{"id":15221,"date":"2024-01-09T22:25:29","date_gmt":"2024-01-09T18:55:29","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/"},"modified":"2024-01-09T22:25:29","modified_gmt":"2024-01-09T18:55:29","slug":"%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/","title":{"rendered":"\u0648\u06cc\u0698\u06af\u06cc \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0628\u0627 Scikit-Learn \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0633\u0631\u0641\u0635\u0644\u0647\u0627\u06cc \u0645\u0637\u0644\u0628<\/p>\n<\/div><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d9%85%d8%b9%d8%b1%d9%81%db%8c\" >\u0645\u0639\u0631\u0641\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d9%85%d9%82%db%8c%d8%a7%d8%b3_%d8%a8%d9%86%d8%af%db%8c_%d9%88%db%8c%da%98%da%af%db%8c_%da%86%db%8c%d8%b3%d8%aa_%e2%80%93_%d8%b9%d8%a7%d8%af%db%8c_%d8%b3%d8%a7%d8%b2%db%8c_%d9%88_%d8%a7%d8%b3%d8%aa%d8%a7%d9%86%d8%af%d8%a7%d8%b1%d8%af%d8%b3%d8%a7%d8%b2%db%8c\" >\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0686\u06cc\u0633\u062a &#8211; \u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc \u0648 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%da%86%d9%87_%d8%b2%d9%85%d8%a7%d9%86%db%8c_%d8%a8%d8%a7%db%8c%d8%af_%d9%85%d9%82%db%8c%d8%a7%d8%b3_%d8%a8%d9%86%d8%af%db%8c_%d9%88%db%8c%da%98%da%af%db%8c_%d9%87%d8%a7_%d8%b1%d8%a7_%d8%a7%d9%86%d8%ac%d8%a7%d9%85_%d8%af%d8%a7%d8%af%d8%9f\" >\u0686\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0627\u06cc\u062f \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u061f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d9%88%d8%a7%d8%b1%d8%af%d8%a7%d8%aa_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7_%d9%88_%d8%aa%d8%ac%d8%b2%db%8c%d9%87_%d9%88_%d8%aa%d8%ad%d9%84%db%8c%d9%84_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7%db%8c_%d8%a7%da%a9%d8%aa%d8%b4%d8%a7%d9%81%db%8c\" >\u0648\u0627\u0631\u062f\u0627\u062a \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u06a9\u062a\u0634\u0627\u0641\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d9%85%d8%b9%db%8c%d8%a7%d8%b1%d9%87%d8%a7%db%8c\" >\u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#minmaxscaler\" >MinMaxScaler<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d8%a7%d8%ab%d8%b1%d8%a7%d8%aa_%d9%be%d8%b1%d8%aa\" >\u0627\u062b\u0631\u0627\u062a \u067e\u0631\u062a<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d9%85%d9%82%db%8c%d8%a7%d8%b3_%d8%a8%d9%86%d8%af%db%8c_%d9%88%db%8c%da%98%da%af%db%8c_%d8%a7%d8%b2_%d8%b7%d8%b1%db%8c%d9%82_%d8%ae%d8%b7%d9%88%d8%b7_%d9%84%d9%88%d9%84%d9%87_scikit-learn\" >\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062e\u0637\u0648\u0637 \u0644\u0648\u0644\u0647 Scikit-Learn<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%88%db%8c%da%98%da%af%db%8c-%d9%85%d9%82%db%8c%d8%a7%d8%b3%d8%a8%d9%86%d8%af%db%8c-%d8%af%d8%a7%d8%af%d9%87-%d8%a8%d8%a7-scikit-learn-%d8%a8%d8%b1%d8%a7%db%8c-%db%8c%d8%a7%d8%af%da%af\/#%d9%86%d8%aa%db%8c%d8%ac%d9%87\" >\u0646\u062a\u06cc\u062c\u0647<\/a><\/li><\/ul><\/nav><\/div>\n<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 7<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b9%d8%b1%d9%81%db%8c\"><\/span>\u0645\u0639\u0631\u0641\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627 \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u06a9\u0644\u06cc\u062f\u06cc \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0627\u063a\u0644\u0628 \u0646\u0627\u062f\u06cc\u062f\u0647 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u062f\u0631 \u0648\u0627\u0642\u0639 &#8211; \u0627\u06cc\u0646 \u0627\u0633\u062a <em>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u0647\u0645<\/em> \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062f\u0644 \u0628\u0631\u0627\u0642\u06cc \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0627 \u0622\u0646 \u062a\u0646\u0627\u0633\u0628 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f.<\/p>\n<blockquote>\n<p><strong>\u0632\u0628\u0627\u0644\u0647 \u062f\u0631 &#8211; \u0632\u0628\u0627\u0644\u0647 \u0628\u06cc\u0631\u0648\u0646.<\/strong><\/p>\n<\/blockquote>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f <em>\u0628\u0647\u062a\u0631\u06cc\u0646<\/em> \u0645\u062f\u0644\u06cc \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u0634\u06a9\u0644\u06cc \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a &#8211; \u0627\u06af\u0631 \u0628\u0647 \u0622\u0646 \u0632\u0628\u0627\u0644\u0647 \u0628\u062f\u0647\u06cc\u062f\u060c \u0632\u0628\u0627\u0644\u0647\u200c\u0647\u0627 \u0631\u0627 \u0628\u06cc\u0631\u0648\u0646 \u0645\u06cc\u200c\u0631\u06cc\u0632\u062f.  \u0634\u0627\u06cc\u0627\u0646 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 <em>&#8220;\u0632\u0628\u0627\u0644\u0647&#8221;<\/em> \u0628\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc \u0627\u0634\u0627\u0631\u0647 \u0646\u0645\u06cc \u06a9\u0646\u062f.  \u0627\u06cc\u0646 \u0628\u0631\u0686\u0633\u0628 \u0633\u062e\u062a\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u0627 \u0628\u0647 \u0647\u0631 \u062f\u0627\u062f\u0647 \u0627\u06cc \u0645\u06cc \u0686\u0633\u0628\u0627\u0646\u06cc\u0645 \u06a9\u0647 \u0628\u0647 \u0645\u062f\u0644 \u0627\u062c\u0627\u0632\u0647 \u0646\u0645\u06cc \u062f\u0647\u062f \u0628\u0647\u062a\u0631\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u062f &#8211; \u0628\u0631\u062e\u06cc \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u062f\u06cc\u06af\u0631\u0627\u0646.  \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u06af\u0641\u062a\u0647 \u0634\u062f &#8211; \u0647\u0645\u0627\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u0645\u062f\u0644 \u0628\u062f \u0628\u0627\u0634\u062f\u060c \u0627\u0645\u0627 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u062f\u06cc\u06af\u0631 \u0639\u0627\u0644\u06cc \u0627\u0633\u062a. <em>\u0628\u0637\u0648\u0631 \u06a9\u0644\u06cc<\/em>\u060c \u0645\u062f\u0644 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0646\u06cc\u0632 \u062a\u0639\u0645\u06cc\u0645 \u0646\u0645\u06cc \u062f\u0647\u0646\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0628\u0627 \u0648\u0627\u0631\u06cc\u0627\u0646\u0633 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0627\u0644\u0627\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u06cc\u062f \u0642\u0628\u0644 \u0627\u0632 \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0622\u0646 \u0628\u0647 \u0645\u062f\u0644\u060c \u0622\u0646\u200c\u0647\u0627 \u0631\u0627 \u0627\u062a\u0648 \u06a9\u0646\u06cc\u062f.