{"id":22166,"date":"2023-05-10T01:07:08","date_gmt":"2023-05-09T21:37:08","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/"},"modified":"2023-05-10T01:07:08","modified_gmt":"2023-05-09T21:37:08","slug":"how-to-build-machine-learning-regression-models-with-python-54a4","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/","title":{"rendered":"\u0646\u062d\u0648\u0647 \u0633\u0627\u062e\u062a \u0645\u062f\u0644 \u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0628\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646"},"content":{"rendered":"<div data-article-id=\"1462665\" id=\"article-body\">\n<blockquote>\n<p>\u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u0648\u0633\u0637 <strong>\u0646\u062c\u06cc\u0628 \u0627\u0644\u062d\u0633\u0646<\/strong>\u060c \u06cc\u06a9\u06cc \u0627\u0632 \u0627\u0639\u0636\u0627\u06cc \u062a\u06cc\u0645 \u0645\u062d\u062a\u0648\u0627\u06cc \u0641\u0646\u06cc Educative.<\/p>\n<\/blockquote>\n<p>\u0645\u0627\u0631\u0648\u0644 \u06a9\u0627\u0645\u06cc\u06a9\u0633 \u06cc\u06a9 \u0634\u062e\u0635\u06cc\u062a \u062f\u0627\u0633\u062a\u0627\u0646\u06cc Destiny \u0631\u0627 \u062f\u0631 \u062f\u0647\u0647 1980 \u0645\u0639\u0631\u0641\u06cc \u06a9\u0631\u062f \u06a9\u0647 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0627\u062a\u0641\u0627\u0642\u0627\u062a \u0622\u06cc\u0646\u062f\u0647 \u0631\u0627 \u062f\u0627\u0634\u062a.  \u062e\u0628\u0631 \u0647\u06cc\u062c\u0627\u0646 \u0627\u0646\u06af\u06cc\u0632 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0631\u0648\u06cc\u062f\u0627\u062f\u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u062f\u06cc\u06af\u0631 \u0641\u0642\u0637 \u06cc\u06a9 \u062e\u06cc\u0627\u0644 \u0646\u06cc\u0633\u062a!  \u0628\u0627 \u067e\u06cc\u0634\u0631\u0641\u062a\u06cc \u06a9\u0647 \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0627\u06cc\u062c\u0627\u062f \u0634\u062f\u0647 \u0627\u0633\u062a\u060c \u06cc\u06a9 \u0645\u0627\u0634\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06af\u0630\u0634\u062a\u0647 \u0628\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0631\u0648\u06cc\u062f\u0627\u062f\u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u06a9\u0645\u06a9 \u06a9\u0646\u062f.<\/p>\n<p><\/p>\n<p>\u0647\u06cc\u062c\u0627\u0646 \u0627\u0646\u06af\u06cc\u0632\u060c \u062f\u0631\u0633\u062a \u0627\u0633\u062a\u061f  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06cc\u0646 \u0633\u0641\u0631 \u0631\u0627 \u0628\u0627 \u06cc\u06a9 \u0645\u062f\u0644 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0627\u062f\u0647 \u0634\u0631\u0648\u0639 \u06a9\u0646\u06cc\u0645. <\/p>\n<p>\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0631\u06cc\u0627\u0636\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0631\u0627\u0628\u0637\u0647 \u0628\u06cc\u0646 \u06cc\u06a9 \u0645\u062a\u063a\u06cc\u0631 \u0648\u0627\u0628\u0633\u062a\u0647 \u0648 \u06cc\u06a9 \u06cc\u0627 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u062f.  \u0628\u0647 \u062c\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u062a\u0626\u0648\u0631\u06cc\u060c \u062a\u0645\u0631\u06a9\u0632 \u0628\u0631 \u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0645\u062e\u062a\u0644\u0641 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter-rtl ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0641\u0647\u0631\u0633\u062a \u0645\u0637\u0627\u0644\u0628<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"\u062a\u063a\u06cc\u06cc\u0631 \u0648\u0636\u0639\u06cc\u062a \u0641\u0647\u0631\u0633\u062a \u0645\u0637\u0627\u0644\u0628\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<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:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%AF%D8%B1%DA%A9_%D8%AF%D8%A7%D8%AF%D9%87_%D9%87%D8%A7%DB%8C_%D9%88%D8%B1%D9%88%D8%AF%DB%8C\" >\u062f\u0631\u06a9 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\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:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%B1%DA%AF%D8%B1%D8%B3%DB%8C%D9%88%D9%86_%D8%AE%D8%B7%DB%8C\" >\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D9%88%DB%8C%DA%98%DA%AF%DB%8C\" >\u0627\u0646\u062a\u062e\u0627\u0628 \u0648\u06cc\u0698\u06af\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%AA%D9%82%D8%B3%DB%8C%D9%85_%D8%AF%D8%A7%D8%AF%D9%87_%D9%87%D8%A7\" >\u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%A7%D8%B9%D9%85%D8%A7%D9%84_%D9%85%D8%AF%D9%84\" >\u0627\u0639\u0645\u0627\u0644 \u0645\u062f\u0644<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%A7%D8%B9%D8%AA%D8%A8%D8%A7%D8%B1%D8%B3%D9%86%D8%AC%DB%8C_%D9%85%D8%AF%D9%84\" >\u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062f\u0644<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%B1%DA%AF%D8%B1%D8%B3%DB%8C%D9%88%D9%86_%D8%AE%D8%B7%DB%8C_%DA%86%D9%86%D8%AF%DA%AF%D8%A7%D9%86%D9%87\" >\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/nabfollower.com\/blog\/how-to-build-machine-learning-regression-models-with-python-54a4\/#%D8%B1%DA%AF%D8%B1%D8%B3%DB%8C%D9%88%D9%86_%DA%86%D9%86%D8%AF_%D8%AC%D9%85%D9%84%D9%87_%D8%A7%DB%8C\" >\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"%D8%AF%D8%B1%DA%A9_%D8%AF%D8%A7%D8%AF%D9%87_%D9%87%D8%A7%DB%8C_%D9%88%D8%B1%D9%88%D8%AF%DB%8C\"><\/span>\n<p>  \u062f\u0631\u06a9 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0642\u0628\u0644 \u0627\u0632 \u0634\u0631\u0648\u0639 \u0633\u0627\u062e\u062a \u06cc\u06a9 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646\u060c \u0628\u0627\u06cc\u062f \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0631\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0627\u06af\u0631 \u0641\u0631\u062f\u06cc \u0645\u0627\u0644\u06a9 \u06cc\u06a9 \u0645\u0632\u0631\u0639\u0647 \u0645\u0627\u0647\u06cc \u0628\u0627\u0634\u062f \u0648 \u0628\u0627\u06cc\u062f \u0648\u0632\u0646 \u0645\u0627\u0647\u06cc \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0627\u0628\u0639\u0627\u062f \u0622\u0646 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u06a9\u0646\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0627 \u06a9\u0644\u06cc\u06a9 \u06a9\u0631\u062f\u0646 \u0631\u0648\u06cc \u062f\u06a9\u0645\u0647 \u00abRUN\u00bb \u0628\u0631\u0627\u06cc \u0646\u0645\u0627\u06cc\u0634 \u0686\u0646\u062f \u0631\u062f\u06cc\u0641 \u0628\u0627\u0644\u0627\u06cc DataFrame \u06a9\u0627\u0648\u0634 \u06a9\u0646\u062f.<code>Fish.txt<\/code>).<\/p>\n<p><strong>DataFrame (<code>Fish.txt<\/code>):<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"n\">Species<\/span> <span class=\"n\">Weight<\/span>  <span class=\"n\">V<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>    <span class=\"n\">D<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>    <span class=\"n\">X<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>    <span class=\"n\">Height<\/span>  <span class=\"n\">Width<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">290<\/span> <span class=\"mi\">24<\/span>  <span class=\"mf\">26.3<\/span>    <span class=\"mf\">31.2<\/span>    <span class=\"mf\">12.48<\/span>   <span class=\"mf\">4.3056<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">340<\/span> <span class=\"mf\">23.9<\/span>    <span class=\"mf\">26.5<\/span>    <span class=\"mf\">31.1<\/span>    <span class=\"mf\">12.3778<\/span> <span class=\"mf\">4.6961<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">363<\/span> <span class=\"mf\">26.3<\/span>    <span class=\"mi\">29<\/span>  <span class=\"mf\">33.5<\/span>    <span class=\"mf\">12.73<\/span>   <span class=\"mf\">4.4555<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">430<\/span> <span class=\"mf\">26.5<\/span>    <span class=\"mi\">29<\/span>  <span class=\"mi\">34<\/span>  <span class=\"mf\">12.444<\/span>  <span class=\"mf\">5.134<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">450<\/span> <span class=\"mf\">26.8<\/span>    <span class=\"mf\">29.7<\/span>    <span class=\"mf\">34.7<\/span>    <span class=\"mf\">13.6024<\/span> <span class=\"mf\">4.9274<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">500<\/span> <span class=\"mf\">26.8<\/span>    <span class=\"mf\">29.7<\/span>    <span class=\"mf\">34.5<\/span>    <span class=\"mf\">14.1795<\/span> <span class=\"mf\">5.2785<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">390<\/span> <span class=\"mf\">27.6<\/span>    <span class=\"mi\">30<\/span>  <span class=\"mi\">35<\/span>  <span class=\"mf\">12.67<\/span>   <span class=\"mf\">4.69<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">450<\/span> <span class=\"mf\">27.6<\/span>    <span class=\"mi\">30<\/span>  <span class=\"mf\">35.1<\/span>    <span class=\"mf\">14.0049<\/span> <span class=\"mf\">4.8438<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">500<\/span> <span class=\"mf\">28.5<\/span>    <span class=\"mf\">30.7<\/span>    <span class=\"mf\">36.2<\/span>    <span class=\"mf\">14.2266<\/span> <span class=\"mf\">4.9594<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">475<\/span> <span class=\"mf\">28.4<\/span>    <span class=\"mi\">31<\/span>  <span class=\"mf\">36.2<\/span>    <span class=\"mf\">14.2628<\/span> <span class=\"mf\">5.1042<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">500<\/span> <span class=\"mf\">28.7<\/span>    <span class=\"mi\">31<\/span>  <span class=\"mf\">36.2<\/span>    <span class=\"mf\">14.3714<\/span> <span class=\"mf\">4.8146<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">500<\/span> <span class=\"mf\">29.1<\/span>    <span class=\"mf\">31.5<\/span>    <span class=\"mf\">36.4<\/span>    <span class=\"mf\">13.7592<\/span> <span class=\"mf\">4.368<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">340<\/span> <span class=\"mf\">29.5<\/span>    <span class=\"mi\">32<\/span>  <span class=\"mf\">37.3<\/span>    <span class=\"mf\">13.9129<\/span> <span class=\"mf\">5.0728<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">600<\/span> <span class=\"mf\">29.4<\/span>    <span class=\"mi\">32<\/span>  <span class=\"mf\">37.2<\/span>    <span class=\"mf\">14.9544<\/span> <span class=\"mf\">5.1708<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">600<\/span> <span class=\"mf\">29.4<\/span>    <span class=\"mi\">32<\/span>  <span class=\"mf\">37.2<\/span>    <span class=\"mf\">15.438<\/span>  <span class=\"mf\">5.58<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">700<\/span> <span class=\"mf\">30.4<\/span>    <span class=\"mi\">33<\/span>  <span class=\"mf\">38.3<\/span>    <span class=\"mf\">14.8604<\/span> <span class=\"mf\">5.2854<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">700<\/span> <span class=\"mf\">30.4<\/span>    <span class=\"mi\">33<\/span>  <span class=\"mf\">38.5<\/span>    <span class=\"mf\">14.938<\/span>  <span class=\"mf\">5.1975<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">610<\/span> <span class=\"mf\">30.9<\/span>    <span class=\"mf\">33.5<\/span>    <span class=\"mf\">38.6<\/span>    <span class=\"mf\">15.633<\/span>  <span class=\"mf\">5.1338<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">650<\/span> <span class=\"mi\">31<\/span>  <span class=\"mf\">33.5<\/span>    <span class=\"mf\">38.7<\/span>    <span class=\"mf\">14.4738<\/span> <span class=\"mf\">5.7276<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">575<\/span> <span class=\"mf\">31.3<\/span>    <span class=\"mi\">34<\/span>  <span class=\"mf\">39.5<\/span>    <span class=\"mf\">15.1285<\/span> <span class=\"mf\">5.5695<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">685<\/span> <span class=\"mf\">31.4<\/span>    <span class=\"mi\">34<\/span>  <span class=\"mf\">39.2<\/span>    <span