From 81438c95f68fc7755846b9e7ff03a1818ab16d79 Mon Sep 17 00:00:00 2001
From: sharayuanuse <sharayu.anuse@gmail.com>
Date: Wed, 23 Oct 2024 20:21:49 +0530
Subject: [PATCH 1/2] adds tourist recommendation system task

---
 .../Top Indian Places to Visit.csv            |  326 +
 .../Tourist_Recommendation_System.ipynb       | 5400 +++++++++++++++++
 2 files changed, 5726 insertions(+)
 create mode 100644 Recommendation Systems/tourist_recommendation_system/Top Indian Places to Visit.csv
 create mode 100644 Recommendation Systems/tourist_recommendation_system/Tourist_Recommendation_System.ipynb

diff --git a/Recommendation Systems/tourist_recommendation_system/Top Indian Places to Visit.csv b/Recommendation Systems/tourist_recommendation_system/Top Indian Places to Visit.csv
new file mode 100644
index 00000000..c7be1fa7
--- /dev/null
+++ b/Recommendation Systems/tourist_recommendation_system/Top Indian Places to Visit.csv	
@@ -0,0 +1,326 @@
+Id,Zone,State,City,Name,Type,Establishment Year,time needed to visit in hrs,Google review rating,Entrance Fee in INR,Airport with 50km Radius,Weekly Off,Significance,DSLR Allowed,Number of google review in lakhs,Best Time to visit
+0,Northern,Delhi,Delhi,India Gate,War Memorial,1921,0.5,4.6,0,Yes,None,Historical,Yes,2.6,Evening
+1,Northern,Delhi,Delhi,Humayun's Tomb,Tomb,1572,2.0,4.5,30,Yes,None,Historical,Yes,0.4,Afternoon
+2,Northern,Delhi,Delhi,Akshardham Temple,Temple,2005,5.0,4.6,60,Yes,None,Religious,No,0.4,Afternoon
+3,Northern,Delhi,Delhi,Waste to Wonder Park,Theme Park,2019,2.0,4.1,50,Yes,Monday,Environmental,Yes,0.27,Evening
+4,Northern,Delhi,Delhi,Jantar Mantar,Observatory,1724,2.0,4.2,15,Yes,None,Scientific,Yes,0.31,Morning
+5,Northern,Delhi,Delhi,Chandni Chowk,Market,1700,3.0,4.2,0,Yes,Sunday,Market,Yes,0.25,Afternoon
+6,Northern,Delhi,Delhi,Lotus Temple,Temple,1986,1.0,4.5,0,Yes,Monday,Religious,Yes,0.59,Evening
+7,Northern,Delhi,Delhi,Red Fort,Fort,1648,2.0,4.5,35,Yes,None,Historical,Yes,1.5,Afternoon
+8,Northern,Delhi,Delhi,Agrasen ki Baoli,Stepwell,1400,1.0,4.2,0,Yes,None,Historical,Yes,0.41,Afternoon
+9,Northern,Delhi,Delhi,Sunder Nursery,Park,1600,2.0,4.6,0,Yes,None,Botanical,Yes,0.16,Afternoon
+10,Northern,Delhi,Delhi,Garden of Five Senses,Park,2003,2.0,4.1,35,Yes,None,Botanical,Yes,0.23,Morning
+11,Northern,Delhi,Delhi,Lodhi Garden,Park,1500,1.0,4.5,0,Yes,None,Botanical,Yes,0.48,All
+12,Northern,Delhi,Delhi,National Gallery of Modern Art,Museum,1954,3.0,4.5,20,Yes,Monday,Artistic,Yes,0.08,All
+13,Northern,Delhi,Delhi,National Zoological Park ,Zoo,1959,3.0,4.1,80,Yes,Friday,Environmental,Yes,0.41,All
+14,Northern,Delhi,Delhi,Qutub Minar,Monument,1192,1.0,4.5,35,Yes,None,Historical,Yes,1.37,Afternoon
+15,Northern,Delhi,Delhi,National Science Centre,Science,1992,5.0,4.4,70,Yes,None,Scientific,Yes,0.23,All
+16,Western,Maharastra,Mumbai,Marine Drive,Promenade,Unknown,2.0,4.5,0,Yes,None,Scenic,Yes,1.5,Evening
+17,Western,Maharastra,Mumbai,Gateway of India,Monument,1924,1.0,4.6,0,Yes,None,Historical,Yes,3.6,All
+18,Western,Maharastra,Mumbai,Chhatrapati Shivaji Maharaj Vastu Sangrahalaya,Museum,1922,1.0,4.6,500,Yes,None,Historical,Yes,0.34,All
+19,Western,Maharastra,Mumbai,Sanjay Gandhi National Park,National Park,1996,3.0,4.3,50,Yes,Monday,Wildlife,Yes,0.6,All
+20,Western,Maharastra,Mumbai,Siddhivinayak Temple,Temple,1881,2.0,4.8,0,Yes,None,Religious,No,1.05,All
+21,Western,Maharastra,Mumbai,Mahalaxmi Temple,Temple,1831,1.0,4.7,0,Yes,None,Religious,No,0.33,All
+22,Western,Maharastra,Mumbai,Haji Ali Dargah,Religious Shrine,1431,2.0,4.4,0,Yes,None,Religious,No,0.16,All
+23,Western,Maharastra,Mumbai,Chowpatty Beach,Beach,Unknown,2.0,4.3,0,Yes,None,Recreational,Yes,0.05,Evening
+24,Western,Maharastra,Mumbai,Essel World,Amusement Park,1986,5.0,4.3,1149,Yes,None,Recreational,Yes,0.27,All
+25,Western,Maharastra,Mumbai,Elephanta Caves,Monument,1987,4.0,4.3,550,Yes,None,Historical,Yes,0.35,All 
+26,Western,Maharastra,Lonavala,Imagicaa,Amusement Park,2013,5.0,1.4,1149,No,Monday,Recreational,Yes,0.95,All
+27,Southern,Karnataka,Bangalore,Bangalore Palace,Palace,1878,2.0,4.2,500,Yes,Monday,Historical,Yes,0.9,Morning
+28,Southern,Karnataka,Bangalore,Lalbagh Botanical Garden,Botanical Garden,1760,1.5,4.4,20,Yes,None,Nature,Yes,1.5,Evening
+29,Southern,Karnataka,Bangalore,Cubbon Park,Park,1870,1.0,4.4,0,Yes,None,Nature,Yes,1.32,Morning
+30,Southern,Karnataka,Bangalore,Vidhana Soudha,Government Building,1956,0.5,4.6,0,Yes,None,Architectural,No,0.8,Morning
+31,Southern,Karnataka,Bangalore,ISKCON Temple Bangalore,Temple,1997,1.0,4.6,0,Yes,None,Religious,Yes,1.14,Evening
+32,Southern,Telangana,Hyderabad,Charminar,Landmark,1591,1.0,4.5,25,Yes,Friday,Historical,Yes,2.1,Morning
+33,Southern,Telangana,Hyderabad,Golconda Fort,Fort,1600,2.0,4.4,30,Yes,None,Historical,Yes,1.2,Morning
+34,Southern,Telangana,Hyderabad,Hussain Sagar Lake,Lake,1563,1.0,4.3,0,Yes,None,Scenic,Yes,0.5,Evening
+35,Southern,Telangana,Hyderabad,Ramoji Film City,Film Studio,1996,4.0,4.4,1150,Yes,None,Entertainment,Yes,0.45,All
+36,Southern,Telangana,Hyderabad,Salar Jung Museum,Museum,1951,2.0,4.4,20,Yes,None,Historical,Yes,0.67,All
+37,Southern,Telangana,Hyderabad,Qutb Shahi Tombs,Tombs,1600,1.0,4.4,25,Yes,None,Historical,Yes,0.2,Morning
+38,Southern,Telangana,Hyderabad,Birla Mandir,Temple,1976,1.0,4.7,0,Yes,None,Religious,No,0.41,All
+39,Southern,Telangana,Hyderabad,Chowmahalla Palace,Palace,1800,1.5,4.4,80,Yes,None,Historical,Yes,0.45,Morning
+40,Southern,Telangana,Hyderabad,Nehru Zoological Park,Zoo,1963,3.0,4.2,50,Yes,None,Wildlife,Yes,0.86,Morning
+41,Southern,Telangana,Hyderabad,Lumbini Park,Park,1994,1.0,4.1,20,Yes,None,Recreational,Yes,0.73,Evening
+42,Eastern,West Bengal,Kolkata,Victoria Memorial,Museum,1921,1.5,4.6,30,Yes,Monday,Historical,Yes,0.73,Morning
+43,Eastern,West Bengal,Kolkata,Howrah Bridge,Bridge,1943,0.5,4.6,0,Yes,None,Architectural,No,1.2,Anytime
+44,Eastern,West Bengal,Kolkata,Indian Museum,Museum,1814,2.0,4.6,50,Yes,Monday,Historical,Yes,0.18,Morning
+45,Eastern,West Bengal,Kolkata,Dakshineswar Kali Temple,Temple,1855,1.0,4.7,0,Yes,None,Religious,Yes,0.82,Morning
+46,Eastern,West Bengal,Kolkata,Kalighat Kali Temple,Temple,1809,1.0,4.4,0,Yes,None,Religious,Yes,0.5,Morning
+47,Eastern,West Bengal,Kolkata,Eden Gardens,Cricket Ground,1864,3.0,4.1,2500,Yes,None,Sports,Yes,0.1,All
+48,Eastern,West Bengal,Kolkata,Alipore Zoological Gardens,Zoo,1876,2.0,4.3,25,Yes,None,Wildlife,Yes,0.66,Afternoon
+49,Eastern,West Bengal,Kolkata,Science City Kolkata,Science,1997,3.0,4.4,60,Yes,None,Educational,Yes,0.88,All
+50,Eastern,West Bengal,Kolkata,Belur Math,Site,1898,1.5,4.7,0,Yes,None,Religious,Yes,0.47,Morning
+51,Eastern,West Bengal,Kolkata,Marble Palace,Palace,1835,1.0,4.4,0,Yes,None,Historical,Yes,0.1,Afternoon
+52,Southern,Goa,Goa,Calangute Beach,Beach,Unknown,2.0,4.4,0,Yes,None,Scenic,Yes,0.26,Evening
+53,Southern,Goa,Goa,Basilica of Bom Jesus,Church,1605,1.0,4.5,0,Yes,None,Historical,Yes,0.59,Afternoon
+54,Southern,Goa,Goa,Fort Aguada,Fort,1612,1.5,4.2,0,Yes,None,Historical,Yes,0.95,Morning
+55,Southern,Goa,Goa,Dudhsagar Falls,Waterfall,Unknown,3.0,4.6,500,Yes,None,Nature,Yes,0.3,Afternoon
+56,Southern,Goa,Goa,Anjuna Beach,Beach,Unknown,2.0,4.4,0,Yes,None,Scenic,Yes,0.18,Evening
+57,Southern,Goa,Goa,Chapora Fort,Fort,1617,1.0,4.2,0,Yes,None,Historical,Yes,0.19,Evening
+58,Southern,Goa,Goa,Se Cathedral,Church,1640,1.0,4.5,0,Yes,None,Historical,Yes,0.05,Afternoon
+59,Southern,Goa,Goa,Baga Beach,Beach,Unknown,2.0,4.5,0,Yes,None,Scenic,Yes,0.35,Evening
+60,Southern,Goa,Goa,Arambol Beach,Beach,Unknown,2.0,4.6,0,Yes,None,Scenic,Yes,0.1,Evening
+61,Southern,Goa,Goa,Palolem Beach,Beach,Unknown,2.0,4.6,0,Yes,None,Scenic,Yes,0.27,Evening
+62,Southern,Goa,Goa,Colva Beach,Beach,Unknown,2.0,4.3,0,Yes,None,Scenic,Yes,0.1,Evening
+63,Southern,Goa,Goa,Miramar Beach,Beach,Unknown,1.5,4.2,0,Yes,None,Scenic,Yes,0.3,Evening
+64,Southern,Goa,Goa,Aguada Beach,Beach,Unknown,2.0,4.5,0,Yes,None,Scenic,Yes,0.01,Evening
+65,Southern,Goa,Goa,Dr. Salim Ali Bird Santuary,Bird Sanctuary,1988,2.0,3.9,10,Yes,None,Wildlife,Yes,0.03,Afternoon
+66,Western,Gujarat,Ahmedabad,Sabarmati Ashram,Historical,1915,1.5,4.6,0,Yes,None,Historical,Yes,0.35,Morning
+67,Western,Gujarat,Dwarka,Dwarkadhish Temple,Temple,-400,2.0,4.7,0,No,None,Religious,No,0.59,Evening
+68,Western,Gujarat,Junagadh,Gir National Park,National Park,1965,3.0,4.5,3500,No,None,Wildlife,Yes,0.08,Morning
+69,Western,Gujarat,Bhuj,White Desert,Site,1950,2.5,4.6,0,Yes,None,Nature,Yes,0.12,Evening
+70,Western,Gujarat,Vadodara,Laxmi Vilas Palace,Palace,1890,2.0,4.4,200,Yes,Monday,Historical,Yes,0.17,Afternoon
+71,Western,Gujarat,Somnath,Somnath Temple,Temple,1951,2.0,4.8,0,No,None,Religious,No,0.39,Morning
+72,Western,Gujarat,Rann of Kutch,Rann Utsav,Cultural,Unknown,3.0,4.9,7500,Yes,None,Cultural,Yes,0.1,Evening
+73,Western,Gujarat,Kevadia,Statue of Unity,Monument,2018,3.0,4.6,350,No,Monday,Historical,Yes,0.67,All
+74,Western,Gujarat,Gandhinagar,Dandi Kutir,Museum,2013,1.5,4.5,0,No,None,Historical,Yes,0.05,All
+75,Western,Gujarat,Ahmedabad,Sabarmati Riverfront,Urban Development Project,2012,1.0,4.6,0,Yes,None,Recreational,Yes,0.1,Evening
+76,Western,Gujarat,Ahmedabad,Manek Chowk,Market,Unknown,2.0,4.4,0,Yes,None,Food,Yes,0.49,Night
+77,Western,Gujarat,Ahmedabad,Kankaria Lake,Lake,1451,3.0,4.5,10,Yes,None,Recreational,Yes,0.3,Afternoon
+78,Western,Gujarat,Ahmedabad,Science City,Science,2002,7.0,4.4,500,Yes,Monday,Educational,Yes,0.11,All
+79,Northern,Rajasthan,Jaipur,Hawa Mahal,Palace,1799,1.0,4.4,50,Yes,None,Architectural,Yes,1.3,Morning
+80,Northern,Rajasthan,Udaipur,City Palace,Palace,1559,2.0,4.4,300,Yes,None,Historical,Yes,0.51,All
+81,Northern,Rajasthan,Jaisalmer,Jaisalmer Fort,Fort,1156,2.5,4.4,50,No,None,Historical,Yes,0.56,All
+82,Northern,Rajasthan,Sawai Madhopur,Ranthambore National Park,Wildlife Sanctuary,1980,3.0,4.6,500,No,None,Wildlife,Yes,0.09,All
+83,Northern,Rajasthan,Pushkar,Pushkar Lake,Temple,1400,1.5,4.4,0,No,None,Religious,Yes,1.6,All
+84,Northern,Rajasthan,Ajmer,Ajmer Sharif Dargah,Shrine,1236,1.0,4.6,0,Yes,None,Religious,No,0.35,All
+85,Northern,Rajasthan,Jodhpur,Mehrangarh Fort,Fort,1459,2.0,4.6,100,Yes,None,Historical,Yes,0.64,All
+86,Northern,Rajasthan,Chittorgarh,Chittorgarh Fort,Fort,700,2.0,4.6,40,No,None,Historical,Yes,1.9,All
+87,Northern,Rajasthan,Mount Abu,Dilwara Temples,Temple,1100,1.0,4.6,0,No,None,Religious,No,0.05,All
+88,Northern,Rajasthan,Bikaner,Junagarh Fort,Fort,1589,2.0,4.5,50,Yes,None,Historical,Yes,0.32,All
+89,Northern,Rajasthan,Jaipur,Amber Fort,Fort,1592,2.0,4.6,100,Yes,None,Historical,Yes,1.5,All
+90,Northern,Rajasthan,Jaipur,Jaigarh Fort,Fort,1726,1.5,4.5,35,Yes,None,Historical,Yes,0.3,All
+91,Northern,Rajasthan,Udaipur,Lake Pichola,Lake,1362,1.0,4.6,0,Yes,None,Nature,Yes,0.5,All
+92,Northern,Punjab,Amritsar,Golden Temple (Harmandir Sahib),Religious Site,1604,1.5,4.9,0,Yes,None,Spiritual,Yes,1.9,All
+93,Northern,Punjab,Amritsar,Jallianwala Bagh,Memorial,1951,1.0,4.8,0,Yes,None,Historical,Yes,0.3,Afternoon
+94,Northern,Punjab,Amritsar,Wagah Border,Border Crossing,1950,2.0,4.8,0,Yes,None,Cultural,Yes,0.17,Evening
+95,Northern,Punjab,Chandigarh,Rock Garden,Sculpture Garden,1976,2.0,4.5,30,Yes,None,Artistic,Yes,0.5,All
+96,Southern,Kerala,Alappuzha,Alappuzha Beach,Beach,Unknown,1.5,4.5,0,Yes,None,Recreational,Yes,0.11,All
+97,Southern,Kerala,Munnar,Munnar Tea Gardens,Scenic Area,Unknown,2.0,4.3,0,No,None,Nature,Yes,0.3,All
+98,Southern,Kerala,Kochi,Fort Kochi,Site,1503,1.0,4.4,0,Yes,None,Historical,Yes,0.1,All
+99,Southern,Kerala,Thiruvananthapuram,Padmanabhaswamy Temple,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,No,0.46,All
+100,Southern,Kerala,Kozhikode,Kozhikode Beach,Beach,Unknown,1.5,3.9,0,Yes,None,Recreational,Yes,0.059,All
+101,Southern,Kerala,Wayanad,Wayanad Wildlife Sanctuary,Wildlife Sanctuary,Unknown,3.0,4.5,300,No,None,Wildlife,Yes,2.2,All
+102,Southern,Kerala,Thekkady,Periyar National Park,National Park,1982,3.0,4.3,50,No,None,Wildlife,Yes,0.14,All
+103,Southern,Kerala,Kumarakom,Kumarakom Bird Sanctuary,Bird Sanctuary,1972,2.0,3.8,50,Yes,None,Wildlife,Yes,0.1,All
+104,Southern,Kerala,Varkala,Varkala Beach,Beach,Unknown,2.0,4.6,0,Yes,None,Recreational,Yes,0.1,All
+105,Southern,Kerala,Bekal,Bekal Fort,Fort,1650,1.5,4.5,20,No,None,Historical,Yes,0.22,All
+106,Southern,Kerala,Kovalam,Kovalam Beach,Beach,Unknown,2.0,4.4,0,Yes,None,Recreational,Yes,0.68,All
+107,Southern,Kerala,Kannur,St. Angelo Fort,Fort,1505,1.0,4.4,20,Yes,None,Historical,Yes,0.11,All
+108,Southern,Kerala,Nelliyampathy,Seethargundu Viewpoint,Viewpoint,Unknown,1.0,4.5,0,No,None,Nature,Yes,0.03,Morning
+109,Southern,Kerala,Kochi,Kerala Folklore Museum,Cultural,2009,1.5,4.4,100,Yes,Monday,Cultural,Yes,0.1,All
+110,Southern,Kerala,Kochi,Wonderla Amusement Park,Amusement Park,2016,5.5,4.6,750,Yes,None,Entertainment,Yes,0.41,All
+111,Southern,Karnataka,Mysore,Mysore Palace,Palace,1912,2.0,4.6,50,Yes,None,Historical,Yes,2.5,All
+112,Southern,Karnataka,Hampi,Hampi Archaeological Ruins,Site,Unknown,3.0,4.7,0,No,None,Historical,Yes,0.05,All
+113,Southern,Karnataka,Coorg,Abbey Falls,Waterfall,Unknown,1.0,4.1,0,No,None,Nature,Yes,0.03,Morning
+114,Southern,Karnataka,Gokarna,Om Beach,Beach,Unknown,2.0,4.5,0,No,None,Nature,Yes,0.09,All
+115,Southern,Karnataka,Chikmagalur,Mullayanagiri,Mountain Peak,Unknown,3.0,4.5,0,No,None,Nature,Yes,0.05,All
+116,Southern,Karnataka,Badami,Badami Cave Temples,Cave,600,1.5,4.6,30,No,None,Religious,No,0.2,All
+117,Southern,Karnataka,Shivamogga,Jog Falls,Waterfall,1900,1.5,4.6,30,No,None,Nature,Yes,0.23,Morning
+118,Southern,Karnataka,Mangalore,Panambur Beach,Beach,Unknown,1.5,4.5,0,Yes,None,Recreational,Yes,0.1,All
+119,Southern,Karnataka,Murudeshwar,Murudeshwar Temple,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,No,0.49,All
+120,Southern,Karnataka,Bijapur,Gol Gumbaz,Mausoleum,1656,1.5,4.5,20,No,None,Historical,Yes,0.25,All
+121,Southern,Karnataka,Bandipur,Bandipur National Park,National Park,1974,3.0,4.4,300,No,None,Wildlife,Yes,0.15,Morning
+122,Southern,Karnataka,Halebidu,Halebidu Hoysaleswara Temple,Temple,1121,1.0,4.7,15,No,None,Religious,No,0.11,All
+123,Western,Maharastra,Pune,Shaniwar Wada,Fort,1732,2.0,4.4,50,Yes,None,Historical,Yes,1.2,All
+124,Western,Maharastra,Aurangabad,Ajanta Caves,Cave,200,3.0,4.6,30,Yes,Monday,Historical,Yes,0.21,Afternoon
+125,Western,Maharastra,Nashik,Sula Vineyards,Vineyard,1999,2.0,4.1,300,Yes,None,Recreational,Yes,0.1,Afternoon
+126,Western,Maharastra,Shirdi,Sai Baba Temple,Temple,1922,1.5,4.7,0,Yes,None,Religious,No,0.69,All
+127,Western,Maharastra,Alibaug,Alibaug Beach,Beach,Unknown,1.5,4.2,0,Yes,None,Recreational,Yes,0.05,Evening
+128,Western,Maharastra,Ratnagiri,Ganapatipule Temple,Temple,1600,1.0,4.7,0,No,None,Religious,No,0.1,All
+129,Western,Maharastra,Nagpur,Deekshabhoomi,Monument,2001,1.0,4.5,0,Yes,None,Religious,Yes,0.11,Afternoon
+130,Western,Maharastra,Kolhapur,Mahalakshmi Temple,Temple,700,1.0,4.8,0,Yes,None,Religious,No,0.9,All
+131,Western,Maharastra,Lonavala,Karla Caves,Cave,200,1.5,4.4,25,Yes,Yes,Historical,Yes,0.27,Afternoon
+132,Western,Maharashtra,Tarkarli,Tarkarli Beach,Beach,Unknown,2.0,4.6,0,No,None,Recreational,Yes,0.065,Evening
+133,Western,Maharashtra,Satara,Kaas Plateau,Valley,Unknown,2.0,4.4,300,No,None,Nature,Yes,0.05,Afternoon
+134,Western,Maharashtra,Matheran,Echo Point,Viewpoint,1828,1.5,4.4,0,Yes,None,Nature,Yes,0.02,Morning
+135,Western,Maharashtra,Ajanta,Ellora Caves,Cave,600,3.0,4.7,30,Yes,Tuesday,Historical,Yes,0.49,Afternoon
+136,Central,Madhya Pradesh,Khajuraho,Khajuraho Group of Monuments,Temples,-850,2.0,4.7,40,No,None,Cultural,Yes,0.09,Afternoon
+137,Central,Madhya Pradesh,Bhopal,Sanchi Stupa,Monument,-300,1.5,4.7,30,Yes,None,Historical,Yes,0.01,Afternoon
+138,Central,Madhya Pradesh,Indore,Rajwada Palace,Palace,1747,1.0,4.4,10,Yes,None,Historical,Yes,0.63,Afternoon
+139,Central,Madhya Pradesh,Gwalior,Gwalior Fort,Fort,900,2.5,4.5,75,Yes,None,Historical,Yes,0.4,Morning
+140,Central,Madhya Pradesh,Ujjain,Mahakaleshwar Jyotirlinga,Temple,-3500,1.5,4.8,0,Yes,None,Religious,No,1.2,All
+141,Central,Madhya Pradesh,Jabalpur,Dhuandhar Falls,Waterfall,Unknown,1.0,4.5,0,No,None,Nature,Yes,0.01,Morning
+142,Central,Madhya Pradesh,Pachmarhi,Bee Falls,Waterfall,Unknown,1.5,4.6,15,No,None,Nature,Yes,0.065,Morning
+143,Central,Madhya Pradesh,Kanha,Kanha National Park,Wildlife Sanctuary,1955,3.0,4.5,100,No,None,Wildlife,Yes,0.1,Morning
+144,Central,Madhya Pradesh,Bandhavgarh,Bandhavgarh National Park,National Park,1968,3.0,4.5,50,No,None,Wildlife,Yes,0.05,Morning
+145,Central,Madhya Pradesh,Orchha,Orchha Fort,Fort,1500,1.5,4.8,10,No,None,Historical,Yes,0.1,Afternoon
+146,Central,Madhya Pradesh,Mandu,Jahaz Mahal,Site,1500,1.0,3.9,50,No,None,Historical,Yes,0.03,Afternoon
+147,Central,Madhya Pradesh,Bhimbetka,Bhimbetka Rock Shelters,Prehistoric Site,1958,2.0,4.6,25,No,None,Archaeological,Yes,0.07,Afternoon
+148,Central,Madhya Pradesh,Amarkantak,Narmada Udgam Temple,Temple,1200,1.0,4.4,0,No,None,Religious,Yes,0.01,All
+149,Central,Madhya Pradesh,Chitrakoot,Chitrakoot Falls,Waterfall,Unknown,1.5,4.4,0,No,None,Nature,Yes,0.1,Morning
+150,Northern,Himachal Pradesh,Shimla,The Ridge,Scenic Point,Unknown,1.0,4.7,0,Yes,None,Recreational,Yes,0.03,Morning
+151,Northern,Himachal Pradesh,Manali,Solang Valley,Valley,Unknown,2.0,4.1,0,No,None,Adventure,Yes,0.05,Morning
+152,Northern,Himachal Pradesh,dalhousie,Dalai Lama Temple,Temple,1959,1.5,4.7,0,Yes,None,Religious,No,0.15,All
+153,Northern,Himachal Pradesh,Dalhousie,Khajjiar Lake,Lake,Unknown,1.5,4.5,0,No,None,Nature,Yes,0.1,Morning
+154,Northern,Himachal Pradesh,Spiti Valley,Key Monastery,Monastery,1100,1.0,4.8,0,No,None,Religious,Yes,0.025,Morning
+155,Northern,Himachal Pradesh,Kullu,Great Himalayan National Park,National Park,1984,3.0,4.5,50,No,None,Wildlife,Yes,0.2,All
+156,Northern,Himachal Pradesh,Chamba,Chamera Lake,Lake,Unknown,2.0,4.4,0,No,None,Recreational,Yes,0.01,Morning
+157,Northern,Himachal Pradesh,Kinnaur,Sangla Valley,Valley,Unknown,2.0,4.5,0,No,None,Nature,Yes,0.01,Morning
+158,Northern,Himachal Pradesh,Kangra,Kangra Fort,Fort,400,2.0,4.4,150,Yes,None,Historical,Yes,0.1,All
+159,Northern,Himachal Pradesh,Palampur,Tea Gardens,Tea Plantation,1950,1.5,4.6,0,Yes,None,Agricultural,Yes,0.015,Morning
+160,Northern,Himachal Pradesh,Mandi,Prashar Lake,Lake,1400,1.5,4.6,0,No,None,Nature,Yes,0.01,Morning
+161,Northern,Himachal Pradesh,Bir Billing,Paragliding Site,Adventure Sport,2005,2.0,4.8,2500,No,None,Adventure,Yes,0.01,All
+162,Northern,Himachal Pradesh,McLeod Ganj,Triund Trek,Trekking,Unknown,5.0,4.8,0,Yes,None,Adventure,Yes,0.01,Morning
+163,Northern,Himachal Pradesh,Manikaran,Manikaran Sahib,Gurudwara,1980,1.0,4.6,0,No,None,Religious,Yes,1.3,Morning
+164,Northern,Himachal Pradesh,Narkanda,Hatu Peak,Viewpoint,Unknown,2.0,4.5,0,Yes,None,Nature,Yes,1.1,All