<\/p>\n<blockquote>\n<p><strong><em>\u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc<\/em><\/strong>  \u0648 <strong><em>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0633\u0627\u0632\u06cc<\/em><\/strong>  \u062f\u0648 \u062a\u06a9\u0646\u06cc\u06a9\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u0645\u0639\u0645\u0648\u0644\u0627 \u062f\u0631 \u0637\u0648\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f <em>\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627<\/em> \u0628\u0631\u0627\u06cc \u062a\u0646\u0638\u06cc\u0645 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u062f\u0631 \u06cc\u06a9 \u0645\u0642\u06cc\u0627\u0633 \u0645\u0634\u062a\u0631\u06a9.<\/p>\n<\/blockquote>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627\u060c \u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u06cc \u067e\u0631\u062f\u0627\u0632\u06cc\u0645 \u06a9\u0647 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0686\u06cc\u0633\u062a \u0648 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0647 \u0645\u0642\u06cc\u0627\u0633 \u0645\u0646\u0627\u0633\u0628 \u062a\u0631 \u0645\u0642\u06cc\u0627\u0633 \u0645\u06cc \u062f\u0647\u06cc\u0645.  \u0633\u067e\u0633\u060c \u0645\u0627 \u06cc\u06a9 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f <code>SGDRegressor<\/code> \u0645\u062f\u0644 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u0635\u0644\u06cc \u0648 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u0627\u06cc\u0646\u06a9\u0647 \u0622\u06cc\u0627 \u062a\u0623\u062b\u06cc\u0631 \u0632\u06cc\u0627\u062f\u06cc \u062f\u0627\u0634\u062a\u0647 \u0627\u0633\u062a \u06cc\u0627 \u062e\u06cc\u0631 \u0631\u0648\u06cc \u0627\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0627\u0635<\/p>\n<h2 id=\"whatisfeaturescalingnormalizationandstandardization\"><span class=\"ez-toc-section\" id=\"%d9%85%d9%82%db%8c%d8%a7%d8%b3_%d8%a8%d9%86%d8%af%db%8c_%d9%88%db%8c%da%98%da%af%db%8c_%da%86%db%8c%d8%b3%d8%aa_%e2%80%93_%d8%b9%d8%a7%d8%af%db%8c_%d8%b3%d8%a7%d8%b2%db%8c_%d9%88_%d8%a7%d8%b3%d8%aa%d8%a7%d9%86%d8%af%d8%a7%d8%b1%d8%af%d8%b3%d8%a7%d8%b2%db%8c\"><\/span>\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0686\u06cc\u0633\u062a &#8211; \u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc \u0648 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong><em>\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc<\/em><\/strong>  \u06cc\u0627 <strong><em>\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627<\/em><\/strong>  \u0647\u0633\u062a process \u062a\u063a\u06cc\u06cc\u0631 \u0645\u0642\u06cc\u0627\u0633 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u062e\u0627\u0635 \u0628\u0647 \u06cc\u06a9 \u0645\u0642\u06cc\u0627\u0633 \u0645\u0634\u062a\u0631\u06a9.  \u0627\u06cc\u0646 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0628\u0647 \u062f\u0633\u062a \u0645\u06cc \u0622\u06cc\u062f <strong>\u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc<\/strong> \u0648 <strong>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0633\u0627\u0632\u06cc<\/strong> (\u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc).<\/p>\n<ul>\n<li><strong><em>\u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc<\/em><\/strong>  \u0647\u0633\u062a process \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u062f\u0631 \u0645\u062d\u062f\u0648\u062f\u0647 (0\u060c 1).  \u0628\u0631\u0627\u06cc \u06a9\u0627\u0631\u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0645\u0641\u06cc\u062f\u062a\u0631 \u0648 \u0631\u0627\u06cc\u062c \u062a\u0631 \u0627\u0633\u062a.<\/li>\n<\/ul>\n<p>$$<br \/>x&#8217; = \\frac{x-x_{min}}{x_{max} &#8211; x_{min}}<br \/>$$<\/p>\n<ul>\n<li><strong><em>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0633\u0627\u0632\u06cc<\/em><\/strong>  \u0647\u0633\u062a process \u0627\u0632 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0647 \u0637\u0648\u0631\u06cc \u06a9\u0647 \u0622\u0646\u0647\u0627 \u06cc\u06a9 <em>\u0645\u0642\u062f\u0627\u0631 \u0645\u062a\u0648\u0633\u0637 \u200b\u200b0<\/em> \u0648 \u0627\u0644\u0641 <em>\u0627\u0646\u062d\u0631\u0627\u0641 \u0645\u0639\u06cc\u0627\u0631 1<\/em>.  \u0628\u0631\u0627\u06cc \u06a9\u0627\u0631\u0647\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u0641\u06cc\u062f\u062a\u0631 \u0648 \u0631\u0627\u06cc\u062c \u062a\u0631 \u0627\u0633\u062a.<\/li>\n<\/ul>\n<p>$$<br \/>x&#8217; = \\frac{x-\\mu}{\\sigma}<br \/>$$<\/p>\n<p>\u062a\u0648\u0632\u06cc\u0639 \u0646\u0631\u0645\u0627\u0644 \u0628\u0627 \u0627\u06cc\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 a \u0646\u0627\u0645\u06cc\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f <em>\u062a\u0648\u0632\u06cc\u0639 \u0646\u0631\u0645\u0627\u0644 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f<\/em>.<\/p>\n<p>\u0634\u0627\u06cc\u0627\u0646 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u062a\u0636\u0645\u06cc\u0646 \u0646\u0645\u06cc \u06a9\u0646\u062f \u06a9\u0647 \u062f\u0631 \u0645\u062d\u062f\u0648\u062f\u0647 (0\u060c 1) \u0642\u0631\u0627\u0631 \u062f\u0627\u0631\u0646\u062f.  \u0628\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 \u0632\u06cc\u0627\u062f \u0627\u06cc\u0646\u0637\u0648\u0631 \u0646\u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f &#8211; \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u062e\u0627\u0635\u06cc \u06a9\u0647 \u0627\u0646\u062a\u0638\u0627\u0631 \u0627\u06cc\u0646 \u0645\u062d\u062f\u0648\u062f\u0647 \u0631\u0627 \u062f\u0627\u0631\u0646\u062f \u0645\u0634\u06a9\u0644 \u0633\u0627\u0632 \u0628\u0627\u0634\u062f.<\/p>\n<p>\u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc\u060c Scikit-Learn \u0628\u0647 \u0645\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u062f <code>StandardScaler<\/code> \u06a9\u0644\u0627\u0633<\/p>\n<p>\u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc \u0646\u06cc\u0632 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a <em>\u0645\u0642\u06cc\u0627\u0633 \u062d\u062f\u0627\u0642\u0644 \u062d\u062f\u0627\u06a9\u062b\u0631\u06cc<\/em> \u0648 Scikit-Learn \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f <code>MinMaxScaler<\/code> \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0646\u0638\u0648\u0631.  \u0627\u0632 \u0637\u0631\u0641\u06cc \u0627\u0644\u0641 \u0631\u0627 \u0646\u06cc\u0632 \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f <code>Normalizer<\/code>\u060c \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0627\u0648\u0636\u0627\u0639 \u0631\u0627 \u06a9\u0645\u06cc \u06af\u06cc\u062c \u06a9\u0646\u0646\u062f\u0647 \u06a9\u0646\u062f.<\/p>\n<div class=\"alert alert-note\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f:<\/strong> \u0627\u06cc\u0646 <code>Normalizer<\/code> \u06a9\u0644\u0627\u0633 <strong>\u0627\u062c\u0631\u0627 \u0646\u0645\u06cc \u06a9\u0646\u062f<\/strong> \u0647\u0645\u0627\u0646 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc <code>MinMaxScaler<\/code>. <code>Normalizer<\/code> \u0622\u062b\u0627\u0631 \u0631\u0648\u06cc <em>\u0631\u062f\u06cc\u0641 \u0647\u0627<\/em>\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0646\u06cc\u0633\u062a\u060c \u0648 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0645\u0633\u062a\u0642\u0644 \u0645\u0642\u06cc\u0627\u0633 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<\/p><\/div><\/div><\/div>\n<h2 id=\"whentoperformfeaturescaling\"><span class=\"ez-toc-section\" id=\"%da%86%d9%87_%d8%b2%d9%85%d8%a7%d9%86%db%8c_%d8%a8%d8%a7%db%8c%d8%af_%d9%85%d9%82%db%8c%d8%a7%d8%b3_%d8%a8%d9%86%d8%af%db%8c_%d9%88%db%8c%da%98%da%af%db%8c_%d9%87%d8%a7_%d8%b1%d8%a7_%d8%a7%d9%86%d8%ac%d8%a7%d9%85_%d8%af%d8%a7%d8%af%d8%9f\"><\/span>\u0686\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0627\u06cc\u062f \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u061f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<blockquote>\n<p>\u0645\u0642\u06cc\u0627\u0633 \u06af\u0630\u0627\u0631\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0646\u0645\u06cc \u06a9\u0646\u062f <em>\u0636\u0645\u0627\u0646\u062a<\/em> \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0628\u0647\u062a\u0631 \u0628\u0631\u0627\u06cc <strong><em>\u0647\u0645\u0647<\/em><\/strong>  \u0645\u062f\u0644 \u0647\u0627.<\/p>\n<\/blockquote>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0627\u06af\u0631 \u0645\u0642\u06cc\u0627\u0633 \u0627\u0647\u0645\u06cc\u062a\u06cc \u0646\u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f\u060c \u00ab\u0645\u0642\u06cc\u0627\u0633\u200c\u0633\u0627\u0632\u06cc \u0648\u06cc\u0698\u06af\u06cc\u00bb \u06a9\u0627\u0631 \u0686\u0646\u062f\u0627\u0646\u06cc \u0627\u0646\u062c\u0627\u0645 \u0646\u0645\u06cc\u200c\u062f\u0647\u062f.  \u0628\u0631\u0627\u06cc <strong><em>K-Means Clustering<\/em><\/strong>\u060c <a target=\"_blank\" href=\"\" rel=\"noopener\"><em>\u0641\u0627\u0635\u0644\u0647 \u0627\u0642\u0644\u06cc\u062f\u0633\u06cc<\/em><\/a>  \u0645\u0647\u0645 \u0627\u0633\u062a\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u062a\u0623\u062b\u06cc\u0631 \u0632\u06cc\u0627\u062f\u06cc \u062f\u0627\u0631\u062f.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u06cc \u06a9\u0647 \u0645\u062a\u06a9\u06cc \u0627\u0633\u062a \u062a\u0623\u062b\u06cc\u0631 \u0632\u06cc\u0627\u062f\u06cc \u0645\u06cc \u06af\u0630\u0627\u0631\u062f \u0631\u0648\u06cc \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0647\u0627\u060c \u0645\u0627\u0646\u0646\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u062e\u0637\u06cc \u06a9\u0647 \u0628\u0627 \u0628\u0647 \u062d\u062f\u0627\u0642\u0644 \u0631\u0633\u0627\u0646\u062f\u0646 \u062a\u0644\u0641\u0627\u062a \u0628\u0631\u0627\u0632\u0634 \u0645\u06cc \u0634\u0648\u0646\u062f <a target=\"_blank\" href=\"\" rel=\"noopener\">\u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0646\u0632\u0648\u0644<\/a>.<\/p>\n<p><strong><em>\u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062c\u0632\u0627\u06cc \u0627\u0635\u0644\u06cc (PCA)<\/em><\/strong>  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc\u06cc \u0631\u0646\u062c \u0645\u06cc \u0628\u0631\u062f \u06a9\u0647 \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0646\u0634\u062f\u0647 \u0627\u0646\u062f.<\/p>\n<p>\u062f\u0631 \u0645\u0648\u0631\u062f Scikit-Learn &#8211; \u0647\u06cc\u0686 \u062a\u0641\u0627\u0648\u062a \u0645\u062d\u0633\u0648\u0633\u06cc \u0628\u0627 a \u0646\u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f <code>LinearRegression<\/code>\u060c \u0627\u0645\u0627 \u062a\u0641\u0627\u0648\u062a \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0628\u0627 a \u062e\u0648\u0627\u0647\u062f \u062f\u06cc\u062f <code>SGDRegressor<\/code>\u060c \u0632\u06cc\u0631\u0627 \u0627\u0644\u0641 <code>SGDRegressor<\/code>\u06a9\u0647 \u06cc\u06a9 \u0645\u062f\u0644 \u062e\u0637\u06cc \u0646\u06cc\u0632 \u0645\u06cc \u0628\u0627\u0634\u062f\u060c \u0628\u0633\u062a\u06af\u06cc \u062f\u0627\u0631\u062f \u0631\u0648\u06cc <strong><em>\u0646\u0632\u0648\u0644 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u062a\u0635\u0627\u062f\u0641\u06cc<\/em><\/strong>  \u0628\u0631\u0627\u06cc \u062a\u0646\u0627\u0633\u0628 \u0628\u0627 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627<\/p>\n<p>\u0622 <strong><em>\u0645\u062f\u0644 \u062f\u0631\u062e\u062a\u06cc<\/em><\/strong>  \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0646\u0634\u062f\u0647 \u0631\u0646\u062c \u0646\u062e\u0648\u0627\u0647\u0646\u062f \u0628\u0631\u062f\u060c \u0632\u06cc\u0631\u0627 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0647 \u0647\u06cc\u0686 \u0648\u062c\u0647 \u0628\u0631 \u0622\u0646\u0647\u0627 \u062a\u0623\u062b\u06cc\u0631 \u0646\u0645\u06cc \u06af\u0630\u0627\u0631\u062f\u060c \u0627\u0645\u0627 \u0627\u06af\u0631 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f <strong><em>\u0627\u0641\u0632\u0627\u06cc\u0634 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0631\u0648\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647 \u0647\u0627<\/em><\/strong>\u060c \u0645\u0642\u06cc\u0627\u0633 <em>\u0645\u06cc\u06a9\u0646\u062f<\/em> \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0631\u0627 \u062a\u062d\u062a \u062a\u0627\u062b\u06cc\u0631 \u0642\u0631\u0627\u0631 \u062f\u0647\u062f.