{"id":145935,"date":"2025-07-16T16:00:18","date_gmt":"2025-07-16T10:30:18","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145935"},"modified":"2025-07-16T16:00:18","modified_gmt":"2025-07-16T10:30:18","slug":"tourist-destination-recommender-system-using-machine-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/tourist-destination-recommender-system-using-machine-learning\/","title":{"rendered":"ML Project &#8211; Tourist Destination Recommender System using Random Forest"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1F3AiTtPpl6Xff-7wbEpod-hoFUL1bMWm\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Tourist Recommendation Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score, classification_report\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\n#Load Dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/tourist\/tourist_recommendation_rf.csv\")\r\n\r\ndf.shape\r\n\r\ndf.shape\r\n\r\ndf.info()\r\n\r\ndf.isnull().sum()\r\n\r\n# Independed and Depended variables\r\nX = df.drop(['Name', 'Recommended'], axis=1) # Independed\r\ny = df['Recommended'] # Depended variables\r\n\r\nX\r\n\r\ny\r\n\r\n# Train Random Forest model\r\nmodel = RandomForestClassifier(n_estimators=100, random_state=42)\r\nmodel.fit(X, y)\r\n\r\n# Find  feature importances\r\nfeature_importances = pd.Series(model.feature_importances_, index=X.columns)\r\nfeature_importances = feature_importances.sort_values(ascending=False)\r\nfeature_importances\r\n\r\n\r\n\r\n# Plot feature importance\r\nplt.figure(figsize=(8, 5))\r\nsns.barplot(x=feature_importances, y=feature_importances.index, palette='viridis')\r\nplt.title(\"Feature Importance in Tourist Destination Recommender\")\r\nplt.xlabel(\"Importance Score\")\r\nplt.ylabel(\"Feature\")\r\nplt.grid(True)\r\nplt.tight_layout()\r\nplt.show()\r\n<\/pre>\n<h3>Program 2<\/h3>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score, classification_report\r\n\r\n#Load Dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/tourist\/tourist_recommendation_rf.csv\")\r\n\r\n# Independed and Depended variables\r\nX = df.drop(['Name', 'Recommended'], axis=1) # Independed\r\ny = df['Recommended'] # Depended variables\r\n\r\n# Split Data Set in Train and Test\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\n\r\nX_train\r\n\r\nX_test\r\n\r\n# Create Model\r\nmodel=RandomForestClassifier()\r\nmodel.fit(X_train,y_train)\r\n\r\nmodel.score(X_train,y_train)\r\n\r\n# Predication\r\n# User input as preference dictionary\r\nna=int(input(\"Nature Friendly(Yes-1 , No-0): \"))\r\ncu=int(input(\"Culture Friendly(Yes-1 , No-0): \"))\r\nad=int(input(\"Adventure Friendly(Yes-1 , No-0): \"))\r\nlx=int(input(\"Luxury Friendly(Yes-1 , No-0): \"))\r\nbd=int(input(\"Budget Friendly(Yes-1 , No-0): \"))\r\nfm=int(input(\"Family Friendly(Yes-1 , No-0): \"))\r\nuser_preferences = {\r\n    'Nature': na,\r\n    'Culture': cu,\r\n    'Adventure': ad,\r\n    'Luxury': lx,\r\n    'Budget': bd,\r\n    'FamilyFriendly': fm\r\n}\r\ndf_input=pd.DataFrame([user_preferences])\r\nresult=model.predict(df_input)\r\n# Show result\r\nif result == 1:\r\n    print(\"\\n Recommended Destination Based on Your Preferences\")\r\nelse:\r\n    print(\"\\n No Suitable Recommendation Found for Your Preferences\")\r\n<\/pre>\n<p><span hidden class=\"__iawmlf-post-loop-links\" 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09:46:46&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-27 09:41:51&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 05:08:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-19 23:19:12&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-19 23:19:12&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Tourist Recommendation Dataset import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, classification_report import matplotlib.pyplot as plt import seaborn as sns import matplotlib.pyplot as plt&#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":[8431,33127,33128,20697,34912,34911,11304,34733,34908,34909,34910],"class_list":["post-145935","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-machine-learning-tourist-destination-recommender-system","tag-machine-learning-tourist-destination-recommender-system-project","tag-random-forest","tag-random-forest-algorithm","tag-tourist-destination-recommender-system","tag-tourist-destination-recommender-system-project","tag-tourist-destination-recommender-system-using-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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