+165,Northern,Himachal Pradesh,Barot,Barot Valley,Valley,Unknown,2.0,4.7,0,No,None,Nature,Yes,1.2,Morning
+166,Northern,Himachal Pradesh,Shoja,Serolsar Lake,Lake,Unknown,2.5,4.4,0,No,None,Nature,Yes,0.9,Morning
+167,Northern,Himachal Pradesh,Kufri,Kufri Fun World,Ski Resort,1975,5.0,3.8,1500,Yes,None,Recreational,Yes,0.1,All
+168,Northern,Uttarakhand,Nainital,Naini Lake,Lake,Unknown,1.5,4.2,0,Yes,None,Nature,Yes,0.01,Morning
+169,Northern,Uttarakhand,Rishikesh,Laxman Jhula,Suspension Bridge,1939,1.0,4.4,0,Yes,None,Cultural,Yes,0.03,Morning
+170,Northern,Uttarakhand,Haridwar,Har Ki Pauri,Ghat,Unknown,1.0,4.5,0,Yes,None,Religious,Yes,0.025,All
+171,Northern,Uttarakhand,Dehradun,Robber's Cave,Cave,Unknown,1.5,4.5,25,Yes,None,Nature,Yes,0.01,Morning
+172,Northern,Uttarakhand,Mussoorie,Kempty Falls,Waterfall,Unknown,1.5,4.2,15,Yes,None,Nature,Yes,0.55,Morning
+173,Northern,Uttarakhand,Auli,Auli Ski Resort,Ski Resort,1990,3.0,4.5,0,No,None,Adventure,Yes,0.01,Morning
+174,Northern,Uttarakhand,Badrinath,Badrinath Temple,Temple,-820,1.0,4.8,0,No,None,Religious,No,0.29,All
+175,Northern,Uttarakhand,Almora,Binsar Wildlife Sanctuary,Wildlife Sanctuary,1988,2.0,4.3,150,No,None,Wildlife,Yes,0.01,All
+176,Northern,Uttarakhand,Ranikhet,Chaubatia Gardens,Orchard,1868,1.0,4.0,50,No,None,Recreational,Yes,0.025,All
+177,Northern,Uttarakhand,Jim Corbett,Jim Corbett National Park,National Park,1936,3.0,4.4,100,Yes,None,Wildlife,Yes,0.3,All
+178,Northern,Uttarakhand,Uttarkashi,Gangotri Temple,Temple,1751,1.0,4.8,0,No,None,Religious,No,0.05,All
+179,Northern,Uttarakhand,Chopta,Tungnath Temple,Temple,751,2.0,4.8,0,No,None,Religious,No,0.09,All
+180,Northern,Uttarakhand,Joshimath,Valley of Flowers,National Park,1982,5.0,4.7,150,No,None,Nature,Yes,0.035,Morning
+181,Central,Uttar Pradesh,Agra,Taj Mahal,Mausoleum,1632,2.0,4.6,50,Yes,Friday,Historical,Yes,2.25,Morning
+182,Central,Uttar Pradesh,Varanasi,Kashi Vishwanath Temple,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,No,0.9,All
+183,Central,Uttar Pradesh,Lucknow,Bara Imambara,Monument,1784,1.5,4.4,50,Yes,Monday,Historical,No,0.45,All
+184,Central,Uttar Pradesh,Mathura,Krishna Janmabhoomi,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,No,0.13,All
+185,Central,Uttar Pradesh,Ayodhya,Ram Janmabhoomi,Religious Site,Unknown,1.0,4.8,0,Yes,None,Religious,No,0.025,All
+186,Central,Uttar Pradesh,Vrindavan,Banke Bihari Temple,Temple,1864,1.0,4.8,0,Yes,None,Religious,No,0.37,All
+187,Central,Uttar Pradesh,Allahabad,Triveni Sangam,Confluence,Unknown,1.0,4.5,0,Yes,None,Religious,Yes,0.09,Morning
+188,Central,Uttar Pradesh,Jhansi,Jhansi Fort,Fort,1613,1.5,4.4,15,Yes,None,Historical,Yes,0.25,All
+189,Central,Uttar Pradesh,Sarnath,Dhamek Stupa,Monument,-500,1.0,4.6,5,Yes,None,Historical,Yes,0.065,All
+190,Central,Uttar Pradesh,Fatehpur Sikri,Buland Darwaza,Monument,1571,2.0,4.4,40,Yes,None,Historical,Yes,0.07,Afternoon
+191,Central,Uttar Pradesh,Noida,Okhla Bird Sanctuary,Bird Sanctuary,1990,1.5,4.3,30,Yes,None,Wildlife,Yes,0.035,All
+192,Central,Uttar Pradesh,Aligarh,Aligarh Fort,Fort,1524,1.0,4.1,20,Yes,None,Historical,Yes,0.0165,Afternoon
+193,Central,Uttar Pradesh,Meerut,Augarnath Temple,Temple,Unknown,1.0,4.8,0,Yes,None,Religious,No,0.045,All
+194,Central,Uttar Pradesh,Kanpur,Allen Forest Zoo,Zoo,1971,2.0,4.2,150,Yes,Monday,Wildlife,Yes,0.21,All
+195,Northern,Jammu and Kashmir,Srinagar,Dal Lake,Lake,Unknown,2.0,4.6,0,Yes,None,Nature,Yes,0.15,Morning
+196,Northern,Ladakh,Leh,Pangong Tso,Lake,Unknown,2.0,4.9,20,Yes,None,Nature,Yes,0.15,Morning
+197,Northern,Jammu and Kashmir,Pahalgam,Betaab Valley,Valley,Unknown,2.0,4.6,100,No,None,Nature,Yes,0.11,All
+198,Northern,Jammu and Kashmir,Jammu,Vaishno Devi,Temple,Unknown,5.0,4.7,0,Yes,None,Religious,No,0.55,All
+199,Northern,Jammu and Kashmir,Udhampur,Patnitop Height,Hill,Unknown,2.0,4.1,0,No,None,Recreational,Yes,0.01,All
+200,Northern,Jammu and Kashmir,Anantnag,Amarnath Cave,Temple,Unknown,6.0,4.5,0,No,None,Religious,Yes,0.11,All
+201,Northern,Ladakh,Leh,Thiksey Monastery,Monastery,1430,1.5,4.7,20,Yes,None,Religious,No,0.05,All
+202,Northern,Ladakh,Nubra Valley,Nubra Valley,Valley,Unknown,2.0,4.5,0,No,None,Nature,Yes,0.1,All
+203,Northern,Ladakh,Kargil,Kargil War Memorial,War Memorial,24,1.0,4.8,0,No,None,Historical,Yes,0.011,All
+204,Northern,Ladakh,Diskit,Diskit Monastery,Monastery,1351,1.0,4.7,20,No,None,Religious,No,0.015,Morning
+205,Northern,Jammu and Kashmir,Kishtwar,Kishtwar National Park,National Park,1981,3.0,4.3,100,No,None,Wildlife,Yes,0.01,All
+206,Northern,Ladakh,Hemis,Hemis National Park,National Park,1981,4.0,4.4,20,No,None,Wildlife,Yes,0.02,All
+207,Northern,Ladakh,Dras,Dras War Memorial,War Memorial,Unknown,1.0,4.8,0,No,None,Historical,Yes,0.012,All
+208,Northern,Ladakh,Leh,Magnetic Hill,Gravity Hill,Unknown,0.5,3.7,0,Yes,None,Nature,Yes,0.1,All
+209,Northern,Ladakh,Leh,Khardung La Pass,Hill,Unknown,1.0,4.5,0,Yes,None,Adventure,Yes,0.05,Afternoon
+210,Northern,Ladakh,Leh,Thiksey Monastery,Monastery,1430,1.5,4.7,30,Yes,None,Religious,No,0.05,All
+211,Central,Uttar Pradesh,Vrindavan,Prem Mandir,Temple,2012,1.5,4.8,0,Yes,None,Religious,Yes,0.49,Evening
+212,Central,Uttar Pradesh,Porbandar,Kirti Mandir,Memorial,1950,1.0,4.8,0,Yes,None,Historical,Yes,0.03,Morning
+213,Central,Uttar Pradesh,Mathura,Nand Gaon,Village,Unknown,1.0,4.1,0,Yes,None,Cultural,Yes,0.01,Morning
+214,Central,Uttar Pradesh,Mathura,Barsana Mandir,Temple,Unknown,1.0,4.8,0,Yes,None,Religious,Yes,0.1,Morning
+215,Eastern,West Bengal,Darjeeling,Tiger Hill,Sunrise Point,Unknown,1.0,4.5,0,No,None,Nature,Yes,0.025,All
+216,Eastern,West Bengal,Siliguri,Jaldapara National Park,Wildlife Sanctuary,1941,3.0,4.4,250,Yes,None,Wildlife,Yes,0.035,All
+217,Eastern,West Bengal,Sundarbans,Sundarbans National Park,National Park,1984,4.0,4.4,60,No,None,Wildlife,Yes,0.065,All
+218,Eastern,West Bengal,Digha,Digha Beach,Beach,Unknown,1.5,4.5,0,No,None,Recreational,Yes,0.09,Morning
+219,Eastern,West Bengal,Murshidabad,Hazarduari Palace,Palace,1837,1.5,4.5,10,Yes,Friday,Historical,Yes,0.18,Morning
+220,Eastern,West Bengal,Bolpur,Kankalitala Temple,Temple,Unknown,0.5,4.7,0,Yes,None,Religious,No,0.045,All
+221,Eastern,West Bengal,Hooghly,Hangseswari Temple,Temple,1814,0.5,4.6,0,Yes,None,Architectural,No,0.07,All
+222,Eastern,West Bengal,Jalpaiguri,Gorumara National Park,National Park,1949,3.0,4.4,100,Yes,None,Wildlife,Yes,0.07,All
+223,Eastern,West Bengal,Cooch Behar,Cooch Behar Palace,Palace,1887,1.0,4.5,20,Yes,Friday,Historical,Yes,0.09,All
+224,Eastern,West Bengal,Purulia,Ayodhya Hills,Hill,Unknown,2.5,4.5,0,No,None,Nature,Yes,0.15,All
+225,Eastern,Odisha,Puri,Jagannath Temple,Temple,12th century,2.0,4.7,0,Yes,None,Religious,No,1.0,All
+226,Eastern,Odisha,Konark,Sun Temple,Temple,1250,1.5,4.7,40,Yes,None,Historical,Yes,0.83,All
+227,Eastern,Odisha,Bhubaneswar,Lingaraj Temple,Temple,11th century,1.0,4.6,0,Yes,None,Religious,No,0.35,All
+228,Eastern,Odisha,Rourkela,Khandadhar Waterfall,Waterfall,Unknown,1.5,4.5,0,No,None,Nature,Yes,1.2,All
+229,Eastern,Odisha,Cuttack,Barabati Fort,Fort,-987,1.0,4.5,0,Yes,None,Historical,Yes,0.13,All
+230,Eastern,Odisha,Sambalpur,Hirakud Dam,Dam,1957,1.0,4.5,0,No,None,Engineering Marvel,Yes,0.01,All
+231,Eastern,Odisha,Chilika,Chilika Lake,Lake,Unknown,2.0,3.9,0,Yes,None,Nature,Yes,0.1,Morning
+232,Eastern,Odisha,Berhampur,Tara Tarini Temple,Temple,Ancient,1.0,4.6,0,Yes,None,Religious,No,0.01,All
+233,Eastern,Odisha,Keonjhar,Badaghagara Waterfall,Waterfall,Unknown,1.0,4.3,0,No,None,Nature,Yes,0.018,Morning
+234,Eastern,Odisha,Balasore,Chandipur Beach,Beach,Unknown,1.5,4.2,0,Yes,None,Recreational,Yes,0.014,Morning
+235,Eastern,Odisha,Kendujhar,Sanaghagara Waterfall,Waterfall,Unknown,1.0,4.4,0,No,None,Nature,Yes,0.055,All
+236,Southern,Tamil Nadu,Chennai,Marina Beach,Beach,Unknown,1.5,3.9,0,Yes,None,Recreational,Yes,0.1,Morning
+237,Southern,Tamil Nadu,Madurai,Meenakshi Amman Temple,Temple,6th century AD,2.0,4.7,0,Yes,None,Religious,No,0.65,All
+238,Southern,Tamil Nadu,Rameswaram,Ramanathaswamy Temple,Temple,12th century,1.5,4.6,0,No,None,Religious,No,0.01,All
+239,Southern,Tamil Nadu,Kanyakumari,Vivekananda Rock Memorial,Memorial,1970,1.0,4.6,20,Yes,None,Historical,No,0.47,Morning
+240,Southern,Tamil Nadu,Ooty,Ooty Lake,Lake,1824,1.0,4.1,10,Yes,None,Recreational,Yes,0.61,Morning
+241,Southern,Tamil Nadu,Coimbatore,Marudamalai Temple,Temple,12th century,1.0,4.7,0,Yes,None,Religious,No,0.3,All
+242,Southern,Tamil Nadu,Kodaikanal,Kodaikanal Lake,Lake,1863,2.0,3.9,0,No,None,Recreational,Yes,0.1,Morning
+243,Southern,Tamil Nadu,Thanjavur,Brihadeeswarar Temple,Temple,110,1.5,4.8,0,Yes,None,Religious,No,0.35,All
+244,Southern,Tamil Nadu,Mahabalipuram,Shore Temple,Temple,7th century,1.0,4.6,40,Yes,None,Historical,No,0.09,All
+245,Southern,Tamil Nadu,Yercaud,Yercaud Lake,Lake,Unknown,1.0,4.2,0,No,None,Recreational,Yes,0.019,Morning
+246,Southern,Tamil Nadu,Tirunelveli,Nellaiappar Temple,Temple,7th century,1.0,4.6,0,Yes,None,Religious,No,0.16,All
+247,Southern,Tamil Nadu,Chidambaram,Nataraja Temple,Temple,10th century,1.0,4.7,0,Yes,None,Religious,No,0.28,All
+248,Southern,Andhra Pradesh,Vijayawada,Kanaka Durga Temple,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,No,0.44,All
+249,Southern,Andhra Pradesh,Visakhapatnam,Rishikonda Beach,Beach,Unknown,1.5,4.5,0,Yes,None,Recreational,Yes,0.39,Morning
+250,Southern,Andhra Pradesh,Srisailam,Mallikarjuna Swamy Temple,Temple,14th century,1.0,4.7,0,No,None,Religious,No,0.49,All
+251,Southern,Andhra Pradesh,Rajahmundry,Papikondalu,Hill,Unknown,2.0,4.3,0,Yes,None,Nature,Yes,0.032,All
+252,Southern,Andhra Pradesh,Anantapur,Lepakshi,Site,16th century,1.5,4.6,0,No,None,Historical,Yes,0.075,All
+253,Southern,Andhra Pradesh,Kurnool,Belum Caves,Cave,Unknown,1.5,4.4,65,Yes,None,Natural Wonder,Yes,0.11,Afternoon
+254,Southern,Andhra Pradesh,Amravati,Amaravathi Temple,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,No,0.041,All
+255,Southern,Andhra Pradesh,Guntur,Uppalapadu Bird Sanctuary,Bird Sanctuary,Unknown,1.0,4.4,10,Yes,None,Wildlife,Yes,0.7,All
+256,Southern,Andhra Pradesh,Kadapa,Gandikota Fort,Fort,12th century,1.5,4.5,0,No,None,Historical,Yes,0.015,Morning
+257,Southern,Andhra Pradesh,Puttaparthi,Prasanthi Nilayam,Spiritual Center,1950,1.0,4.7,0,Yes,None,Religious,Yes,0.12,All
+258,Southern,Andhra Pradesh,Vizianagaram,Simhachalam Temple,Temple,198,1.0,4.7,0,Yes,None,Religious,No,0.59,All
+259,Southern,Andhra Pradesh,Visakhapatnam,Kailasagiri,Hill,Unknown,2.0,4.5,0,Yes,None,Recreational,Yes,0.019,All
+260,Southern,Andhra Pradesh,Visakhapatnam,Submarine Museum,Museum,22,1.0,4.6,40,Yes,None,Historical,Yes,0.49,All
+261,Southern,Andhra Pradesh,Visakhapatnam,Borra Caves,Cave,Unknown,2.0,4.5,60,No,None,Natural Wonder,Yes,0.31,Afternoon
+262,Southern,Andhra Pradesh,Visakhapatnam,War Memorial,War Memorial,Unknown,1.0,4.6,0,Yes,None,Historical,Yes,0.059,All
+263,Southern,Andhra Pradesh,Visakhapatnam,Indira Gandhi Zoological Park,Zoo,1977,2.0,4.1,20,Yes,Monday,Wildlife,Yes,0.25,Afternoon
+264,Southern,Andhra Pradesh,Visakhapatnam,Matsyadarshini Aquarium,Aquarium,Unknown,1.0,3.8,20,Yes,None,Recreational,Yes,0.03,All
+265,Southern,Andhra Pradesh,Visakhapatnam,Visakha Museum,Museum,1991,1.0,4.3,10,Yes,None,Cultural,Yes,0.065,All
+266,Eastern,Sikkim,Gangtok,Nathula Pass,Hill,Unknown,2.0,4.3,20,Yes,None,Historical,Yes,0.15,Afternoon
+267,Eastern,Sikkim,Pelling,Pemayangtse Monastery,Monastery,1705,1.0,4.6,20,No,None,Religious,No,0.015,Morning
+268,Eastern,Sikkim,Namchi,Char Dham,Religious Complex,211,2.0,4.7,50,Yes,None,Religious,No,0.12,All
+269,Eastern,Sikkim,Gangtok,Rumtek Monastery,Monastery,1960s,1.0,4.6,10,Yes,None,Religious,No,0.035,Morning
+270,Eastern,Sikkim,Ravangla,Buddha Park,Park,213,1.0,4.8,50,Yes,None,Cultural,Yes,0.1,All
+271,Eastern,Sikkim,Gangtok,Baba Harbhajan Singh Temple,Temple,1967,1.0,4.7,0,Yes,None,Religious,No,0.075,All
+272,Eastern,Sikkim,Gangtok,Tsomgo Lake,Lake,Unknown,2.0,4.5,0,Yes,None,Nature,Yes,0.15,Morning
+273,North Eastern,Assam,Guwahati,Kamakhya Temple,Temple,Unknown,2.0,4.6,0,Yes,None,Religious,No,0.21,All
+274,North Eastern,Assam,Kaziranga,Kaziranga National Park,National Park,1905,3.0,4.5,650,No,None,Wildlife,No,0.068,Morning
+275,North Eastern,Assam,Guwahati,Umananda Island,Island,Unknown,1.0,4.1,0,Yes,None,Nature,Yes,0.01,Morning
+276,North Eastern,Assam,Sivasagar,Sivasagar Sivadol,Temple,1734,1.0,4.7,0,No,None,Historical,Yes,0.065,All
+277,North Eastern,Assam,Majuli,Majuli Island,River Island,Unknown,2.0,4.7,0,No,None,Cultural,Yes,0.01,Morning
+278,North Eastern,Assam,Manas,Manas National Park,National Park,1990,3.0,4.6,500,No,None,Wildlife,Yes,0.19,Morning
+279,North Eastern,Assam,Hajo,Hayagriva Madhava Temple,Temple,Unknown,1.0,4.5,0,No,None,Religious,Yes,0.9,All
+280,North Eastern,Assam,Guwahati,Pobitora Wildlife Sanctuary,Wildlife Sanctuary,1987,2.0,4.4,500,Yes,None,Wildlife,Yes,0.037,All
+281,North Eastern,Arunachal Pradesh,Tawang,Tawang Monastery,Monastery,1680,2.0,4.7,0,No,None,Religious,No,0.032,Morning
+282,North Eastern,Tripura,Agartala,Ujjayanta Palace,Palace,1901,1.5,4.5,10,Yes,Monday,Historical,Yes,0.035,All
+283,North Eastern,Tripura,Dumboor,Dumboor Lake,Lake,Unknown,1.0,4.5,0,No,None,Nature,Yes,0.01,Morning
+284,North Eastern,Tripura,Unakoti,Unakoti Rock Carvings,Rock Carvings,700,1.5,4.5,20,No,None,Historical,Yes,0.025,Morning
+285,Central,Chhattisgarh,Bastar,Chitrakote Falls,Waterfall,Unknown,1.5,4.6,0,No,None,Nature,Yes,0.19,Morning
+286,North Eastern,Nagaland,Dzükou Valley,Dzükou Valley,Valley,Unknown,3.0,4.7,0,No,None,Trekking,Yes,0.01,Afternoon
+287,Southern,Puducherry,Puducherry,Promenade Beach,Beach,Unknown,1.0,4.5,0,Yes,None,Recreational,Yes,0.09,Morning
+288,Southern,Puducherry,Auroville,Auroville,Township,1968,2.0,4.1,0,Yes,None,Cultural,Yes,0.035,All
+289,Southern,Puducherry,Puducherry,Paradise Beach,Beach,Unknown,1.0,4.5,200,Yes,None,Recreational,Yes,0.015,Morning
+290,Southern,Andaman and Nicobar Islands,Port Blair,Cellular Jail,Landmark,1906,1.5,4.7,30,Yes,Monday,Historical,Yes,0.12,Afternoon
+291,Southern,Andaman and Nicobar Islands,Havelock Island,Radhanagar Beach,Beach,Unknown,1.0,4.8,0,No,None,Nature,Yes,0.09,Morning
+292,Southern,Andaman and Nicobar Islands,Neil Island,Bharatpur Beach,Beach,Unknown,1.0,4.5,0,No,None,Nature,Yes,0.04,Morning
+293,Southern,Andaman and Nicobar Islands,Baratang Island,Limestone Caves,Natural Feature,Unknown,1.5,4.4,250,No,None,Nature,Yes,0.015,Morning
+294,Western,Daman and Diu,Diu,Naida Caves,Cave,Unknown,1.0,4.5,0,No,None,Nature,Yes,0.6,Afternoon
+295,Western,Daman and Diu,Diu,Diu Fort,Fort,1535,1.5,4.6,0,No,None,Historical,Yes,1.2,Afternoon
+296,Eastern,Jharkhand,Deoghar,Baba Baidyanath Temple,Temple,Unknown,1.0,4.7,0,Yes,None,Religious,Yes,1.8,All
+297,Eastern,Jharkhand,Ranchi,Pahari Mandir,Temple,Unknown,1.0,4.6,0,Yes,None,Religious,Yes,0.13,All
+298,Eastern,Bihar,Bodh Gaya,Mahabodhi Temple,Temple,-260,1.5,4.7,0,Yes,None,Religious,Yes,0.2,All
+299,Eastern,Bihar,Patna,Sanjay Gandhi Biological Park,Zoo,Unknown,2.5,4.3,30,Yes,None,Wildlife,Yes,0.5,All
+300,Eastern,Bihar,Patna,Takhat Shri Harimandir Ji Patna Sahib,Gurudwara,Unknown,1.0,4.7,0,Yes,None,Religious,Yes,0.25,All
+301,Eastern,Bihar,Patna,Budhha Smriti Park,Park,Unknown,1.0,4.4,10,Yes,None,Cultural,Yes,0.31,All
+302,Northern,Haryana,Gurugram,Kingdom of Dreams,Entertainment,2010,3.0,4.4,1100,Yes,Monday,Entertainment,Yes,0.3,Afternoon
+303,Northern,Haryana,Gurugram,Ambience Mall,Mall,Unknown,2.0,4.6,0,Yes,None,Shopping,Yes,1.2,Afternoon
+304,Northern,Haryana,Gurugram,DLF CyberHub,Commercial Complex,Unknown,2.0,4.7,0,Yes,None,Entertainment,Yes,0.71,Afternoon
+305,Northern,Delhi,New Delhi,Gurudwara Bangla Sahib,Gurudwara,Unknown,1.0,4.8,0,Yes,None,Religious,Yes,1.05,All
+306,Northern,Uttarakhand,Kedarnath,Kedarnath,Temple,Unknown,1.5,4.8,0,No,None,Religious,No,2.0,All
+307,Central,Uttar Pradesh,Noida,DLF Mall of India,Mall,2016,2.0,4.6,0,Yes,None,Shopping,Yes,1.5,All
+308,Central,Uttar Pradesh,Greater Noida,The Grand Venice Mall,Mall,Unknown,2.0,4.2,0,Yes,None,Shopping,Yes,0.45,All
+309,Southern,Karnataka,Bengaluru,Wonderla Amusement Park,Amusement Park,2005,4.0,4.5,890,Yes,None,Entertainment,Yes,0.95,All
+310,Eastern,Odisha,Bhubaneswar,Nandankanan Zoological Park,Zoo,1960,3.0,4.4,50,Yes,Monday,Wildlife,Yes,0.81,Afternoon
+311,Southern,Karnataka,Bengaluru,Orion Mall,Mall,2012,2.0,4.5,0,Yes,None,Shopping,Yes,1.8,All
+312,Southern,Telangana,Hyderabad,Inorbit Mall Cyberabad,Mall,2004,2.0,4.5,0,Yes,None,Shopping,Yes,1.2,All
+313,Northern,Delhi,New Delhi,Jama Masjid,Mosque,1656,1.0,4.5,0,Yes,None,Historical,Yes,0.49,All
+314,Southern,Tamil Nadu,Rameswaram,Ramanathaswamy Temple,Temple,Unknown,1.5,4.7,0,Yes,None,Religious,No,0.1,All
+315,Central,Uttar Pradesh,Greater Noida,Buddh International Circuit,Race Track,2011,2.0,4.6,1500,Yes,Sunday,Sports,Yes,7.4,All
+316,Central,Uttar Pradesh,Lucknow,Phoenix Palassio,Mall,2020,2.0,4.6,0,Yes,None,Shopping,Yes,0.35,All
+317,Southern,Kerala,Kochi,LuLu International Shopping Mall,Mall,2013,3.0,4.6,0,Yes,None,Shopping,Yes,1.9,All
+318,Northern,Delhi,New Delhi,Rail Museum,Museum,1977,2.0,4.4,50,Yes,Monday,Cultural,Yes,0.24,Morning
+319,North Eastern,Meghalaya,Cherrapunji,Living Root Bridge,Natural Feature,Unknown,2.0,4.6,0,No,None,Nature,Yes,0.06,Morning
+320,Western,Gujarat,Gandhinagar,Akshardham,Temple,1992,3.0,4.6,0,Yes,Monday,Religious,No,0.18,All
+321,Central,Uttar Pradesh,Agra,Agra Fort,Fort,1565,2.0,4.5,40,Yes,None,Historical,Yes,1.3,Afternoon
+322,Central,Madhya Pradesh,Bhopal,Madhya Pradesh Tribal Museum,Museum,2013,2.0,4.7,10,Yes,Monday,Cultural,Yes,0.15,All
+323,Northern,Rajasthan,Jaipur,City Palace,Palace,1727,2.0,4.4,200,Yes,None,Historical,Yes,0.51,Morning
+324,Northern,Rajasthan,Jaipur,Albert Hall Museum,Museum,1887,2.0,4.5,200,Yes,None,Historical,Yes,0.63,All
diff --git a/Recommendation Systems/tourist_recommendation_system/Tourist_Recommendation_System.ipynb b/Recommendation Systems/tourist_recommendation_system/Tourist_Recommendation_System.ipynb
new file mode 100644
index 00000000..a5ad494f
--- /dev/null
+++ b/Recommendation Systems/tourist_recommendation_system/Tourist_Recommendation_System.ipynb	
@@ -0,0 +1,5400 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "from sklearn.svm import SVR\n",
+        "\n",
+        "from sklearn.ensemble import RandomForestRegressor"
+      ],
+      "metadata": {
+        "id": "VwK4i3H3vJio"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('Top Indian Places to Visit.csv')\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 721
+        },
+        "id": "N3rwara3zBXe",
+        "outputId": "950389bb-2966-4dcb-863e-0623e88170c9"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Unnamed: 0      Zone           State         City  \\\n",