<\/p>\n<h2 id=\"importingdataandexploratorydataanalysis\"><span class=\"ez-toc-section\" id=\"%d9%88%d8%a7%d8%b1%d8%af%d8%a7%d8%aa_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7_%d9%88_%d8%aa%d8%ac%d8%b2%db%8c%d9%87_%d9%88_%d8%aa%d8%ad%d9%84%db%8c%d9%84_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7%db%8c_%d8%a7%da%a9%d8%aa%d8%b4%d8%a7%d9%81%db%8c\"><\/span>\u0648\u0627\u0631\u062f\u0627\u062a \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u06a9\u062a\u0634\u0627\u0641\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u0627 \u0628\u0627 \u0622\u0646 \u06a9\u0627\u0631 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.kaggle.com\/prevek18\/ames-housing-dataset\">\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0633\u06a9\u0646 \u0627\u06cc\u0645\u0632<\/a> \u06a9\u0647 \u0634\u0627\u0645\u0644 79 \u0648\u06cc\u0698\u06af\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0641\u0631\u0648\u062e\u062a\u0647 \u0634\u062f\u0647 \u062f\u0631 \u0627\u06cc\u0645\u0632\u060c \u0622\u06cc\u0648\u0648\u0627 \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0642\u06cc\u0645\u062a \u0641\u0631\u0648\u0634 \u0622\u0646\u0647\u0627 \u0627\u0633\u062a.  \u0627\u06cc\u0646 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0639\u0627\u0644\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0627\u0648\u0644\u06cc\u0647 \u0648 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0627\u0633\u062a\u060c \u0632\u06cc\u0631\u0627 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u06a9\u0627\u0631\u06cc \u0648 \u06a9\u0645\u0627\u0646\u0686\u0647 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0628\u0647 \u0637\u0631\u06cc\u0642\u06cc \u0628\u0631 \u0642\u06cc\u0645\u062a \u0641\u0631\u0648\u0634 \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc\u200c\u06af\u0630\u0627\u0631\u0646\u062f.<\/p>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f import \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\n\ndf = pd.read_csv(<span class=\"hljs-string\">'AmesHousing.csv'<\/span>)\n\nx = df((<span class=\"hljs-string\">'Gr Liv Area'<\/span>, <span class=\"hljs-string\">'Overall Qual'<\/span>)).values\ny = df(<span class=\"hljs-string\">'SalePrice'<\/span>).values\n\nfig, ax = plt.subplots(ncols=<span class=\"hljs-number\">2<\/span>, figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nax(<span class=\"hljs-number\">0<\/span>).scatter(x(:,<span class=\"hljs-number\">0<\/span>), y)\nax(<span class=\"hljs-number\">1<\/span>).scatter(x(:,<span class=\"hljs-number\">1<\/span>), y)\n\nplt.show()\n<\/code><\/pre>\n<p>\u06cc\u06a9 \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0645\u062b\u0628\u062a \u0642\u0648\u06cc \u0628\u06cc\u0646 <em>&#8220;Gr Liv Area&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc \u0648 <em>&#8220;\u0642\u06cc\u0645\u062a \u0641\u0631\u0648\u0634&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc &#8211; \u062a\u0646\u0647\u0627 \u0628\u0627 \u0686\u0646\u062f \u0646\u0642\u0637\u0647 \u067e\u0631\u062a.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u06cc\u06a9 \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0645\u062b\u0628\u062a \u0642\u0648\u06cc \u0628\u06cc\u0646 <em>&#8220;\u06a9\u0644\u06cc \u06a9\u06cc\u0641\u06cc&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc \u0648 <em>&#8220;\u0642\u06cc\u0645\u062a \u0641\u0631\u0648\u0634&#8221;<\/em>:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-1.png\" alt=\"\" title=\"\"><\/p>\n<p>\u06af\u0631\u0686\u0647 \u0627\u06cc\u0646\u0647\u0627 \u0647\u0633\u062a\u0646\u062f \u0631\u0648\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0628\u0633\u06cc\u0627\u0631 \u0645\u062a\u0641\u0627\u0648\u062a &#8211; <em>&#8220;\u0645\u0646\u0637\u0642\u0647 Gr liv&#8221;<\/em> \u062a\u0627 5000 ~ (\u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0634\u062f\u0647 \u062f\u0631 \u0641\u0648\u062a \u0645\u0631\u0628\u0639)\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 <em>&#8220;\u06a9\u0644\u06cc \u06a9\u06cc\u0641\u06cc&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc \u062a\u0627 10 (\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u062c\u0632\u0627 \u0627\u0632 \u06a9\u06cc\u0641\u06cc\u062a) \u0631\u0627 \u067e\u0648\u0634\u0634 \u0645\u06cc \u062f\u0647\u062f.  \u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u0627\u06cc\u0646 \u062f\u0648 \u0631\u0627 \u0637\u0631\u062d \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u0647\u0645\u0627\u0646 \u0645\u062d\u0648\u0631\u0647\u0627\u060c \u0645\u0627 \u0646\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0686\u06cc\u0632 \u0632\u06cc\u0627\u062f\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u0622\u0646 \u0628\u06af\u0648\u06cc\u06cc\u0645 <em>&#8220;\u06a9\u0644\u06cc \u06a9\u06cc\u0641\u06cc&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nax.scatter(x(:,<span class=\"hljs-number\">0<\/span>), y)\nax.scatter(x(:,<span class=\"hljs-number\">1<\/span>), y)\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-2.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646\u060c \u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u062a\u0648\u0632\u06cc\u0639 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645\u060c \u0634\u0627\u0646\u0633 \u0632\u06cc\u0627\u062f\u06cc \u0647\u0645 \u0646\u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u0634\u062a:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nax.hist(x(:,<span class=\"hljs-number\">0<\/span>))\nax.hist(x(:,<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-3.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0645\u0642\u06cc\u0627\u0633 \u0627\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627 \u0628\u0647 \u0642\u062f\u0631\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u0627\u0633\u062a \u06a9\u0647 \u0645\u0627 \u0646\u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u0627 \u062a\u0631\u0633\u06cc\u0645 \u0622\u0646\u0647\u0627 \u062f\u0631 \u06a9\u0646\u0627\u0631 \u0647\u0645 \u0686\u06cc\u0632 \u0632\u06cc\u0627\u062f\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u06cc\u0645. <em>\u0627\u06cc\u0646<\/em> \u062c\u0627\u06cc\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0634\u0631\u0648\u0639 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<h2 id=\"standardscaler\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b9%db%8c%d8%a7%d8%b1%d9%87%d8%a7%db%8c\"><\/span>\u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 <code>StandardScaler<\/code> \u06a9\u0644\u0627\u0633 \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627 \u062a\u0648\u0633\u0637 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f <em>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u06a9\u0631\u062f\u0646<\/em> \u0622\u06cc \u062a\u06cc.  \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f import \u0622\u0646 \u0648 <em>\u0645\u0642\u06cc\u0627\u0633<\/em> \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0622\u0646 <code>fit_transform()<\/code> \u0631\u0648\u0634:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\n<span class=\"hljs-keyword\">from<\/span> sklearn.preprocessing <span class=\"hljs-keyword\">import<\/span> StandardScaler\n\nfig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nscaler = StandardScaler()\nx_std = scaler.fit_transform(x)\n\nax.hist(x_std(:,<span class=\"hljs-number\">0<\/span>))\nax.hist(x_std(:,<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<div class=\"alert alert-note\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f:<\/strong> \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>fit_transform()<\/code> \u0631\u0648\u06cc  \u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0628\u0631\u0627\u06cc \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>StandardScaler<\/code> \u06a9\u0644\u0627\u0633 \u0648 \u0627\u062b\u0631\u0627\u062a \u0622\u0646 \u0631\u0627 \u062a\u062c\u0633\u0645 \u06a9\u0646\u06cc\u062f.  \u0647\u0646\u06af\u0627\u0645 \u0633\u0627\u062e\u062a \u06cc\u06a9 \u0645\u062f\u0644 \u06cc\u0627 \u062e\u0637 \u0644\u0648\u0644\u0647\u060c \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0628\u0647 \u0632\u0648\u062f\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f &#8211; \u0646\u0628\u0627\u06cc\u062f \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f <code>fit_transform()<\/code> \u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u060c \u0628\u0644\u06a9\u0647 \u0641\u0642\u0637 <code>fit()<\/code> \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u060c \u0648 <code>transform()<\/code> \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a<\/p>\n<\/p><\/div><\/div><\/div>\n<p>\u0627\u062c\u0631\u0627\u06cc \u0627\u06cc\u0646 \u0642\u0637\u0639\u0647 \u06a9\u062f\u060c \u0645\u0642\u062f\u0627\u0631 \u0631\u0627 \u0645\u062d\u0627\u0633\u0628\u0647 \u0645\u06cc \u06a9\u0646\u062f <strong>\u03bc<\/strong> \u0648 <strong>\u03c3<\/strong> \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 &#8211; \u0627\u06cc\u0646 process \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a <em>\u0628\u0631\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627<\/em>\u060c \u0648 \u0633\u067e\u0633 <em>\u0622\u0646 \u0631\u0627 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u062f<\/em> \u0628\u0647 \u0637\u0648\u0631\u06cc \u06a9\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0637\u0627\u0628\u0642\u062a \u062f\u0627\u0631\u0646\u062f <em>1<\/em> \u0648 <em>0<\/em> \u0628\u0647 \u062a\u0631\u062a\u06cc\u0628.<\/p>\n<p>\u0648\u0642\u062a\u06cc \u062a\u0648\u0632\u06cc\u0639 \u0627\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0627\u06a9\u0646\u0648\u0646 \u062a\u0631\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645\u060c \u0628\u0627 \u0637\u0631\u062d \u0628\u0633\u06cc\u0627\u0631 \u0642\u0627\u0628\u0644 \u0645\u062f\u06cc\u0631\u06cc\u062a \u062a\u0631\u06cc \u0645\u0648\u0627\u062c\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u0634\u062f:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-4.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0648\u0628\u0627\u0631\u0647 \u0627\u06cc\u0646\u0647\u0627 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 Scatter Plots \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645\u060c \u0634\u0627\u06cc\u062f \u0628\u0647 \u0648\u0636\u0648\u062d \u0627\u062b\u0631\u0627\u062a \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f\u0633\u0627\u0632\u06cc \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nscaler = StandardScaler()\nx_std = scaler.fit_transform(x)\n\nax.scatter(x_std(:,<span class=\"hljs-number\">0<\/span>), y)\nax.scatter(x_std(:,<span class=\"hljs-number\">1<\/span>), y)\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-5.png\" alt=\"\" title=\"\"><\/p>\n<h2 id=\"minmaxscaler\"><span class=\"ez-toc-section\" id=\"minmaxscaler\"><\/span>MinMaxScaler<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0647 <em>\u0639\u0627\u062f\u06cc \u06a9\u0631\u062f\u0646<\/em> \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u060c \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>MinMaxScaler<\/code> \u06a9\u0644\u0627\u0633  \u062a\u0642\u0631\u06cc\u0628\u0627\u064b \u0628\u0647 \u0647\u0645\u0627\u0646 \u0631\u0648\u0634\u06cc \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f <code>StandardScaler<\/code>\u060c \u0627\u0645\u0627 \u0627\u0632 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0627\u0633\u0627\u0633\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u0628\u0631\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nscaler = MinMaxScaler()\nx_minmax = scaler.fit_transform(x)\n\nax.hist(x_minmax (:,<span class=\"hljs-number\">0<\/span>))\nax.hist(x_minmax (:,<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p>\u0622\u0646 \u0647\u0627 \u0647\u0633\u062a\u0646\u062f <em>\u0639\u0627\u062f\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a<\/em> \u062f\u0631 \u0645\u062d\u062f\u0648\u062f\u0647 <em>(0\u060c 1)<\/em>.  \u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0648\u0628\u0627\u0631\u0647 \u062a\u0648\u0632\u06cc\u0639 \u0647\u0627 \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645\u060c \u0628\u0627 \u0627\u0633\u062a\u0642\u0628\u0627\u0644 \u0645\u0648\u0627\u062c\u0647 \u0645\u06cc \u0634\u0648\u06cc\u0645:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-6.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0627\u06cc\u0646 <em>\u0686\u0648\u0644\u06af\u06cc<\/em> \u0627\u0632 \u062a\u0648\u0632\u06cc\u0639 \u062d\u0641\u0638 \u0634\u062f\u0647 \u0627\u0633\u062a\u060c \u0628\u0631 \u062e\u0644\u0627\u0641 \u0628\u0627 <em>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0633\u0627\u0632\u06cc<\/em> \u06a9\u0647 \u0628\u0627\u0639\u062b \u0645\u06cc \u0634\u0648\u062f \u0622\u0646\u0647\u0627 \u062e\u06cc\u0644\u06cc \u0628\u06cc\u0634\u062a\u0631 \u0647\u0645\u067e\u0648\u0634\u0627\u0646\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u0646\u062f.  \u0627\u06af\u0631\u0686\u0647\u060c \u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0648\u0628\u0627\u0631\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 Scatter Plots \u0631\u0633\u0645 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nscaler = MinMaxScaler()\nx_minmax = scaler.fit_transform(x)\n\nax.scatter(x_minmax (:,<span class=\"hljs-number\">0<\/span>), y)\nax.scatter(x_minmax (:,<span class=\"hljs-number\">1<\/span>), y)\n<\/code><\/pre>\n<p>\u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0645\u062b\u0628\u062a \u0642\u0648\u06cc \u0628\u06cc\u0646 \u0647\u0631 \u062f\u0648\u06cc \u0627\u06cc\u0646\u0647\u0627 \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u0645 <em>&#8220;\u0642\u06cc\u0645\u062a \u0641\u0631\u0648\u0634&#8221;<\/em> \u0628\u0627 \u0648\u06cc\u0698\u06af\u06cc\u060c \u0627\u0645\u0627 <em>&#8220;\u06a9\u06cc\u0641\u06cc\u062a \u06a9\u0644\u06cc&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc \u0628\u0647 \u0637\u0631\u0632 \u0639\u062c\u06cc\u0628\u06cc \u0628\u0647 \u0633\u0645\u062a \u0631\u0627\u0633\u062a \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u06af\u0633\u062a\u0631\u0634 \u0645\u06cc \u06cc\u0627\u0628\u062f\u060c \u0632\u06cc\u0631\u0627 \u0646\u0642\u0627\u0637 \u067e\u0631\u062a \u0627\u0632 <em>&#8220;Gr Liv Area&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc \u0627\u06a9\u062b\u0631 \u062a\u0648\u0632\u06cc\u0639 \u0622\u0646 \u0631\u0627 \u0645\u062c\u0628\u0648\u0631 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u06a9\u0631\u062f\u0646 \u06a9\u0631\u062f \u0631\u0648\u06cc \u0633\u0645\u062a \u0686\u067e<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-7.png\" alt=\"\" title=\"\"><\/p>\n<h2 id=\"effectsofoutliers\"><span class=\"ez-toc-section\" id=\"%d8%a7%d8%ab%d8%b1%d8%a7%d8%aa_%d9%be%d8%b1%d8%aa\"><\/span>\u0627\u062b\u0631\u0627\u062a \u067e\u0631\u062a<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0647\u0631 \u062f\u0648 <em>\u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc<\/em> \u0648 <em>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0633\u0627\u0632\u06cc<\/em> \u0628\u0647 \u0645\u0648\u0627\u0631\u062f \u067e\u0631\u062a \u062d\u0633\u0627\u0633 \u0647\u0633\u062a\u0646\u062f &#8211; \u06a9\u0627\u0641\u06cc \u0627\u0633\u062a \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 \u0628\u0627\u0634\u062f <em>\u062a\u0646\u0647\u0627<\/em> \u062f\u0648\u0631 \u0627\u0632 \u0630\u0647\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0631\u0627\u0647 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u062a\u0627 \u0647\u0645\u0647 \u0686\u06cc\u0632 \u0648\u0627\u0642\u0639\u0627 \u0639\u062c\u06cc\u0628 \u0648 \u063a\u0631\u06cc\u0628 \u0628\u0647 \u0646\u0638\u0631 \u0628\u0631\u0633\u062f.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0648\u0631\u0648\u062f\u06cc \u0645\u0635\u0646\u0648\u0639\u06cc \u0628\u0647 \u0622\u0646 \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u0645 <em>&#8220;Gr Liv Area&#8221;<\/em> \u0648\u06cc\u0698\u06af\u06cc \u0628\u0631\u0627\u06cc \u062f\u06cc\u062f\u0646 \u0627\u06cc\u0646\u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0628\u0631 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc \u06af\u0630\u0627\u0631\u062f process:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nscaler = MinMaxScaler()\nx_minmax = scaler.fit_transform(x)\n\nax.scatter(x_minmax (:,<span class=\"hljs-number\">0<\/span>), y)\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-8.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0648\u0627\u062d\u062f \u067e\u0631\u062a\u060c \u0631\u0648\u06cc \u0633\u0645\u062a \u0631\u0627\u0633\u062a \u0627\u0641\u0631\u0627\u0637\u06cc \u0637\u0631\u062d \u0648\u0627\u0642\u0639\u0627\u064b \u0628\u0631 \u062a\u0648\u0632\u06cc\u0639 \u062c\u062f\u06cc\u062f \u062a\u0623\u062b\u06cc\u0631 \u06af\u0630\u0627\u0634\u062a\u0647 \u0627\u0633\u062a. <em>\u0647\u0645\u0647<\/em> \u0627\u0632 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u060c \u0628\u0647 \u062c\u0632 \u062e\u0631\u0648\u062c\u06cc\u060c \u062f\u0631 \u062f\u0648 \u0686\u0627\u0631\u06a9 \u0627\u0648\u0644 \u0642\u0631\u0627\u0631 \u062f\u0627\u0631\u062f:<\/p>\n<pre><code class=\"hljs\">fig, ax = plt.subplots(figsize=(<span class=\"hljs-number\">12<\/span>, <span class=\"hljs-number\">4<\/span>))\n\nscaler = MinMaxScaler()\nx_minmax = scaler.fit_transform(x)\n\nax.hist(x_minmax (:,<span class=\"hljs-number\">0<\/span>))\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python-9.png\" alt=\"\" title=\"\"><\/p>\n<h2 id=\"featurescalingthroughscikitlearnpipelines\"><span class=\"ez-toc-section\" id=\"%d9%85%d9%82%db%8c%d8%a7%d8%b3_%d8%a8%d9%86%d8%af%db%8c_%d9%88%db%8c%da%98%da%af%db%8c_%d8%a7%d8%b2_%d8%b7%d8%b1%db%8c%d9%82_%d8%ae%d8%b7%d9%88%d8%b7_%d9%84%d9%88%d9%84%d9%87_scikit-learn\"><\/span>\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062e\u0637\u0648\u0637 \u0644\u0648\u0644\u0647 Scikit-Learn<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0628\u06cc\u0627\u06cc\u06cc\u062f \u067e\u06cc\u0634 \u0628\u0631\u0648\u06cc\u0645 \u0648 \u06cc\u06a9 \u0645\u062f\u0644 \u0628\u0627 \u0648 \u0628\u062f\u0648\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0631\u0627 \u0627\u0632 \u0642\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645.  \u0647\u0646\u06af\u0627\u0645 \u06a9\u0627\u0631 \u0631\u0648\u06cc \u067e\u0631\u0648\u0698\u0647 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 &#8211; \u0645\u0627 \u0645\u0639\u0645\u0648\u0644\u0627 \u06cc\u06a9 <em>\u062e\u0637 \u0644\u0648\u0644\u0647<\/em> \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0642\u0628\u0644 \u0627\u0632 \u0631\u0633\u06cc\u062f\u0646 \u0628\u0647 \u0645\u062f\u0644\u06cc \u06a9\u0647 \u0645\u0627 \u0628\u0631\u0627\u0632\u0634 \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0645\u0627 \u0627\u0632 <code>Pipeline<\/code> \u06a9\u0644\u0627\u0633\u06cc \u06a9\u0647 \u0628\u0647 \u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0627\u06cc\u0646 \u0631\u0627 \u0628\u0647 \u062d\u062f\u0627\u0642\u0644 \u0628\u0631\u0633\u0627\u0646\u06cc\u0645 \u0648 \u062a\u0627 \u062d\u062f\u06cc \u0622\u0646 \u0631\u0627 \u062e\u0648\u062f\u06a9\u0627\u0631 \u06a9\u0646\u06cc\u0645 process\u060c \u062d\u062a\u06cc \u0627\u06af\u0631 \u0645\u0627 \u0641\u0642\u0637 \u062f\u0648 \u0645\u0631\u062d\u0644\u0647 \u062f\u0627\u0631\u06cc\u0645 &#8211; \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u0628\u0631\u0627\u0632\u0634 \u06cc\u06a9 \u0645\u062f\u0644:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n<span class=\"hljs-keyword\">from<\/span> sklearn.pipeline <span