class=\"mf\">15.9936<\/span> <span class=\"mf\">5.3704<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">620<\/span> <span class=\"mf\">31.5<\/span>    <span class=\"mf\">34.5<\/span>    <span class=\"mf\">39.7<\/span>    <span class=\"mf\">15.5227<\/span> <span class=\"mf\">5.2801<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">680<\/span> <span class=\"mf\">31.8<\/span>    <span class=\"mi\">35<\/span>  <span class=\"mf\">40.6<\/span>    <span class=\"mf\">15.4686<\/span> <span class=\"mf\">6.1306<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">700<\/span> <span class=\"mf\">31.9<\/span>    <span class=\"mi\">35<\/span>  <span class=\"mf\">40.5<\/span>    <span class=\"mf\">16.2405<\/span> <span class=\"mf\">5.589<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">725<\/span> <span class=\"mf\">31.8<\/span>    <span class=\"mi\">35<\/span>  <span class=\"mf\">40.9<\/span>    <span class=\"mf\">16.36<\/span>   <span class=\"mf\">6.0532<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">720<\/span> <span class=\"mi\">32<\/span>  <span class=\"mi\">35<\/span>  <span class=\"mf\">40.6<\/span>    <span class=\"mf\">16.3618<\/span> <span class=\"mf\">6.09<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">714<\/span> <span class=\"mf\">32.7<\/span>    <span class=\"mi\">36<\/span>  <span class=\"mf\">41.5<\/span>    <span class=\"mf\">16.517<\/span>  <span class=\"mf\">5.8515<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">850<\/span> <span class=\"mf\">32.8<\/span>    <span class=\"mi\">36<\/span>  <span class=\"mf\">41.6<\/span>    <span class=\"mf\">16.8896<\/span> <span class=\"mf\">6.1984<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">1000<\/span>    <span class=\"mf\">33.5<\/span>    <span class=\"mi\">37<\/span>  <span class=\"mf\">42.6<\/span>    <span class=\"mf\">18.957<\/span>  <span class=\"mf\">6.603<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">920<\/span> <span class=\"mi\">35<\/span>  <span class=\"mf\">38.5<\/span>    <span class=\"mf\">44.1<\/span>    <span class=\"mf\">18.0369<\/span> <span class=\"mf\">6.3063<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">955<\/span> <span class=\"mi\">35<\/span>  <span class=\"mf\">38.5<\/span>    <span class=\"mi\">44<\/span>  <span class=\"mf\">18.084<\/span>  <span class=\"mf\">6.292<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">925<\/span> <span class=\"mf\">36.2<\/span>    <span class=\"mf\">39.5<\/span>    <span class=\"mf\">45.3<\/span>    <span class=\"mf\">18.7542<\/span> <span class=\"mf\">6.7497<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">975<\/span> <span class=\"mf\">37.4<\/span>    <span class=\"mi\">41<\/span>  <span class=\"mf\">45.9<\/span>    <span class=\"mf\">18.6354<\/span> <span class=\"mf\">6.7473<\/span>\n<span class=\"n\">Bream<\/span>   <span class=\"mi\">950<\/span> <span class=\"mi\">38<\/span>  <span class=\"mi\">41<\/span>  <span class=\"mf\">46.5<\/span>    <span class=\"mf\">17.6235<\/span> <span class=\"mf\">6.3705<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">40<\/span>  <span class=\"mf\">12.9<\/span>    <span class=\"mf\">14.1<\/span>    <span class=\"mf\">16.2<\/span>    <span class=\"mf\">4.1472<\/span>  <span class=\"mf\">2.268<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">69<\/span>  <span class=\"mf\">16.5<\/span>    <span class=\"mf\">18.2<\/span>    <span class=\"mf\">20.3<\/span>    <span class=\"mf\">5.2983<\/span>  <span class=\"mf\">2.8217<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">78<\/span>  <span class=\"mf\">17.5<\/span>    <span class=\"mf\">18.8<\/span>    <span class=\"mf\">21.2<\/span>    <span class=\"mf\">5.5756<\/span>  <span class=\"mf\">2.9044<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">87<\/span>  <span class=\"mf\">18.2<\/span>    <span class=\"mf\">19.8<\/span>    <span class=\"mf\">22.2<\/span>    <span class=\"mf\">5.6166<\/span>  <span class=\"mf\">3.1746<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">120<\/span> <span class=\"mf\">18.6<\/span>    <span class=\"mi\">20<\/span>  <span class=\"mf\">22.2<\/span>    <span class=\"mf\">6.216<\/span>   <span class=\"mf\">3.5742<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">0<\/span>   <span class=\"mi\">19<\/span>  <span class=\"mf\">20.5<\/span>    <span class=\"mf\">22.8<\/span>    <span class=\"mf\">6.4752<\/span>  <span class=\"mf\">3.3516<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">110<\/span> <span class=\"mf\">19.1<\/span>    <span class=\"mf\">20.8<\/span>    <span class=\"mf\">23.1<\/span>    <span class=\"mf\">6.1677<\/span>  <span class=\"mf\">3.3957<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">120<\/span> <span class=\"mf\">19.4<\/span>    <span class=\"mi\">21<\/span>  <span class=\"mf\">23.7<\/span>    <span class=\"mf\">6.1146<\/span>  <span class=\"mf\">3.2943<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">150<\/span> <span class=\"mf\">20.4<\/span>    <span class=\"mi\">22<\/span>  <span class=\"mf\">24.7<\/span>    <span class=\"mf\">5.8045<\/span>  <span class=\"mf\">3.7544<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">145<\/span> <span class=\"mf\">20.5<\/span>    <span class=\"mi\">22<\/span>  <span class=\"mf\">24.3<\/span>    <span class=\"mf\">6.6339<\/span>  <span class=\"mf\">3.5478<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">160<\/span> <span class=\"mf\">20.5<\/span>    <span class=\"mf\">22.5<\/span>    <span class=\"mf\">25.3<\/span>    <span class=\"mf\">7.0334<\/span>  <span class=\"mf\">3.8203<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">140<\/span> <span class=\"mi\">21<\/span>  <span class=\"mf\">22.5<\/span>    <span class=\"mi\">25<\/span>  <span class=\"mf\">6.55<\/span>    <span class=\"mf\">3.325<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">160<\/span> <span class=\"mf\">21.1<\/span>    <span class=\"mf\">22.5<\/span>    <span class=\"mi\">25<\/span>  <span class=\"mf\">6.4<\/span> <span class=\"mf\">3.8<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">169<\/span> <span class=\"mi\">22<\/span>  <span class=\"mi\">24<\/span>  <span class=\"mf\">27.2<\/span>    <span class=\"mf\">7.5344<\/span>  <span class=\"mf\">3.8352<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">161<\/span> <span class=\"mi\">22<\/span>  <span class=\"mf\">23.4<\/span>    <span class=\"mf\">26.7<\/span>    <span class=\"mf\">6.9153<\/span>  <span class=\"mf\">3.6312<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">200<\/span> <span class=\"mf\">22.1<\/span>    <span class=\"mf\">23.5<\/span>    <span class=\"mf\">26.8<\/span>    <span class=\"mf\">7.3968<\/span>  <span class=\"mf\">4.1272<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">180<\/span> <span class=\"mf\">23.6<\/span>    <span class=\"mf\">25.2<\/span>    <span class=\"mf\">27.9<\/span>    <span class=\"mf\">7.0866<\/span>  <span class=\"mf\">3.906<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">290<\/span> <span class=\"mi\">24<\/span>  <span class=\"mi\">26<\/span>  <span class=\"mf\">29.2<\/span>    <span class=\"mf\">8.8768<\/span>  <span class=\"mf\">4.4968<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">272<\/span> <span class=\"mi\">25<\/span>  <span class=\"mi\">27<\/span>  <span class=\"mf\">30.6<\/span>    <span class=\"mf\">8.568<\/span>   <span class=\"mf\">4.7736<\/span>\n<span class=\"n\">Roach<\/span>   <span class=\"mi\">390<\/span> <span class=\"mf\">29.5<\/span>    <span class=\"mf\">31.7<\/span>    <span class=\"mi\">35<\/span>  <span class=\"mf\">9.485<\/span>   <span class=\"mf\">5.355<\/span>\n<span class=\"n\">Whitefish<\/span>   <span class=\"mi\">270<\/span> <span class=\"mf\">23.6<\/span>    <span class=\"mi\">26<\/span>  <span class=\"mf\">28.7<\/span>    <span class=\"mf\">8.3804<\/span>  <span class=\"mf\">4.2476<\/span>\n<span class=\"n\">Whitefish<\/span>   <span class=\"mi\">270<\/span> <span class=\"mf\">24.1<\/span>    <span class=\"mf\">26.5<\/span>    <span class=\"mf\">29.3<\/span>    <span class=\"mf\">8.1454<\/span>  <span class=\"mf\">4.2485<\/span>\n<span class=\"n\">Whitefish<\/span>   <span class=\"mi\">306<\/span> <span class=\"mf\">25.6<\/span>    <span class=\"mi\">28<\/span>  <span class=\"mf\">30.8<\/span>    <span class=\"mf\">8.778<\/span>   <span class=\"mf\">4.6816<\/span>\n<span class=\"n\">Whitefish<\/span>   <span class=\"mi\">540<\/span> <span class=\"mf\">28.5<\/span>    <span class=\"mi\">31<\/span>  <span class=\"mi\">34<\/span>  <span class=\"mf\">10.744<\/span>  <span class=\"mf\">6.562<\/span>\n<span class=\"n\">Whitefish<\/span>   <span class=\"mi\">800<\/span> <span class=\"mf\">33.7<\/span>    <span class=\"mf\">36.4<\/span>    <span class=\"mf\">39.6<\/span>    <span class=\"mf\">11.7612<\/span> <span class=\"mf\">6.5736<\/span>\n<span class=\"n\">Whitefish<\/span>   <span class=\"mi\">1000<\/span>    <span class=\"mf\">37.3<\/span>    <span class=\"mi\">40<\/span>  <span class=\"mf\">43.5<\/span>    <span class=\"mf\">12.354<\/span>  <span class=\"mf\">6.525<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">55<\/span>  <span class=\"mf\">13.5<\/span>    <span class=\"mf\">14.7<\/span>    <span class=\"mf\">16.5<\/span>    <span class=\"mf\">6.8475<\/span>  <span class=\"mf\">2.3265<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">60<\/span>  <span class=\"mf\">14.3<\/span>    <span class=\"mf\">15.5<\/span>    <span class=\"mf\">17.4<\/span>    <span class=\"mf\">6.5772<\/span>  <span class=\"mf\">2.3142<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">90<\/span>  <span class=\"mf\">16.3<\/span>    <span class=\"mf\">17.7<\/span>    <span class=\"mf\">19.8<\/span>    <span class=\"mf\">7.4052<\/span>  <span class=\"mf\">2.673<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">120<\/span> <span class=\"mf\">17.5<\/span>    <span class=\"mi\">19<\/span>  <span class=\"mf\">21.3<\/span>    <span class=\"mf\">8.3922<\/span>  <span class=\"mf\">2.9181<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">150<\/span> <span class=\"mf\">18.4<\/span>    <span class=\"mi\">20<\/span>  <span class=\"mf\">22.4<\/span>    <span class=\"mf\">8.8928<\/span>  <span class=\"mf\">3.2928<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">140<\/span> <span class=\"mi\">19<\/span>  <span class=\"mf\">20.7<\/span>    <span class=\"mf\">23.2<\/span>    <span class=\"mf\">8.5376<\/span>  <span class=\"mf\">3.2944<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">170<\/span> <span class=\"mi\">19<\/span>  <span class=\"mf\">20.7<\/span>    <span class=\"mf\">23.2<\/span>    <span class=\"mf\">9.396<\/span>   <span class=\"mf\">3.4104<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">145<\/span> <span class=\"mf\">19.8<\/span>    <span class=\"mf\">21.5<\/span>    <span class=\"mf\">24.1<\/span>    <span class=\"mf\">9.7364<\/span>  <span class=\"mf\">3.1571<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">200<\/span> <span class=\"mf\">21.2<\/span>    <span class=\"mi\">23<\/span>  <span class=\"mf\">25.8<\/span>    <span class=\"mf\">10.3458<\/span> <span class=\"mf\">3.6636<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">273<\/span> <span class=\"mi\">23<\/span>  <span class=\"mi\">25<\/span>  <span class=\"mi\">28<\/span>  <span class=\"mf\">11.088<\/span>  <span class=\"mf\">4.144<\/span>\n<span class=\"n\">Parkki<\/span>  <span class=\"mi\">300<\/span> <span class=\"mi\">24<\/span>  <span class=\"mi\">26<\/span>  <span class=\"mi\">29<\/span>  <span class=\"mf\">11.368<\/span>  <span class=\"mf\">4.234<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mf\">5.9<\/span> <span