+              "0             0  Northern           Delhi        Delhi   \n",
+              "1             1  Northern           Delhi        Delhi   \n",
+              "2             2  Northern           Delhi        Delhi   \n",
+              "3             3  Northern           Delhi        Delhi   \n",
+              "4             4  Northern           Delhi        Delhi   \n",
+              "..          ...       ...             ...          ...   \n",
+              "320         320   Western         Gujarat  Gandhinagar   \n",
+              "321         321   Central   Uttar Pradesh         Agra   \n",
+              "322         322   Central  Madhya Pradesh       Bhopal   \n",
+              "323         323  Northern       Rajasthan       Jaipur   \n",
+              "324         324  Northern       Rajasthan       Jaipur   \n",
+              "\n",
+              "                             Name          Type Establishment Year  \\\n",
+              "0                      India Gate  War Memorial               1921   \n",
+              "1                  Humayun's Tomb          Tomb               1572   \n",
+              "2               Akshardham Temple        Temple               2005   \n",
+              "3            Waste to Wonder Park    Theme Park               2019   \n",
+              "4                   Jantar Mantar   Observatory               1724   \n",
+              "..                            ...           ...                ...   \n",
+              "320                    Akshardham        Temple               1992   \n",
+              "321                     Agra Fort          Fort               1565   \n",
+              "322  Madhya Pradesh Tribal Museum        Museum               2013   \n",
+              "323                   City Palace        Palace               1727   \n",
+              "324            Albert Hall Museum        Museum               1887   \n",
+              "\n",
+              "     time needed to visit in hrs  Google review rating  Entrance Fee in INR  \\\n",
+              "0                            0.5                   4.6                    0   \n",
+              "1                            2.0                   4.5                   30   \n",
+              "2                            5.0                   4.6                   60   \n",
+              "3                            2.0                   4.1                   50   \n",
+              "4                            2.0                   4.2                   15   \n",
+              "..                           ...                   ...                  ...   \n",
+              "320                          3.0                   4.6                    0   \n",
+              "321                          2.0                   4.5                   40   \n",
+              "322                          2.0                   4.7                   10   \n",
+              "323                          2.0                   4.4                  200   \n",
+              "324                          2.0                   4.5                  200   \n",
+              "\n",
+              "    Airport with 50km Radius Weekly Off   Significance DSLR Allowed  \\\n",
+              "0                        Yes        NaN     Historical          Yes   \n",
+              "1                        Yes        NaN     Historical          Yes   \n",
+              "2                        Yes        NaN      Religious           No   \n",
+              "3                        Yes     Monday  Environmental          Yes   \n",
+              "4                        Yes        NaN     Scientific          Yes   \n",
+              "..                       ...        ...            ...          ...   \n",
+              "320                      Yes     Monday      Religious           No   \n",
+              "321                      Yes        NaN     Historical          Yes   \n",
+              "322                      Yes     Monday       Cultural          Yes   \n",
+              "323                      Yes        NaN     Historical          Yes   \n",
+              "324                      Yes        NaN     Historical          Yes   \n",
+              "\n",
+              "     Number of google review in lakhs Best Time to visit  \n",
+              "0                                2.60            Evening  \n",
+              "1                                0.40          Afternoon  \n",
+              "2                                0.40          Afternoon  \n",
+              "3                                0.27            Evening  \n",
+              "4                                0.31            Morning  \n",
+              "..                                ...                ...  \n",
+              "320                              0.18                All  \n",
+              "321                              1.30          Afternoon  \n",
+              "322                              0.15                All  \n",
+              "323                              0.51            Morning  \n",
+              "324                              0.63                All  \n",
+              "\n",
+              "[325 rows x 16 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-90ba700e-6dd7-452b-8fd7-78f183044088\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>Zone</th>\n",
+              "      <th>State</th>\n",
+              "      <th>City</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Establishment Year</th>\n",
+              "      <th>time needed to visit in hrs</th>\n",
+              "      <th>Google review rating</th>\n",
+              "      <th>Entrance Fee in INR</th>\n",
+              "      <th>Airport with 50km Radius</th>\n",
+              "      <th>Weekly Off</th>\n",
+              "      <th>Significance</th>\n",
+              "      <th>DSLR Allowed</th>\n",
+              "      <th>Number of google review in lakhs</th>\n",
+              "      <th>Best Time to visit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>India Gate</td>\n",
+              "      <td>War Memorial</td>\n",
+              "      <td>1921</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Historical</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>Evening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Humayun's Tomb</td>\n",
+              "      <td>Tomb</td>\n",
+              "      <td>1572</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>30</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Historical</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>Afternoon</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Akshardham Temple</td>\n",
+              "      <td>Temple</td>\n",
+              "      <td>2005</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>60</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Religious</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>Afternoon</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Waste to Wonder Park</td>\n",
+              "      <td>Theme Park</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>50</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Monday</td>\n",
+              "      <td>Environmental</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>Evening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>Jantar Mantar</td>\n",
+              "      <td>Observatory</td>\n",
+              "      <td>1724</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>15</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Scientific</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0.31</td>\n",
+              "      <td>Morning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>320</th>\n",
+              "      <td>320</td>\n",
+              "      <td>Western</td>\n",
+              "      <td>Gujarat</td>\n",
+              "      <td>Gandhinagar</td>\n",
+              "      <td>Akshardham</td>\n",
+              "      <td>Temple</td>\n",
+              "      <td>1992</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Monday</td>\n",
+              "      <td>Religious</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0.18</td>\n",
+              "      <td>All</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>321</th>\n",
+              "      <td>321</td>\n",
+              "      <td>Central</td>\n",
+              "      <td>Uttar Pradesh</td>\n",
+              "      <td>Agra</td>\n",
+              "      <td>Agra Fort</td>\n",
+              "      <td>Fort</td>\n",
+              "      <td>1565</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>40</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Historical</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1.30</td>\n",
+              "      <td>Afternoon</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>322</th>\n",
+              "      <td>322</td>\n",
+              "      <td>Central</td>\n",
+              "      <td>Madhya Pradesh</td>\n",
+              "      <td>Bhopal</td>\n",
+              "      <td>Madhya Pradesh Tribal Museum</td>\n",
+              "      <td>Museum</td>\n",
+              "      <td>2013</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>10</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Monday</td>\n",
+              "      <td>Cultural</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0.15</td>\n",
+              "      <td>All</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>323</th>\n",
+              "      <td>323</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>Jaipur</td>\n",
+              "      <td>City Palace</td>\n",
+              "      <td>Palace</td>\n",
+              "      <td>1727</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>200</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Historical</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0.51</td>\n",
+              "      <td>Morning</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>324</th>\n",
+              "      <td>324</td>\n",
+              "      <td>Northern</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>Jaipur</td>\n",
+              "      <td>Albert Hall Museum</td>\n",
+              "      <td>Museum</td>\n",
+              "      <td>1887</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>200</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Historical</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0.63</td>\n",
+              "      <td>All</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>325 rows × 16 columns</p>\n",
+              "</div>\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-90ba700e-6dd7-452b-8fd7-78f183044088 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-90ba700e-6dd7-452b-8fd7-78f183044088');\n",
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+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-135da624-dfd7-4de0-8142-9a7caa718d47\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-135da624-dfd7-4de0-8142-9a7caa718d47')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-135da624-dfd7-4de0-8142-9a7caa718d47 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_9c88d770-5ac3-49af-a96b-070bf0aaebc2\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_9c88d770-5ac3-49af-a96b-070bf0aaebc2 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 325,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93,\n        \"min\": 0,\n        \"max\": 324,\n        \"num_unique_values\": 325,\n        \"samples\": [\n          234,\n          110,\n          248\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Northern\",\n          \"Western\",\n          \"North Eastern\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"Haryana\",\n          \"Jammu and Kashmir\",\n          \"Puducherry\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 214,\n        \"samples\": [\n          \"Junagadh\",\n          \"Puducherry\",\n          \"Bhopal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 321,\n        \"samples\": [\n          \"Auli Ski Resort\",\n          \"Tarkarli Beach\",\n          \"Betaab Valley\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 78,\n        \"samples\": [\n          \"Urban Development Project\",\n          \"War Memorial\",\n          \"Wildlife Sanctuary\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Establishment Year\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 162,\n        \"samples\": [\n          \"2011\",\n          \"1632\",\n          \"1970\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time needed to visit in hrs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9713976967649001,\n        \"min\": 0.5,\n        \"max\": 7.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          4.0,\n          0.5,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Google review rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2745798828406383,\n        \"min\": 1.4,\n        \"max\": 4.9,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          3.9,\n          3.8,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entrance Fee in INR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 530,\n        \"min\": 0,\n        \"max\": 7500,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          1100,\n          10,\n          1500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Airport with 50km Radius\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly Off\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Sunday\",\n          \"Tuesday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Significance\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 25,\n        \"samples\": [\n          \"Wildlife\",\n          \"Food\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DSLR Allowed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of google review in lakhs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6466684463731491,\n        \"min\": 0.01,\n        \"max\": 7.4,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          2.25,\n          0.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Best Time to visit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Evening\",\n          \"Afternoon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Distribution of Google review ratings\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.histplot(df['Google review rating'], bins=20, kde=True, color='skyblue')\n",
+        "plt.title('Distribution of Google Review Ratings')\n",
+        "plt.xlabel('Google Review Rating')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "44it_mL0zOUt",
+        "outputId": "e08f5034-5e78-4a71-c5fd-cd2a52864c4a"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Boxplot of Google review ratings by Zone\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "sns.boxplot(x='Zone', y='Google review rating', data=df, palette='Set3')\n",
+        "plt.title('Boxplot of Google Review Ratings by Zone')\n",
+        "plt.xlabel('Zone')\n",
+        "plt.ylabel('Google Review Rating')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "id": "GXPgOzy3zWgM",
+        "outputId": "9c831afd-0d1a-414c-f64b-06271ebeacf3"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-13-7264f19cd147>:3: FutureWarning: \n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "  sns.boxplot(x='Zone', y='Google review rating', data=df, palette='Set3')\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Pairplot to visualize relationships between numerical variables\n",
+        "numerical_cols = ['time needed to visit in hrs', 'Entrance Fee in INR', 'Number of google review in lakhs']\n",
+        "sns.pairplot(df[numerical_cols])\n",
+        "plt.suptitle('Pairplot of Numerical Variables', y=1.02)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 787
+        },
+        "id": "lr-PA1x6zY95",
+        "outputId": "c03153ae-9313-4b29-b546-f28394b07445"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 750x750 with 12 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation heatmap of numerical variables\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "corr = df[numerical_cols].corr()\n",
+        "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
+        "plt.title('Correlation Heatmap of Numerical Variables')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 699
+        },
+        "id": "TSb6u_-ezapr",
+        "outputId": "e0ac75c6-f38d-40dc-8838-0727452fe99a"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# remove all rows which contain NaN value\n",
+        "df.dropna(inplace=True)"
+      ],
+      "metadata": {
+        "id": "kRUY0gmpzdGg"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_encoded = pd.get_dummies(df, columns=['Zone', 'State', 'City', 'Name', 'Type', 'Establishment Year',\n",
+        "                                         'Weekly Off', 'Significance', 'Best Time to visit',\n",
+        "                                         'Airport with 50km Radius', 'DSLR Allowed'])\n",
+        "\n",
+        "df_encoded.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 342
+        },
+        "id": "3hT40hQZzfTo",
+        "outputId": "8cf1ae99-50ea-4476-8ff1-34a12028d0d1"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Unnamed: 0  time needed to visit in hrs  Google review rating  \\\n",
+              "3            3                          2.0                   4.1   \n",
+              "5            5                          3.0                   4.2   \n",
+              "6            6                          1.0                   4.5   \n",
+              "12          12                          3.0                   4.5   \n",
+              "13          13                          3.0                   4.1   \n",
+              "\n",
+              "    Entrance Fee in INR  Number of google review in lakhs  Zone_Central  \\\n",
+              "3                    50                              0.27         False   \n",
+              "5                     0                              0.25         False   \n",
+              "6                     0                              0.59         False   \n",
+              "12                   20                              0.08         False   \n",
+              "13                   80                              0.41         False   \n",
+              "\n",
+              "    Zone_Eastern  Zone_North Eastern  Zone_Northern  Zone_Southern  ...  \\\n",
+              "3          False               False           True          False  ...   \n",
