans<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u0947 \u0932\u093f\u090f \u0938\u0902\u092a\u0942\u0930\u094d\u0923 \u0926\u0938\u094d\u0924\u093e\u0935\u0947\u091c\u093c \u092f\u0939\u093e\u0902<\/a> \u092a\u093e \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u0905\u0924\u093f\u0930\u093f\u0915\u094d\u0924 \u0938\u0902\u0938\u093e\u0927\u0928<\/strong><\/span><\/h2>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u092c\u0924\u093e\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0905\u0928\u094d\u092f \u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0915\u093e\u0930\u094d\u092f \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/span><\/p>\n
 \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0932\u0940\u0928\u093f\u092f\u0930 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a>
 \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a>
 \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0915\u0947-\u092b\u094b\u0932\u094d\u0921 \u0915\u094d\u0930\u0949\u0938-\u0935\u0948\u0932\u093f\u0921\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u092e\u0947\u0902 \u0938\u092c\u0938\u0947 \u0906\u092e \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0932\u094d\u0917\u094b\u0930\u093f\u0926\u092e \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0915\u094b \u0915\u0947-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u091c\u093e\u0928\u093e \u091c\u093e\u0924\u093e \u0939\u0948\u0964 \u0915\u0947-\u092e\u0940\u0928\u094d\u0938 \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0915 \u0910\u0938\u0940 \u0924\u0915\u0928\u0940\u0915 \u0939\u0948 \u091c\u093f\u0938\u092e\u0947\u0902 \u0939\u092e \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0938\u0947 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u094b \u0915\u0947 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u092e\u0947\u0902 \u0930\u0916\u0924\u0947 \u0939\u0948\u0902\u0964 \u0905\u0902\u0924\u093f\u092e \u0932\u0915\u094d\u0937\u094d\u092f K \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u092c\u0928\u093e\u0928\u093e \u0939\u0948 \u091c\u093f\u0938\u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0947 \u092d\u0940\u0924\u0930 \u0905\u0935\u0932\u094b\u0915\u0928 \u090f\u0915-\u0926\u0942\u0938\u0930\u0947 \u0915\u0947 \u0938\u092e\u093e\u0928 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-4061","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\n\u0907\u0937\u094d\u091f\u0924\u092e \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0932\u094d\u092c\u094b \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902 - \u0938\u094d\u091f\u0947\u091f\u094b\u0930\u093f\u092f\u0932\u094d\u0938<\/title>\n<meta name=\"description\" content=\"\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u092c\u0924\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0932\u094d\u0917\u094b\u0930\u093f\u0926\u092e \u092e\u0947\u0902 \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0940 \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0902\u0916\u094d\u092f\u093e \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0932\u094d\u092c\u094b \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902\u0964\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/hi\/\u092a\u093e\u092f\u0925\u0928-\u092e\u0947\u0902-\u0915\u094b\u0939\u0928\u0940-\u0935\u093f\u0927\u093f\/\" \/>\n<meta property=\"og:locale\" content=\"hi_IN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0907\u0937\u094d\u091f\u0924\u092e \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0932\u094d\u092c\u094b \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902 - \u0938\u094d\u091f\u0947\u091f\u094b\u0930\u093f\u092f\u0932\u094d\u0938\" \/>\n<meta property=\"og:description\" content=\"\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u092c\u0924\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u094d\u0932\u0938\u094d\u091f\u0930\u093f\u0902\u0917 \u090f\u0932\u094d\u0917\u094b\u0930\u093f\u0926\u092e \u092e\u0947\u0902 \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u094d\u0932\u0938\u094d\u091f\u0930 \u0915\u0940 \u0907\u0937\u094d\u091f\u0924\u092e \u0938\u0902\u0916\u094d\u092f\u093e \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0932\u094d\u092c\u094b \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902\u0964\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/hi\/\u092a\u093e\u092f\u0925\u0928-\u092e\u0947\u0902-\u0915\u094b\u0939\u0928\u0940-\u0935\u093f\u0927\u093f\/\" \/>\n<meta property=\"og:site_name\" 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