class=\"mf\">7.5<\/span> <span class=\"mf\">8.4<\/span> <span class=\"mf\">8.8<\/span> <span class=\"mf\">2.112<\/span>   <span class=\"mf\">1.408<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">32<\/span>  <span class=\"mf\">12.5<\/span>    <span class=\"mf\">13.7<\/span>    <span class=\"mf\">14.7<\/span>    <span class=\"mf\">3.528<\/span>   <span class=\"mf\">1.9992<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">40<\/span>  <span class=\"mf\">13.8<\/span>    <span class=\"mi\">15<\/span>  <span class=\"mi\">16<\/span>  <span class=\"mf\">3.824<\/span>   <span class=\"mf\">2.432<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mf\">51.5<\/span>    <span class=\"mi\">15<\/span>  <span class=\"mf\">16.2<\/span>    <span class=\"mf\">17.2<\/span>    <span class=\"mf\">4.5924<\/span>  <span class=\"mf\">2.6316<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">70<\/span>  <span class=\"mf\">15.7<\/span>    <span class=\"mf\">17.4<\/span>    <span class=\"mf\">18.5<\/span>    <span class=\"mf\">4.588<\/span>   <span class=\"mf\">2.9415<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">100<\/span> <span class=\"mf\">16.2<\/span>    <span class=\"mi\">18<\/span>  <span class=\"mf\">19.2<\/span>    <span class=\"mf\">5.2224<\/span>  <span class=\"mf\">3.3216<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">78<\/span>  <span class=\"mf\">16.8<\/span>    <span class=\"mf\">18.7<\/span>    <span class=\"mf\">19.4<\/span>    <span class=\"mf\">5.1992<\/span>  <span class=\"mf\">3.1234<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">80<\/span>  <span class=\"mf\">17.2<\/span>    <span class=\"mi\">19<\/span>  <span class=\"mf\">20.2<\/span>    <span class=\"mf\">5.6358<\/span>  <span class=\"mf\">3.0502<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">85<\/span>  <span class=\"mf\">17.8<\/span>    <span class=\"mf\">19.6<\/span>    <span class=\"mf\">20.8<\/span>    <span class=\"mf\">5.1376<\/span>  <span class=\"mf\">3.0368<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">85<\/span>  <span class=\"mf\">18.2<\/span>    <span class=\"mi\">20<\/span>  <span class=\"mi\">21<\/span>  <span class=\"mf\">5.082<\/span>   <span class=\"mf\">2.772<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">110<\/span> <span class=\"mi\">19<\/span>  <span class=\"mi\">21<\/span>  <span class=\"mf\">22.5<\/span>    <span class=\"mf\">5.6925<\/span>  <span class=\"mf\">3.555<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">115<\/span> <span class=\"mi\">19<\/span>  <span class=\"mi\">21<\/span>  <span class=\"mf\">22.5<\/span>    <span class=\"mf\">5.9175<\/span>  <span class=\"mf\">3.3075<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">125<\/span> <span class=\"mi\">19<\/span>  <span class=\"mi\">21<\/span>  <span class=\"mf\">22.5<\/span>    <span class=\"mf\">5.6925<\/span>  <span class=\"mf\">3.6675<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">130<\/span> <span class=\"mf\">19.3<\/span>    <span class=\"mf\">21.3<\/span>    <span class=\"mf\">22.8<\/span>    <span class=\"mf\">6.384<\/span>   <span class=\"mf\">3.534<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">120<\/span> <span class=\"mi\">20<\/span>  <span class=\"mi\">22<\/span>  <span class=\"mf\">23.5<\/span>    <span class=\"mf\">6.11<\/span>    <span class=\"mf\">3.4075<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">120<\/span> <span class=\"mi\">20<\/span>  <span class=\"mi\">22<\/span>  <span class=\"mf\">23.5<\/span>    <span class=\"mf\">5.64<\/span>    <span class=\"mf\">3.525<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">130<\/span> <span class=\"mi\">20<\/span>  <span class=\"mi\">22<\/span>  <span class=\"mf\">23.5<\/span>    <span class=\"mf\">6.11<\/span>    <span class=\"mf\">3.525<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">135<\/span> <span class=\"mi\">20<\/span>  <span class=\"mi\">22<\/span>  <span class=\"mf\">23.5<\/span>    <span class=\"mf\">5.875<\/span>   <span class=\"mf\">3.525<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">110<\/span> <span class=\"mi\">20<\/span>  <span class=\"mi\">22<\/span>  <span class=\"mf\">23.5<\/span>    <span class=\"mf\">5.5225<\/span>  <span class=\"mf\">3.995<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">130<\/span> <span class=\"mf\">20.5<\/span>    <span class=\"mf\">22.5<\/span>    <span class=\"mi\">24<\/span>  <span class=\"mf\">5.856<\/span>   <span class=\"mf\">3.624<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">150<\/span> <span class=\"mf\">20.5<\/span>    <span class=\"mf\">22.5<\/span>    <span class=\"mi\">24<\/span>  <span class=\"mf\">6.792<\/span>   <span class=\"mf\">3.624<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">145<\/span> <span class=\"mf\">20.7<\/span>    <span class=\"mf\">22.7<\/span>    <span class=\"mf\">24.2<\/span>    <span class=\"mf\">5.9532<\/span>  <span class=\"mf\">3.63<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">150<\/span> <span class=\"mi\">21<\/span>  <span class=\"mi\">23<\/span>  <span class=\"mf\">24.5<\/span>    <span class=\"mf\">5.2185<\/span>  <span class=\"mf\">3.626<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">170<\/span> <span class=\"mf\">21.5<\/span>    <span class=\"mf\">23.5<\/span>    <span class=\"mi\">25<\/span>  <span class=\"mf\">6.275<\/span>   <span class=\"mf\">3.725<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">225<\/span> <span class=\"mi\">22<\/span>  <span class=\"mi\">24<\/span>  <span class=\"mf\">25.5<\/span>    <span class=\"mf\">7.293<\/span>   <span class=\"mf\">3.723<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">145<\/span> <span class=\"mi\">22<\/span>  <span class=\"mi\">24<\/span>  <span class=\"mf\">25.5<\/span>    <span class=\"mf\">6.375<\/span>   <span class=\"mf\">3.825<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">188<\/span> <span class=\"mf\">22.6<\/span>    <span class=\"mf\">24.6<\/span>    <span class=\"mf\">26.2<\/span>    <span class=\"mf\">6.7334<\/span>  <span class=\"mf\">4.1658<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">180<\/span> <span class=\"mi\">23<\/span>  <span class=\"mi\">25<\/span>  <span class=\"mf\">26.5<\/span>    <span class=\"mf\">6.4395<\/span>  <span class=\"mf\">3.6835<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">197<\/span> <span class=\"mf\">23.5<\/span>    <span class=\"mf\">25.6<\/span>    <span class=\"mi\">27<\/span>  <span class=\"mf\">6.561<\/span>   <span class=\"mf\">4.239<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">218<\/span> <span class=\"mi\">25<\/span>  <span class=\"mf\">26.5<\/span>    <span class=\"mi\">28<\/span>  <span class=\"mf\">7.168<\/span>   <span class=\"mf\">4.144<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">300<\/span> <span class=\"mf\">25.2<\/span>    <span class=\"mf\">27.3<\/span>    <span class=\"mf\">28.7<\/span>    <span class=\"mf\">8.323<\/span>   <span class=\"mf\">5.1373<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">260<\/span> <span class=\"mf\">25.4<\/span>    <span class=\"mf\">27.5<\/span>    <span class=\"mf\">28.9<\/span>    <span class=\"mf\">7.1672<\/span>  <span class=\"mf\">4.335<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">265<\/span> <span class=\"mf\">25.4<\/span>    <span class=\"mf\">27.5<\/span>    <span class=\"mf\">28.9<\/span>    <span class=\"mf\">7.0516<\/span>  <span class=\"mf\">4.335<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">250<\/span> <span class=\"mf\">25.4<\/span>    <span class=\"mf\">27.5<\/span>    <span class=\"mf\">28.9<\/span>    <span class=\"mf\">7.2828<\/span>  <span class=\"mf\">4.5662<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">250<\/span> <span class=\"mf\">25.9<\/span>    <span class=\"mi\">28<\/span>  <span class=\"mf\">29.4<\/span>    <span class=\"mf\">7.8204<\/span>  <span class=\"mf\">4.2042<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">300<\/span> <span class=\"mf\">26.9<\/span>    <span class=\"mf\">28.7<\/span>    <span class=\"mf\">30.1<\/span>    <span class=\"mf\">7.5852<\/span>  <span class=\"mf\">4.6354<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">320<\/span> <span class=\"mf\">27.8<\/span>    <span class=\"mi\">30<\/span>  <span class=\"mf\">31.6<\/span>    <span class=\"mf\">7.6156<\/span>  <span class=\"mf\">4.7716<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">514<\/span> <span class=\"mf\">30.5<\/span>    <span class=\"mf\">32.8<\/span>    <span class=\"mi\">34<\/span>  <span class=\"mf\">10.03<\/span>   <span class=\"mf\">6.018<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">556<\/span> <span class=\"mi\">32<\/span>  <span class=\"mf\">34.5<\/span>    <span class=\"mf\">36.5<\/span>    <span class=\"mf\">10.2565<\/span> <span class=\"mf\">6.3875<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">840<\/span> <span class=\"mf\">32.5<\/span>    <span class=\"mi\">35<\/span>  <span class=\"mf\">37.3<\/span>    <span class=\"mf\">11.4884<\/span> <span class=\"mf\">7.7957<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">685<\/span> <span class=\"mi\">34<\/span>  <span class=\"mf\">36.5<\/span>    <span class=\"mi\">39<\/span>  <span class=\"mf\">10.881<\/span>  <span class=\"mf\">6.864<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">700<\/span> <span class=\"mi\">34<\/span>  <span class=\"mi\">36<\/span>  <span class=\"mf\">38.3<\/span>    <span class=\"mf\">10.6091<\/span> <span class=\"mf\">6.7408<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">700<\/span> <span class=\"mf\">34.5<\/span>    <span class=\"mi\">37<\/span>  <span class=\"mf\">39.4<\/span>    <span class=\"mf\">10.835<\/span>  <span class=\"mf\">6.2646<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">690<\/span> <span class=\"mf\">34.6<\/span>    <span class=\"mi\">37<\/span>  <span class=\"mf\">39.3<\/span>    <span class=\"mf\">10.5717<\/span> <span class=\"mf\">6.3666<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">900<\/span> <span class=\"mf\">36.5<\/span>    <span class=\"mi\">39<\/span>  <span class=\"mf\">41.4<\/span>    <span class=\"mf\">11.1366<\/span> <span class=\"mf\">7.4934<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">650<\/span> <span class=\"mf\">36.5<\/span>    <span class=\"mi\">39<\/span>  <span class=\"mf\">41.4<\/span>    <span class=\"mf\">11.1366<\/span> <span class=\"mf\">6.003<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">820<\/span> <span class=\"mf\">36.6<\/span>    <span class=\"mi\">39<\/span>  <span class=\"mf\">41.3<\/span>    <span class=\"mf\">12.4313<\/span> <span class=\"mf\">7.3514<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">850<\/span> <span class=\"mf\">36.9<\/span>    <span class=\"mi\">40<\/span>  <span class=\"mf\">42.3<\/span>    <span class=\"mf\">11.9286<\/span> <span class=\"mf\">7.1064<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">900<\/span> <span class=\"mi\">37<\/span>  <span class=\"mi\">40<\/span>  <span class=\"mf\">42.5<\/span>    <span class=\"mf\">11.73<\/span>   <span class=\"mf\">7.225<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">1015<\/span>    <span class=\"mi\">37<\/span>  <span class=\"mi\">40<\/span>  <span class=\"mf\">42.4<\/span>    <span class=\"mf\">12.3808<\/span> <span class=\"mf\">7.4624<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">820<\/span> <span class=\"mf\">37.1<\/span>    <span class=\"mi\">40<\/span>  <span class=\"mf\">42.5<\/span>    <span class=\"mf\">11.135<\/span>  <span class=\"mf\">6.63<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">1100<\/span>    <span