Capstone_Test_v4.ipynb

{
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   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mVzfF5vHjbHc"
   },
   "source": [
    "# Data Preparation (Pre-processing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jTzyaLbhjXyG"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://storage.googleapis.com/data-hotel/list-hotel/Data%20API.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TYminsbynnMD",
    "outputId": "a79d006f-a0fa-44b3-8a52-4835194df511"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'hotel', 'star', 'rating', 'reviews', 'harga', 'places_nearby',\n",
       "       'facil_+_akomod', 'food_and_drinks_list', 'hotel_services_list',\n",
       "       'in-room_facilities_list', 'business_facilities_list',\n",
       "       'nearby_facilities_list', 'public_facilities_list', 'general_list',\n",
       "       'things_to_do_list', 'accessibilty_list', 'connectivity_list',\n",
       "       'transportation_list', 'kids_and_pets_list',\n",
       "       'sports_and_recreations_list', 'shuttle_service_list', 'description'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "e1643060-b827-4a32-d123-b11bc0f3def0"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 298,
     "metadata": {},
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   ],
   "source": [
    "facilities_columns = ['Food and Drinks','Hotel Services','In-room Facilities', 'Business Facilities', 'Nearby Facilities',\n",
    "                      'Public Facilities', 'General', 'Things to Do', 'Accessibilty', 'Connectivity', 'Transportation',\n",
    "                      'Kids and Pets', 'Sports and Recreations', 'Shuttle Service']\n",
    "ind = []\n",
    "for i in range(len(df)):\n",
    "    list_subfacil = {}\n",
    "    facils = df['facil_+_akomod'][i].splitlines()\n",
    "    facils = [facil for facil in facils if facil != '']\n",
    "        \n",
    "    for facil in list_subfacil:\n",
    "        if facil not in facils:\n",
    "            list_subfacil[facil] = []   \n",
    "\n",
    "    for facil in facils:     \n",
    "        if facil in facilities_columns:\n",
    "            main_facil = facil\n",
    "            list_subfacil[main_facil] = []\n",
    "        else:\n",
    "            try:\n",
    "                list_subfacil[main_facil] += [facil]\n",
    "            except:\n",
    "                pass\n",
    "    ind.append(list_subfacil)\n",
    "\n",
    "len(ind)"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviews</th>\n",
       "      <th>harga</th>\n",
       "      <th>places_nearby</th>\n",
       "      <th>facil_+_akomod</th>\n",
       "      <th>food_and_drinks_list</th>\n",
       "      <th>hotel_services_list</th>\n",
       "      <th>...</th>\n",
       "      <th>public_facilities_list</th>\n",
       "      <th>general_list</th>\n",
       "      <th>things_to_do_list</th>\n",
       "      <th>accessibilty_list</th>\n",
       "      <th>connectivity_list</th>\n",
       "      <th>transportation_list</th>\n",
       "      <th>kids_and_pets_list</th>\n",
       "      <th>sports_and_recreations_list</th>\n",
       "      <th>shuttle_service_list</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Hotel Indonesia Kempinski Jakarta</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4363</td>\n",
       "      <td>2.480.500</td>\n",
       "      <td>Nearby Places\\n\\nJia Jia - Grand Indonesia (De...</td>\n",
       "      <td>Food and Drinks\\nA la carte breakfast\\nA la ca...</td>\n",
       "      <td>A la carte breakfast\\nA la carte dinner\\nA la ...</td>\n",
       "      <td>Bellhop\\nConcierge\\nDoorman\\nExpress check-in\\...</td>\n",
       "      <td>...</td>\n",
       "      <td>Parking\\nCoffee shop\\nElevator\\n24-hour room s...</td>\n",
       "      <td>AC\\nBallroom\\nBanquet\\nConnecting rooms\\nNon-s...</td>\n",
       "      <td>Fitness center\\nHealth club\\nMassage\\nOutdoor ...</td>\n",
       "      <td>Accessible bathroom\\nAccessible parking\\nRoll-...</td>\n",
       "      <td>INTERNATIONAL_CALL\\nFree WiFi\\nWiFi (surcharge)</td>\n",
       "      <td>Car rental\\nValet parking (surcharge)</td>\n",
