{"id":3121,"date":"2023-07-19T03:04:16","date_gmt":"2023-07-19T03:04:16","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%aa%e0%a4%be%e0%a4%82%e0%a4%a1%e0%a4%be-%e0%a4%9f%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%a8-%e0%a4%aa%e0%a4%b0%e0%a5%80%e0%a4%95%e0%a5%8d%e0%a4%b7%e0%a4%a3\/"},"modified":"2023-07-19T03:04:16","modified_gmt":"2023-07-19T03:04:16","slug":"%e0%a4%aa%e0%a4%be%e0%a4%82%e0%a4%a1%e0%a4%be-%e0%a4%9f%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%a8-%e0%a4%aa%e0%a4%b0%e0%a5%80%e0%a4%95%e0%a5%8d%e0%a4%b7%e0%a4%a3","status":"publish","type":"post","link":"https:\/\/statorials.org\/hi\/%e0%a4%aa%e0%a4%be%e0%a4%82%e0%a4%a1%e0%a4%be-%e0%a4%9f%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%a8-%e0%a4%aa%e0%a4%b0%e0%a5%80%e0%a4%95%e0%a5%8d%e0%a4%b7%e0%a4%a3\/","title":{"rendered":"\u092a\u093e\u0902\u0921\u093e\u0938 \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u091f\u094d\u0930\u0947\u0928 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u090f\u0902"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\" target=\"_blank\" rel=\"noopener\">\u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u092e\u0949\u0921\u0932 \u0915\u094b<\/a> \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 \u092b\u093f\u091f \u0915\u0930\u0924\u0947 \u0938\u092e\u092f, \u0939\u092e \u0905\u0915\u094d\u0938\u0930 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u094b \u0926\u094b \u0938\u0947\u091f\u094b\u0902 \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0924\u0947 \u0939\u0948\u0902:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f:<\/strong> \u092e\u0949\u0921\u0932 \u0915\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0909\u092a\u092f\u094b\u0917 \u0915\u093f\u092f\u093e \u091c\u093e\u0924\u093e \u0939\u0948 (\u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e 70-80%)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f:<\/strong> \u092e\u0949\u0921\u0932 \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u0928 \u0915\u093e \u0928\u093f\u0937\u094d\u092a\u0915\u094d\u0937 \u0905\u0928\u0941\u092e\u093e\u0928 \u092a\u094d\u0930\u093e\u092a\u094d\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0909\u092a\u092f\u094b\u0917 \u0915\u093f\u092f\u093e \u091c\u093e\u0924\u093e \u0939\u0948 (\u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e 20-30%)<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902, \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0915\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0926\u094b \u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0924\u0930\u0940\u0915\u0947 \u0939\u0948\u0902:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0935\u093f\u0927\u093f 1: \u0938\u094d\u0915\u0947\u0932\u0947\u0930\u0928 \u0915\u0940 \u091f\u094d\u0930\u0947\u0928_\u091f\u0947\u0938\u094d\u091f_\u0938\u094d\u092a\u094d\u0932\u093f\u091f() \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\ntrain, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u0935\u093f\u0927\u093f 2: \u092a\u093e\u0902\u0921\u093e \u0938\u0947 \u0928\u092e\u0942\u0928\u093e() \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0909\u0926\u093e\u0939\u0930\u0923 \u0926\u093f\u0916\u093e\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0915\u0947 \u0938\u093e\u0925 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create DataFrame with 1,000 rows and 3 columns\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> <span style=\"color: #3366ff;\">(<\/span> {' <span style=\"color: #ff0000;\">x1<\/span> ': <span style=\"color: #3366ff;\">np.random.randint<\/span> (30,size=1000),\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (12, size=1000),\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (2, size=1000)})\n\n<span style=\"color: #008080;\">#view first few rows of DataFrame<\/span>\ndf. <span style=\"color: #3366ff;\">head<\/span> ()\n\n        x1 x2 y\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u0909\u0926\u093e\u0939\u0930\u0923 1: \u0938\u094d\u0915\u0947\u0932\u0947\u0930\u0928 \u0938\u0947 \u091f\u094d\u0930\u0947\u0928_\u091f\u0947\u0938\u094d\u091f_\u0938\u094d\u092a\u094d\u0932\u093f\u091f() \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0915\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f <strong>\u0938\u094d\u0915\u0947\u0932\u0947\u0930<\/strong> \u0915\u0947 <strong>\u091f\u094d\u0930\u0947\u0928_\u091f\u0947\u0938\u094d\u091f_\u0938\u094d\u092a\u094d\u0932\u093f\u091f()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\n<span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n687 16 2 0\n500 18 2 1\n332 4 10 1\n979 2 8 1\n817 11 1 0\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u092a\u0930\u093f\u0923\u093e\u092e \u0938\u0947 \u0939\u092e \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0926\u094b \u0938\u0947\u091f \u092c\u0928\u093e\u090f \u0917\u090f:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f: 800 \u092a\u0902\u0915\u094d\u0924\u093f\u092f\u093e\u0901 \u0914\u0930 3 \u0938\u094d\u0924\u0902\u092d<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f: 200 \u092a\u0902\u0915\u094d\u0924\u093f\u092f\u093e\u0901 \u0914\u0930 3 \u0915\u0949\u0932\u092e<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f <strong>test_size<\/strong> \u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u0905\u0935\u0932\u094b\u0915\u0928\u094b\u0902 \u0915\u0947 \u092a\u094d\u0930\u0924\u093f\u0936\u0924 \u0915\u094b \u0928\u093f\u092f\u0902\u0924\u094d\u0930\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u094b \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0938\u0947 \u0938\u0902\u092c\u0902\u0927\u093f\u0924 \u0939\u094b\u0917\u093e \u0914\u0930 <strong>\u0930\u0948\u0902\u0921\u092e_\u0938\u094d\u091f\u0947\u091f<\/strong> \u092e\u093e\u0928 \u0935\u093f\u092d\u093e\u091c\u0928 \u0915\u094b \u092a\u094d\u0930\u0924\u093f\u0932\u093f\u092a\u093f \u092a\u094d\u0930\u0938\u094d\u0924\u0941\u0924 \u0915\u0930\u0928\u0947 \u092f\u094b\u0917\u094d\u092f \u092c\u0928\u093e\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0909\u0926\u093e\u0939\u0930\u0923 2: \u092a\u093e\u0902\u0921\u093e \u0938\u0947 \u0928\u092e\u0942\u0928\u093e() \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0915\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f <b>\u092a\u093e\u0902\u0921\u093e<\/b> <strong>\u0928\u092e\u0942\u0928\u093e()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n    x1 x2 y\n9 16 5 0\n11 12 10 0\n19 5 9 0\n23 28 1 1\n28 18 0 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u092a\u0930\u093f\u0923\u093e\u092e \u0938\u0947 \u0939\u092e \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0926\u094b \u0938\u0947\u091f \u092c\u0928\u093e\u090f \u0917\u090f:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f: 800 \u092a\u0902\u0915\u094d\u0924\u093f\u092f\u093e\u0901 \u0914\u0930 3 \u0938\u094d\u0924\u0902\u092d<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f: 200 \u092a\u0902\u0915\u094d\u0924\u093f\u092f\u093e\u0901 \u0914\u0930 3 \u0915\u0949\u0932\u092e<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f <b>\u092b\u093c\u094d\u0930\u0947\u0915<\/b> \u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u0905\u0935\u0932\u094b\u0915\u0928\u094b\u0902 \u0915\u0947 \u092a\u094d\u0930\u0924\u093f\u0936\u0924 \u0915\u094b \u0928\u093f\u092f\u0902\u0924\u094d\u0930\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0938\u0947 \u0938\u0902\u092c\u0902\u0927\u093f\u0924 \u0939\u094b\u0917\u093e \u0914\u0930 <strong>\u0930\u0948\u0902\u0921\u092e_\u0938\u094d\u091f\u0947\u091f<\/strong> \u092e\u093e\u0928 \u0935\u093f\u092d\u093e\u091c\u0928 \u0915\u094b \u092a\u094d\u0930\u0924\u093f\u0932\u093f\u092a\u093f \u092a\u094d\u0930\u0938\u094d\u0924\u0941\u0924 \u0915\u0930\u0928\u0947 \u092f\u094b\u0917\u094d\u092f \u092c\u0928\u093e\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0905\u0924\u093f\u0930\u093f\u0915\u094d\u0924 \u0938\u0902\u0938\u093e\u0927\u0928<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\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<p> <a href=\"https:\/\/statorials.org\/hi\/\u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915-\u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928-\u092a\u093e\u092f\u0925\u0928\/\" target=\"_blank\" rel=\"noopener\">\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><br \/> <a href=\"https:\/\/statorials.org\/hi\/\u092a\u093e\u092f\u0925\u0928-\u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938-\u092d\u094d\u0930\u092e\/\" target=\"_blank\" rel=\"noopener\">\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0915\u0928\u094d\u092b\u094d\u092f\u0942\u091c\u0928 \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u090f\u0902<\/a><br \/> <a href=\"https:\/\/statorials.org\/hi\/\u0938\u0902\u0924\u0941\u0932\u093f\u0924-\u092a\u0930\u093f\u0936\u0941\u0926\u094d\u0927\u0924\u093e-\u092a\u093e\u092f\u0925\u0928-\u0938\u094d\u0915\u0947\u0932\u0947\u0930\/\">\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0938\u0902\u0924\u0941\u0932\u093f\u0924 \u092a\u0930\u093f\u0936\u0941\u0926\u094d\u0927\u0924\u093e \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u092e\u0949\u0921\u0932 \u0915\u094b \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 \u092b\u093f\u091f \u0915\u0930\u0924\u0947 \u0938\u092e\u092f, \u0939\u092e \u0905\u0915\u094d\u0938\u0930 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u094b \u0926\u094b \u0938\u0947\u091f\u094b\u0902 \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0924\u0947 \u0939\u0948\u0902: 1. \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f: \u092e\u0949\u0921\u0932 \u0915\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0909\u092a\u092f\u094b\u0917 \u0915\u093f\u092f\u093e \u091c\u093e\u0924\u093e \u0939\u0948 (\u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e 70-80%) 2. \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f: \u092e\u0949\u0921\u0932 \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u0928 \u0915\u093e \u0928\u093f\u0937\u094d\u092a\u0915\u094d\u0937 \u0905\u0928\u0941\u092e\u093e\u0928 \u092a\u094d\u0930\u093e\u092a\u094d\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0909\u092a\u092f\u094b\u0917 \u0915\u093f\u092f\u093e \u091c\u093e\u0924\u093e \u0939\u0948 (\u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e [&hellip;]<\/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-3121","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u092a\u093e\u0902\u0921\u093e\u0938 \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u090f\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 \u0915\u0908 \u0924\u0930\u0940\u0915\u094b\u0902 \u0915\u0940 \u0935\u094d\u092f\u093e\u0916\u094d\u092f\u093e \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u093f\u0928\u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0906\u092a \u090f\u0915\u0932 \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092c\u0928\u093e\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\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\u0902\u0921\u093e-\u091f\u094d\u0930\u0947\u0928-\u092a\u0930\u0940\u0915\u094d\u0937\u0923\/\" \/>\n<meta property=\"og:locale\" content=\"hi_IN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u092a\u093e\u0902\u0921\u093e\u0938 \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u090f\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 \u0915\u0908 \u0924\u0930\u0940\u0915\u094b\u0902 \u0915\u0940 \u0935\u094d\u092f\u093e\u0916\u094d\u092f\u093e \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u093f\u0928\u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0906\u092a \u090f\u0915\u0932 \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0938\u0947 \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092c\u0928\u093e\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/hi\/\u092a\u093e\u0902\u0921\u093e-\u091f\u094d\u0930\u0947\u0928-\u092a\u0930\u0940\u0915\u094d\u0937\u0923\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-19T03:04:16+00:00\" \/>\n<meta name=\"author\" content=\"\u0921\u0949. \u092c\u0947\u0902\u091c\u093e\u092e\u093f\u0928 \u090f\u0902\u0921\u0930\u0938\u0928\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u0926\u094d\u0935\u093e\u0930\u093e \u0932\u093f\u0916\u093f\u0924\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0921\u0949. \u092c\u0947\u0902\u091c\u093e\u092e\u093f\u0928 \u090f\u0902\u0921\u0930\u0938\u0928\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u0905\u0928\u0941\u092e\u093e\u0928\u093f\u0924 \u092a\u0922\u093c\u0928\u0947 \u0915\u093e \u0938\u092e\u092f\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u092e\u093f\u0928\u091f\" \/>\n<script type=\"application\/ld+json\" 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