class=\"hljs-keyword\">import<\/span> Pipeline\n<span class=\"hljs-keyword\">from<\/span> sklearn.linear_model <span class=\"hljs-keyword\">import<\/span> SGDRegressor\n<span class=\"hljs-keyword\">from<\/span> sklearn.preprocessing <span class=\"hljs-keyword\">import<\/span> StandardScaler\n<span class=\"hljs-keyword\">from<\/span> sklearn.preprocessing <span class=\"hljs-keyword\">import<\/span> MinMaxScaler\n<span class=\"hljs-keyword\">from<\/span> sklearn.metrics <span class=\"hljs-keyword\">import<\/span> mean_absolute_error\n<span class=\"hljs-keyword\">import<\/span> sklearn.metrics <span class=\"hljs-keyword\">as<\/span> metrics\n\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\n\ndf = pd.read_csv(<span class=\"hljs-string\">'AmesHousing.csv'<\/span>)\nx = df((<span class=\"hljs-string\">'Gr Liv Area'<\/span>, <span class=\"hljs-string\">'Overall Qual'<\/span>)).values\ny = df(<span class=\"hljs-string\">'SalePrice'<\/span>).values\n\n\nX_train, X_test, Y_train, Y_test = train_test_split(x, y)\n\n\npipeline = Pipeline((\n    (<span class=\"hljs-string\">\"MinMax Scaling\"<\/span>, MinMaxScaler()),\n    (<span class=\"hljs-string\">\"SGD Regression\"<\/span>, SGDRegressor())\n))\n\n\npipeline.fit(X_train, Y_train)\n\n\nY_pred = pipeline.predict(X_test)\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Mean Absolute Error: '<\/span>, mean_absolute_error(Y_pred, Y_test))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Score'<\/span>, pipeline.score(X_test, Y_test))\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">Mean Absolute Error:  27614.031131858766\nScore 0.7536086980531018\n<\/code><\/pre>\n<p>\u0645\u06cc\u0627\u0646\u06af\u06cc\u0646 \u062e\u0637\u0627\u06cc \u0645\u0637\u0644\u0642 \u0627\u0633\u062a <em>27000 ~<\/em>\u060c \u0648 \u0646\u0645\u0631\u0647 \u062f\u0642\u062a \u0627\u0633\u062a <em>~ 75\u066a<\/em>.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0631\u0648\u06cc \u0628\u0647 \u0637\u0648\u0631 \u0645\u062a\u0648\u0633\u0637\u060c \u0645\u062f\u0644 \u0645\u0627 \u0642\u06cc\u0645\u062a \u0631\u0627 \u0627\u0632 \u062f\u0633\u062a \u0645\u06cc \u062f\u0647\u062f <em>27000 \u062f\u0644\u0627\u0631<\/em>\u060c \u06a9\u0647 \u0686\u0646\u062f\u0627\u0646 \u0628\u062f \u0628\u0647 \u0646\u0638\u0631 \u0646\u0645\u06cc \u0631\u0633\u062f\u060c \u0627\u06af\u0631\u0686\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0622\u0646 \u0631\u0627 \u0641\u0631\u0627\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0628\u0647\u0628\u0648\u062f \u062f\u0627\u062f.<\/p>\n<p>\u0645\u0647\u0645\u200c\u062a\u0631 \u0627\u0632 \u0647\u0645\u0647\u060c \u0646\u0648\u0639 \u0645\u062f\u0644\u06cc \u06a9\u0647 \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645 \u06a9\u0645\u06cc \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0633\u0641\u062a \u0648 \u0633\u062e\u062a \u0627\u0633\u062a \u0648 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u0631\u0627 \u062f\u0631 \u0622\u0646 \u0627\u0631\u0627\u0626\u0647 \u0646\u06a9\u0631\u062f\u0647\u200c\u0627\u06cc\u0645\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0642\u0637\u0639\u0627\u064b \u0627\u06cc\u0646 \u062f\u0648 \u0645\u06a9\u0627\u0646\u200c\u0647\u0627\u06cc\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647\u0628\u0648\u062f \u0628\u062e\u0634\u06cc\u062f.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 &#8211; \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0645\u0631\u06a9\u0632 \u062e\u0648\u062f \u0631\u0627 \u0628\u0631 \u0622\u0646\u0686\u0647 \u06a9\u0647 \u0628\u0647 \u0622\u0646 \u0639\u0644\u0627\u0642\u0647 \u0645\u0646\u062f\u06cc\u0645 \u0627\u0632 \u062f\u0633\u062a \u0646\u062f\u0647\u06cc\u0645. \u0639\u0645\u0644\u06a9\u0631\u062f \u0627\u06cc\u0646 \u0645\u062f\u0644 \u0686\u06af\u0648\u0646\u0647 \u0627\u0633\u062a <em>\u0628\u062f\u0648\u0646<\/em> \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc\u061f  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062e\u0637 \u0644\u0648\u0644\u0647 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u0645 \u062a\u0627 \u0645\u0631\u062d\u0644\u0647 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0631\u0627 \u0631\u062f \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">pipeline = Pipeline((\n    (<span class=\"hljs-string\">\"SGD Regression\"<\/span>, SGDRegressor())\n))\n<\/code><\/pre>\n<p>\u0622\u0646\u0686\u0647 \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0634\u0645\u0627 \u0631\u0627 \u0634\u06af\u0641\u062a \u0632\u062f\u0647 \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">Mean Absolute Error:  1260383513716205.8\nScore -2.772781517117743e+20\n<\/code><\/pre>\n<p>\u0645\u0627 \u0631\u0641\u062a\u06cc\u0645 \u0627\u0632 <strong><em>~ 75\u066a<\/em><\/strong>  \u062f\u0642\u062a \u0628\u0647 <strong><em>~-3\u066a<\/em><\/strong>  \u062f\u0642\u062a \u0641\u0642\u0637 \u0628\u0627 \u067e\u0631\u0634 \u0628\u0631\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0645\u0627. <em>\u0647\u0631<\/em> \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u06a9\u0647 \u0628\u0633\u062a\u06af\u06cc \u062f\u0627\u0631\u062f \u0631\u0648\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0645\u0632\u0627\u06cc\u0627\u06cc \u0639\u0645\u062f\u0647\u200c\u0627\u06cc \u0631\u0627 \u0627\u0632 \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u0645\u06cc\u200c\u06a9\u0646\u062f.  \u06a9\u0633\u0627\u0646\u06cc \u06a9\u0647 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0646\u0645\u06cc \u06a9\u0646\u0646\u062f\u060c \u062a\u0641\u0627\u0648\u062a \u0686\u0646\u062f\u0627\u0646\u06cc \u0646\u062e\u0648\u0627\u0647\u0646\u062f \u062f\u06cc\u062f.