       "      <td>CRIBS\\nHighchairs</td>\n",
       "      <td>Fitness center</td>\n",
       "      <td>Airport transfer (surcharge)</td>\n",
       "      <td>This Hotel offers ultimate comfort and luxury....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>The Langham, Jakarta</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>238</td>\n",
       "      <td>3.823.600</td>\n",
       "      <td>Nearby Places\\n\\nPig Me Up! - Ashta District 8...</td>\n",
       "      <td>Food and Drinks\\nA la carte dinner\\nA la carte...</td>\n",
       "      <td>A la carte dinner\\nA la carte lunch\\nBar\\nBrea...</td>\n",
       "      <td>Bellhop\\nConcierge\\nDoorman\\nFront desk\\n24-ho...</td>\n",
       "      <td>...</td>\n",
       "      <td>Coffee/tea in lobby\\nCoffee shop\\nElevator\\nRe...</td>\n",
       "      <td>AC\\nBallroom\\nBanquet\\nNon-smoking room\\nSwimm...</td>\n",
       "      <td>Fitness center\\nHealth club\\nIndoor pool\\nOutd...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Free WiFi</td>\n",
       "      <td>Secure parking\\nValet parking</td>\n",
       "      <td>CRIBS</td>\n",
       "      <td>Fitness center</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This Hotel offers ultimate comfort and luxury....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Manhattan Hotel</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8405</td>\n",
       "      <td>6.231.500</td>\n",
       "      <td>Nearby Places\\n\\nJia Jia - Grand Indonesia (De...</td>\n",
       "      <td>Hotel Services\\nBellhop\\nConcierge\\nMoney chan...</td>\n",
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       "      <td>Bellhop\\nConcierge\\nMoney changer\\nDoorman\\nEa...</td>\n",
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       "      <td>Fitness center\\nOutdoor pool</td>\n",
       "      <td>Disabled access friendly\\nAccessible bathroom\\...</td>\n",
       "      <td>INTERNATIONAL_CALL\\nLAN Internet (surcharge)\\n...</td>\n",
       "      <td>Area shuttle\\nParking with surcharge\\nSecure p...</td>\n",
       "      <td>Babysitting</td>\n",
       "      <td>Fitness center</td>\n",
       "      <td>Airport transfer (surcharge)</td>\n",
       "      <td>This Hotel offers ultimate comfort and luxury....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Aloft South Jakarta</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>424</td>\n",
       "      <td>762.3</td>\n",
       "      <td>Nearby Places\\n\\nSouthside Rooftop Bar &amp; Loung...</td>\n",
       "      <td>Public Facilities\\nParking\\nCoffee shop\\nEleva...</td>\n",
       "      <td>Bar\\nPoolside bar\\nBar</td>\n",
       "      <td>24-hour Receptionist\\nLaundry service\\nLuggage...</td>\n",
       "      <td>...</td>\n",
       "      <td>Parking\\nCoffee shop\\nElevator\\nRestaurant\\nRo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fitness center\\nGarden\\nOutdoor pool</td>\n",
       "      <td>Disabled access friendly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car rental</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Billiard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This Hotel offers ultimate comfort and luxury....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>The Mayflower, Jakarta - Marriott Executive Ap...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>393</td>\n",
       "      <td>1.756.254</td>\n",
       "      <td>Nearby Places\\n\\nSudirman Plaza\\nBusiness\\n2 m...</td>\n",
       "      <td>Food and Drinks\\nA la carte breakfast\\nA la ca...</td>\n",
       "      <td>A la carte breakfast\\nA la carte dinner\\nA la ...</td>\n",
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       "      <td>Fitness center\\nHealth club\\nHot tub\\nIndoor h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Free WiFi</td>\n",
       "      <td>Airport transfer\\nCar rental\\nSecure parking\\n...</td>\n",
       "      <td>CRIBS\\nHighchairs\\nFamily-friendly Facilities\\...</td>\n",
       "      <td>Fitness center</td>\n",
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       "      <td>This Hotel offers ultimate comfort and luxury....</td>\n",