class=\"mi\">39<\/span>  <span class=\"mi\">42<\/span>  <span class=\"mf\">44.6<\/span>    <span class=\"mf\">12.8002<\/span> <span class=\"mf\">6.8684<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">1000<\/span>    <span class=\"mf\">39.8<\/span>    <span class=\"mi\">43<\/span>  <span class=\"mf\">45.2<\/span>    <span class=\"mf\">11.9328<\/span> <span class=\"mf\">7.2772<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">1100<\/span>    <span class=\"mf\">40.1<\/span>    <span class=\"mi\">43<\/span>  <span class=\"mf\">45.5<\/span>    <span class=\"mf\">12.5125<\/span> <span class=\"mf\">7.4165<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">1000<\/span>    <span class=\"mf\">40.2<\/span>    <span class=\"mf\">43.5<\/span>    <span class=\"mi\">46<\/span>  <span class=\"mf\">12.604<\/span>  <span class=\"mf\">8.142<\/span>\n<span class=\"n\">Perch<\/span>   <span class=\"mi\">1000<\/span>    <span class=\"mf\">41.1<\/span>    <span class=\"mi\">44<\/span>  <span class=\"mf\">46.6<\/span>    <span class=\"mf\">12.4888<\/span> <span class=\"mf\">7.5958<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">200<\/span> <span class=\"mi\">30<\/span>  <span class=\"mf\">32.3<\/span>    <span class=\"mf\">34.8<\/span>    <span class=\"mf\">5.568<\/span>   <span class=\"mf\">3.3756<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">300<\/span> <span class=\"mf\">31.7<\/span>    <span class=\"mi\">34<\/span>  <span class=\"mf\">37.8<\/span>    <span class=\"mf\">5.7078<\/span>  <span class=\"mf\">4.158<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">300<\/span> <span class=\"mf\">32.7<\/span>    <span class=\"mi\">35<\/span>  <span class=\"mf\">38.8<\/span>    <span class=\"mf\">5.9364<\/span>  <span class=\"mf\">4.3844<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">300<\/span> <span class=\"mf\">34.8<\/span>    <span class=\"mf\">37.3<\/span>    <span class=\"mf\">39.8<\/span>    <span class=\"mf\">6.2884<\/span>  <span class=\"mf\">4.0198<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">430<\/span> <span class=\"mf\">35.5<\/span>    <span class=\"mi\">38<\/span>  <span class=\"mf\">40.5<\/span>    <span class=\"mf\">7.29<\/span>    <span class=\"mf\">4.5765<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">345<\/span> <span class=\"mi\">36<\/span>  <span class=\"mf\">38.5<\/span>    <span class=\"mi\">41<\/span>  <span class=\"mf\">6.396<\/span>   <span class=\"mf\">3.977<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">456<\/span> <span class=\"mi\">40<\/span>  <span class=\"mf\">42.5<\/span>    <span class=\"mf\">45.5<\/span>    <span class=\"mf\">7.28<\/span>    <span class=\"mf\">4.3225<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">510<\/span> <span class=\"mi\">40<\/span>  <span class=\"mf\">42.5<\/span>    <span class=\"mf\">45.5<\/span>    <span class=\"mf\">6.825<\/span>   <span class=\"mf\">4.459<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">540<\/span> <span class=\"mf\">40.1<\/span>    <span class=\"mi\">43<\/span>  <span class=\"mf\">45.8<\/span>    <span class=\"mf\">7.786<\/span>   <span class=\"mf\">5.1296<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">500<\/span> <span class=\"mi\">42<\/span>  <span class=\"mi\">45<\/span>  <span class=\"mi\">48<\/span>  <span class=\"mf\">6.96<\/span>    <span class=\"mf\">4.896<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">567<\/span> <span class=\"mf\">43.2<\/span>    <span class=\"mi\">46<\/span>  <span class=\"mf\">48.7<\/span>    <span class=\"mf\">7.792<\/span>   <span class=\"mf\">4.87<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">770<\/span> <span class=\"mf\">44.8<\/span>    <span class=\"mi\">48<\/span>  <span class=\"mf\">51.2<\/span>    <span class=\"mf\">7.68<\/span>    <span class=\"mf\">5.376<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">950<\/span> <span class=\"mf\">48.3<\/span>    <span class=\"mf\">51.7<\/span>    <span class=\"mf\">55.1<\/span>    <span class=\"mf\">8.9262<\/span>  <span class=\"mf\">6.1712<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">1250<\/span>    <span class=\"mi\">52<\/span>  <span class=\"mi\">56<\/span>  <span class=\"mf\">59.7<\/span>    <span class=\"mf\">10.6863<\/span> <span class=\"mf\">6.9849<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">1600<\/span>    <span class=\"mi\">56<\/span>  <span class=\"mi\">60<\/span>  <span class=\"mi\">64<\/span>  <span class=\"mf\">9.6<\/span> <span class=\"mf\">6.144<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">1550<\/span>    <span class=\"mi\">56<\/span>  <span class=\"mi\">60<\/span>  <span class=\"mi\">64<\/span>  <span class=\"mf\">9.6<\/span> <span class=\"mf\">6.144<\/span>\n<span class=\"n\">Pike<\/span>    <span class=\"mi\">1650<\/span>    <span class=\"mi\">59<\/span>  <span class=\"mf\">63.4<\/span>    <span class=\"mi\">68<\/span>  <span class=\"mf\">10.812<\/span>  <span class=\"mf\">7.48<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">6.7<\/span> <span class=\"mf\">9.3<\/span> <span class=\"mf\">9.8<\/span> <span class=\"mf\">10.8<\/span>    <span class=\"mf\">1.7388<\/span>  <span class=\"mf\">1.0476<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">7.5<\/span> <span class=\"mi\">10<\/span>  <span class=\"mf\">10.5<\/span>    <span class=\"mf\">11.6<\/span>    <span class=\"mf\">1.972<\/span>   <span class=\"mf\">1.16<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mi\">7<\/span>   <span class=\"mf\">10.1<\/span>    <span class=\"mf\">10.6<\/span>    <span class=\"mf\">11.6<\/span>    <span class=\"mf\">1.7284<\/span>  <span class=\"mf\">1.1484<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">9.7<\/span> <span class=\"mf\">10.4<\/span>    <span class=\"mi\">11<\/span>  <span class=\"mi\">12<\/span>  <span class=\"mf\">2.196<\/span>   <span class=\"mf\">1.38<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">9.8<\/span> <span class=\"mf\">10.7<\/span>    <span class=\"mf\">11.2<\/span>    <span class=\"mf\">12.4<\/span>    <span class=\"mf\">2.0832<\/span>  <span class=\"mf\">1.2772<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">8.7<\/span> <span class=\"mf\">10.8<\/span>    <span class=\"mf\">11.3<\/span>    <span class=\"mf\">12.6<\/span>    <span class=\"mf\">1.9782<\/span>  <span class=\"mf\">1.2852<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mi\">10<\/span>  <span class=\"mf\">11.3<\/span>    <span class=\"mf\">11.8<\/span>    <span class=\"mf\">13.1<\/span>    <span class=\"mf\">2.2139<\/span>  <span class=\"mf\">1.2838<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">9.9<\/span> <span class=\"mf\">11.3<\/span>    <span class=\"mf\">11.8<\/span>    <span class=\"mf\">13.1<\/span>    <span class=\"mf\">2.2139<\/span>  <span class=\"mf\">1.1659<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">9.8<\/span> <span class=\"mf\">11.4<\/span>    <span class=\"mi\">12<\/span>  <span class=\"mf\">13.2<\/span>    <span class=\"mf\">2.2044<\/span>  <span class=\"mf\">1.1484<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">12.2<\/span>    <span class=\"mf\">11.5<\/span>    <span class=\"mf\">12.2<\/span>    <span class=\"mf\">13.4<\/span>    <span class=\"mf\">2.0904<\/span>  <span class=\"mf\">1.3936<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">13.4<\/span>    <span class=\"mf\">11.7<\/span>    <span class=\"mf\">12.4<\/span>    <span class=\"mf\">13.5<\/span>    <span class=\"mf\">2.43<\/span>    <span class=\"mf\">1.269<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">12.2<\/span>    <span class=\"mf\">12.1<\/span>    <span class=\"mi\">13<\/span>  <span class=\"mf\">13.8<\/span>    <span class=\"mf\">2.277<\/span>   <span class=\"mf\">1.2558<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">19.7<\/span>    <span class=\"mf\">13.2<\/span>    <span class=\"mf\">14.3<\/span>    <span class=\"mf\">15.2<\/span>    <span class=\"mf\">2.8728<\/span>  <span class=\"mf\">2.0672<\/span>\n<span class=\"n\">Smelt<\/span>   <span class=\"mf\">19.9<\/span>    <span class=\"mf\">13.8<\/span>    <span class=\"mi\">15<\/span>  <span class=\"mf\">16.2<\/span>    <span class=\"mf\">2.9322<\/span>  <span class=\"mf\">1.8792<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u06a9\u062f \u0627\u062c\u0631\u0627\u06cc\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 1: Importing libraries \n<\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"n\">pd<\/span>\n\n<span class=\"c1\"># Step 2: Defining the columns of and reading our DataFrame \n<\/span><span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s\">'Species'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Weight'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">Fish<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"p\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">'Fish.txt'<\/span><span class=\"p\">,<\/span> <span class=\"n\">sep<\/span><span class=\"o\">=<\/span><span class=\"s\">'<\/span><span class=\"se\">\\t<\/span><span class=\"s\">'<\/span><span class=\"p\">,<\/span> <span class=\"n\">usecols<\/span><span class=\"o\">=<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Printing the head of our DataFrame\n<\/span><span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">Fish<\/span><span class=\"p\">.<\/span><span class=\"n\">head<\/span><span class=\"p\">())<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code>  <span class=\"n\">Species<\/span>  <span class=\"n\">Weight<\/span>  <span class=\"n\">V<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"n\">D<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"n\">X<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>   <span class=\"n\">Height<\/span>   <span class=\"n\">Width<\/span>\n<span class=\"mi\">0<\/span>   <span class=\"n\">Bream<\/span>   <span class=\"mf\">290.0<\/span>      <span class=\"mf\">24.0<\/span>      <span class=\"mf\">26.3<\/span>      <span class=\"mf\">31.2<\/span>  <span class=\"mf\">12.4800<\/span>  <span class=\"mf\">4.3056<\/span>\n<span class=\"mi\">1<\/span>   <span class=\"n\">Bream<\/span>   <span class=\"mf\">340.0<\/span>      <span class=\"mf\">23.9<\/span>      <span class=\"mf\">26.5<\/span>      <span class=\"mf\">31.1<\/span>  <span class=\"mf\">12.3778<\/span>  <span class=\"mf\">4.6961<\/span>\n<span class=\"mi\">2<\/span>   <span class=\"n\">Bream<\/span>   <span class=\"mf\">363.0<\/span>      <span class=\"mf\">26.3<\/span>      <span class=\"mf\">29.0<\/span>      <span class=\"mf\">33.5<\/span>  <span class=\"mf\">12.7300<\/span>  <span class=\"mf\">4.4555<\/span>\n<span class=\"mi\">3<\/span>   <span class=\"n\">Bream<\/span>   <span class=\"mf\">430.0<\/span>      <span class=\"mf\">26.5<\/span>      <span class=\"mf\">29.0<\/span>      <span class=\"mf\">34.0<\/span>  <span class=\"mf\">12.4440<\/span>  <span class=\"mf\">5.1340<\/span>\n<span class=\"mi\">4<\/span>   <span class=\"n\">Bream<\/span>   <span class=\"mf\">450.0<\/span>      <span class=\"mf\">26.8<\/span>      <span class=\"mf\">29.7<\/span>      <span class=\"mf\">34.7<\/span>  <span class=\"mf\">13.6024<\/span>  <span class=\"mf\">4.9274<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<ul>\n<li>\n<strong>\u062e\u0637 2:<\/strong> \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 pandas \u0628\u0631\u0627\u06cc \u062e\u0648\u0627\u0646\u062f\u0646 DataFrame \u0648\u0627\u0631\u062f \u0634\u062f\u0647 \u0627\u0633\u062a.<\/li>\n<li>\n<p><strong>\u062e\u0637 6:<\/strong> \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0627\u0632 <code>Fish.txt<\/code> \u0641\u0627\u06cc\u0644 \u0628\u0627 \u0633\u062a\u0648\u0646 \u0647\u0627\u06cc \u062a\u0639\u0631\u06cc\u0641 \u0634\u062f\u0647 \u062f\u0631 <strong>\u062e\u0637 5<\/strong>.