+              "5          False               False           True          False  ...   \n",
+              "6          False               False           True          False  ...   \n",
+              "12         False               False           True          False  ...   \n",
+              "13         False               False           True          False  ...   \n",
+              "\n",
+              "    Significance_Sports  Significance_Wildlife  Best Time to visit_Afternoon  \\\n",
+              "3                 False                  False                         False   \n",
+              "5                 False                  False                          True   \n",
+              "6                 False                  False                         False   \n",
+              "12                False                  False                         False   \n",
+              "13                False                  False                         False   \n",
+              "\n",
+              "    Best Time to visit_All  Best Time to visit_Evening  \\\n",
+              "3                    False                        True   \n",
+              "5                    False                       False   \n",
+              "6                    False                        True   \n",
+              "12                    True                       False   \n",
+              "13                    True                       False   \n",
+              "\n",
+              "    Best Time to visit_Morning  Airport with 50km Radius_No  \\\n",
+              "3                        False                        False   \n",
+              "5                        False                        False   \n",
+              "6                        False                        False   \n",
+              "12                       False                        False   \n",
+              "13                       False                        False   \n",
+              "\n",
+              "    Airport with 50km Radius_Yes  DSLR Allowed_No  DSLR Allowed_Yes  \n",
+              "3                           True            False              True  \n",
+              "5                           True            False              True  \n",
+              "6                           True            False              True  \n",
+              "12                          True            False              True  \n",
+              "13                          True            False              True  \n",
+              "\n",
+              "[5 rows x 153 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c0cbc2de-018b-4cfc-a740-d43f976de0eb\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>time needed to visit in hrs</th>\n",
+              "      <th>Google review rating</th>\n",
+              "      <th>Entrance Fee in INR</th>\n",
+              "      <th>Number of google review in lakhs</th>\n",
+              "      <th>Zone_Central</th>\n",
+              "      <th>Zone_Eastern</th>\n",
+              "      <th>Zone_North Eastern</th>\n",
+              "      <th>Zone_Northern</th>\n",
+              "      <th>Zone_Southern</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Significance_Sports</th>\n",
+              "      <th>Significance_Wildlife</th>\n",
+              "      <th>Best Time to visit_Afternoon</th>\n",
+              "      <th>Best Time to visit_All</th>\n",
+              "      <th>Best Time to visit_Evening</th>\n",
+              "      <th>Best Time to visit_Morning</th>\n",
+              "      <th>Airport with 50km Radius_No</th>\n",
+              "      <th>Airport with 50km Radius_Yes</th>\n",
+              "      <th>DSLR Allowed_No</th>\n",
+              "      <th>DSLR Allowed_Yes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>5</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>6</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.59</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>12</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.08</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>13</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>80</td>\n",
+              "      <td>0.41</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 153 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0cbc2de-018b-4cfc-a740-d43f976de0eb')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c0cbc2de-018b-4cfc-a740-d43f976de0eb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c0cbc2de-018b-4cfc-a740-d43f976de0eb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-dd0b8aac-1d63-40d7-98c7-b109bc2ec94e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd0b8aac-1d63-40d7-98c7-b109bc2ec94e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-dd0b8aac-1d63-40d7-98c7-b109bc2ec94e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_encoded"
+            }
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# user input for finding prediction\n",
+        "zone = input('Zone: ')\n",
+        "state = input('State: ')\n",
+        "city = input('City: ')\n",
+        "name = input('Name: ')\n",
+        "type_travel = input('Type: ')\n",
+        "establishment_year = input('Establishment Year: ')\n",
+        "time_needed_to_visit_in_hrs = float(input('Time needed to visit (in hrs): '))\n",
+        "entrance_fee = float(input('Entrance Fee (in INR): '))\n",
+        "airport = input('Airport with 50km Radius (Yes or No): ')\n",
+        "weekoff = input('Weekly Off (if not - None): ')\n",
+        "significance = input('Significance: ')\n",
+        "dslr_allowed = input('DSLR Allowed (Yes or No): ')\n",
+        "no_of_google_review_in_lakh = float(input('Number of google review in lakhs (input `1` mean `1 lakh`): '))\n",
+        "best_time_to_visit = input('Best time to visit (ex. Evening, Morning, Afternoon, Night, All): ')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_-60P83Qzh0m",
+        "outputId": "6a26b473-5637-4864-cb15-1220d90e152a"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Zone: Eastern\n",
+            "State: West Bengal\n",
+            "City: kolkata\n",
+            "Name: Victoria\n",
+            "Type: Historical\n",
+            "Establishment Year: 1200\n",
+            "Time needed to visit (in hrs): 5\n",
+            "Entrance Fee (in INR): 0\n",
+            "Airport with 50km Radius (Yes or No): Yes\n",
+            "Weekly Off (if not - None): None\n",
+            "Significance: Palace\n",
+            "DSLR Allowed (Yes or No): Yes\n",
+            "Number of google review in lakhs (input `1` mean `1 lakh`): 2\n",
+            "Best time to visit (ex. Evening, Morning, Afternoon, Night, All): Evening\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example prediction\n",
+        "user_data = {\n",
+        "    'Zone': [zone],\n",
+        "    'State': [state],\n",
+        "    'City': [city],\n",
+        "    'Name': [name],\n",
+        "    'Type': [type_travel],\n",
+        "    'Establishment Year': [establishment_year],\n",
+        "    'time needed to visit in hrs': [time_needed_to_visit_in_hrs],\n",
+        "    'Entrance Fee in INR': [entrance_fee],\n",
+        "    'Airport with 50km Radius': [airport],\n",
+        "    'Weekly Off': [weekoff],\n",
+        "    'Significance': [significance],\n",
+        "    'DSLR Allowed': [dslr_allowed],\n",
+        "    'Number of google review in lakhs': [no_of_google_review_in_lakh],  # Assuming it has 1.1 lakh reviews\n",
+        "    'Best Time to visit': [best_time_to_visit]\n",
+        "}\n",
+        "\n",
+        "user_data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cKrTyAHqzlEO",
+        "outputId": "627707b1-1d8b-4c1f-fe41-dfc8dc319566"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Zone': ['Eastern'],\n",
+              " 'State': ['West Bengal'],\n",
+              " 'City': ['kolkata'],\n",
+              " 'Name': ['Victoria'],\n",
+              " 'Type': ['Historical'],\n",
+              " 'Establishment Year': ['1200'],\n",
+              " 'time needed to visit in hrs': [5.0],\n",
+              " 'Entrance Fee in INR': [0.0],\n",
+              " 'Airport with 50km Radius': ['Yes'],\n",
+              " 'Weekly Off': ['None'],\n",
+              " 'Significance': ['Palace'],\n",
+              " 'DSLR Allowed': ['Yes'],\n",
+              " 'Number of google review in lakhs': [2.0],\n",
+              " 'Best Time to visit': ['Evening']}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example prediction\n",
+        "example_data = {\n",
+        "    'Zone': ['Northern'],\n",
+        "    'State': ['Delhi'],\n",
+        "    'City': ['New Delhi'],\n",
+        "    'Name': ['India Gate'],\n",
+        "    'Type': ['War Memorial'],\n",
+        "    'Establishment Year': ['1931'],\n",
+        "    'time needed to visit in hrs': [2],\n",
+        "    'Entrance Fee in INR': [0],  # Assuming it's free\n",
+        "    'Airport with 50km Radius': ['Yes'],\n",
+        "    'Weekly Off': ['None'],\n",
+        "    'Significance': ['Historical'],\n",
+        "    'DSLR Allowed': ['Yes'],\n",
+        "    'Number of google review in lakhs': [1.1],  # Assuming it has 1.1 lakh reviews\n",
+        "    'Best Time to visit': ['Evening']\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "ME_153Dez7v2"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "user_df = pd.DataFrame(user_data)\n",
+        "user_df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 185
+        },
+        "id": "6KbUxFypz_NQ",
+        "outputId": "79605900-1fb9-4916-fbb3-641c230af79f"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Zone        State     City      Name        Type Establishment Year  \\\n",
+              "0  Eastern  West Bengal  kolkata  Victoria  Historical               1200   \n",
+              "\n",
+              "   time needed to visit in hrs  Entrance Fee in INR Airport with 50km Radius  \\\n",
+              "0                          5.0                  0.0                      Yes   \n",
+              "\n",
+              "  Weekly Off Significance DSLR Allowed  Number of google review in lakhs  \\\n",
+              "0       None       Palace          Yes                               2.0   \n",
+              "\n",
+              "  Best Time to visit  \n",
+              "0            Evening  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-44c834d3-26e1-4842-80b1-7d20929b28fc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Zone</th>\n",
+              "      <th>State</th>\n",
+              "      <th>City</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Establishment Year</th>\n",
+              "      <th>time needed to visit in hrs</th>\n",
+              "      <th>Entrance Fee in INR</th>\n",
+              "      <th>Airport with 50km Radius</th>\n",
+              "      <th>Weekly Off</th>\n",
+              "      <th>Significance</th>\n",
+              "      <th>DSLR Allowed</th>\n",
+              "      <th>Number of google review in lakhs</th>\n",
+              "      <th>Best Time to visit</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Eastern</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>kolkata</td>\n",
+              "      <td>Victoria</td>\n",
+              "      <td>Historical</td>\n",
+              "      <td>1200</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>None</td>\n",
+              "      <td>Palace</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>Evening</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44c834d3-26e1-4842-80b1-7d20929b28fc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
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+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-44c834d3-26e1-4842-80b1-7d20929b28fc button.colab-df-convert');\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "  <div id=\"id_2c354c7d-adc2-441b-b2ce-ab744834dba6\">\n",
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+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('user_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
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+              "        document.querySelector('#id_2c354c7d-adc2-441b-b2ce-ab744834dba6 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('user_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "user_df",
+              "summary": "{\n  \"name\": \"user_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Zone\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Eastern\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"West Bengal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"kolkata\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Victoria\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Historical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Establishment Year\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1200\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time needed to visit in hrs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Entrance Fee in INR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Airport with 50km Radius\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly Off\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"None\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Significance\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Palace\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DSLR Allowed\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of google review in lakhs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Best Time to visit\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Evening\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Splitting the data into train and test sets\n",
+        "X = df_encoded.drop(['Unnamed: 0','Google review rating'], axis=1)\n",
+        "y = df_encoded['Google review rating']\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ],
+      "metadata": {
+        "id": "v5_pLmz40CXw"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 969
+        },
+        "id": "g61hPCmq0Eab",
+        "outputId": "55c3f031-4ae8-402a-b0cd-89d3e782dd04"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     time needed to visit in hrs  Entrance Fee in INR  \\\n",
+              "290                          1.5                   30   \n",
+              "73                           3.0                  350   \n",
+              "3                            2.0                   50   \n",
+              "13                           3.0                   80   \n",
+              "131                          1.5                   25   \n",
+              "19                           3.0                   50   \n",
+              "78                           7.0                  500   \n",
+              "70                           2.0                  200   \n",
+              "263                          2.0                   20   \n",
+              "5                            3.0                    0   \n",
+              "6                            1.0                    0   \n",
+              "302                          3.0                 1100   \n",
+              "12                           3.0                   20   \n",
+              "219                          1.5                   10   \n",
+              "310                          3.0                   50   \n",
+              "223                          1.0                   20   \n",
+              "181                          2.0                   50   \n",
+              "322                          2.0                   10   \n",
+              "194                          2.0                  150   \n",
+              "27                           2.0                  500   \n",
+              "44                           2.0                   50   \n",
+              "109                          1.5                  100   \n",
+              "315                          2.0                 1500   \n",
+              "183                          1.5                   50   \n",
+              "26                           5.0                 1149   \n",
+              "\n",
+              "     Number of google review in lakhs  Zone_Central  Zone_Eastern  \\\n",
+              "290                              0.12         False         False   \n",
+              "73                               0.67         False         False   \n",
+              "3                                0.27         False         False   \n",
+              "13                               0.41         False         False   \n",
+              "131                              0.27         False         False   \n",
+              "19                               0.60         False         False   \n",
+              "78                               0.11         False         False   \n",
+              "70                               0.17         False         False   \n",
+              "263                              0.25         False         False   \n",
+              "5                                0.25         False         False   \n",
+              "6                                0.59         False         False   \n",
+              "302                              0.30         False         False   \n",
+              "12                               0.08         False         False   \n",
+              "219                              0.18         False          True   \n",
+              "310                              0.81         False          True   \n",
+              "223                              0.09         False          True   \n",
+              "181                              2.25          True         False   \n",
+              "322                              0.15          True         False   \n",
+              "194                              0.21          True         False   \n",
+              "27                               0.90         False         False   \n",
+              "44                               0.18         False          True   \n",
+              "109                              0.10         False         False   \n",
+              "315                              7.40          True         False   \n",
+              "183                              0.45          True         False   \n",
+              "26                               0.95         False         False   \n",
+              "\n",