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      "text/plain": [
       "   id                                              hotel  star  rating  \\\n",
       "0   0                  Hotel Indonesia Kempinski Jakarta   5.0     8.9   \n",
       "1   1                               The Langham, Jakarta   5.0     8.8   \n",
       "2   2                                    Manhattan Hotel   5.0     8.5   \n",
       "3   3                                Aloft South Jakarta   4.0     8.9   \n",
       "4   4  The Mayflower, Jakarta - Marriott Executive Ap...   5.0     9.0   \n",
       "\n",
       "  reviews      harga                                      places_nearby  \\\n",
       "0    4363  2.480.500  Nearby Places\\n\\nJia Jia - Grand Indonesia (De...   \n",
       "1     238  3.823.600  Nearby Places\\n\\nPig Me Up! - Ashta District 8...   \n",
       "2    8405  6.231.500  Nearby Places\\n\\nJia Jia - Grand Indonesia (De...   \n",
       "3     424      762.3  Nearby Places\\n\\nSouthside Rooftop Bar & Loung...   \n",
       "4     393  1.756.254  Nearby Places\\n\\nSudirman Plaza\\nBusiness\\n2 m...   \n",
       "\n",
       "                                      facil_+_akomod  \\\n",
       "0  Food and Drinks\\nA la carte breakfast\\nA la ca...   \n",
       "1  Food and Drinks\\nA la carte dinner\\nA la carte...   \n",
       "2  Hotel Services\\nBellhop\\nConcierge\\nMoney chan...   \n",
       "3  Public Facilities\\nParking\\nCoffee shop\\nEleva...   \n",
       "4  Food and Drinks\\nA la carte breakfast\\nA la ca...   \n",
       "\n",
       "                                food_and_drinks_list  \\\n",
       "0  A la carte breakfast\\nA la carte dinner\\nA la ...   \n",
       "1  A la carte dinner\\nA la carte lunch\\nBar\\nBrea...   \n",
       "2  A la carte dinner\\nA la carte lunch\\nRestauran...   \n",
       "3                             Bar\\nPoolside bar\\nBar   \n",
       "4  A la carte breakfast\\nA la carte dinner\\nA la ...   \n",
       "\n",
       "                                 hotel_services_list  ...  \\\n",
       "0  Bellhop\\nConcierge\\nDoorman\\nExpress check-in\\...  ...   \n",
       "1  Bellhop\\nConcierge\\nDoorman\\nFront desk\\n24-ho...  ...   \n",
       "2  Bellhop\\nConcierge\\nMoney changer\\nDoorman\\nEa...  ...   \n",
       "3  24-hour Receptionist\\nLaundry service\\nLuggage...  ...   \n",
       "4  Welcoming drinks\\nConcierge\\nMoney changer\\nDo...  ...   \n",
       "\n",
       "                              public_facilities_list  \\\n",
       "0  Parking\\nCoffee shop\\nElevator\\n24-hour room s...   \n",
       "1  Coffee/tea in lobby\\nCoffee shop\\nElevator\\nRe...   \n",
       "2  Parking\\nCoffee/tea in lobby\\nCoffee shop\\nEar...   \n",
       "3  Parking\\nCoffee shop\\nElevator\\nRestaurant\\nRo...   \n",
       "4  Parking\\nCoffee shop\\nElevator\\n24-hour room s...   \n",
       "\n",
       "                                        general_list  \\\n",
       "0  AC\\nBallroom\\nBanquet\\nConnecting rooms\\nNon-s...   \n",
       "1  AC\\nBallroom\\nBanquet\\nNon-smoking room\\nSwimm...   \n",
       "2  AC\\nBallroom\\nBanquet\\nConnecting rooms\\nFamil...   \n",
       "3                                                NaN   \n",
       "4  AC\\nFamily room\\nNon-smoking room\\nSwimming po...   \n",
       "\n",
       "                                   things_to_do_list  \\\n",
       "0  Fitness center\\nHealth club\\nMassage\\nOutdoor ...   \n",
       "1  Fitness center\\nHealth club\\nIndoor pool\\nOutd...   \n",
       "2                       Fitness center\\nOutdoor pool   \n",
       "3               Fitness center\\nGarden\\nOutdoor pool   \n",
       "4  Fitness center\\nHealth club\\nHot tub\\nIndoor h...   \n",
       "\n",
       "                                   accessibilty_list  \\\n",
       "0  Accessible bathroom\\nAccessible parking\\nRoll-...   \n",
       "1                                                NaN   \n",
       "2  Disabled access friendly\\nAccessible bathroom\\...   \n",
       "3                           Disabled access friendly   \n",
       "4                                                NaN   \n",