<\/p>\n<\/li>\n<li>\n<p><strong>\u062e\u0637 9:<\/strong> \u067e\u0646\u062c \u0631\u062f\u06cc\u0641 \u0628\u0627\u0644\u0627\u06cc DataFrame \u0631\u0627 \u0686\u0627\u067e \u0645\u06cc \u06a9\u0646\u062f.  \u0633\u0647 \u0637\u0648\u0644\u060c \u0637\u0648\u0644 \u0647\u0627\u06cc \u0639\u0645\u0648\u062f\u06cc\u060c \u0645\u0648\u0631\u0628 \u0648 \u0645\u062a\u0642\u0627\u0637\u0639 \u0631\u0627 \u0628\u0631 \u062d\u0633\u0628 \u0633\u0627\u0646\u062a\u06cc \u0645\u062a\u0631 \u0645\u0634\u062e\u0635 \u0645\u06cc \u06a9\u0646\u0646\u062f.<\/p>\n<\/li>\n<\/ul>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627\u060c \u0637\u0648\u0644\u060c \u0642\u062f \u0648 \u0639\u0631\u0636 \u0645\u0627\u0647\u06cc \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0645\u0633\u062a\u0642\u0644 \u0647\u0633\u062a\u0646\u062f \u0648 \u0648\u0632\u0646 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062a\u063a\u06cc\u0631 \u0648\u0627\u0628\u0633\u062a\u0647 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u062f.  \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u060c \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0645\u0633\u062a\u0642\u0644 \u0627\u063a\u0644\u0628 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0646\u0627\u0645\u06cc\u062f\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f <strong>\u0627\u0645\u06a9\u0627\u0646\u0627\u062a<\/strong> \u0648 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0648\u0627\u0628\u0633\u062a\u0647 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 <strong>\u0628\u0631\u0686\u0633\u0628 \u0647\u0627<\/strong>\u060c \u0648 \u0627\u06cc\u0646 \u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u062f\u0631 \u0633\u0631\u0627\u0633\u0631 \u0627\u06cc\u0646 \u0648\u0628\u0644\u0627\u06af \u0628\u0647 \u062c\u0627\u06cc \u06cc\u06a9\u062f\u06cc\u06af\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u0646\u062f \u0634\u062f.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"%D8%B1%DA%AF%D8%B1%D8%B3%DB%8C%D9%88%D9%86_%D8%AE%D8%B7%DB%8C\"><\/span>\n<p>  \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u062f\u0644 \u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0628\u0647 \u0637\u0648\u0631 \u06af\u0633\u062a\u0631\u062f\u0647 \u062f\u0631 \u0622\u0645\u0627\u0631 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0627 \u0627\u0632 \u06cc\u06a9 \u062e\u0637 \u0645\u0633\u062a\u0642\u06cc\u0645 \u0628\u0631\u0627\u06cc \u062a\u0648\u0635\u06cc\u0641 \u0631\u0627\u0628\u0637\u0647 \u0628\u06cc\u0646 \u06cc\u06a9 \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u0648 \u06cc\u06a9 \u0645\u062a\u063a\u06cc\u0631 \u0648\u0627\u0628\u0633\u062a\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u0646\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0647\u0646\u06af\u0627\u0645 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0648\u0632\u0646 \u0645\u0627\u0647\u06cc\u060c \u0627\u0632 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0628\u0631\u0627\u06cc \u062a\u0648\u0635\u06cc\u0641 \u0631\u0627\u0628\u0637\u0647 \u0628\u06cc\u0646 \u0648\u0632\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f. <em>y<\/em> \u0645\u0627\u0647\u06cc \u0648 \u06cc\u06a9\u06cc \u0627\u0632 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0645\u0633\u062a\u0642\u0644 <em>\u0627\u06cc\u06a9\u0633<\/em> \u0628\u0647 \u0634\u0631\u062d \u0632\u06cc\u0631 \u0627\u0633\u062a\u060c<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/nabfollower.com\/blog\/wp-content\/uploads\/2023\/05\/1683668228_910_\u0646\u062d\u0648\u0647-\u0633\u0627\u062e\u062a-\u0645\u062f\u0644-\u0647\u0627\u06cc-\u0631\u06af\u0631\u0633\u06cc\u0648\u0646-\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc-\u0645\u0627\u0634\u06cc\u0646-\u0628\u0627-\u067e\u0627\u06cc\u062a\u0648\u0646.png\" alt=\"\u0645\u062d\u0648\u0631 Y\" loading=\"lazy\" width=\"636\" height=\"77\" title=\"\"><\/p>\n<p>\u062c\u0627\u06cc\u06cc \u06a9\u0647 <em>\u0645\u062a\u0631<\/em> \u0647\u0633\u062a <strong>\u0634\u06cc\u0628<\/strong> \u0627\u0632 \u062e\u0637\u06cc \u06a9\u0647 \u0634\u06cc\u0628 \u0622\u0646 \u0631\u0627 \u0645\u0634\u062e\u0635 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0648 <em>\u062c<\/em> \u0647\u0633\u062a <strong>y-\u0628\u0631\u0642<\/strong>\u060c \u0646\u0642\u0637\u0647 \u0627\u06cc \u06a9\u0647 \u062e\u0637 \u0627\u0632 \u0645\u062d\u0648\u0631 y \u0639\u0628\u0648\u0631 \u0645\u06cc \u06a9\u0646\u062f. <\/p>\n<p><img decoding=\"async\" src=\"https:\/\/nabfollower.com\/blog\/wp-content\/uploads\/2023\/05\/1683668228_685_\u0646\u062d\u0648\u0647-\u0633\u0627\u062e\u062a-\u0645\u062f\u0644-\u0647\u0627\u06cc-\u0631\u06af\u0631\u0633\u06cc\u0648\u0646-\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc-\u0645\u0627\u0634\u06cc\u0646-\u0628\u0627-\u067e\u0627\u06cc\u062a\u0648\u0646.png\" alt=\"\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc\" loading=\"lazy\" width=\"800\" height=\"550\" title=\"\"><\/p>\n<h3><span class=\"ez-toc-section\" id=\"%D8%A7%D9%86%D8%AA%D8%AE%D8%A7%D8%A8_%D9%88%DB%8C%DA%98%DA%AF%DB%8C\"><\/span>\n<p>  \u0627\u0646\u062a\u062e\u0627\u0628 \u0648\u06cc\u0698\u06af\u06cc<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0627\u0645\u0644 \u067e\u0646\u062c \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u0627\u0633\u062a.  \u06cc\u06a9 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0633\u0627\u062f\u0647 \u0628\u0627 \u062a\u0646\u0647\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u0642\u0648\u06cc \u062a\u0631\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0645\u0627\u0647\u06cc \u0622\u063a\u0627\u0632 \u06a9\u0631\u062f. <code>Weight<\/code>.  \u06cc\u06a9 \u0631\u0648\u0634 \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u0627\u0645\u0631\u060c \u0645\u062d\u0627\u0633\u0628\u0647 \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0645\u062a\u0642\u0627\u0628\u0644 \u0628\u06cc\u0646 \u0627\u0633\u062a <code>Weight<\/code> \u0648 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627<\/p>\n<p><strong>\u06a9\u062f \u0645\u062e\u0641\u06cc:<\/strong> (\u0627\u0632 \u0628\u0644\u0648\u06a9 \u06a9\u062f \u0642\u0628\u0644\u06cc)<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 1: Importing libraries \n<\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"n\">pd<\/span>\n\n<span class=\"c1\"># Step 2: Defining the columns of and reading our data frame \n<\/span><span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s\">'Species'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Weight'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">Fish<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"p\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">'Fish.txt'<\/span><span class=\"p\">,<\/span> <span class=\"n\">sep<\/span><span class=\"o\">=<\/span><span class=\"s\">'<\/span><span class=\"se\">\\t<\/span><span class=\"s\">'<\/span><span class=\"p\">,<\/span> <span class=\"n\">usecols<\/span><span class=\"o\">=<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u06a9\u062f \u0627\u062c\u0631\u0627\u06cc\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Finding the cross-correlation matrix\n<\/span><span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"n\">Fish<\/span><span class=\"p\">.<\/span><span class=\"n\">corr<\/span><span class=\"p\">())<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code>            <span class=\"n\">Weight<\/span>  <span class=\"n\">V<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"n\">D<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"n\">X<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>    <span class=\"n\">Height<\/span>     <span class=\"n\">Width<\/span>\n<span class=\"n\">Weight<\/span>    <span class=\"mf\">1.000000<\/span>  <span class=\"mf\">0.915691<\/span>  <span class=\"mf\">0.918625<\/span>  <span class=\"mf\">0.923343<\/span>  <span class=\"mf\">0.727260<\/span>  <span class=\"mf\">0.886546<\/span>\n<span class=\"n\">V<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"mf\">0.915691<\/span>  <span class=\"mf\">1.000000<\/span>  <span class=\"mf\">0.999519<\/span>  <span class=\"mf\">0.992155<\/span>  <span class=\"mf\">0.627425<\/span>  <span class=\"mf\">0.867002<\/span>\n<span class=\"n\">D<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"mf\">0.918625<\/span>  <span class=\"mf\">0.999519<\/span>  <span class=\"mf\">1.000000<\/span>  <span class=\"mf\">0.994199<\/span>  <span class=\"mf\">0.642392<\/span>  <span class=\"mf\">0.873499<\/span>\n<span class=\"n\">X<\/span><span class=\"o\">-<\/span><span class=\"n\">Length<\/span>  <span class=\"mf\">0.923343<\/span>  <span class=\"mf\">0.992155<\/span>  <span class=\"mf\">0.994199<\/span>  <span class=\"mf\">1.000000<\/span>  <span class=\"mf\">0.704628<\/span>  <span class=\"mf\">0.878548<\/span>\n<span class=\"n\">Height<\/span>    <span class=\"mf\">0.727260<\/span>  <span class=\"mf\">0.627425<\/span>  <span class=\"mf\">0.642392<\/span>  <span class=\"mf\">0.704628<\/span>  <span class=\"mf\">1.000000<\/span>  <span class=\"mf\">0.794810<\/span>\n<span class=\"n\">Width<\/span>     <span class=\"mf\">0.886546<\/span>  <span class=\"mf\">0.867002<\/span>  <span class=\"mf\">0.873499<\/span>  <span class=\"mf\">0.878548<\/span>  <span class=\"mf\">0.794810<\/span>  <span class=\"mf\">1.000000<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0628\u0627 \u0628\u0631\u0631\u0633\u06cc \u0633\u062a\u0648\u0646 \u0627\u0648\u0644\u060c \u0645\u0648\u0627\u0631\u062f \u0632\u06cc\u0631 \u0645\u0634\u0627\u0647\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f: <\/p>\n<ul>\n<li>\u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0642\u0648\u06cc \u0628\u06cc\u0646 <code>Weight<\/code>\u0648 \u0648\u06cc\u0698\u06af\u06cc <code>X-Length<\/code>. <\/li>\n<li>\u0631\u0627 <code>Weight<\/code> \u0636\u0639\u06cc\u0641 \u062a\u0631\u06cc\u0646 \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0631\u0627 \u0628\u0627 <code>Height<\/code>.<\/li>\n<\/ul>\n<p>\u0628\u0627 \u062a\u0648\u062c\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u0627\u0637\u0644\u0627\u0639\u0627\u062a\u060c \u0648\u0627\u0636\u062d \u0627\u0633\u062a \u06a9\u0647 \u0627\u06af\u0631 \u0641\u0631\u062f \u0645\u062d\u062f\u0648\u062f \u0628\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u062a\u063a\u06cc\u0631 \u0648\u0627\u0628\u0633\u062a\u0647 \u0628\u0627\u0634\u062f\u060c \u0628\u0627\u06cc\u062f \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u062f. <code>X-Length<\/code> \u0648 \u0646\u0647 <code>Height<\/code>.