+              "     Zone_North Eastern  Zone_Northern  Zone_Southern  Zone_Western  \\\n",
+              "290               False          False           True         False   \n",
+              "73                False          False          False          True   \n",
+              "3                 False           True          False         False   \n",
+              "13                False           True          False         False   \n",
+              "131               False          False          False          True   \n",
+              "19                False          False          False          True   \n",
+              "78                False          False          False          True   \n",
+              "70                False          False          False          True   \n",
+              "263               False          False           True         False   \n",
+              "5                 False           True          False         False   \n",
+              "6                 False           True          False         False   \n",
+              "302               False           True          False         False   \n",
+              "12                False           True          False         False   \n",
+              "219               False          False          False         False   \n",
+              "310               False          False          False         False   \n",
+              "223               False          False          False         False   \n",
+              "181               False          False          False         False   \n",
+              "322               False          False          False         False   \n",
+              "194               False          False          False         False   \n",
+              "27                False          False           True         False   \n",
+              "44                False          False          False         False   \n",
+              "109               False          False           True         False   \n",
+              "315               False          False          False         False   \n",
+              "183               False          False          False         False   \n",
+              "26                False          False          False          True   \n",
+              "\n",
+              "     State_Andaman and Nicobar Islands  ...  Significance_Sports  \\\n",
+              "290                               True  ...                False   \n",
+              "73                               False  ...                False   \n",
+              "3                                False  ...                False   \n",
+              "13                               False  ...                False   \n",
+              "131                              False  ...                False   \n",
+              "19                               False  ...                False   \n",
+              "78                               False  ...                False   \n",
+              "70                               False  ...                False   \n",
+              "263                              False  ...                False   \n",
+              "5                                False  ...                False   \n",
+              "6                                False  ...                False   \n",
+              "302                              False  ...                False   \n",
+              "12                               False  ...                False   \n",
+              "219                              False  ...                False   \n",
+              "310                              False  ...                False   \n",
+              "223                              False  ...                False   \n",
+              "181                              False  ...                False   \n",
+              "322                              False  ...                False   \n",
+              "194                              False  ...                False   \n",
+              "27                               False  ...                False   \n",
+              "44                               False  ...                False   \n",
+              "109                              False  ...                False   \n",
+              "315                              False  ...                 True   \n",
+              "183                              False  ...                False   \n",
+              "26                               False  ...                False   \n",
+              "\n",
+              "     Significance_Wildlife  Best Time to visit_Afternoon  \\\n",
+              "290                  False                          True   \n",
+              "73                   False                         False   \n",
+              "3                    False                         False   \n",
+              "13                   False                         False   \n",
+              "131                  False                          True   \n",
+              "19                    True                         False   \n",
+              "78                   False                         False   \n",
+              "70                   False                          True   \n",
+              "263                   True                          True   \n",
+              "5                    False                          True   \n",
+              "6                    False                         False   \n",
+              "302                  False                          True   \n",
+              "12                   False                         False   \n",
+              "219                  False                         False   \n",
+              "310                   True                          True   \n",
+              "223                  False                         False   \n",
+              "181                  False                         False   \n",
+              "322                  False                         False   \n",
+              "194                   True                         False   \n",
+              "27                   False                         False   \n",
+              "44                   False                         False   \n",
+              "109                  False                         False   \n",
+              "315                  False                         False   \n",
+              "183                  False                         False   \n",
+              "26                   False                         False   \n",
+              "\n",
+              "     Best Time to visit_All  Best Time to visit_Evening  \\\n",
+              "290                   False                       False   \n",
+              "73                     True                       False   \n",
+              "3                     False                        True   \n",
+              "13                     True                       False   \n",
+              "131                   False                       False   \n",
+              "19                     True                       False   \n",
+              "78                     True                       False   \n",
+              "70                    False                       False   \n",
+              "263                   False                       False   \n",
+              "5                     False                       False   \n",
+              "6                     False                        True   \n",
+              "302                   False                       False   \n",
+              "12                     True                       False   \n",
+              "219                   False                       False   \n",
+              "310                   False                       False   \n",
+              "223                    True                       False   \n",
+              "181                   False                       False   \n",
+              "322                    True                       False   \n",
+              "194                    True                       False   \n",
+              "27                    False                       False   \n",
+              "44                    False                       False   \n",
+              "109                    True                       False   \n",
+              "315                    True                       False   \n",
+              "183                    True                       False   \n",
+              "26                     True                       False   \n",
+              "\n",
+              "     Best Time to visit_Morning  Airport with 50km Radius_No  \\\n",
+              "290                       False                        False   \n",
+              "73                        False                         True   \n",
+              "3                         False                        False   \n",
+              "13                        False                        False   \n",
+              "131                       False                        False   \n",
+              "19                        False                        False   \n",
+              "78                        False                        False   \n",
+              "70                        False                        False   \n",
+              "263                       False                        False   \n",
+              "5                         False                        False   \n",
+              "6                         False                        False   \n",
+              "302                       False                        False   \n",
+              "12                        False                        False   \n",
+              "219                        True                        False   \n",
+              "310                       False                        False   \n",
+              "223                       False                        False   \n",
+              "181                        True                        False   \n",
+              "322                       False                        False   \n",
+              "194                       False                        False   \n",
+              "27                         True                        False   \n",
+              "44                         True                        False   \n",
+              "109                       False                        False   \n",
+              "315                       False                        False   \n",
+              "183                       False                        False   \n",
+              "26                        False                         True   \n",
+              "\n",
+              "     Airport with 50km Radius_Yes  DSLR Allowed_No  DSLR Allowed_Yes  \n",
+              "290                          True            False              True  \n",
+              "73                          False            False              True  \n",
+              "3                            True            False              True  \n",
+              "13                           True            False              True  \n",
+              "131                          True            False              True  \n",
+              "19                           True            False              True  \n",
+              "78                           True            False              True  \n",
+              "70                           True            False              True  \n",
+              "263                          True            False              True  \n",
+              "5                            True            False              True  \n",
+              "6                            True            False              True  \n",
+              "302                          True            False              True  \n",
+              "12                           True            False              True  \n",
+              "219                          True            False              True  \n",
+              "310                          True            False              True  \n",
+              "223                          True            False              True  \n",
+              "181                          True            False              True  \n",
+              "322                          True            False              True  \n",
+              "194                          True            False              True  \n",
+              "27                           True            False              True  \n",
+              "44                           True            False              True  \n",
+              "109                          True            False              True  \n",
+              "315                          True            False              True  \n",
+              "183                          True             True             False  \n",
+              "26                          False            False              True  \n",
+              "\n",
+              "[25 rows x 151 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9f9da79d-197a-4baa-a8c9-b3695ae10354\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>time needed to visit in hrs</th>\n",
+              "      <th>Entrance Fee in INR</th>\n",
+              "      <th>Number of google review in lakhs</th>\n",
+              "      <th>Zone_Central</th>\n",
+              "      <th>Zone_Eastern</th>\n",
+              "      <th>Zone_North Eastern</th>\n",
+              "      <th>Zone_Northern</th>\n",
+              "      <th>Zone_Southern</th>\n",
+              "      <th>Zone_Western</th>\n",
+              "      <th>State_Andaman and Nicobar Islands</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Significance_Sports</th>\n",
+              "      <th>Significance_Wildlife</th>\n",
+              "      <th>Best Time to visit_Afternoon</th>\n",
+              "      <th>Best Time to visit_All</th>\n",
+              "      <th>Best Time to visit_Evening</th>\n",
+              "      <th>Best Time to visit_Morning</th>\n",
+              "      <th>Airport with 50km Radius_No</th>\n",
+              "      <th>Airport with 50km Radius_Yes</th>\n",
+              "      <th>DSLR Allowed_No</th>\n",
+              "      <th>DSLR Allowed_Yes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0.12</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>73</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>350</td>\n",
+              "      <td>0.67</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>80</td>\n",
+              "      <td>0.41</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>131</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>25</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.60</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>78</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>500</td>\n",
+              "      <td>0.11</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>70</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>200</td>\n",
+              "      <td>0.17</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>263</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.25</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.59</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>302</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1100</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.08</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>219</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.18</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.81</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>223</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.09</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>181</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>2.25</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>322</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.15</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>194</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>0.21</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>500</td>\n",
+              "      <td>0.90</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.18</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>109</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>100</td>\n",
+              "      <td>0.10</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>315</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1500</td>\n",
+              "      <td>7.40</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>183</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.45</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1149</td>\n",
+              "      <td>0.95</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>25 rows × 151 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
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+              "        document.querySelector('#df-9f9da79d-197a-4baa-a8c9-b3695ae10354 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9f9da79d-197a-4baa-a8c9-b3695ae10354');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "  .colab-df-quickchart {\n",
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+              "      --disabled-bg-color: #DDD;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "  .colab-df-quickchart {\n",
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+              "    height: 32px;\n",
+              "    padding: 0;\n",
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+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "\n",
+              "  @keyframes spin {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "    }\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "    90% {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "    (() => {\n",
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+              "        document.querySelector('#df-e242d62e-3d8a-46af-9e83-7221d982bef7 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "  </script>\n",
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+              "\n",
+              "  <div id=\"id_ffffe5ad-8f35-4fba-98d0-33dd1f6f9698\">\n",
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+              "      .colab-df-generate {\n",
