<\/div>\n\n\n\n
nbflu=pd.read_csv('\/kaggle\/input\/naivebayes.csv')<\/code><\/pre>\n\n\n\n<\/div>\n\n\n\n
\u062a\u0628\u062f\u06cc\u0644 \u062f\u0627\u062f\u0647\u200c\u0647\u0627<\/strong><\/h3>\n\n\n\n\u0645\u0627 \u0633\u062a\u0648\u0646\u200c\u0647\u0627 \u0631\u0627 \u062f\u0631 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u062a\u0628\u062f\u06cc\u0644 \u0648 \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645.<\/p>\n\n\n\n
# Collecting the Variables\nx1= nbflu.iloc[:,0]\nx2= nbflu.iloc[:,1]\nx3= nbflu.iloc[:,2]\nx4= nbflu.iloc[:,3]\ny=nbflu.iloc[:,4]\n# Encoding the categorical variables\nle = preprocessing.LabelEncoder()\nx1= le.fit_transform(x1)\nx2= le.fit_transform(x2)\nx3= le.fit_transform(x3)\nx4= le.fit_transform(x4)\ny=le.fit_transform(y)\n# Getting the Encoded in Data Frame\nX = pd.DataFrame(list(zip(x1,x2,x3,x4)))<\/code><\/pre>\n\n\n\n<\/div>\n\n\n\n
\u062a\u0637\u0628\u06cc\u0642 \u0645\u062f\u0644<\/strong><\/h3>\n\n\n\n\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647 \u0627\u0628\u062a\u062f\u0627 \u0645\u062f\u0644 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc\u200c\u062f\u0647\u06cc\u0645 \u0648 \u0633\u067e\u0633 \u0628\u0631\u0627\u06cc \u06cc\u06a9 \u0628\u06cc\u0645\u0627\u0631 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n\n\n\n
model = CategoricalNB()\n\n# Train the model using the training sets\nmodel.fit(X,y)\n\n#Predict Output\n#['Y','N','Mild','Y']\npredicted = model.predict([[1,0,0,1]]) \nprint(\"Predicted Value:\",model.predict([[1,0,0,1]]))\nprint(model.predict_proba([[1,0,0,1]]))<\/code><\/pre>\n\n\n\n<\/div>\n\n\n\n
\u062e\u0631\u0648\u062c\u06cc<\/strong><\/h3>\n\n\n\n<\/div>\n\n\n\n
Predicted Value: [1]\n[[0.30509228 0.69490772]]<\/code><\/pre>\n\n\n\n\u062e\u0631\u0648\u062c\u06cc \u0645\u06cc\u200c\u06af\u0648\u06cc\u062f \u06a9\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 \u0639\u062f\u0645 \u0622\u0646\u0641\u0648\u0644\u0627\u0646\u0632\u0627 0.31 \u0648 \u0622\u0646\u0641\u0648\u0644\u0627\u0646\u0632\u0627 0.69 \u0627\u0633\u062a. \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u0622\u0646\u0641\u0648\u0644\u0627\u0646\u0632\u0627 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n\n\n\n
\n\n\n\nNaive Bayes \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u067e\u0627\u06cc\u0647 \u0628\u0633\u06cc\u0627\u0631 \u062e\u0648\u0628 \u0639\u0645\u0644 \u0645\u06cc\u200c\u06a9\u0646\u062f. \u0633\u0631\u06cc\u0639 \u0627\u0633\u062a \u0648 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0631\u0648\u06cc \u062a\u0639\u062f\u0627\u062f \u0646\u0645\u0648\u0646\u0647\u200c\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u06a9\u0645\u062a\u0631 \u0648 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u067e\u0631\u062a (Noisy Data) \u06a9\u0627\u0631 \u06a9\u0646\u062f. \u06cc\u06a9\u06cc \u0627\u0632 \u0686\u0627\u0644\u0634\u200c\u0647\u0627 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627 \u0631\u0627 \u0645\u0633\u062a\u0642\u0644 \u0641\u0631\u0636 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n","protected":false},"excerpt":{"rendered":"
\u062f\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u0628\u06cc\u0632 \u0633\u0627\u062f\u0647 (Naive Bayes Classification) \u06cc\u06a9\u06cc \u0627\u0632 \u0633\u0627\u062f\u0647\u200c\u062a\u0631\u06cc\u0646 \u0648 \u0645\u062d\u0628\u0648\u0628\u200c\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627 \u062f\u0631 \u062f\u0627\u062f\u0647\u200c\u06a9\u0627\u0648\u06cc (data mining) \u06cc\u0627 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 (machine…<\/p>\n","protected":false},"author":6,"featured_media":32848,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[80,46,48],"tags":[],"class_list":["post-32475","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-data-analysis","category-developer","category-technology"],"_links":{"self":[{"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/posts\/32475","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/comments?post=32475"}],"version-history":[{"count":33,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/posts\/32475\/revisions"}],"predecessor-version":[{"id":43106,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/posts\/32475\/revisions\/43106"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/media\/32848"}],"wp:attachment":[{"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/media?parent=32475"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/categories?post=32475"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/quera.org\/blog\/wp-json\/wp\/v2\/tags?post=32475"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}