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 3: Separating the data into features and labels\n<\/span><span class=\"n\">X<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[[<\/span><span class=\"s\">'X-Length'<\/span><span class=\"p\">]]<\/span>\n<span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[<\/span><span class=\"s\">'Weight'<\/span><span class=\"p\">]<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<h3><span class=\"ez-toc-section\" id=\"%D8%AA%D9%82%D8%B3%DB%8C%D9%85_%D8%AF%D8%A7%D8%AF%D9%87_%D9%87%D8%A7\"><\/span>\n<p>  \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u0627 \u0648\u062c\u0648\u062f \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u060c \u0627\u06a9\u0646\u0648\u0646 DataFrame \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u0634\u0648\u062f. <br \/>\u0631\u0627 <strong>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc<\/strong> \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 <strong>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc<\/strong> \u0639\u0645\u0644\u06a9\u0631\u062f \u0622\u0646 \u0631\u0627 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u062f. <\/p>\n<p>\u0631\u0627 <code>train_test_split<\/code> \u062a\u0627\u0628\u0639 \u0627\u0632 \u0648\u0627\u0631\u062f \u0634\u062f\u0647 \u0627\u0633\u062a <code>sklearn<\/code> \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0628\u0631\u0627\u06cc \u062a\u0642\u0633\u06cc\u0645 \u062f\u0627\u062f\u0647 \u0647\u0627<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.model_selection<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">train_test_split<\/span>\n\n<span class=\"c1\"># Step 4: Dividing the dataset into test and train data\n<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">X_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_test<\/span> <span class=\"o\">=<\/span> \n    <span class=\"n\">train_test_split<\/span><span class=\"p\">(<\/span>\n                <span class=\"n\">X<\/span><span class=\"p\">,<\/span> <span class=\"n\">y<\/span><span class=\"p\">,<\/span> \n                <span class=\"n\">test_size<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.3<\/span><span class=\"p\">,<\/span> \n                <span class=\"n\">random_state<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> \n                <span class=\"n\">shuffle<\/span><span class=\"o\">=<\/span><span class=\"bp\">True<\/span>\n                <span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0647\u0627\u06cc <code>train_test_split<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0631\u062f:<\/p>\n<ul>\n<li>\n<strong>\u062e\u0637 6:<\/strong> \u0648\u06cc\u0698\u06af\u06cc \u0648 \u0628\u0631\u0686\u0633\u0628 \u0631\u0627 \u067e\u0627\u0633 \u06a9\u0646\u06cc\u062f. <\/li>\n<li>\n<strong>\u062e\u0637 7:<\/strong> \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>test_size=0.3<\/code> \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u062e\u0627\u0628 70 \u062f\u0631\u0635\u062f \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0648 30 \u062f\u0631\u0635\u062f \u0628\u0627\u0642\u06cc \u0645\u0627\u0646\u062f\u0647 \u0628\u0631\u0627\u06cc \u0627\u0647\u062f\u0627\u0641 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc. <\/li>\n<li>\n<strong>\u062e\u0637\u0648\u0637 8-9:<\/strong> \u062a\u0642\u0633\u06cc\u0645 \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u062a\u0635\u0627\u062f\u0641\u06cc \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f \u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>shuffle=True<\/code> \u0628\u0631\u0627\u06cc \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u0627\u0632 \u0639\u062f\u0645 \u062a\u0637\u0627\u0628\u0642 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u062f\u0644 \u0628\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062e\u0627\u0635\u06cc \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627. <\/li>\n<\/ul>\n<p>\u062f\u0631 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u062f\u0631 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627 <code>X_train<\/code> \u0648 <code>y_train<\/code> \u0648 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a \u062f\u0631 <code>X_test<\/code> \u0648 <code>y_test<\/code> \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647 \u0627\u0633\u062a.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"%D8%A7%D8%B9%D9%85%D8%A7%D9%84_%D9%85%D8%AF%D9%84\"><\/span>\n<p>  \u0627\u0639\u0645\u0627\u0644 \u0645\u062f\u0644<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f. <\/p>\n<p><strong>\u06a9\u062f \u0645\u062e\u0641\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 1: Importing libraries \n<\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"n\">pd<\/span>\n\n<span class=\"c1\"># 1.2\n<\/span><span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.model_selection<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">train_test_split<\/span>\n\n<span class=\"c1\"># Step 2: Defining the columns of and reading our data frame \n<\/span><span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s\">'Species'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Weight'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">Fish<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"p\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">'Fish.txt'<\/span><span class=\"p\">,<\/span> <span class=\"n\">sep<\/span><span class=\"o\">=<\/span><span class=\"s\">'<\/span><span class=\"se\">\\t<\/span><span class=\"s\">'<\/span><span class=\"p\">,<\/span> <span class=\"n\">usecols<\/span><span class=\"o\">=<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 3: Seperating the data into features and labels\n<\/span><span class=\"n\">X<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[[<\/span><span class=\"s\">'X-Length'<\/span><span class=\"p\">]]<\/span>\n<span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[<\/span><span class=\"s\">'Weight'<\/span><span class=\"p\">]<\/span>\n\n<span class=\"c1\"># Step 4: Dividing the data into test and train set\n<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">X_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_test<\/span> <span class=\"o\">=<\/span> <span class=\"n\">train_test_split<\/span><span class=\"p\">(<\/span><span class=\"n\">X<\/span><span class=\"p\">,<\/span> <span class=\"n\">y<\/span><span class=\"p\">,<\/span> <span class=\"n\">test_size<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.3<\/span><span class=\"p\">,<\/span> <span class=\"n\">random_state<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"n\">shuffle<\/span><span class=\"o\">=<\/span><span class=\"bp\">True<\/span><span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u06a9\u062f \u0627\u062c\u0631\u0627\u06cc\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.linear_model<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">LinearRegression<\/span>\n\n<span class=\"c1\"># Step 5: Selecting the linear regression method from scikit-learn library\n<\/span><span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">LinearRegression<\/span><span class=\"p\">().<\/span><span class=\"n\">fit<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<ul>\n<li>\n<strong>\u062e\u0637 1:<\/strong> \u0631\u0627 <code>LinearRegression<\/code> \u062a\u0627\u0628\u0639 \u0627\u0632 <code>sklearn<\/code> \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0648\u0627\u0631\u062f \u0634\u062f\u0647 \u0627\u0633\u062a<\/li>\n<li>\n<strong>\u062e\u0637 4:<\/strong> \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0645\u062f\u0644 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0648 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f <code>X_train<\/code> \u0648 <code>y_train<\/code>.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"%D8%A7%D8%B9%D8%AA%D8%A8%D8%A7%D8%B1%D8%B3%D9%86%D8%AC%DB%8C_%D9%85%D8%AF%D9%84\"><\/span>\n<p>  \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062f\u0644<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u0647 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f\u060c 30\u066a \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634 \u06a9\u0646\u0627\u0631 \u06af\u0630\u0627\u0634\u062a\u0647 \u0634\u062f.  \u0645\u06cc\u0627\u0646\u06af\u06cc\u0646 \u062e\u0637\u0627\u06cc \u0645\u0637\u0644\u0642 (MAE) \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u06cc\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0634\u0627\u062e\u0635 \u0645\u06cc\u0627\u0646\u06af\u06cc\u0646 \u0627\u062e\u062a\u0644\u0627\u0641 \u0645\u0637\u0644\u0642 \u0628\u06cc\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0648 \u0648\u0627\u0642\u0639\u06cc \u0645\u062d\u0627\u0633\u0628\u0647 \u06a9\u0631\u062f\u060c \u0628\u0627 \u0645\u0642\u062f\u0627\u0631 MAE \u067e\u0627\u06cc\u06cc\u0646 \u062a\u0631 \u06a9\u0647 \u0646\u0634\u0627\u0646 \u062f\u0647\u0646\u062f\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u062f\u0642\u06cc\u0642 \u062a\u0631 \u0627\u0633\u062a.  \u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc \u062f\u06cc\u06af\u0631\u06cc \u0628\u0631\u0627\u06cc \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0645\u062f\u0644 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f\u060c \u0627\u0645\u0627 \u062f\u0631 \u0627\u06cc\u0646 \u0632\u0645\u06cc\u0646\u0647 \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u0646\u062e\u0648\u0627\u0647\u0646\u062f \u06af\u0631\u0641\u062a.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u06cc\u06a9 \u0645\u062b\u0627\u0644 \u062f\u0631 \u062d\u0627\u0644 \u0627\u062c\u0631\u0627 \u06a9\u0627\u0645\u0644\u060c \u0634\u0627\u0645\u0644 \u062a\u0645\u0627\u0645 \u0645\u0631\u0627\u062d\u0644 \u0630\u06a9\u0631 \u0634\u062f\u0647 \u062f\u0631 \u0628\u0627\u0644\u0627 \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u06cc\u06a9 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0627\u0633\u062a.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 1: Importing libraries \n<\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"n\">pd<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.model_selection<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">train_test_split<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.linear_model<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">LinearRegression<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">metrics<\/span>\n\n<span class=\"c1\"># Step 2: Defining the columns of and reading the DataFrame \n<\/span><span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s\">'Species'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Weight'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">Fish<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"p\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">'Fish.txt'<\/span><span class=\"p\">,<\/span> <span class=\"n\">sep<\/span><span class=\"o\">=<\/span><span class=\"s\">'<\/span><span class=\"se\">\\t<\/span><span class=\"s\">'<\/span><span class=\"p\">,<\/span> <span class=\"n\">usecols<\/span><span class=\"o\">=<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 3: Seperating the data into features and labels\n<\/span><span class=\"n\">X<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[[<\/span><span class=\"s\">'X-Length'<\/span><span class=\"p\">]]<\/span>\n<span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[<\/span><span class=\"s\">'Weight'<\/span><span class=\"p\">]<\/span>\n\n<span class=\"c1\"># Step 4: Dividing the dataset into test and train data\n<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">X_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_test<\/span> <span class=\"o\">=<\/span> <span class=\"n\">train_test_split<\/span><span class=\"p\">(<\/span><span class=\"n\">X<\/span><span class=\"p\">,<\/span> <span class=\"n\">y<\/span><span class=\"p\">,<\/span> <span class=\"n\">test_size<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.3<\/span><span class=\"p\">,<\/span> <span class=\"n\">random_state<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"n\">shuffle<\/span><span class=\"o\">=<\/span><span class=\"bp\">True<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 5: Selecting the linear regression method from the scikit-learn library\n<\/span><span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">LinearRegression<\/span><span class=\"p\">().