<\/p>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u060c \u0627\u06af\u0631 \u0645\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u06cc\u0645 <code>LinearRegression<\/code> \u0631\u0648\u06cc  \u0647\u0645\u06cc\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627\u060c \u0628\u0627 \u0648 \u0628\u062f\u0648\u0646 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc\u060c \u0646\u062a\u0627\u06cc\u062c \u063a\u06cc\u0631\u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f \u0631\u0648\u06cc \u0627\u0632 \u0637\u0631\u0641 \u0645\u0642\u06cc\u0627\u0633 \u0648 \u0646\u062a\u0627\u06cc\u062c \u0645\u0646\u0627\u0633\u0628 \u0631\u0648\u06cc \u0627\u0632 \u0637\u0631\u0641 \u062e\u0648\u062f \u0645\u062f\u0644:<\/p>\n<pre><code class=\"hljs\">pipeline1 = Pipeline((\n    (<span class=\"hljs-string\">\"Linear Regression\"<\/span>, LinearRegression())\n))\n\npipeline2 = Pipeline((\n    (<span class=\"hljs-string\">\"Scaling\"<\/span>, StandardScaler()),\n    (<span class=\"hljs-string\">\"Linear Regression\"<\/span>, LinearRegression())\n))\n\npipeline1.fit(X_train, Y_train)\npipeline2.fit(X_train, Y_train)\n\nY_pred1 = pipeline1.predict(X_test)\nY_pred2 = pipeline2.predict(X_test)\n\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Pipeline 1 Mean Absolute Error: '<\/span>, mean_absolute_error(Y_pred1, Y_test))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Pipeline 1 Score'<\/span>, pipeline1.score(X_test, Y_test))\n\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Pipeline 2 Mean Absolute Error: '<\/span>, mean_absolute_error(Y_pred2, Y_test))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Pipeline 2 Score'<\/span>, pipeline2.score(X_test, Y_test))\n<\/code><\/pre>\n<pre><code class=\"hljs\">Pipeline 1 Mean Absolute Error:  27706.61376199076\nPipeline 1 Score 0.7641840816646945\n\nPipeline 2 Mean Absolute Error:  27706.613761990764\nPipeline 2 Score 0.7641840816646945\n<\/code><\/pre>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%aa%db%8c%d8%ac%d9%87\"><\/span>\u0646\u062a\u06cc\u062c\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0627\u0633\u062a process \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0628\u0647 \u0645\u0642\u06cc\u0627\u0633\u06cc \u0642\u0627\u0628\u0644 \u0645\u062f\u06cc\u0631\u06cc\u062a \u062a\u0631.  \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0642\u0628\u0644 \u0627\u0632 \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0627\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627 \u0628\u0647 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627\u06cc\u06cc \u06a9\u0647 \u062a\u062d\u062a \u062a\u0623\u062b\u06cc\u0631 \u0645\u0642\u06cc\u0627\u0633 \u0647\u0633\u062a\u0646\u062f\u060c \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634\u060c \u0622\u0646 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc\u200c\u062f\u0647\u06cc\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627\u060c \u0645\u0627 \u0646\u06af\u0627\u0647\u06cc \u0627\u0646\u062f\u0627\u062e\u062a\u0647\u200c\u0627\u06cc\u0645 \u0628\u0647 \u0627\u06cc\u0646\u06a9\u0647 \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0686\u06cc\u0633\u062a \u0648 \u0686\u06af\u0648\u0646\u0647 \u0622\u0646 \u0631\u0627 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0627 Scikit-Learn \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645\u060c \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>StandardScaler<\/code> \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0633\u0627\u0632\u06cc \u0648 <code>MinMaxScaler<\/code> \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc.  \u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0686\u06af\u0648\u0646\u06af\u06cc \u062a\u0623\u062b\u06cc\u0631 \u0639\u0648\u0627\u0645\u0644 \u067e\u0631\u062a \u0628\u0631 \u0627\u06cc\u0646 \u0641\u0631\u0622\u06cc\u0646\u062f\u0647\u0627 \u0648 \u062a\u0641\u0627\u0648\u062a \u0628\u06cc\u0646 \u06cc\u06a9 \u0645\u062f\u0644 \u062d\u0633\u0627\u0633 \u0628\u0647 \u0645\u0642\u06cc\u0627\u0633 \u06a9\u0647 \u0628\u0627 \u0648 \u0628\u062f\u0648\u0646 \u0645\u0642\u06cc\u0627\u0633\u200c\u06af\u0630\u0627\u0631\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0627\u0646\u062f\u0627\u062e\u062a\u0647\u200c\u0627\u06cc\u0645.<\/p>\n<\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python(T)# Learning Machine (T)# Data Science<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-09 22:25:04<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;15221&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628&quot;,&quot;legend&quot;:&quot;0\\\/5 (0 \u0631\u0627\u06cc)&quot;,&quot;size&quot;:&quot;30&quot;,&quot;title&quot;:&quot;\u0648\u06cc\u0698\u06af\u06cc \u0645\u0642\u06cc\u0627\u0633\u200c\u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0628\u0627 Scikit-Learn \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} ({count} \u0631\u0627\u06cc)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 24px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 7<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0645\u0639\u0631\u0641\u06cc \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627 \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u06a9\u0644\u06cc\u062f\u06cc \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0627\u063a\u0644\u0628 \u0646\u0627\u062f\u06cc\u062f\u0647 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u0634\u0648\u062f. \u062f\u0631 \u0648\u0627\u0642\u0639 &#8211; \u0627\u06cc\u0646 \u0627\u0633\u062a \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u0647\u0645 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062f\u0644 \u0628\u0631\u0627\u0642\u06cc \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0627 \u0622\u0646 \u062a\u0646\u0627\u0633\u0628 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f. \u0632\u0628\u0627\u0644\u0647 \u062f\u0631 &#8211; \u0632\u0628\u0627\u0644\u0647 \u0628\u06cc\u0631\u0648\u0646. \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f \u0628\u0647\u062a\u0631\u06cc\u0646 \u0645\u062f\u0644\u06cc \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u0634\u06a9\u0644\u06cc \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":15222,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[1747,3744,1746,1754,1779,1814,1753,1744,1796,2750,1883,3743,1745,2446,1911],"class_list":["post-15221","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-programming","tag-python-vps","tag-scikitlearn","tag-vps-","tag-1754","tag-1779","tag-1814","tag-1753","tag-1744","tag-1796","tag-2750","tag-1883","tag-3743","tag-1745","tag-2446","tag-1911"],"acf":[],"_links":{"self":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/15221","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/comments?post=15221"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/15221\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/15222"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=15221"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=15221"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=15221"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}