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+              "        border: none;\n",
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+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_ffffe5ad-8f35-4fba-98d0-33dd1f6f9698 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('X_train');\n",
+              "      }\n",
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+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "X_train"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 405
+        },
+        "id": "7G7iyGwg0FJZ",
+        "outputId": "e08b33cb-864d-4fdc-f806-c5886edf5a34"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     time needed to visit in hrs  Entrance Fee in INR  \\\n",
+              "318                          2.0                   50   \n",
+              "124                          3.0                   30   \n",
+              "282                          1.5                   10   \n",
+              "135                          3.0                   30   \n",
+              "32                           1.0                   25   \n",
+              "42                           1.5                   30   \n",
+              "320                          3.0                    0   \n",
+              "\n",
+              "     Number of google review in lakhs  Zone_Central  Zone_Eastern  \\\n",
+              "318                             0.240         False         False   \n",
+              "124                             0.210         False         False   \n",
+              "282                             0.035         False         False   \n",
+              "135                             0.490         False         False   \n",
+              "32                              2.100         False         False   \n",
+              "42                              0.730         False          True   \n",
+              "320                             0.180         False         False   \n",
+              "\n",
+              "     Zone_North Eastern  Zone_Northern  Zone_Southern  Zone_Western  \\\n",
+              "318               False           True          False         False   \n",
+              "124               False          False          False          True   \n",
+              "282                True          False          False         False   \n",
+              "135               False          False          False          True   \n",
+              "32                False          False           True         False   \n",
+              "42                False          False          False         False   \n",
+              "320               False          False          False          True   \n",
+              "\n",
+              "     State_Andaman and Nicobar Islands  ...  Significance_Sports  \\\n",
+              "318                              False  ...                False   \n",
+              "124                              False  ...                False   \n",
+              "282                              False  ...                False   \n",
+              "135                              False  ...                False   \n",
+              "32                               False  ...                False   \n",
+              "42                               False  ...                False   \n",
+              "320                              False  ...                False   \n",
+              "\n",
+              "     Significance_Wildlife  Best Time to visit_Afternoon  \\\n",
+              "318                  False                         False   \n",
+              "124                  False                          True   \n",
+              "282                  False                         False   \n",
+              "135                  False                          True   \n",
+              "32                   False                         False   \n",
+              "42                   False                         False   \n",
+              "320                  False                         False   \n",
+              "\n",
+              "     Best Time to visit_All  Best Time to visit_Evening  \\\n",
+              "318                   False                       False   \n",
+              "124                   False                       False   \n",
+              "282                    True                       False   \n",
+              "135                   False                       False   \n",
+              "32                    False                       False   \n",
+              "42                    False                       False   \n",
+              "320                    True                       False   \n",
+              "\n",
+              "     Best Time to visit_Morning  Airport with 50km Radius_No  \\\n",
+              "318                        True                        False   \n",
+              "124                       False                        False   \n",
+              "282                       False                        False   \n",
+              "135                       False                        False   \n",
+              "32                         True                        False   \n",
+              "42                         True                        False   \n",
+              "320                       False                        False   \n",
+              "\n",
+              "     Airport with 50km Radius_Yes  DSLR Allowed_No  DSLR Allowed_Yes  \n",
+              "318                          True            False              True  \n",
+              "124                          True            False              True  \n",
+              "282                          True            False              True  \n",
+              "135                          True            False              True  \n",
+              "32                           True            False              True  \n",
+              "42                           True            False              True  \n",
+              "320                          True             True             False  \n",
+              "\n",
+              "[7 rows x 151 columns]"
+            ],
+            "text/html": [
+              "\n",
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>time needed to visit in hrs</th>\n",
+              "      <th>Entrance Fee in INR</th>\n",
+              "      <th>Number of google review in lakhs</th>\n",
+              "      <th>Zone_Central</th>\n",
+              "      <th>Zone_Eastern</th>\n",
+              "      <th>Zone_North Eastern</th>\n",
+              "      <th>Zone_Northern</th>\n",
+              "      <th>Zone_Southern</th>\n",
+              "      <th>Zone_Western</th>\n",
+              "      <th>State_Andaman and Nicobar Islands</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Significance_Sports</th>\n",
+              "      <th>Significance_Wildlife</th>\n",
+              "      <th>Best Time to visit_Afternoon</th>\n",
+              "      <th>Best Time to visit_All</th>\n",
+              "      <th>Best Time to visit_Evening</th>\n",
+              "      <th>Best Time to visit_Morning</th>\n",
+              "      <th>Airport with 50km Radius_No</th>\n",
+              "      <th>Airport with 50km Radius_Yes</th>\n",
+              "      <th>DSLR Allowed_No</th>\n",
+              "      <th>DSLR Allowed_Yes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>318</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0.240</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>124</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>282</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.035</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>135</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0.490</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>25</td>\n",
+              "      <td>2.100</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0.730</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>320</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.180</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>...</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>7 rows × 151 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-844bf4cd-2ac1-4d73-8a81-5fbf82d9512a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-844bf4cd-2ac1-4d73-8a81-5fbf82d9512a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-844bf4cd-2ac1-4d73-8a81-5fbf82d9512a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-416ae043-88cb-4353-a11c-3f77aec4d2bf\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-416ae043-88cb-4353-a11c-3f77aec4d2bf')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-416ae043-88cb-4353-a11c-3f77aec4d2bf button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_0162d584-37c4-45cb-8240-f62fbb8f97e0\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_test')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0162d584-37c4-45cb-8240-f62fbb8f97e0 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('X_test');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "X_test"
+            }
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 868
+        },
+        "id": "IgPCRanw0HjY",
+        "outputId": "fa4a7b2f-e2cc-43f2-850b-18a0c4d99f8d"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "290    4.7\n",
+              "73     4.6\n",
+              "3      4.1\n",
+              "13     4.1\n",
+              "131    4.4\n",
+              "19     4.3\n",
+              "78     4.4\n",
+              "70     4.4\n",
+              "263    4.1\n",
+              "5      4.2\n",
+              "6      4.5\n",
+              "302    4.4\n",
+              "12     4.5\n",
+              "219    4.5\n",
+              "310    4.4\n",
+              "223    4.5\n",
+              "181    4.6\n",
+              "322    4.7\n",
+              "194    4.2\n",
+              "27     4.2\n",
+              "44     4.6\n",
+              "109    4.4\n",
+              "315    4.6\n",
+              "183    4.4\n",
+              "26     1.4\n",
+              "Name: Google review rating, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Google review rating</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>73</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>131</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>4.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>78</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>70</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>263</th>\n",
+              "      <td>4.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>302</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>4.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>219</th>\n",
+              "      <td>4.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>223</th>\n",
+              "      <td>4.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>181</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>322</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>194</th>\n",
+              "      <td>4.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>4.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>109</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>315</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>183</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "_vzW6WZu0IfN",
+        "outputId": "44759cc2-88ac-4909-9615-ce2e7ed63a3e"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "318    4.4\n",
+              "124    4.6\n",
+              "282    4.5\n",
+              "135    4.7\n",
+              "32     4.5\n",
+              "42     4.6\n",
+              "320    4.6\n",
+              "Name: Google review rating, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Google review rating</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>318</th>\n",
+              "      <td>4.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>124</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>282</th>\n",
+              "      <td>4.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>135</th>\n",
+              "      <td>4.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>4.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>320</th>\n",
+              "      <td>4.6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Machine Learning Regression Model - Linear Regression\n",
+        "model = LinearRegression()\n",
+        "model.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "v8qY08j40LiI",
+        "outputId": "a24356c3-ea17-4911-b0c8-77136db595f0"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-2 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-2 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-2 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predicting on test set\n",
+        "y_pred = model.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "LI5XTJGc0Pi1"
+      },
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AOkUesiX0TeP",
+        "outputId": "61e9447c-3fd0-4d36-eb8f-7a3713d8534e"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Iv_Sv3ym0VtV",
+        "outputId": "09a41004-aab3-486d-c6d3-24b0b9b4d719"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4.52241218, 4.40734644, 4.5739958 , 4.62103419, 4.86677035,\n",
+              "       4.75152727, 4.52225633])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Model evaluation\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "print(\"Mean Squared Error:\", mse)\n",
+        "print(\"Mean Squared Error %:\", mse*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gaUY83ZI0XTm",
+        "outputId": "2ad4fdcd-7d6e-4963-e9f9-bd125e7a7387"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Squared Error: 0.03247659875482853\n",
+            "Mean Squared Error %: 3.247659875482853\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "user_df_encoded = pd.get_dummies(user_df, columns=['Zone', 'State', 'City', 'Name', 'Type',\n",
+        "                                                         'Establishment Year', 'Weekly Off',\n",
+        "                                                         'Significance', 'Best Time to visit',\n",
+        "                                                         'Airport with 50km Radius', 'DSLR Allowed'])"
+      ],
+      "metadata": {
+        "id": "SbMRVyX40Z4r"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Ensure that the columns in the example data match the columns used during model training\n",
+        "# If there are missing columns, add them and fill with zeros\n",
+        "missing_cols = list(set(X_train.columns) - set(user_df_encoded.columns))\n",
+        "if missing_cols:\n",
+        "    user_df_encoded = pd.concat([user_df_encoded, pd.DataFrame(0, index=user_df_encoded.index, columns=missing_cols)], axis=1)\n",
+        ""
+      ],
+      "metadata": {
+        "id": "UvASK2Hi0cQd"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Ensure the order of columns is the same as in the training data\n",
+        "user_df_encoded = user_df_encoded.reindex(columns=X_train.columns, fill_value=0)"
+      ],
+      "metadata": {
+        "id": "twX4lIuX0eZ1"
+      },
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict the Google Review Rating\n",
+        "predicted_rating = model.predict(user_df_encoded)\n",
+        "print(\"Predicted Google Review Rating:\", predicted_rating[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qtntbImN0gk2",
+        "outputId": "845fe306-1a85-419c-fa9e-44238cad7e18"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Google Review Rating: 4.555226686231576\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "svr_model = SVR(kernel='linear')\n",
+        "svr_model.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "cdhYKClk0ikh",
+        "outputId": "8e75c343-6561-4622-bdfe-a4b2463b9df0"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVR(kernel='linear')"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-4 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-4 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-4 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-4 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_svr = svr_model.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "J3qnW52k0mPl"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(y_pred_svr))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NX82llmq0q7p",
+        "outputId": "edf70d8f-233b-4987-ee19-b440c85315f3"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_svr"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b4Y8q41V0rfD",
+        "outputId": "e9de1a0f-8193-46ec-a4bd-1e05679e94e0"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4.49027335, 4.33034819, 4.58030777, 4.58177   , 4.83906346,\n",
+              "       4.70140935, 4.53381704])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Model evaluation for SVR\n",
+        "mse_svr = mean_squared_error(y_test, y_pred_svr)\n",
+        "print(\"SVR Mean Squared Error:\", mse_svr)\n",
+        "print(\"SVR Mean Squared Error %:\", mse_svr*100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "K2rfNHAx0uqG",
+        "outputId": "5ca45414-dde8-42c5-d657-27a0812bd8dd"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "SVR Mean Squared Error: 0.03298816029965159\n",
+            "SVR Mean Squared Error %: 3.298816029965159\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predicted_rating_svr = svr_model.predict(user_df_encoded)\n",
+        "print(\"Predicted Google Review Rating using SVR:\", predicted_rating_svr[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bVKyP7q-0xLN",
+        "outputId": "cf2e67a6-1f65-42b1-df3a-3320fa1cf1ff"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Google Review Rating using SVR: 4.55734453953711\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Machine Learning Regression Model - Random Forest Regression\n",
+        "rf_model = RandomForestRegressor(random_state=42)\n",
+        "rf_model.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "9c6ZW9rd0znW",
+        "outputId": "d42a8ace-fafd-404c-f881-15bcb7afe666"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestRegressor(random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-5 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-5 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-5 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-5 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predicting on the test set\n",