<\/span><span class=\"n\">fit<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 6: Validation\n# Evaluating the trained model on training data\n<\/span><span class=\"n\">y_prediction<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"s\">\"MAE on train data= \"<\/span> <span class=\"p\">,<\/span> <span class=\"n\">metrics<\/span><span class=\"p\">.<\/span><span class=\"n\">mean_absolute_error<\/span><span class=\"p\">(<\/span><span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_prediction<\/span><span class=\"p\">))<\/span>\n<span class=\"c1\"># Evaluating the trained model on test data\n<\/span><span class=\"n\">y_prediction<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">X_test<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"s\">\"MAE on test data = \"<\/span> <span class=\"p\">,<\/span> <span class=\"n\">metrics<\/span><span class=\"p\">.<\/span><span class=\"n\">mean_absolute_error<\/span><span class=\"p\">(<\/span><span class=\"n\">y_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_prediction<\/span><span class=\"p\">))<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"p\">(<\/span><span class=\"s\">'MAE on train data=\"<\/span><span class=\"p\">,<\/span> <span class=\"mf\">105.08242420291623<\/span><span class=\"p\">)<\/span>\n<span class=\"p\">(<\/span><span class=\"s\">\"MAE on test data=\"<\/span><span class=\"p\">,<\/span> <span class=\"mf\">108.7817508976745<\/span><span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u062b\u0627\u0644\u060c <code>model.predict()<\/code> \u062a\u0627\u0628\u0639 \u0628\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f <strong>\u062e\u0637 23<\/strong>\u060c \u0648 \u062f\u0631 <strong>\u062e\u0637 26<\/strong>\u060c \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u0645\u0627 \u0686\u0647 \u0686\u06cc\u0632\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f\u061f  <\/p>\n<p>\u0627\u0633\u0627\u0633\u0627\u064b\u060c \u0627\u06cc\u0646 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0646\u0627\u0622\u0634\u0646\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.<br \/>\u062f\u0648 \u0645\u0642\u062f\u0627\u0631 MAE \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627 \u062f\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0642\u0637\u0627\u0631 \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0645\u0634\u0627\u0628\u0647 \u0647\u0633\u062a\u0646\u062f. <\/p>\n<blockquote>\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f:<\/strong> \u06cc\u0627\u062f\u0622\u0648\u0631\u06cc \u0627\u06cc\u0646 \u0646\u06a9\u062a\u0647 \u0636\u0631\u0648\u0631\u06cc \u0627\u0633\u062a \u06a9\u0647 <code>X-Length<\/code> \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0628\u0627\u0644\u0627\u06cc \u0622\u0646 \u0628\u0627 \u0628\u0631\u0686\u0633\u0628 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0627\u0646\u062a\u062e\u0627\u0628 \u0634\u062f.  \u0628\u0631\u0627\u06cc \u062a\u0623\u06cc\u06cc\u062f \u0627\u0646\u062a\u062e\u0627\u0628 \u0648\u06cc\u0698\u06af\u06cc\u060c \u0645\u06cc \u062a\u0648\u0627\u0646 \u0622\u0646 \u0631\u0627 \u0628\u0627 \u0648\u06cc\u0698\u06af\u06cc \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u06a9\u0631\u062f <code>Height<\/code> \u0628\u0631 <strong>\u062e\u0637 12<\/strong> \u0648 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0631\u0627 \u062f\u0648\u0628\u0627\u0631\u0647 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f\u060c \u0633\u067e\u0633 \u062f\u0648 \u0645\u0642\u062f\u0627\u0631 MAE \u0631\u0627 \u0645\u0642\u0627\u06cc\u0633\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"%D8%B1%DA%AF%D8%B1%D8%B3%DB%8C%D9%88%D9%86_%D8%AE%D8%B7%DB%8C_%DA%86%D9%86%D8%AF%DA%AF%D8%A7%D9%86%D9%87\"><\/span>\n<p>  \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062a\u0627 \u06a9\u0646\u0648\u0646 \u062a\u0646\u0647\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc\u060c <code>X-Length<\/code> \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc\u06cc \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u0627\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0634\u0627\u0645\u0644 \u0637\u0648\u0644 \u0639\u0645\u0648\u062f\u06cc\u060c \u0637\u0648\u0644 \u0645\u0648\u0631\u0628\u060c \u0627\u0631\u062a\u0641\u0627\u0639 \u0648 \u0639\u0631\u0636 \u0645\u0627\u0647\u06cc \u0627\u0633\u062a \u0648 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646 \u0628\u0631\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u062c\u062f\u062f \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 3: Separating the data into features and labels\n<\/span><span class=\"n\">X<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[[<\/span><span class=\"s\">\"V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]]<\/span>\n<span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[<\/span><span class=\"s\">'Weight'<\/span><span class=\"p\">]<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0627\u0632 \u0646\u0638\u0631 \u0631\u06cc\u0627\u0636\u06cc\u060c \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0646\u0648\u0634\u062a:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/nabfollower.com\/blog\/wp-content\/uploads\/2023\/05\/1683668228_322_\u0646\u062d\u0648\u0647-\u0633\u0627\u062e\u062a-\u0645\u062f\u0644-\u0647\u0627\u06cc-\u0631\u06af\u0631\u0633\u06cc\u0648\u0646-\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc-\u0645\u0627\u0634\u06cc\u0646-\u0628\u0627-\u067e\u0627\u06cc\u062a\u0648\u0646.png\" alt=\"\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc\" loading=\"lazy\" width=\"800\" height=\"61\" title=\"\"><\/p>\n<p>\u062c\u0627\u06cc\u06cc \u06a9\u0647 <em>m_i<\/em> \u0648\u0632\u0646 \u0628\u0631\u0627\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f <em>X_i<\/em> \u062f\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc <em>y<\/em> \u0648 <em>n<\/em> \u062a\u0639\u062f\u0627\u062f \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.<\/p>\n<p>\u0628\u0627 \u067e\u06cc\u0631\u0648\u06cc \u0627\u0632 \u0645\u0631\u0627\u062d\u0644 \u0645\u0634\u0627\u0628\u0647 \u0642\u0628\u0644\u06cc\u060c \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0645\u0627\u0645 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0645\u062d\u0627\u0633\u0628\u0647 \u06a9\u0631\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 1: Importing libraries \n<\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"n\">pd<\/span>\n\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.model_selection<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">train_test_split<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.linear_model<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">LinearRegression<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">metrics<\/span>\n\n<span class=\"c1\"># Step 2: Defining the columns and reading the DataFrame \n<\/span><span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s\">'Species'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Weight'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">Fish<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"p\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">'Fish.txt'<\/span><span class=\"p\">,<\/span> <span class=\"n\">sep<\/span><span class=\"o\">=<\/span><span class=\"s\">'<\/span><span class=\"se\">\\t<\/span><span class=\"s\">'<\/span><span class=\"p\">,<\/span> <span class=\"n\">usecols<\/span><span class=\"o\">=<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 3: Seperating the data into features and labels\n<\/span><span class=\"n\">X<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[[<\/span><span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]]<\/span>\n<span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[<\/span><span class=\"s\">'Weight'<\/span><span class=\"p\">]<\/span>\n\n<span class=\"c1\"># Step 4: Dividing the dataset into test and train data\n<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">X_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_test<\/span> <span class=\"o\">=<\/span> <span class=\"n\">train_test_split<\/span><span class=\"p\">(<\/span><span class=\"n\">X<\/span><span class=\"p\">,<\/span> <span class=\"n\">y<\/span><span class=\"p\">,<\/span> <span class=\"n\">test_size<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.3<\/span><span class=\"p\">,<\/span> <span class=\"n\">random_state<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"n\">shuffle<\/span><span class=\"o\">=<\/span><span class=\"bp\">True<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 5: Selecting the linear regression method from the scikit-learn library\n<\/span><span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">LinearRegression<\/span><span class=\"p\">().<\/span><span class=\"n\">fit<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 6: Validation\n# Evaluating the trained model on training data\n<\/span><span class=\"n\">y_prediction<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"s\">\"MAE on train data= \"<\/span> <span class=\"p\">,<\/span> <span class=\"n\">metrics<\/span><span class=\"p\">.<\/span><span class=\"n\">mean_absolute_error<\/span><span class=\"p\">(<\/span><span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_prediction<\/span><span class=\"p\">))<\/span>\n<span class=\"c1\"># Evaluating the trained model on test data\n<\/span><span class=\"n\">y_prediction<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">X_test<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"s\">\"MAE on test data = \"<\/span> <span class=\"p\">,<\/span> <span class=\"n\">metrics<\/span><span class=\"p\">.<\/span><span class=\"n\">mean_absolute_error<\/span><span class=\"p\">(<\/span><span class=\"n\">y_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_prediction<\/span><span class=\"p\">))<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"p\">(<\/span><span class=\"s\">'MAE on train data=\"<\/span><span class=\"p\">,<\/span> <span class=\"mf\">88.6176233769433<\/span><span class=\"p\">)<\/span>\n<span class=\"p\">(<\/span><span class=\"s\">\"MAE on test data=\"<\/span><span class=\"p\">,<\/span> <span class=\"mf\">104.71922684746642<\/span><span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0645\u0642\u0627\u062f\u06cc\u0631 MAE \u0645\u0634\u0627\u0628\u0647 \u0646\u062a\u0627\u06cc\u062c \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0648\u0627\u062d\u062f \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"%D8%B1%DA%AF%D8%B1%D8%B3%DB%8C%D9%88%D9%86_%DA%86%D9%86%D8%AF_%D8%AC%D9%85%D9%84%D9%87_%D8%A7%DB%8C\"><\/span>\n<p>  \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0648\u0628\u0644\u0627\u06af \u0645\u0641\u0647\u0648\u0645 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u0645\u06cc \u062f\u0647\u062f\u060c \u06a9\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0641\u0631\u0636 \u0631\u0627\u0628\u0637\u0647 \u062e\u0637\u06cc \u0628\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0648 \u0628\u0631\u0686\u0633\u0628 \u062f\u0642\u06cc\u0642 \u0646\u0628\u0627\u0634\u062f.  \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u062c\u0645\u0644\u0647\u200c\u0627\u06cc \u0628\u0627 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u062a\u0646\u0627\u0633\u0628 \u0627\u0646\u0639\u0637\u0627\u0641\u200c\u067e\u0630\u06cc\u0631\u062a\u0631 \u0628\u0627 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0631\u0648\u0627\u0628\u0637 \u067e\u06cc\u0686\u06cc\u062f\u0647\u200c\u062a\u0631\u06cc \u0631\u0627 \u062b\u0628\u062a \u06a9\u0646\u062f \u0648 \u0645\u0646\u062c\u0631 \u0628\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627\u06cc \u062f\u0642\u06cc\u0642\u200c\u062a\u0631 \u0634\u0648\u062f.