{"id":145702,"date":"2025-06-30T18:48:26","date_gmt":"2025-06-30T13:18:26","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145702"},"modified":"2025-06-30T18:48:26","modified_gmt":"2025-06-30T13:18:26","slug":"restaurant-preference-classifier-using-decision-tree","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/restaurant-preference-classifier-using-decision-tree\/","title":{"rendered":"ML Project &#8211; Restaurant Preference Classifier using Decision Tree"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1zfbL8uew99TBhtBhE147nNLlsToXV-vh\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Restaurant Preference Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import accuracy_score ,classification_report\r\n\r\n# Load Dataset\r\ndf_rest=pd.read_csv(\"D:\/\/scikit_data\/rest\/restaurant_preference.csv\")\r\n\r\ndf_rest\r\n\r\ndf_rest.shape\r\n\r\ndf_rest.info()\r\n\r\ndf_rest.isnull().sum()\r\n\r\n# Label Encoding\r\nle=LabelEncoder()\r\ndf_rest['Cuisine_new']=le.fit_transform(df_rest['Cuisine'])\r\ndf_rest['MealTime_new']=le.fit_transform(df_rest['MealTime'])\r\ndf_rest['AgeGroup_new']=le.fit_transform(df_rest['AgeGroup'])\r\ndf_rest['Location_new']=le.fit_transform(df_rest['Location'])\r\ndf_rest['RestaurantType']=le.fit_transform(df_rest['RestaurantType'])\r\n\r\ndf_rest\r\n\r\ndf_rest.info()\r\n\r\ndf_rest=df_rest.drop(['Cuisine','MealTime','AgeGroup','Location'],axis='columns')\r\n\r\ndf_rest.info()\r\n\r\ndf_rest.head()\r\n\r\nX = df_rest.drop('RestaurantType', axis=1)\r\ny = df_rest['RestaurantType']\r\n\r\nX\r\n\r\ny\r\n\r\n# Split data\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\n\r\nlen(X_train)\r\n\r\nlen(X_test)\r\n\r\nmodel=DecisionTreeClassifier()\r\n\r\nmodel.fit(X_train,y_train)\r\n\r\nmodel.score(X_train,y_train)\r\n\r\ny_pred=model.predict(X_test)\r\n\r\n\"Accuracy:\", accuracy_score(y_test, y_pred)\r\n\r\nprint(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\r\n\r\ndf_rest\r\n\r\nmodel.predict([[2,2,0,2]])\r\n\r\nmodel.fit(X,y)\r\n\r\nmodel.score(X,y)\r\n\r\nmodel.predict([[1,1,2,2]])\r\n\r\ncus=int(input(\"Enter Cuisine Type(Indian-0,Chinese-1,Italian-2,Mexican-3) :\"))\r\nmt=int(input(\"Enter MealTime(Breakfast-0,Dinner-1,Lunch-2) :\"))\r\nag=int(input(\"Enter AgeGroup:(Adult-0,Senior-1,Teen-2) :\"))\r\nloc=int(input(\"Enter Location(Suburban-0,Rural-1,Urban-2) :\"))\r\nresult=model.predict([[cus,mt,ag,loc]])\r\nif(result==0):\r\n    print('Casual Dining')\r\nelif(result==1):\r\n    print('Fast Food')\r\nelse:\r\n    print('Fine Dining')\r\n<\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:39,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1zfbL8uew99TBhtBhE147nNLlsToXV-vh\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205110900\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1zfbL8uew99TBhtBhE147nNLlsToXV-vh\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2026-01-03 11:59:09&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-10 13:03:04&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-26 15:25:30&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-11 19:14:48&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-29 04:47:13&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-04 14:41:40&quot;,&quot;http_code&quot;:503}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-05-04 14:41:40&quot;,&quot;http_code&quot;:503},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Restaurant Preference Dataset import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score ,classification_report # Load Dataset df_rest=pd.read_csv(&#8220;D:\/\/scikit_data\/rest\/restaurant_preference.csv&#8221;) df_rest df_rest.shape df_rest.info() df_rest.isnull().sum()&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[3626,8431,33127,33128,20697,34772,34771,34769,34770,34768],"class_list":["post-145702","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-decision-tree","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-ml-restaurant-preference-classifier","tag-ml-restaurant-preference-classifier-using-decision-tree-project","tag-restaurant-preference-classifier","tag-restaurant-preference-classifier-project","tag-restaurant-preference-classifier-using-decision-tree"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ML Project - Restaurant Preference Classifier using Decision Tree - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/restaurant-preference-classifier-using-decision-tree\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ML Project - Restaurant Preference Classifier using Decision Tree - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 Restaurant Preference Dataset import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score ,classification_report # 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