+        "y_pred_rf = rf_model.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "hKiUMy_w020z"
+      },
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(y_pred_rf))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JDfShxwa04-S",
+        "outputId": "c9ce375c-1449-41d1-c82f-4aa72337cef0"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "7\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_rf"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "egPGGUiI07V9",
+        "outputId": "e1d135e4-665d-4861-a192-3ecc4cc978fa"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4.405, 4.38 , 4.488, 4.397, 4.444, 4.497, 4.454])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Model evaluation for Random Forest Regression\n",
+        "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
+        "print(\"Random Forest Mean Squared Error:\", mse_rf)\n",
+        "print(\"Random Forest Mean Squared Error %:\", mse_rf*100)\n",
+        ""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Sg2WfPUO09so",
+        "outputId": "b14a8efd-2e39-4154-a6de-481727da1549"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Forest Mean Squared Error: 0.025062714285715153\n",
+            "Random Forest Mean Squared Error %: 2.506271428571515\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict the Google Review Rating for the example data using Random Forest Regression\n",
+        "predicted_rating_rf = rf_model.predict(user_df_encoded)\n",
+        "print(\"Predicted Google Review Rating using Random Forest:\", predicted_rating_rf[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jZYo8Rkb0_qc",
+        "outputId": "0a0a6f00-68c8-4cfb-9567-0e798a520c30"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Google Review Rating using Random Forest: 4.230999999999997\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define model names and their corresponding mean squared errors\n",
+        "models = ['Linear Regression', 'SVR', 'Random Forest']\n",
+        "mse_values = [mse, mse_svr, mse_rf]  # Assuming these variables are defined from your previous code\n",
+        "\n",
+        "# Create a bar plot\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.bar(models, mse_values, color='skyblue')\n",
+        "plt.title('Mean Squared Error for Linear Regression, SVR, and Random Forest')\n",
+        "plt.xlabel('Regression Models')\n",
+        "plt.ylabel('Mean Squared Error')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "qTmD7EmA1B0m",
+        "outputId": "312161ac-1176-4efd-adc1-7ea258a6884a"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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xf//3f+ZpDRs2NLy9vS3Ogbi4OKNmzZpGsWLFzNMePHhgPm5Pbsfe3t4YP368edqLHFNJRmBgoHH9+nXj2rVrxu7du43GjRsbkoxPPvnE3O7HH380bGxsLH6vhmEYM2fONCQZ27ZtMwzDMPbs2WNIMgYMGGDRrmvXrgnOmaSeX2fPnjVsbW2Njz76yGL6wYMHjUyZMpmn792715BkLF68OMn9++yzzwxJxvXr15Nsk9h5XqFCBSNXrlzGzZs3zdP2799v2NjYGJ07d06wD927d7dYZ8uWLY3s2bMnuc1nOXTokOHo6GhIMipUqGD079/fWLZsmREVFZWg7a+//mpIMr7++muL6TVq1DDy5s1r8byJf+1Zt26dcf36dePChQvGkiVLjJw5cxr29vbGhQsXXqjemJgYIzo62mLa33//bXh4eFgcl5ScG6nxGleyZEnj+vXrxvXr141jx44Z77//viEpwTmd2Lny888/G5KMLVu2mKcl93e9b98+Q5LRu3dvi3YdOnRIcA74+/sbdnZ2xqlTp8zTLl++bGTNmtWoW7eueVr8787Pz8+Ii4szT/fx8TFMJpPx7rvvmqfFxMQY+fLlM+rVq/eco5T0e0GXLl0s9uWdd96xWG7w4MGGJGPDhg3maUm9J0yYMMFwcnIy/vrrL4vpw4YNM2xtbY3z588bhmEY/fv3N1xcXCzeF5+2ePFiQ5KxcePG5+7by4AuE6+wtm3b6v79+1q5cqXu3LmjlStXJtldYvHixXJ1ddXrr7+uGzdumB+VK1eWs7OzxcdlT/YhunPnjm7cuKE6dero3r17Ce5kdXZ2tujPZGdnp2rVqun06dPPrN1kMmnNmjX68MMPlS1bNv3888/q06ePChQooHbt2pn7EMfGxmrNmjXy9/dX/vz5zcuXKlXqH/WXfHIfHz16pJs3b6po0aJyc3NTWFhYgvY9evSw6MO2du1a3b59WwEBARbH09bWVtWrVzcfzytXrmjfvn3q0qWLxQ1wr7/+eor61xYsWFBr165N8EhsKLN333030XUUKlQowTFbtWqVqlWrZvHRt7Ozs3r27KmzZ88m6KLQpUuXf9THbNWqVfL09FRAQIB5WubMmRUUFKS7d+9q8+bNFu1btWqlnDlzvvD2kqtdu3YWV9ziP5aOfx7funVLGzZsUNu2bc3nxI0bN3Tz5k35+fnpxIkT5jv/7e3tzX2AY2NjdfPmTfNH/4k9t1J6TL/77jvlzJlTuXLlUpUqVbR+/XoNGTJEwcHB5jaLFy9WqVKlVLJkSYvnZ/wVzPjn5+rVqyU9/gTkSf369Uty+08/v5YuXaq4uDi1bdvWYluenp4qVqyYeVvxz/81a9Yk2T0h/krg8uXLk33zY/w51rVrV4urruXKldPrr7+uVatWPXcf6tSpo5s3b6a4u4oklSlTRvv27VOnTp109uxZTZs2Tf7+/vLw8NA333xj0Tb+k64nu02cOXNGf/75pwICAhLtO+7r66ucOXPKy8tLrVu3lpOTk1asWPHCV19tbW3Nn0LExcXp1q1biomJUZUqVRJ9fj7v3Eit17hjx44pZ86cypkzp0qWLKlPPvlELVq0SNA15slz5cGDB7px44Zq1KghSYnW/7zfdfzzIygoyKLd06+tsbGx+v333+Xv76/ChQubp+fOnVsdOnTQ1q1bEzx/AgMDLa5YV69eXYZhKDAw0DzN1tZWVapUee57ZrzE3guGDBlisS9PvhZIMn9K93RXuMTeExYvXqw6deooW7ZsFuezr6+vYmNjtWXLFkmPz9WoqCiLbpuvOrpMvMJy5swpX19fzZ8/X/fu3VNsbKxat26daNsTJ04oIiLCok/bk57so3X48GGNHDlSGzZsSHCCP9nXT3p8x+vTH1Fly5ZNBw4ceG799vb2+uCDD/TBBx/oypUr2rx5s6ZNm6ZFixYpc+bM+umnn3T9+nXdv39fxYoVS7B8iRIlEn2zS4779+8rJCREs2fP1qVLlyy6Fjy9j9LjF44nnThxQpKS/IjUxcVF0uO+ypKSrD+xF/DEODk5ydfXN1ltn671WdPPnTuX4GM+6X8fa507d05ly5Z97rqT69y5cypWrFiCN/4nt/e8mtPCk39sSf/7ODq+f/3JkydlGIZGjRqlUaNGJbqOa9euKW/evIqLi9O0adP05Zdf6syZMxZ975/uGy+lfB/ffPNN9e3bVw8fPtSuXbv08ccf6969exbH9MSJEzp69GiSf0zEn+/nzp2TjY1NghqKFi2a5PYTOxcMw0j0OS7970bGQoUKKTg4WJ9++qnmzZunOnXqqEWLFurUqZM5SLVr107ffvut3nnnHQ0bNkwNGzbUW2+9pdatWyd5o2H8c6ZEiRIJ5pUqVUpr1qxJcPPfs37f8eduShQvXlw//vijYmNjdeTIEa1cuVKTJ09Wz549VahQIfO5mylTJrVr105ffvmlLl26pLx585rDcVLdJWbMmKHixYsrIiJC33//vbZs2fKPRxGaO3eupkyZomPHjunRo0fm6Yk9F593bqTWa1zBggXNo8CcOnVKH330ka5fv57gBspbt25p3LhxWrBggcX7lpT4a/fzftfx58CTXY7ia3/S9evXde/evSSfZ3Fxcbpw4YLKlCmT5Lbjn+deXl4Jpj99L09SnvVeEL8vT5+/np6ecnNzS9br64kTJ3TgwIHnvnb07t1bixYtUpMmTZQ3b141atRIbdu2VePGjZO1Hy8jAvErrkOHDurRo4fCw8PVpEkTi75WT4qLi1OuXLk0b968ROfHP/lv376tevXqycXFRePHjzePqxkWFqahQ4cmuGqT1J2/RgpvEMidO7fat2+vVq1aqUyZMlq0aFGKB95/OpjHe/pmQOnxFbDZs2drwIAB8vHxkaurq0wmk9q3b5/olamnr+DFt/nxxx/l6emZoP2z+kSntaSuNqbG3cPpfQdyem3vec/j+N/34MGDk/xkIv5N6OOPP9aoUaPUvXt3TZgwQe7u7rKxsdGAAQOS9dx6nnz58pnfEJs2baocOXKob9++atCggXkM27i4OHl7e+vTTz9NdB1PvyGnRGLngslk0m+//ZbocYzv2yhJU6ZMUdeuXbV8+XL9/vvvCgoKUkhIiP7880/ly5dPjo6O2rJlizZu3Khff/1Vq1ev1sKFC/Xaa6/p999/T7WRBlLrdSux9Xp7e8vb21s+Pj5q0KCB5s2bZxFgOnXqpOnTp+vnn3/W4MGD9fPPP6t06dIJ+u7Gq1atmnmUCX9/f9WuXVsdOnTQ8ePHLY5tcv3000/q2rWr/P399f777ytXrlyytbVVSEhIojfqpdWxetrTQa9WrVqqVKmSRowYYXFPTNu2bbV9+3a9//77qlChgpydnRUXF6fGjRsnen6lV/2JSWrbiU1PzXqSei98WmKvPXFxcXr99dfNV52fVrx4cUmPbxjdt2+f1qxZo99++02//fabZs+erc6dOye4SfpVQSB+xbVs2VK9evXSn3/+qYULFybZrkiRIlq3bp1q1ar1zDfgTZs26ebNm1q6dKnq1q1rnn7mzJlUrTspmTNnVrly5XTixAnduHFDOXPmlKOjo/mK7JOOHz9u8XP8X/5PD9n29F/FkrRkyRJ16dJFU6ZMMU978OBBsod7i7+akCtXrmdeuY2/eTA59WeEAgUKJFpHfNeYxO6c/6fbO3DggOLi4iyu+KXV9lJL/EekmTNnfu6V+iVLlqhBgwb67rvvLKbfvn1bOXLkSPXaevXqpc8++0wjR45Uy5YtZTKZVKRIEe3fv18NGzZ85ptjgQIFFBcXpzNnzlhc4Tt58mSyt1+kSBEZhqFChQqZ3yyfJT4wjhw5Utu3b1etWrU0c+ZMffjhh5IkGxsbNWzYUA0bNtSnn36qjz/+WB988IE2btyY6LGPf84k9TzOkSNHug8NJ8kcYq9cuWIxvXr16ipSpIjmz5+v119/XYcPH072OLvxwbVBgwaaPn26hg0bluK6lixZosKFC2vp0qUWz40XHS88rV7jypUrp06dOunrr7/W4MGDlT9/fv39999av369xo0bp9GjR5vbJrbt5Io/B06dOmVx9ffp2nPmzKksWbIk+TyzsbH5R39opob4fTlx4oTFzWtXr17V7du3k/X6WqRIEd29ezdZn0ja2dmpefPmat68ueLi4tS7d299/fXXGjVqlIoWLZrsYP6yoA/xK87Z2VlfffWVxo4dq+bNmyfZrm3btoqNjdWECRMSzIuJiTEHwfi/XJ/8a/Xhw4f68ssvU7XuEydO6Pz58wmm3759W6GhocqWLZty5swpW1tb+fn5admyZRbtjx49qjVr1lgs6+Liohw5cpj7OMVLrHZbW9sEf5F/8cUXiV5NToyfn59cXFz08ccfW3zkGO/69euSHl/5rlChgubOnZtgaKl/OoRYamjatKl27typ0NBQ87SoqCjNmjVLBQsWTNVxhOO3Fx4ebvHHW0xMjL744gs5OzurXr16qbq91JIrVy7Vr19fX3/9dYKAI/3v9y0l/txavHhxmn27WKZMmTRo0CAdPXpUy5cvl/T4fL906VKCPqzS4+5C8SOlxF/tfvoc+eKLL5K9/bfeeku2trYaN25cgv02DMM8HFZkZKRiYmIs5nt7e8vGxsY8JNStW7cSrD/+yunTw0bFe/Ice/IP2kOHDun3339X06ZNk70vL+KPP/5I9DUgvjtXYh+xd+zYUXv37tWYMWNkMpmSvPcjMfXr11e1atU0depUPXjwIMX1JvYav2PHDovXgJRIy9e4IUOG6NGjR+ZPOhKrXZJ55IMXET8a0tMjMz29TltbWzVq1EjLly+3GJ7z6tWr5i/JepHuNqkp/rn+dO3xx+9Zo8fEa9u2rUJDQxO8v0qP35/jz+Gnh7mzsbFRuXLlJP3vXI3/Q/Rl/l6BJ3GF+F+gS5cuz21Tr1499erVSyEhIdq3b58aNWqkzJkz68SJE1q8eLGmTZum1q1bq2bNmsqWLZu6dOmioKAgmUwm/fjjj6n+8dL+/fvVoUMHNWnSRHXq1JG7u7suXbqkuXPn6vLly5o6dar5xW/cuHFavXq16tSpo969e5sDVJkyZRL0VX7nnXc0ceJEvfPOO6pSpYq2bNmiv/76K8H233jjDf34449ydXVV6dKlFRoaqnXr1iXaxzMxLi4u+uqrr/T222+rUqVKat++vXLmzKnz58/r119/Va1atTR9+nRJUkhIiJo1a6batWure/fuunXrlrn+u3fvJmt7ERER+umnnxKd908GaR82bJh56L6goCC5u7tr7ty5OnPmjH755ZdnfkHEi+jZs6e+/vprde3aVXv27FHBggW1ZMkSbdu2TVOnTlXWrFn/0fpPnjxpvtL4pIoVKybrzeBZZsyYodq1a8vb21s9evRQ4cKFdfXqVYWGhurixYvmcYbfeOMNjR8/Xt26dVPNmjV18OBBzZs3z+JGnNTWtWtXjR49WpMmTZK/v7/efvttLVq0SO+++642btyoWrVqKTY2VseOHdOiRYvMY49WrlxZrVq10tSpU3Xz5k3zsGvx50xyrvAUKVJEH374oYYPH66zZ8/K399fWbNm1ZkzZ/Tf//5XPXv21ODBg7Vhwwb17dtXbdq0UfHixRUTE6Mff/xRtra2atWqlaTH38q2ZcsWNWvWTAUKFNC1a9f05ZdfKl++fAm+lfNJn3zyiZo0aSIfHx8FBgaah11zdXW1GEs5JcaOHatx48Zp48aNql+/fpLtJk2apD179uitt94yB4KwsDD98MMPcnd3T/TG106dOmn8+PFavny5atWqleJv8Xr//ffVpk0bzZkzx3zTWPyQmrNnz1bXrl2TXPaNN97Q0qVL1bJlSzVr1kxnzpzRzJkzVbp06WS/Hj0tNV7jElO6dGk1bdpU3377rUaNGqXs2bOrbt26mjx5sh49eqS8efPq999//0efYFaoUEEBAQH68ssvFRERoZo1a2r9+vWJfkry4YcfmsfJ7t27tzJlyqSvv/5a0dHRyRp/P62VL19eXbp00axZs8zdH3fu3Km5c+fK399fDRo0eO463n//fa1YsUJvvPGGunbtqsqVKysqKkoHDx7UkiVLdPbsWeXIkUPvvPOObt26pddee0358uXTuXPn9MUXX6hChQrmq9MVKlSQra2tJk2apIiICNnb2+u1115L8l6mDJfOo1rgH3py2LVnSWoIqlmzZhmVK1c2HB0djaxZsxre3t7GkCFDjMuXL5vbbNu2zahRo4bh6Oho5MmTxxgyZIixZs2aBMOn1KtXzyhTpkyCbTw9tFVirl69akycONGoV6+ekTt3biNTpkxGtmzZjNdee81YsmRJgvabN282KleubNjZ2RmFCxc2Zs6caR5W50n37t0zAgMDDVdXVyNr1qxG27ZtjWvXriUYPufvv/82unXrZuTIkcNwdnY2/Pz8jGPHjhkFChQwD2FjGM8/3hs3bjT8/PwMV1dXw8HBwShSpIjRtWtXY/fu3RbtfvnlF6NUqVKGvb29Ubp0aWPp0qXJOk6G8exh157c//jjkdiQVc8akuzUqVNG69atDTc3N8PBwcGoVq2asXLlygT7qecMmZXcbV69etV87O3s7Axvb+8EQ+XFD/n05FBiydleUscoMDDQMIykh11LbDtPP2cM4/Gx6ty5s+Hp6WlkzpzZyJs3r/HGG29YPGcfPHhgDBo0yMidO7fh6Oho1KpVywgNDTXq1atnMbTSixxTSUafPn0SnTd27FiLc/Thw4fGpEmTjDJlyhj29vZGtmzZjMqVKxvjxo0zIiIizMtFRUUZffr0Mdzd3Q1nZ2fD39/fOH78uCHJmDhxornds55fhvH4OV67dm3DycnJcHJyMkqWLGn06dPHOH78uGEYhnH69Gmje/fuRpEiRQwHBwfD3d3daNCggbFu3TrzOtavX2+8+eabRp48eQw7OzsjT548RkBAgMUQUEkNr7hu3TqjVq1ahqOjo+Hi4mI0b97cOHLkiEWbpPYh/jw/c+aMedqgQYMMk8lkHD16NNH9jbdt2zajT58+RtmyZQ1XV1cjc+bMRv78+Y2uXbtaDM/1tKpVqxqSjC+//DLR+c967YmNjTWKFCliFClSxDzs1RdffJHoMFpPi4uLMz7++GOjQIEChr29vVGxYkVj5cqV//jc+KevcYm9lxiGYWzatMliexcvXjRatmxpuLm5Ga6urkabNm2My5cvJzlMYHJ+1/fv3zeCgoKM7NmzG05OTkbz5s2NCxcuJLqfYWFhhp+fn+Hs7GxkyZLFaNCggbF9+/ZEt/H07y6pmrp06WI4OTn9o+MU79GjR8a4ceOMQoUKGZkzZza8vLyM4cOHWwwXaRjPfk+4c+eOMXz4cKNo0aKGnZ2dkSNHDqNmzZrGf/7zH+Phw4eGYRjGkiVLjEaNGhm5cuUy7OzsjPz58xu9evUyrly5YrGub775xihcuLB5mNSXeQg2k2GkQ89yIA3EX8HhKQykrn379qlixYr66aefkvVlEf9G1apVU4ECBbR48eKMLiVZ2rZtq7Nnz2rnzp0ZXQrwSqLLBABYsfv37ye40Xbq1KmysbGxuLHWmkRGRmr//v2vzN3yhmFo06ZNSXarAvB8BGIAsGKTJ0/Wnj171KBBA2XKlMk8hFLPnj0z/K75jOLi4pLkTXwvI5PJlGBMXgApQyAGACtWs2ZNrV27VhMmTNDdu3eVP39+jR07Vh988EFGlwYA6YY+xAAAALBqjEMMAAAAq0YgBgAAgFWjD/ELiouL0+XLl5U1a9ZX7usJAQAArIFhGLpz547y5MnzzC+bIhC/oMuXL1vtHdgAAACvkgsXLihfvnxJzicQv6D4r5i9cOFChn9/OQAAABKKjIyUl5eXObclhUD8guK7Sbi4uBCIAQAAXmLP697KTXUAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArFqmjC4AAFLLxL03MroEWLlhFXNkdAkAXgBXiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq8awa68QhpRCRmNIKQDAvxFXiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKzaSxGIZ8yYoYIFC8rBwUHVq1fXzp07n9l+8eLFKlmypBwcHOTt7a1Vq1ZZzB87dqxKliwpJycnZcuWTb6+vtqxY4dFm1u3bqljx45ycXGRm5ubAgMDdffu3VTfNwAAALzcMjwQL1y4UMHBwRozZozCwsJUvnx5+fn56dq1a4m23759uwICAhQYGKi9e/fK399f/v7+OnTokLlN8eLFNX36dB08eFBbt25VwYIF1ahRI12/ft3cpmPHjjp8+LDWrl2rlStXasuWLerZs2ea7y8AAABeLibDMIyMLKB69eqqWrWqpk+fLkmKi4uTl5eX+vXrp2HDhiVo365dO0VFRWnlypXmaTVq1FCFChU0c+bMRLcRGRkpV1dXrVu3Tg0bNtTRo0dVunRp7dq1S1WqVJEkrV69Wk2bNtXFixeVJ0+e59Ydv86IiAi5uLi8yK6n2MS9N9JlO0BShlXMkdElPBPnCDLay36OANYmuXktQ68QP3z4UHv27JGvr695mo2NjXx9fRUaGproMqGhoRbtJcnPzy/J9g8fPtSsWbPk6uqq8uXLm9fh5uZmDsOS5OvrKxsbmwRdK+JFR0crMjLS4gEAAIBXX4YG4hs3big2NlYeHh4W0z08PBQeHp7oMuHh4clqv3LlSjk7O8vBwUGfffaZ1q5dqxw5cpjXkStXLov2mTJlkru7e5LbDQkJkaurq/nh5eWVon0FAADAyynD+xCnlQYNGmjfvn3avn27GjdurLZt2ybZLzk5hg8froiICPPjwoULqVgtAAAAMkqGBuIcOXLI1tZWV69etZh+9epVeXp6JrqMp6dnsto7OTmpaNGiqlGjhr777jtlypRJ3333nXkdT4fjmJgY3bp1K8nt2tvby8XFxeIBAACAV1+GBmI7OztVrlxZ69evN0+Li4vT+vXr5ePjk+gyPj4+Fu0lae3atUm2f3K90dHR5nXcvn1be/bsMc/fsGGD4uLiVL169RfdHQAAALyCMmV0AcHBwerSpYuqVKmiatWqaerUqYqKilK3bt0kSZ07d1bevHkVEhIiSerfv7/q1aunKVOmqFmzZlqwYIF2796tWbNmSZKioqL00UcfqUWLFsqdO7du3LihGTNm6NKlS2rTpo0kqVSpUmrcuLF69OihmTNn6tGjR+rbt6/at2+frBEmAAAA8O+R4YG4Xbt2un79ukaPHq3w8HBVqFBBq1evNt84d/78ednY/O9Cds2aNTV//nyNHDlSI0aMULFixbRs2TKVLVtWkmRra6tjx45p7ty5unHjhrJnz66qVavqjz/+UJkyZczrmTdvnvr27auGDRvKxsZGrVq10ueff56+Ow8AAIAMl+HjEL+qGIcY1uhlH2OVcwQZ7WU/RwBr80qMQwwAAABkNAIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVXopAPGPGDBUsWFAODg6qXr26du7c+cz2ixcvVsmSJeXg4CBvb2+tWrXKPO/Ro0caOnSovL295eTkpDx58qhz5866fPmyxToKFiwok8lk8Zg4cWKa7B8AAABeXhkeiBcuXKjg4GCNGTNGYWFhKl++vPz8/HTt2rVE22/fvl0BAQEKDAzU3r175e/vL39/fx06dEiSdO/ePYWFhWnUqFEKCwvT0qVLdfz4cbVo0SLBusaPH68rV66YH/369UvTfQUAAMDLx2QYhpGRBVSvXl1Vq1bV9OnTJUlxcXHy8vJSv379NGzYsATt27Vrp6ioKK1cudI8rUaNGqpQoYJmzpyZ6DZ27dqlatWq6dy5c8qfP7+kx1eIBwwYoAEDBrxQ3ZGRkXJ1dVVERIRcXFxeaB0pNXHvjXTZDpCUYRVzZHQJz8Q5goz2sp8jgLVJbl7LlI41JfDw4UPt2bNHw4cPN0+zsbGRr6+vQkNDE10mNDRUwcHBFtP8/Py0bNmyJLcTEREhk8kkNzc3i+kTJ07UhAkTlD9/fnXo0EEDBw5UpkyJH5Lo6GhFR0ebf46MjHzO3gEA8PLhD0dktJfxD8cMDcQ3btxQbGysPDw8LKZ7eHjo2LFjiS4THh6eaPvw8PBE2z948EBDhw5VQECAxV8GQUFBqlSpktzd3bV9+3YNHz5cV65c0aeffproekJCQjRu3LiU7B4AAABeARkaiNPao0eP1LZtWxmGoa+++spi3pNXmcuVKyc7Ozv16tVLISEhsre3T7Cu4cOHWywTGRkpLy+vtCseAAAA6SJDA3GOHDlka2urq1evWky/evWqPD09E13G09MzWe3jw/C5c+e0YcOG5/bzrV69umJiYnT27FmVKFEiwXx7e/tEgzIAAABebRk6yoSdnZ0qV66s9evXm6fFxcVp/fr18vHxSXQZHx8fi/aStHbtWov28WH4xIkTWrdunbJnz/7cWvbt2ycbGxvlypXrBfcGAAAAr6IM7zIRHBysLl26qEqVKqpWrZqmTp2qqKgodevWTZLUuXNn5c2bVyEhIZKk/v37q169epoyZYqaNWumBQsWaPfu3Zo1a5akx2G4devWCgsL08qVKxUbG2vuX+zu7i47OzuFhoZqx44datCggbJmzarQ0FANHDhQnTp1UrZs2TLmQAAAACBDZHggbteuna5fv67Ro0crPDxcFSpU0OrVq803zp0/f142Nv+7kF2zZk3Nnz9fI0eO1IgRI1SsWDEtW7ZMZcuWlSRdunRJK1askCRVqFDBYlsbN25U/fr1ZW9vrwULFmjs2LGKjo5WoUKFNHDgwASjVwAAAODfL8PHIX5VMQ4xrNHLOFTOkzhHkNFe9nNE4jxBxkvP8yS5eS3Dv6kOAAAAyEgEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVi1FgTgmJkbjx4/XxYsX06oeAAAAIF2lKBBnypRJn3zyiWJiYtKqHgAAACBdpbjLxGuvvabNmzenRS0AAABAusuU0gWaNGmiYcOG6eDBg6pcubKcnJws5rdo0SLVigMAAADSWooDce/evSVJn376aYJ5JpNJsbGx/7wqAAAAIJ2kOBDHxcWlRR0AAABAhmDYNQAAAFi1FwrEmzdvVvPmzVW0aFEVLVpULVq00B9//JHatQEAAABpLsWB+KeffpKvr6+yZMmioKAgBQUFydHRUQ0bNtT8+fPTokYAAAAgzaS4D/FHH32kyZMna+DAgeZpQUFB+vTTTzVhwgR16NAhVQsEAAAA0lKKrxCfPn1azZs3TzC9RYsWOnPmTKoUBQAAAKSXFAdiLy8vrV+/PsH0devWycvLK1WKAgAAANJLirtMDBo0SEFBQdq3b59q1qwpSdq2bZvmzJmjadOmpXqBAAAAQFpKcSB+77335OnpqSlTpmjRokWSpFKlSmnhwoV68803U71AAAAAIC2lKBDHxMTo448/Vvfu3bV169a0qgkAAABINynqQ5wpUyZNnjxZMTExaVUPAAAAkK5SfFNdw4YNtXnz5rSoBQAAAEh3Ke5D3KRJEw0bNkwHDx5U5cqV5eTkZDG/RYsWqVYcAAAAkNZSHIh79+4tSfr0008TzDOZTIqNjf3nVQEAAADpJMWBOC4uLi3qAAAAADJEivoQP3r0SJkyZdKhQ4fSqh4AAAAgXaUoEGfOnFn58+enWwQAAAD+NVI8ysQHH3ygESNG6NatW2lRDwAAAJCuUtyHePr06Tp58qTy5MmjAgUKJBhlIiwsLNWKAwAAANJaigOxv79/GpQBAAAAZIwUB+IxY8akRR0AAABAhkh2H+KdO3c+82a66OhoLVq0KFWKAgAAANJLsgOxj4+Pbt68af7ZxcVFp0+fNv98+/ZtBQQEpG51AAAAQBpLdiA2DOOZPyc1LTlmzJihggULysHBQdWrV9fOnTuf2X7x4sUqWbKkHBwc5O3trVWrVpnnPXr0SEOHDpW3t7ecnJyUJ08ede7cWZcvX7ZYx61bt9SxY0e5uLjIzc1NgYGBunv37gvVDwAAgFdXioddexaTyZTiZRYuXKjg4GCNGTNGYWFhKl++vPz8/HTt2rVE22/fvl0BAQEKDAzU3r175e/vL39/f/OXhdy7d09hYWEaNWqUwsLCtHTpUh0/flwtWrSwWE/Hjh11+PBhrV27VitXrtSWLVvUs2fPlO80AAAAXmkmI5mXdW1sbBQeHq5cuXJJkrJmzar9+/ercOHCkqSrV68qT548Kf7SjurVq6tq1aqaPn26pMdfDe3l5aV+/fpp2LBhCdq3a9dOUVFRWrlypXlajRo1VKFCBc2cOTPRbezatUvVqlXTuXPnlD9/fh09elSlS5fWrl27VKVKFUnS6tWr1bRpU128eFF58uR5bt2RkZFydXVVRESEXFxcUrTPL2ri3hvpsh0gKcMq5sjoEp6JcwQZ7WU/RyTOE2S89DxPkpvXUjTKxJEjRxQeHi7pcfeIY8eOmbsZ3LiR8hPs4cOH2rNnj4YPH26eZmNjI19fX4WGhia6TGhoqIKDgy2m+fn5admyZUluJyIiQiaTSW5ubuZ1uLm5mcOwJPn6+srGxkY7duxQy5YtE6wjOjpa0dHR5p8jIyOTs4sAAAB4yaUoEDds2NCin/Abb7wh6XFXCcMwUtxl4saNG4qNjZWHh4fFdA8PDx07dizRZcLDwxNtHx/Un/bgwQMNHTpUAQEB5r8MnrzSHS9Tpkxyd3dPcj0hISEaN25csvYLAAAAr45kB+IzZ86kZR1p4tGjR2rbtq0Mw9BXX331j9Y1fPhwiyvTkZGR8vLy+qclAgAAIIMlOxAXKFAg1TeeI0cO2dra6urVqxbTr169Kk9Pz0SX8fT0TFb7+DB87tw5bdiwwaLfiKenZ4Kb9mJiYnTr1q0kt2tvby97e/tk7xsAAABeDak6ykRK2dnZqXLlylq/fr15WlxcnNavXy8fH59El/Hx8bFoL0lr1661aB8fhk+cOKF169Ype/bsCdZx+/Zt7dmzxzxtw4YNiouLU/Xq1VNj1wAAAPCKSPFXN6e24OBgdenSRVWqVFG1atU0depURUVFqVu3bpKkzp07K2/evAoJCZEk9e/fX/Xq1dOUKVPUrFkzLViwQLt379asWbMkPQ7DrVu3VlhYmFauXKnY2Fhzv2B3d3fZ2dmpVKlSaty4sXr06KGZM2fq0aNH6tu3r9q3b5+sESYAAADw75Hhgbhdu3a6fv26Ro8erfDwcFWoUEGrV6823zh3/vx52dj870J2zZo1NX/+fI0cOVIjRoxQsWLFtGzZMpUtW1aSdOnSJa1YsUKSVKFCBYttbdy4UfXr15ckzZs3T3379lXDhg1lY2OjVq1a6fPPP0/7HQYAAMBLJdnjEMMS4xDDGr3sY6xyjiCjvezniMR5goz3Mo5DnKF9iAEAAICMlqwuExUrVkz2GMNhYWH/qCAAAAAgPSUrEPv7+5v//+DBA3355ZcqXbq0eWSHP//8U4cPH1bv3r3TpEgAAAAgrSQrEI8ZM8b8/3feeUdBQUGaMGFCgjYXLlxI3eoAAACANJbiPsSLFy9W586dE0zv1KmTfvnll1QpCgAAAEgvKQ7Ejo6O2rZtW4Lp27Ztk4ODQ6oUBQAAAKSXFI9DPGDAAL333nsKCwtTtWrVJEk7duzQ999/r1GjRqV6gQAAAEBaSnEgHjZsmAoXLqxp06bpp59+kiSVKlVKs2fPVtu2bVO9QAAAACAtvdA31bVt25bwCwAAgH+FF/pijtu3b+vbb7/ViBEjdOvWLUmPxx++dOlSqhYHAAAApLUUXyE+cOCAfH195erqqrNnz+qdd96Ru7u7li5dqvPnz+uHH35IizoBAACANJHiK8TBwcHq2rWrTpw4YTGqRNOmTbVly5ZULQ4AAABIaykOxLt27VKvXr0STM+bN6/Cw8NTpSgAAAAgvaQ4ENvb2ysyMjLB9L/++ks5c+ZMlaIAAACA9JLiQNyiRQuNHz9ejx49kiSZTCadP39eQ4cOVatWrVK9QAAAACAtpTgQT5kyRXfv3lWuXLl0//591atXT0WLFlXWrFn10UcfpUWNAAAAQJpJ8SgTrq6uWrt2rbZt26b9+/fr7t27qlSpknx9fdOiPgAAACBNpSgQP3r0SI6Ojtq3b59q1aqlWrVqpVVdAAAAQLpIUZeJzJkzK3/+/IqNjU2regAAAIB0leI+xB988IHFN9QBAAAAr7IU9yGePn26Tp48qTx58qhAgQJycnKymB8WFpZqxQEAAABpLcWB2N/fPw3KAAAAADJGigPxmDFj0qIOAAAAIEOkuA8xAAAA8G+S4ivEsbGx+uyzz7Ro0SKdP39eDx8+tJjPzXYAAAB4laT4CvG4ceP06aefql27doqIiFBwcLDeeust2djYaOzYsWlQIgAAAJB2UhyI582bp2+++UaDBg1SpkyZFBAQoG+//VajR4/Wn3/+mRY1AgAAAGkmxYE4PDxc3t7ekiRnZ2dFRERIkt544w39+uuvqVsdAAAAkMZSHIjz5cunK1euSJKKFCmi33//XZK0a9cu2dvbp251AAAAQBpLcSBu2bKl1q9fL0nq16+fRo0apWLFiqlz587q3r17qhcIAAAApKUUjzIxceJE8//btWun/PnzKzQ0VMWKFVPz5s1TtTgAAAAgraU4ED/Nx8dHPj4+qVELAAAAkO5SHIh/+OGHZ87v3LnzCxcDAAAApLcUB+L+/ftb/Pzo0SPdu3dPdnZ2ypIlC4EYAAAAr5QU31T3999/Wzzu3r2r48ePq3bt2vr555/TokYAAAAgzaQ4ECemWLFimjhxYoKrxwAAAMDLLlUCsSRlypRJly9fTq3VAQAAAOkixX2IV6xYYfGzYRi6cuWKpk+frlq1aqVaYQAAAEB6SHEg9vf3t/jZZDIpZ86ceu211zRlypTUqgsAAABIFykOxHFxcWlRBwAAAJAhUq0PMQAAAPAqSvEV4uDg4GS3/fTTT1O6egAAACBdpTgQ7927V3v37tWjR49UokQJSdJff/0lW1tbVapUydzOZDKlXpUAAABAGklxIG7evLmyZs2quXPnKlu2bJIef1lHt27dVKdOHQ0aNCjViwQAAADSSor7EE+ZMkUhISHmMCxJ2bJl04cffsgoEwAAAHjlpDgQR0ZG6vr16wmmX79+XXfu3EmVogAAAID0kuJA3LJlS3Xr1k1Lly7VxYsXdfHiRf3yyy8KDAzUW2+9lRY1AgAAAGkmxX2IZ86cqcGDB6tDhw569OjR45VkyqTAwEB98sknqV4gAAAAkJZSHIizZMmiL7/8Up988olOnTolSSpSpIicnJxSvTgAAAAgrb3wF3M4OTmpXLlycnV11blz5/gGOwAAALySkh2Iv//++wRftNGzZ08VLlxY3t7eKlu2rC5cuJDqBQIAAABpKdmBeNasWRZDra1evVqzZ8/WDz/8oF27dsnNzU3jxo1LkyIBAACAtJLsPsQnTpxQlSpVzD8vX75cb775pjp27ChJ+vjjj9WtW7fUrxAAAABIQ8m+Qnz//n25uLiYf96+fbvq1q1r/rlw4cIKDw9P3eoAAACANJbsQFygQAHt2bNHknTjxg0dPnxYtWrVMs8PDw+Xq6tr6lcIAAAApKFkd5no0qWL+vTpo8OHD2vDhg0qWbKkKleubJ6/fft2lS1bNk2KBAAAANJKsq8QDxkyRD169NDSpUvl4OCgxYsXW8zftm2bAgICUlzAjBkzVLBgQTk4OKh69erauXPnM9svXrxYJUuWlIODg7y9vbVq1SqL+UuXLlWjRo2UPXt2mUwm7du3L8E66tevL5PJZPF49913U1w7AAAAXn3JDsQ2NjYaP3689u7dq99++02lSpWymL948WIFBgamaOMLFy5UcHCwxowZo7CwMJUvX15+fn66du1aou23b9+ugIAABQYGau/evfL395e/v78OHTpkbhMVFaXatWtr0qRJz9x2jx49dOXKFfNj8uTJKaodAAAA/w4v/MUcqeHTTz9Vjx491K1bN5UuXVozZ85UlixZ9P333yfaftq0aWrcuLHef/99lSpVShMmTFClSpU0ffp0c5u3335bo0ePlq+v7zO3nSVLFnl6epofT94wCAAAAOuRYYH44cOH2rNnj0VwtbGxka+vr0JDQxNdJjQ0NEHQ9fPzS7L9s8ybN085cuRQ2bJlNXz4cN27d++Z7aOjoxUZGWnxAAAAwKsv2TfVpbYbN24oNjZWHh4eFtM9PDx07NixRJcJDw9PtH1Kh3vr0KGDChQooDx58ujAgQMaOnSojh8/rqVLlya5TEhICF88AgAA8C+UYYE4I/Xs2dP8f29vb+XOnVsNGzbUqVOnVKRIkUSXGT58uIKDg80/R0ZGysvLK81rBQAAQNrKsECcI0cO2dra6urVqxbTr169Kk9Pz0SX8fT0TFH75Kpevbok6eTJk0kGYnt7e9nb2/+j7QAAAODlk+JAHBsbqzlz5mj9+vW6du2a4uLiLOZv2LAhWeuxs7NT5cqVtX79evn7+0uS4uLitH79evXt2zfRZXx8fLR+/XoNGDDAPG3t2rXy8fFJ6W5YiB+aLXfu3P9oPQAAAHj1pDgQ9+/fX3PmzFGzZs1UtmxZmUymF954cHCwunTpoipVqqhatWqaOnWqoqKi1K1bN0lS586dlTdvXoWEhJi3Xa9ePU2ZMkXNmjXTggULtHv3bs2aNcu8zlu3bun8+fO6fPmyJOn48eOSZB5N4tSpU5o/f76aNm2q7Nmz68CBAxo4cKDq1q2rcuXKvfC+AAAA4NWU4kC8YMECLVq0SE2bNv3HG2/Xrp2uX7+u0aNHKzw8XBUqVNDq1avNN86dP39eNjb/GwijZs2amj9/vkaOHKkRI0aoWLFiWrZsmcU35K1YscIcqCWpffv2kqQxY8Zo7NixsrOz07p168zh28vLS61atdLIkSP/8f4AAADg1WMyDMNIyQJ58uTRpk2bVLx48bSq6ZUQGRkpV1dXRUREpNsYxhP33kiX7QBJGVYxR0aX8EycI8hoL/s5InGeIOOl53mS3LyW4nGIBw0apGnTpimFORoAAAB4KaW4y8TWrVu1ceNG/fbbbypTpowyZ85sMf9ZY/kCAAAAL5sUB2I3Nze1bNkyLWoBAAAA0l2KA/Hs2bPTog4AAAAgQ6S4DzEAAADwb/JC31S3ZMkSLVq0SOfPn9fDhw8t5oWFhaVKYQAAAEB6SPEV4s8//1zdunWTh4eH9u7dq2rVqil79uw6ffq0mjRpkhY1AgAAAGkmxYH4yy+/1KxZs/TFF1/Izs5OQ4YM0dq1axUUFKSIiIi0qBEAAABIMykOxOfPn1fNmjUlSY6Ojrpz544k6e2339bPP/+cutUBAAAAaSzFgdjT01O3bt2SJOXPn19//vmnJOnMmTN8WQcAAABeOSkOxK+99ppWrFghSerWrZsGDhyo119/Xe3atWN8YgAAALxyUjzKxKxZsxQXFydJ6tOnj7Jnz67t27erRYsW6tWrV6oXCAAAAKSlFAdiGxsb2dj878Jy+/bt1b59+1QtCgAAAEgvL/TFHH/88Yc6deokHx8fXbp0SZL0448/auvWralaHAAAAJDWUhyIf/nlF/n5+cnR0VF79+5VdHS0JCkiIkIff/xxqhcIAAAApKUUB+IPP/xQM2fO1DfffKPMmTObp9eqVYtvqQMAAMArJ8WB+Pjx46pbt26C6a6urrp9+3Zq1AQAAACkmxcah/jkyZMJpm/dulWFCxdOlaIAAACA9JLiQNyjRw/1799fO3bskMlk0uXLlzVv3jwNHjxY7733XlrUCAAAAKSZFA+7NmzYMMXFxalhw4a6d++e6tatK3t7ew0ePFj9+vVLixoBAACANJPiQGwymfTBBx/o/fff18mTJ3X37l2VLl1azs7OaVEfAAAAkKZSHIjj2dnZqXTp0qlZCwAAAJDukh2Iu3fvnqx233///QsXAwAAAKS3ZAfiOXPmqECBAqpYsaIMw0jLmgAAAIB0k+xA/N577+nnn3/WmTNn1K1bN3Xq1Enu7u5pWRsAAACQ5pI97NqMGTN05coVDRkyRP/3f/8nLy8vtW3bVmvWrOGKMQAAAF5ZKRqH2N7eXgEBAVq7dq2OHDmiMmXKqHfv3ipYsKDu3r2bVjUCAAAAaSbFX8xhXtDGRiaTSYZhKDY2NjVrAgAAANJNigJxdHS0fv75Z73++usqXry4Dh48qOnTp+v8+fOMQwwAAIBXUrJvquvdu7cWLFggLy8vde/eXT///LNy5MiRlrUBAAAAaS7ZgXjmzJnKnz+/ChcurM2bN2vz5s2Jtlu6dGmqFQcAAACktWQH4s6dO8tkMqVlLQAAAEC6S9EXcwAAAAD/Ni88ygQAAADwb0AgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWLUMD8QzZsxQwYIF5eDgoOrVq2vnzp3PbL948WKVLFlSDg4O8vb21qpVqyzmL126VI0aNVL27NllMpm0b9++BOt48OCB+vTpo+zZs8vZ2VmtWrXS1atXU3O3AAAA8IrI0EC8cOFCBQcHa8yYMQoLC1P58uXl5+ena9euJdp++/btCggIUGBgoPbu3St/f3/5+/vr0KFD5jZRUVGqXbu2Jk2alOR2Bw4cqP/7v//T4sWLtXnzZl2+fFlvvfVWqu8fAAAAXn4mwzCMjNp49erVVbVqVU2fPl2SFBcXJy8vL/Xr10/Dhg1L0L5du3aKiorSypUrzdNq1KihChUqaObMmRZtz549q0KFCmnv3r2qUKGCeXpERIRy5syp+fPnq3Xr1pKkY8eOqVSpUgoNDVWNGjWSVXtkZKRcXV0VEREhFxeXlO76C5m490a6bAdIyrCKOTK6hGfiHEFGe9nPEYnzBBkvPc+T5Oa1DLtC/PDhQ+3Zs0e+vr7/K8bGRr6+vgoNDU10mdDQUIv2kuTn55dk+8Ts2bNHjx49slhPyZIllT9//meuJzo6WpGRkRYPAAAAvPoyLBDfuHFDsbGx8vDwsJju4eGh8PDwRJcJDw9PUfuk1mFnZyc3N7cUrSckJESurq7mh5eXV7K3CQAAgJdXht9U96oYPny4IiIizI8LFy5kdEkAAABIBZkyasM5cuSQra1tgtEdrl69Kk9Pz0SX8fT0TFH7pNbx8OFD3b592+Iq8fPWY29vL3t7+2RvBwAAAK+GDLtCbGdnp8qVK2v9+vXmaXFxcVq/fr18fHwSXcbHx8eivSStXbs2yfaJqVy5sjJnzmyxnuPHj+v8+fMpWg8AAAD+HTLsCrEkBQcHq0uXLqpSpYqqVaumqVOnKioqSt26dZMkde7cWXnz5lVISIgkqX///qpXr56mTJmiZs2aacGCBdq9e7dmzZplXuetW7d0/vx5Xb58WdLjsCs9vjLs6ekpV1dXBQYGKjg4WO7u7nJxcVG/fv3k4+OT7BEmAAAA8O+RoYG4Xbt2un79ukaPHq3w8HBVqFBBq1evNt84d/78ednY/O8ids2aNTV//nyNHDlSI0aMULFixbRs2TKVLVvW3GbFihXmQC1J7du3lySNGTNGY8eOlSR99tlnsrGxUatWrRQdHS0/Pz99+eWX6bDHAAAAeNlk6DjErzLGIYY1etnHWOUcQUZ72c8RifMEGY9xiAEAAICXDIEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYtZciEM+YMUMFCxaUg4ODqlevrp07dz6z/eLFi1WyZEk5ODjI29tbq1atsphvGIZGjx6t3Llzy9HRUb6+vjpx4oRFm4IFC8pkMlk8Jk6cmOr7BgAAgJdbhgfihQsXKjg4WGPGjFFYWJjKly8vPz8/Xbt2LdH227dvV0BAgAIDA7V37175+/vL399fhw4dMreZPHmyPv/8c82cOVM7duyQk5OT/Pz89ODBA4t1jR8/XleuXDE/+vXrl6b7CgAAgJdPhgfiTz/9VD169FC3bt1UunRpzZw5U1myZNH333+faPtp06apcePGev/991WqVClNmDBBlSpV0vTp0yU9vjo8depUjRw5Um+++abKlSunH374QZcvX9ayZcss1pU1a1Z5enqaH05OTmm9uwAAAHjJZGggfvjwofbs2SNfX1/zNBsbG/n6+io0NDTRZUJDQy3aS5Kfn5+5/ZkzZxQeHm7RxtXVVdWrV0+wzokTJyp79uyqWLGiPvnkE8XExCRZa3R0tCIjIy0eAAAAePVlysiN37hxQ7GxsfLw8LCY7uHhoWPHjiW6THh4eKLtw8PDzfPjpyXVRpKCgoJUqVIlubu7a/v27Ro+fLiuXLmiTz/9NNHthoSEaNy4cSnbQQAAALz0MjQQZ6Tg4GDz/8uVKyc7Ozv16tVLISEhsre3T9B++PDhFstERkbKy8srXWoFAABA2snQLhM5cuSQra2trl69ajH96tWr8vT0THQZT0/PZ7aP/zcl65Sk6tWrKyYmRmfPnk10vr29vVxcXCweAAAAePVlaCC2s7NT5cqVtX79evO0uLg4rV+/Xj4+Poku4+PjY9FektauXWtuX6hQIXl6elq0iYyM1I4dO5JcpyTt27dPNjY2ypUr1z/ZJQAAALxiMrzLRHBwsLp06aIqVaqoWrVqmjp1qqKiotStWzdJUufOnZU3b16FhIRIkvr376969eppypQpatasmRYsWKDdu3dr1qxZkiSTyaQBAwboww8/VLFixVSoUCGNGjVKefLkkb+/v6THN+bt2LFDDRo0UNasWRUaGqqBAweqU6dOypYtW4YcBwAAAGSMDA/E7dq10/Xr1zV69GiFh4erQoUKWr16tfmmuPPnz8vG5n8XsmvWrKn58+dr5MiRGjFihIoVK6Zly5apbNmy5jZDhgxRVFSUevbsqdu3b6t27dpavXq1HBwcJD3u/rBgwQKNHTtW0dHRKlSokAYOHGjRRxgAAADWwWQYhpHRRbyKIiMj5erqqoiIiHTrTzxx74102Q6QlGEVc2R0Cc/EOYKM9rKfIxLnCTJeep4nyc1rGf7FHAAAAEBGIhADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiAAAAWDUCMQAAAKwagRgAAABWjUAMAAAAq0YgBgAAgFUjEAMAAMCqEYgBAABg1QjEAAAAsGoEYgAAAFg1AjEAAACsGoEYAAAAVo1ADAAAAKtGIAYAAIBVIxADAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACr9lIE4hkzZqhgwYJycHBQ9erVtXPnzme2X7x4sUqWLCkHBwd5e3tr1apVFvMNw9Do0aOVO3duOTo6ytfXVydOnLBoc+vWLXXs2FEuLi5yc3NTYGCg7t69m+r7BgAAgJdbhgfihQsXKjg4WGPGjFFYWJjKly8vPz8/Xbt2LdH227dvV0BAgAIDA7V37175+/vL399fhw4dMreZPHmyPv/8c82cOVM7duyQk5OT/Pz89ODBA3Objh076vDhw1q7dq1WrlypLVu2qGfPnmm+vwAAAHi5mAzDMDKygOrVq6tq1aqaPn26JCkuLk5eXl7q16+fhg0blqB9u3btFBUVpZUrV5qn1ahRQxUqVNDMmTNlGIby5MmjQYMGafDgwZKkiIgIeXh4aM6cOWrfvr2OHj2q0qVLa9euXapSpYokafXq1WratKkuXryoPHnyPLfuyMhIubq6KiIiQi4uLqlxKJ5r4t4b6bIdICnDKubI6BKeiXMEGe1lP0ckzhNkvPQ8T5Kb1zKlW0WJePjwofbs2aPhw4ebp9nY2MjX11ehoaGJLhMaGqrg4GCLaX5+flq2bJkk6cyZMwoPD5evr695vqurq6pXr67Q0FC1b99eoaGhcnNzM4dhSfL19ZWNjY127Nihli1bJthudHS0oqOjzT9HRERIenyg08uDu3fSbVtAYiIj7TK6hGfiHEFGe9nPEYnzBBkvPc+T+Jz2vOu/GRqIb9y4odjYWHl4eFhM9/Dw0LFjxxJdJjw8PNH24eHh5vnx057VJleuXBbzM2XKJHd3d3Obp4WEhGjcuHEJpnt5eSW1e8C/TsIzAMCTOEeA58uI8+TOnTtydXVNcn6GBuJXyfDhwy2uTMfFxenWrVvKnj27TCZTBlaG5IiMjJSXl5cuXLiQbl1cgFcN5wnwbJwjrx7DMHTnzp3ndofN0ECcI0cO2dra6urVqxbTr169Kk9Pz0SX8fT0fGb7+H+vXr2q3LlzW7SpUKGCuc3TN+3FxMTo1q1bSW7X3t5e9vb2FtPc3NyevYN46bi4uPAiBjwH5wnwbJwjr5ZnXRmOl6GjTNjZ2aly5cpav369eVpcXJzWr18vHx+fRJfx8fGxaC9Ja9euNbcvVKiQPD09LdpERkZqx44d5jY+Pj66ffu29uzZY26zYcMGxcXFqXr16qm2fwAAAHj5ZXiXieDgYHXp0kVVqlRRtWrVNHXqVEVFRalbt26SpM6dOytv3rwKCQmRJPXv31/16tXTlClT1KxZMy1YsEC7d+/WrFmzJEkmk0kDBgzQhx9+qGLFiqlQoUIaNWqU8uTJI39/f0lSqVKl1LhxY/Xo0UMzZ87Uo0eP1LdvX7Vv3z5ZI0wAAADg3yPDA3G7du10/fp1jR49WuHh4apQoYJWr15tvinu/PnzsrH534XsmjVrav78+Ro5cqRGjBihYsWKadmyZSpbtqy5zZAhQxQVFaWePXvq9u3bql27tlavXi0HBwdzm3nz5qlv375q2LChbGxs1KpVK33++efpt+NIV/b29hozZkyCbi8A/ofzBHg2zpF/rwwfhxgAAADISBn+TXUAAABARiIQAwAAwKoRiAEAAGDVCMRIkslkMn8lNtLP2LFjzWNmAwD+Od7P8DwEYivWtWtX81B0ibly5YqaNGmSfgWlkMlkMj9cXFxUtWpVLV++PKPL+scGDx6cYKxt4EVdv35d7733nvLnzy97e3t5enrKz89PmzdvVo4cOTRx4sREl5swYYI8PDz06NEjzZkzx3yu2djYKHfu3GrXrp3Onz+fznuDV1XXrl3Nz6HMmTOrUKFCGjJkiB48eJDRpaWpJ/f7ycfJkycztKZnvfdbKwIxkuTp6ZnhQ8sYhqGYmJgk58+ePVtXrlzR7t27VatWLbVu3VoHDx5M05oePnyYput3dnZW9uzZ03QbsB6tWrXS3r17NXfuXP31119asWKF6tevr4iICHXq1EmzZ89OsIxhGJozZ446d+6szJkzS3r8zVxXrlzRpUuX9Msvv+j48eNq06ZNeu8OXmGNGzfWlStXdPr0aX322Wf6+uuvNWbMmIwuK83F7/eTj0KFCr3QutL6/ceaEYiRpCc/Yjp79qxMJpOWLl2qBg0aKEuWLCpfvrxCQ0Mtltm6davq1KkjR0dHeXl5KSgoSFFRUeb5P/74o6pUqaKsWbPK09NTHTp0sPga7U2bNslkMum3335T5cqVZW9vr61btyZZo5ubmzw9PVW8eHFNmDBBMTEx2rhxo3n+hQsX1LZtW7m5ucnd3V1vvvmmzp49a54fExOjoKAgubm5KXv27Bo6dKi6dOli8ddz/fr11bdvXw0YMEA5cuSQn5+fJOnQoUNq0qSJnJ2d5eHhobfffls3btwwL7dkyRJ5e3vL0dFR2bNnl6+vr/lYbNq0SdWqVZOTk5Pc3NxUq1YtnTt3TlLCLhNxcXEaP3688uXLJ3t7e/NY3fGS+7uB9bl9+7b++OMPTZo0SQ0aNFCBAgVUrVo1DR8+XC1atFBgYKD++uuvBOfY5s2bdfr0aQUGBpqnmUwmeXp6Knfu3KpZs6YCAwO1c+dORUZGpvdu4RUV/wmFl5eX/P395evrq7Vr15rn37x5UwEBAcqbN6+yZMkib29v/fzzzxbrqF+/voKCgjRkyBC5u7vL09NTY8eOtWhz4sQJ1a1bVw4ODipdurTFNuIdPHhQr732mvn1uWfPnrp79655fvxV1I8//lgeHh5yc3PT+PHjFRMTo/fff1/u7u7Kly9fon9QJrXfTz5sbW0lPT7XqlWrJnt7e+XOnVvDhg2zuAiU2u8/Y8eO1dy5c7V8+XLz1epNmzY9dx+sAYEYKfLBBx9o8ODB2rdvn4oXL66AgADzyXvq1Ck1btxYrVq10oEDB7Rw4UJt3bpVffv2NS//6NEjTZgwQfv379eyZct09uxZde3aNcF2hg0bpokTJ+ro0aMqV67cc+uKiYnRd999J+nxV4LHb8vPz09Zs2bVH3/8oW3btsnZ2VmNGzc2/5U9adIkzZs3T7Nnz9a2bdsUGRmZaD+zuXPnys7OTtu2bdPMmTN1+/Ztvfbaa6pYsaJ2796t1atX6+rVq2rbtq2kx91NAgIC1L17dx09elSbNm3SW2+9Zb7i7e/vr3r16unAgQMKDQ1Vz549ZTKZEt23adOmacqUKfrPf/6jAwcOyM/PTy1atNCJEyeS/buBdXJ2dpazs7OWLVum6OjoBPO9vb1VtWpVff/99xbTZ8+erZo1a6pkyZKJrvfatWv673//K1tbW/MbO5AShw4d0vbt282v15L04MEDVa5cWb/++qsOHTqknj176u2339bOnTstlp07d66cnJy0Y8cOTZ48WePHjzeH3ri4OL311luys7PTjh07NHPmTA0dOtRi+aioKPn5+SlbtmzatWuXFi9erHXr1lm8V0nShg0bdPnyZW3ZskWffvqpxowZozfeeEPZsmXTjh079O6776pXr166ePHiCx2DS5cuqWnTpqpatar279+vr776St99950+/PDDBPubWu8/gwcPVtu2bS2uWtesWfOF6v/XMWC1unTpYrz55ptJzpdk/Pe//zUMwzDOnDljSDK+/fZb8/zDhw8bkoyjR48ahmEYgYGBRs+ePS3W8ccffxg2NjbG/fv3E93Grl27DEnGnTt3DMMwjI0bNxqSjGXLlj23fkmGg4OD4eTkZNjY2BiSjIIFCxo3b940DMMwfvzxR6NEiRJGXFyceZno6GjD0dHRWLNmjWEYhuHh4WF88skn5vkxMTFG/vz5LY5LvXr1jIoVK1pse8KECUajRo0spl24cMGQZBw/ftzYs2ePIck4e/Zsgrpv3rxpSDI2bdqU6H6NGTPGKF++vPnnPHnyGB999JFFm6pVqxq9e/c2DCN5vxtYryVLlhjZsmUzHBwcjJo1axrDhw839u/fb54/c+ZMw9nZ2XwORkZGGlmyZLF4Ps2ePduQZDg5ORlZsmQxJBmSjKCgoHTfH7yaunTpYtja2hpOTk6Gvb29IcmwsbExlixZ8szlmjVrZgwaNMj8c7169YzatWtbtKlataoxdOhQwzAMY82aNUamTJmMS5cumef/9ttvFu9ns2bNMrJly2bcvXvX3ObXX381bGxsjPDwcHO9BQoUMGJjY81tSpQoYdSpU8f8c0xMjOHk5GT8/PPPydrv+Efr1q0NwzCMESNGJHiPmjFjhuHs7Gzebmq//8TX9Kz3fmvFFWKkyJNXa3Pnzi1J5i4P+/fv15w5c8xXpZydneXn56e4uDidOXNGkrRnzx41b95c+fPnV9asWVWvXj1JSnBzTpUqVZJVz2effaZ9+/bpt99+U+nSpfXtt9/K3d3dXM/JkyeVNWtWcz3u7u568OCBTp06pYiICF29elXVqlUzr8/W1laVK1dOsJ2np+3fv18bN2602Nf4q2mnTp1S+fLl1bBhQ3l7e6tNmzb65ptv9Pfff0uS3N3d1bVrV/n5+al58+aaNm2arly5kuj+RUZG6vLly6pVq5bF9Fq1auno0aMW0571u4H1atWqlS5fvqwVK1aocePG2rRpkypVqqQ5c+ZIkgICAhQbG6tFixZJkhYuXCgbGxu1a9fOYj1Zs2bVvn37tHv3bk2ZMkWVKlXSRx99lN67g1dYgwYNtG/fPu3YsUNdunRRt27d1KpVK/P82NhYTZgwQd7e3nJ3d5ezs7PWrFmT4P3h6U8Nc+fObX6tO3r0qLy8vJQnTx7zfB8fH4v2R48eVfny5eXk5GSeVqtWLcXFxen48ePmaWXKlJGNzf9ikoeHh7y9vc0/29raKnv27M99nY3f7/jH559/bq7Dx8fH4tPBWrVq6e7duxZXnVPz/QdJIxAjReJvsJFkPonj4uIkSXfv3lWvXr0sTvz9+/frxIkTKlKkiPljKhcXF82bN0+7du3Sf//7X0kJbxR48oXqWTw9PVW0aFE1atRIs2fPVrt27cwvTnfv3lXlypUt6tm3b5/++usvdejQIUX7/XQ9d+/eVfPmzROsO77vmq2trdauXWsO6l988YVKlChh/sNg9uzZCg0NVc2aNbVw4UIVL15cf/75Z4pqetqzfjewbg4ODnr99dc1atQobd++XV27djXfzOTi4qLWrVub+0LOnj1bbdu2lbOzs8U6bGxsVLRoUZUqVUrBwcGqUaOG3nvvvXTfF7y6nJycVLRoUZUvX17ff/+9duzYYe7qJkmffPKJpk2bpqFDh2rjxo3at2+f/Pz8Erw/PPlaJz1+vUuL17rEtvMi247f7/hH/AWL5Ert9x8kjkCMVFOpUiUdOXLE4sSPf9jZ2enYsWO6efOmJk6cqDp16qhkyZKpegWzWrVqqly5svmqVaVKlXTixAnlypUrQT2urq5ydXWVh4eHdu3aZV5HbGyswsLCkrWvhw8fVsGCBROsO/7Fy2QyqVatWho3bpz27t0rOzs78x8AklSxYkUNHz5c27dvV9myZTV//vwE23FxcVGePHm0bds2i+nbtm1T6dKlX+g4AaVLl7a42TUwMFBbt27VypUrtX37doub6ZIybNgwLVy4MFnnC/A0GxsbjRgxQiNHjtT9+/clPX5de/PNN9WpUyeVL19ehQsX1l9//ZWi9ZYqVUoXLlyw+NTt6YsNpUqV0v79+y3OgW3btsnGxkYlSpT4B3uVMqVKlVJoaKgMw7CoI2vWrMqXL1+Sy/3T9x87OzvFxsam7c69ggjEVi4iIiLBX5kXLlx4oXUNHTpU27dvV9++fc1/rS5fvtx8o0L+/PllZ2enL774QqdPn9aKFSs0YcKE1NwdDRgwQF9//bUuXbqkjh07KkeOHHrzzTf1xx9/6MyZM9q0aZOCgoLMH0f169dPISEhWr58uY4fP67+/fvr77//TvIGt3h9+vTRrVu3FBAQoF27dunUqVNas2aNunXrptjYWO3YsUMff/yxdu/erfPnz2vp0qW6fv26SpUqpTNnzmj48OEKDQ3VuXPn9Pvvv+vEiRMqVapUott6//33NWnSJC1cuFDHjx/XsGHDtG/fPvXv3z9Vjx3+fW7evKnXXntNP/30kw4cOKAzZ85o8eLFmjx5st58801zu7p166po0aLq3LmzSpYsmaybbLy8vNSyZUuNHj06LXcB/2Jt2rSRra2tZsyYIUkqVqyY1q5dq+3bt+vo0aPq1auXrl69mqJ1+vr6qnjx4urSpYv279+vP/74Qx988IFFm44dO8rBwUFdunTRoUOHtHHjRvXr109vv/22PDw8Um3/nqd37966cOGC+vXrp2PHjmn58uUaM2aMgoODLbpqPO2fvP9IUsGCBXXgwAEdP35cN27c0KNHj9Jrl19qBGIrt2nTJlWsWNHiMW7cuBdaV7ly5bR582b99ddfqlOnjipWrKjRo0eb+3LlzJlTc+bM0eLFi1W6dGlNnDhR//nPf1Jzd9S4cWMVKlRIH330kbJkyaItW7Yof/78euutt1SqVCkFBgbqwYMHcnFxkfQ4xAcEBKhz587y8fEx93t2cHB45nbir9rGxsaqUaNG8vb21oABA+Tm5iYbGxu5uLhoy5Ytatq0qYoXL66RI0dqypQpatKkibJkyaJjx46pVatWKl68uHr27Kk+ffqoV69eiW4rKChIwcHBGjRokLy9vbV69WqtWLFCxYoVS9Vjh38fZ2dnVa9eXZ999pnq1q2rsmXLatSoUerRo4emT59ubmcymdS9e3f9/fff6t69e7LXP3DgQP36668JRgEAkiNTpkzq27evJk+erKioKI0cOVKVKlWSn5+f6tevL09PzxR/gYSNjY3++9//6v79+6pWrZreeeedBH3ds2TJojVr1ujWrVuqWrWqWrdurYYNG1qcE+khb968WrVqlXbu3Kny5cvr3XffVWBgoEaOHPnM5f7J+48k9ejRQyVKlFCVKlWUM2fOBJ9AWiuT8eS1esDKxcXFqVSpUmrbtm2qX70GAAAvp0wZXQCQkeK7LNSrV0/R0dGaPn26zpw5k+Kb7gAAwKuLLhOwajY2NpozZ46qVq2qWrVq6eDBg1q3bl2S/XkBAMC/D10mAAAAYNW4QgwAAACrRiAGAACAVSMQAwAAwKoRiAEAAGDVCMQAAACwagRiALACZ8+elclk0r59+zK6lH/sRfalfv36GjBgQJrVBODVRiAGgCd07dpVJpNJJpNJmTNnVqFChTRkyBA9ePAgo0v7R7y8vHTlyhWVLVs2TbczduxYmUwmNW7cOMG8Tz75RCaTSfXr10/TGgAgpfimOgB4SuPGjTV79mw9evRIe/bsUZcuXWQymTRp0qQ022ZsbKxMJpNsbNLmOoWtra08PT3TZN1Py507tzZu3KiLFy8qX7585unff/+98ufPny41AEBKcIUYAJ5ib28vT09PeXl5yd/fX76+vlq7dq15flxcnEJCQlSoUCE5OjqqfPnyWrJkicU6VqxYoWLFisnBwUENGjTQ3LlzZTKZdPv2bUnSnDlz5ObmphUrVqh06dKyt7fX+fPnFR0drcGDBytv3rxycnJS9erVtWnTJvN6z507p+bNmytbtmxycnJSmTJltGrVKknS33//rY4dOypnzpxydHRUsWLFNHv2bEmJdzPYvHmzqlWrJnt7e+XOnVvDhg1TTEyMeX79+vUVFBSkIUOGyN3dXZ6enho7duxzj1+uXLnUqFEjzZ071zxt+/btunHjhpo1a2bRNi4uTuPHj1e+fPlkb2+vChUqaPXq1RZtdu7cqYoVK8rBwUFVqlTR3r17E2zz0KFDatKkiZydneXh4aG3335bN27cSLLGL7/80vz78fDwUOvWrZ+7XwD+vQjEAPAMhw4d0vbt22VnZ2eeFhISoh9++EEzZ87U4cOHNXDgQHXq1EmbN2+WJJ05c0atW7eWv7+/9u/fr169eumDDz5IsO579+5p0qRJ+vbbb3X48GHlypVLffv2VWhoqBYsWKADBw6oTZs2aty4sU6cOCFJ6tOnj6Kjo7VlyxYdPHhQkyZNkrOzsyRp1KhROnLkiH777TcdPXpUX331lXLkyJHofl26dElNmzZV1apVtX//fn311Vf67rvv9OGHH1q0mzt3rpycnLRjxw5NnjxZ48ePt/jjICndu3fXnDlzzD9///336tixo8VxlKRp06ZpypQp+s9//qMDBw7Iz89PLVq0MO/v3bt39cYbb6h06dLas2ePxo4dq8GDB1us4/bt23rttddUsWJF7d69W6tXr9bVq1fVtm3bRGvbvXu3goKCNH78eB0/flyrV69W3bp1n7tPAP7FDACAWZcuXQxbW1vDycnJsLe3NyQZNjY2xpIlSwzDMIwHDx4YWbJkMbZv326xXGBgoBEQEGAYhmEMHTrUKFu2rMX8Dz74wJBk/P3334ZhGMbs2bMNSca+ffvMbc6dO2fY2toaly5dsli2YcOGxvDhww3DMAxvb29j7NixidbevHlzo1u3bonOO3PmjCHJ2Lt3r2EYhjFixAijRIkSRlxcnLnNjBkzDGdnZyM2NtYwDMOoV6+eUbt2bYv1VK1a1Rg6dGii2zAMwxgzZoxRvnx54+HDh0auXLmMzZs3G3fv3jWyZs1q7N+/3+jfv79Rr149c/s8efIYH330UYJt9O7d2zAMw/j666+N7NmzG/fv3zfP/+qrryz2ZcKECUajRo0s1nHhwgVDknH8+HHzvvTv398wDMP45ZdfDBcXFyMyMjLJ/QBgXehDDABPadCggb766itFRUXps88+U6ZMmdSqVStJ0smTJ3Xv3j29/vrrFss8fPhQFStWlCQdP35cVatWtZhfrVq1BNuxs7NTuXLlzD8fPHhQsbGxKl68uEW76OhoZc+eXZIUFBSk9957T7///rt8fX3VqlUr8zree+89tWrVSmFhYWrUqJH8/f1Vs2bNRPfx6NGj8vHxkclkMk+rVauW7t69q4sXL5r7+j5Zn/S4f/C1a9eSOHL/kzlzZnXq1EmzZ8/W6dOnVbx48QTrioyM1OXLl1WrVi2L6bVq1dL+/fvNdZYrV04ODg7m+T4+Phbt9+/fr40bN5qvlD/p1KlTCY7n66+/rgIFCqhw4cJq3LixGjdurJYtWypLlizP3S8A/04EYgB4ipOTk4oWLSrp8Uf95cuX13fffafAwEDdvXtXkvTrr78qb968FsvZ29unaDuOjo4WgfTu3buytbXVnj17ZGtra9E2Puy988478vPz06+//qrff/9dISEhmjJlivr166cmTZro3LlzWrVqldauXauGDRuqT58++s9//pPiYxAvc+bMFj+bTCbFxcUla9nu3burevXqOnTokLp37/7CNTzP3bt31bx580RvesydO3eCaVmzZlVYWJg2bdqk33//XaNHj9bYsWO1a9cuubm5pVmdAF5e9CEGgGewsbHRiBEjNHLkSN2/f9/iBriiRYtaPLy8vCRJJUqU0O7duy3Ws2vXruduq2LFioqNjdW1a9cSrPvJESK8vLz07rvvaunSpRo0aJC++eYb87ycOXOqS5cu+umnnzR16lTNmjUr0W2VKlVKoaGhMgzDPG3btm3KmjWrxcgQ/0SZMmVUpkwZHTp0SB06dEgw38XFRXny5NG2bdsspm/btk2lS5c213ngwAGLYe/+/PNPi/aVKlXS4cOHVbBgwQTHzcnJKdHaMmXKJF9fX02ePFkHDhzQ2bNntWHDhn+6ywBeUQRiAHiONm3ayNbWVjNmzFDWrFk1ePBgDRw4UHPnztWpU6cUFhamL774wjyqQq9evXTs2DENHTpUf/31lxYtWmS+wezJK8JPK168uDp27KjOnTtr6dKlOnPmjHbu3KmQkBD9+uuvkqQBAwZozZo1OnPmjMLCwrRx40aVKlVKkjR69GgtX75cJ0+e1OHDh7Vy5UrzvKf17t1bFy5cUL9+/XTs2DEtX75cY8aMUXBwcKoO/bZhwwZduXIlySuv77//viZNmqSFCxfq+PHjGjZsmPbt26f+/ftLkjp06CCTyaQePXroyJEjWrVqVYIr3n369NGtW7cUEBCgXbt26dSpU1qzZo26deum2NjYBNtcuXKlPv/8c+3bt0/nzp3TDz/8oLi4OJUoUSLV9hvAq4UuEwDwHJkyZVLfvn01efJkvffee5owYYJy5sypkJAQnT59Wm5ubqpUqZJGjBghSSpUqJCWLFmiQYMGadq0afLx8dEHH3yg995777ndKmbPnq0PP/xQgwYN0qVLl5QjRw7VqFFDb7zxhqTH4xX36dNHFy9elIuLixo3bqzPPvtM0uM+ycOHD9fZs2fl6OioOnXqaMGCBYluJ2/evFq1apXef/99lS9fXu7u7goMDNTIkSNT8cgpySu08YKCghQREaFBgwbp2rVrKl26tHnIOulxV5H/+7//07vvvquKFSuqdOnSmjRpkrlPtyTzVeahQ4eqUaNGio6OVoECBdS4ceNEw72bm5uWLl2qsWPH6sGDBypWrJh+/vlnlSlTJlX3HcCrw2Q8+XkZACBNfPTRR5o5c6YuXLiQ0aUAAJ7CFWIASANffvmlqlatquzZs2vbtm365JNP1Ldv34wuCwCQCAIxAKSBEydO6MMPP9StW7eUP39+DRo0SMOHD8/osgAAiaDLBAAAAKwao0wAAADAqhGIAQAAYNUIxAAAALBqBGIAAABYNQIxAAAArBqBGAAAAFaNQAwAAACrRiAGAACAVft/ornOMIWDSFQAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 49972b31540b5cd243950553a6a2731fbea67409 Mon Sep 17 00:00:00 2001
From: sharayuanuse <114616759+sharayuanuse@users.noreply.github.com>
Date: Wed, 23 Oct 2024 20:27:19 +0530
Subject: [PATCH 2/2] Create README.md

---
 .../tourist_recommendation_system/README.md   | 30 +++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 Recommendation Systems/tourist_recommendation_system/README.md

diff --git a/Recommendation Systems/tourist_recommendation_system/README.md b/Recommendation Systems/tourist_recommendation_system/README.md
new file mode 100644
index 00000000..19fbbc82
--- /dev/null
+++ b/Recommendation Systems/tourist_recommendation_system/README.md	
@@ -0,0 +1,30 @@
+## Tourist Recommendation System
+
+### 🎯 **Goal**
+
+The primary goal of the Tourist Recommendation System is to provide personalized travel recommendations to users based on their preferences, past travel experiences, and interests. The purpose of this project is to enhance the travel planning experience by suggesting popular tourist attractions, activities, and accommodations tailored to individual users, ultimately making their trips more enjoyable and efficient.
+
+### 🧵 **Dataset**
+
+Link: https://www.kaggle.com/datasets/saketk511/travel-dataset-guide-to-indias-must-see-places
+
+### 🧮 **What I had done!**
+
+- Data Collection: Gathered the dataset from Kaggle, which includes details about tourist attractions and user ratings.
+- Data Preprocessing: Cleaned the dataset by handling missing values, standardizing formats, and removing duplicates.
+- Exploratory Data Analysis (EDA): Analyzed the dataset to understand trends, distributions, and insights about popular attractions.
+- Feature Engineering: Created relevant features such as average ratings, location tags, and user preferences to enhance the recommendation process.
+- Model Selection: Chose collaborative filtering and content-based filtering methods for generating recommendations based on user preferences.
+- Model Training: Implemented the selected algorithms and trained the models using the prepared dataset.
+- Evaluation: Assessed the performance of the recommendation system using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
+
+### 🚀 **Models Implemented**
+
+- Content-Based Filtering: This model is selected to recommend attractions based on their features and attributes (e.g., type of attraction, location). It ensures that users receive recommendations aligned with their specific interests.
+
+### 📚 **Libraries Needed**
+
+- Pandas: For data manipulation and analysis.
+- NumPy: For numerical operations and handling arrays.
+- Scikit-learn: For implementing machine learning algorithms and evaluation metrics.
+- Matplotlib: For data visualization.