<\/p>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0627\u06af\u0631 \u0631\u0627\u0628\u0637\u0647 \u0628\u06cc\u0646 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0648\u0627\u0628\u0633\u062a\u0647 \u0648 \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u06cc\u06a9 \u062e\u0637 \u0645\u0633\u062a\u0642\u06cc\u0645 \u0646\u0628\u0627\u0634\u062f\u060c \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0633\u0627\u0632\u06cc \u062f\u0642\u06cc\u0642 \u062a\u0631 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u0627\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0645\u0646\u062c\u0631 \u0628\u0647 \u062a\u0646\u0627\u0633\u0628 \u0628\u0647\u062a\u0631 \u0628\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u062f\u0642\u06cc\u0642 \u062a\u0631 \u0634\u0648\u062f.<\/p>\n<p>\u0627\u0632 \u0646\u0638\u0631 \u0631\u06cc\u0627\u0636\u06cc\u060c \u0631\u0627\u0628\u0637\u0647 \u0628\u06cc\u0646 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0648\u0627\u0628\u0633\u062a\u0647 \u0648 \u0645\u0633\u062a\u0642\u0644 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u0639\u0627\u062f\u0644\u0647 \u0632\u06cc\u0631 \u062a\u0648\u0635\u06cc\u0641 \u0645\u06cc\u200c\u0634\u0648\u062f:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/nabfollower.com\/blog\/wp-content\/uploads\/2023\/05\/1683668228_924_\u0646\u062d\u0648\u0647-\u0633\u0627\u062e\u062a-\u0645\u062f\u0644-\u0647\u0627\u06cc-\u0631\u06af\u0631\u0633\u06cc\u0648\u0646-\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc-\u0645\u0627\u0634\u06cc\u0646-\u0628\u0627-\u067e\u0627\u06cc\u062a\u0648\u0646.png\" alt=\"\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647\" loading=\"lazy\" width=\"800\" height=\"60\" title=\"\"><\/p>\n<p>\u0645\u0639\u0627\u062f\u0644\u0647 \u0641\u0648\u0642 \u0628\u0633\u06cc\u0627\u0631 \u0634\u0628\u06cc\u0647 \u0628\u0647 \u0645\u0639\u0627\u062f\u0644\u0647 \u0627\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0642\u0628\u0644\u0627 \u0628\u0631\u0627\u06cc \u062a\u0648\u0635\u06cc\u0641 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u062f.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0634\u0627\u0645\u0644 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647 \u0628\u0647 \u0646\u0627\u0645 \u0627\u0633\u062a <em>Z_i<\/em>&#8220;\u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0646\u0633\u062e\u0647 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u0647\u0633\u062a\u0646\u062f <em>X_i<\/em>&#8216;s \u062f\u0631 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f. <\/p>\n<p>\u0627\u06cc\u0646 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0645\u062b\u0627\u0644 \u0627\u0632 \u062f\u0648 \u0648\u06cc\u0698\u06af\u06cc \u0628\u06cc\u0634\u062a\u0631 \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f <em>X_1<\/em> \u0648 <em>X_2<\/em> \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0645\u0627\u0646\u0646\u062f: <\/p>\n<p><img decoding=\"async\" src=\"https:\/\/nabfollower.com\/blog\/wp-content\/uploads\/2023\/05\/1683668228_612_\u0646\u062d\u0648\u0647-\u0633\u0627\u062e\u062a-\u0645\u062f\u0644-\u0647\u0627\u06cc-\u0631\u06af\u0631\u0633\u06cc\u0648\u0646-\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc-\u0645\u0627\u0634\u06cc\u0646-\u0628\u0627-\u067e\u0627\u06cc\u062a\u0648\u0646.png\" alt=\"\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062f\u0631 ML\" loading=\"lazy\" width=\"800\" height=\"46\" title=\"\"><\/p>\n<p>\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u062c\u062f\u06cc\u062f \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0622\u0632\u0645\u0648\u0646 \u0648 \u062e\u0637\u0627 \u06cc\u0627 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u0646\u062c\u06cc \u0645\u062a\u0642\u0627\u0637\u0639 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f.  \u062f\u0631\u062c\u0647 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u0631\u0627 \u0646\u06cc\u0632 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0631 \u0627\u0633\u0627\u0633 \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u0631\u0627\u0628\u0637\u0647 \u0628\u06cc\u0646 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0631\u062f.<\/p>\n<p>\u0645\u062b\u0627\u0644 \u0632\u06cc\u0631 \u06cc\u06a9 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u062f \u0648 \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0647\u0627 \u0631\u0627 \u062a\u0627\u06cc\u06cc\u062f \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"c1\"># Step 1: Importing libraries \n<\/span><span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"n\">pd<\/span>\n\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.model_selection<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">train_test_split<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.linear_model<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">LinearRegression<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">metrics<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.preprocessing<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">PolynomialFeatures<\/span>\n\n<span class=\"c1\"># Step 2: Defining the columns and reading the DataFrame \n<\/span><span class=\"n\">columns<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[<\/span><span class=\"s\">'Species'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Weight'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]<\/span>\n<span class=\"n\">Fish<\/span> <span class=\"o\">=<\/span> <span class=\"n\">pd<\/span><span class=\"p\">.<\/span><span class=\"n\">read_csv<\/span><span class=\"p\">(<\/span><span class=\"s\">'Fish.txt'<\/span><span class=\"p\">,<\/span> <span class=\"n\">sep<\/span><span class=\"o\">=<\/span><span class=\"s\">'<\/span><span class=\"se\">\\t<\/span><span class=\"s\">'<\/span><span class=\"p\">,<\/span> <span class=\"n\">usecols<\/span><span class=\"o\">=<\/span><span class=\"n\">columns<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 3: Seperating the data into features and labels\n<\/span><span class=\"n\">X<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[[<\/span><span class=\"s\">'V-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'D-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'X-Length'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Height'<\/span><span class=\"p\">,<\/span> <span class=\"s\">'Width'<\/span><span class=\"p\">]]<\/span>\n<span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Fish<\/span><span class=\"p\">[<\/span><span class=\"s\">'Weight'<\/span><span class=\"p\">]<\/span>\n\n<span class=\"c1\"># Step 4: Generating polynomial features \n<\/span><span class=\"n\">Z<\/span> <span class=\"o\">=<\/span> <span class=\"n\">PolynomialFeatures<\/span><span class=\"p\">(<\/span><span class=\"n\">degree<\/span><span class=\"o\">=<\/span><span class=\"mi\">2<\/span><span class=\"p\">,<\/span> <span class=\"n\">include_bias<\/span><span class=\"o\">=<\/span><span class=\"bp\">False<\/span><span class=\"p\">).<\/span><span class=\"n\">fit_transform<\/span><span class=\"p\">(<\/span><span class=\"n\">X<\/span><span class=\"p\">)<\/span>\n<span class=\"c1\"># Dividing the dataset into test and train data\n<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">X_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_test<\/span> <span class=\"o\">=<\/span> <span class=\"n\">train_test_split<\/span><span class=\"p\">(<\/span><span class=\"n\">Z<\/span><span class=\"p\">,<\/span> <span class=\"n\">y<\/span><span class=\"p\">,<\/span> <span class=\"n\">test_size<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.3<\/span><span class=\"p\">,<\/span> <span class=\"n\">random_state<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 5: Selecting the linear regression method from the scikit-learn library\n<\/span><span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">LinearRegression<\/span><span class=\"p\">().<\/span><span class=\"n\">fit<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Step 6: Validation\n# Evaluating the trained model on training data\n<\/span><span class=\"n\">y_prediction<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"s\">\"MAE on train data= \"<\/span> <span class=\"p\">,<\/span> <span class=\"n\">metrics<\/span><span class=\"p\">.<\/span><span class=\"n\">mean_absolute_error<\/span><span class=\"p\">(<\/span><span class=\"n\">y_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_prediction<\/span><span class=\"p\">))<\/span>\n<span class=\"c1\"># Evaluating our trained model on test data\n<\/span><span class=\"n\">y_prediction<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">X_test<\/span><span class=\"p\">)<\/span>\n<span class=\"k\">print<\/span><span class=\"p\">(<\/span><span class=\"s\">\"MAE on test data = \"<\/span> <span class=\"p\">,<\/span> <span class=\"n\">metrics<\/span><span class=\"p\">.<\/span><span class=\"n\">mean_absolute_error<\/span><span class=\"p\">(<\/span><span class=\"n\">y_test<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_prediction<\/span><span class=\"p\">))<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<div class=\"highlight js-code-highlight\">\n<pre class=\"highlight python\"><code><span class=\"p\">(<\/span><span class=\"s\">'MAE on train data=\"<\/span><span class=\"p\">,<\/span> <span class=\"mf\">30.44121990999409<\/span><span class=\"p\">)<\/span>\n<span class=\"p\">(<\/span><span class=\"s\">\"MAE on test data=\"<\/span><span class=\"p\">,<\/span> <span class=\"mf\">32.558434580499224<\/span><span class=\"p\">)<\/span>\n<\/code><\/pre>\n<div class=\"highlight__panel js-actions-panel\">\n<div class=\"highlight__panel-action js-fullscreen-code-action\">\n    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-on\"><title>\u0648\u0627\u0631\u062f \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M16 3h6v6h-2V5h-4V3zM2 3h6v2H4v4H2V3zm18 16v-4h2v6h-6v-2h4zM4 19h4v2H2v-6h2v4z\"\/>\n<\/svg><\/p>\n<p>    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" class=\"highlight-action crayons-icon highlight-action--fullscreen-off\"><title>\u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    <path d=\"M18 7h4v2h-6V3h2v4zM8 9H2V7h4V3h2v6zm10 8v4h-2v-6h6v2h-4zM8 15v6H6v-4H2v-2h6z\"\/>\n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u063a\u06cc\u06cc\u0631 \u0634\u06a9\u0644 \u062f\u0627\u062f\u0646\u062f <code>PolynomialFeatures<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0648\u0634\u0646 <strong>\u062e\u0637 18<\/strong>.  \u0631\u0627 <code>PolynomialFeatures<\/code> \u062a\u0627\u0628\u0639\u060c \u0648\u0627\u0631\u062f \u0634\u062f\u0647 \u0627\u0632 <code>sklearn<\/code> \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0631\u0648\u0634\u0646 <strong>\u062e\u0637 7<\/strong>\u060c \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u0645\u0646\u0638\u0648\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u062f.<\/p>\n<p>\u0644\u0627\u0632\u0645 \u0628\u0647 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u0645\u0642\u062f\u0627\u0631 MAE \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0648\u0631\u062f \u0646\u0633\u0628\u062a \u0628\u0647 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u062e\u0637\u06cc \u0628\u0631\u062a\u0631\u06cc \u062f\u0627\u0631\u062f\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u0641\u0631\u0636 \u062e\u0637\u06cc \u06a9\u0627\u0645\u0644\u0627\u064b \u062f\u0642\u06cc\u0642 \u0646\u0628\u0648\u062f. <\/p>\n<p>\u0627\u06cc\u0646 \u0648\u0628\u0644\u0627\u06af \u0645\u0639\u0631\u0641\u06cc \u0633\u0631\u06cc\u0639 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0628\u0627 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