       "\n",
       "                                   connectivity_list  \\\n",
       "0    INTERNATIONAL_CALL\\nFree WiFi\\nWiFi (surcharge)   \n",
       "1                                          Free WiFi   \n",
       "2  INTERNATIONAL_CALL\\nLAN Internet (surcharge)\\n...   \n",
       "3                                                NaN   \n",
       "4                                          Free WiFi   \n",
       "\n",
       "                                 transportation_list  \\\n",
       "0              Car rental\\nValet parking (surcharge)   \n",
       "1                      Secure parking\\nValet parking   \n",
       "2  Area shuttle\\nParking with surcharge\\nSecure p...   \n",
       "3                                         Car rental   \n",
       "4  Airport transfer\\nCar rental\\nSecure parking\\n...   \n",
       "\n",
       "                                  kids_and_pets_list  \\\n",
       "0                                  CRIBS\\nHighchairs   \n",
       "1                                              CRIBS   \n",
       "2                                        Babysitting   \n",
       "3                                                NaN   \n",
       "4  CRIBS\\nHighchairs\\nFamily-friendly Facilities\\...   \n",
       "\n",
       "  sports_and_recreations_list          shuttle_service_list  \\\n",
       "0              Fitness center  Airport transfer (surcharge)   \n",
       "1              Fitness center                           NaN   \n",
       "2              Fitness center  Airport transfer (surcharge)   \n",
       "3                    Billiard                           NaN   \n",
       "4              Fitness center  Airport transfer (surcharge)   \n",
       "\n",
       "                                         description  \n",
       "0  This Hotel offers ultimate comfort and luxury....  \n",
       "1  This Hotel offers ultimate comfort and luxury....  \n",
       "2  This Hotel offers ultimate comfort and luxury....  \n",
       "3  This Hotel offers ultimate comfort and luxury....  \n",
       "4  This Hotel offers ultimate comfort and luxury....  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for index, facil in enumerate(ind):\n",
    "    for key, value in facil.items():\n",
    "        key2 = key.lower().split()\n",
    "        key2 = '_'.join(key2)\n",
    "        df.at[index, \"{}_list\".format(key2)] = '\\n'.join(value)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "P5PyrN5zn769"
   },
   "outputs": [],
   "source": [
    "facilities_columns = ['Food and Drinks','Hotel Services','In-room Facilities', 'Business Facilities', 'Nearby Facilities', 'Public Facilities', 'General', 'Things to Do', 'Accessibilty', 'Connectivity', 'Transportation', 'Kids and Pets', 'Sports and Recreations', 'Shuttle Service']\n",
    "\n",
    "facilities_columns.reverse()\n",
    "\n",
    "for index , row in df.iterrows():\n",
    "    \n",
    "    # split per fasil and akomod\n",
    "    arr = row['facil_+_akomod'].splitlines() \n",
    "    arr = [ar for ar in arr if ar != '']\n",
    "    #iterate over fasil and akomod\n",
    "\n",
    "    i = 0\n",
    "    count = 0\n",
    "\n",
    "    for  item in reversed(arr):\n",
    "      count += 1\n",
    "      if item in facilities_columns:\n",
    "        df.at[index,item ] = count-1\n",
    "        count = 0\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bHlrrmrcjhBb"
   },
   "outputs": [],
   "source": [
    "# Replace Null Values\n",
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RZHvSdYVjiTK"
   },
   "outputs": [],
   "source": [
    "for index , row in df.iterrows():\n",
    "    \n",
    "    # split per fasil and akomod\n",
    "    arr = row['places_nearby'].splitlines() \n",
    "    \n",
    "    #iterate over fasil and akomod\n",
    "\n",
    "    i = 0\n",
    "    count = 0\n",
    "    \n",
    "    for ind, item in enumerate(arr):\n",
    "        itemsplits = item.split()\n",
    "        for x in itemsplits:\n",
    "            if x.isdigit():\n",
    "                if itemsplits[1] == \"km\":\n",
    "                    meters = itemsplits[0] * 1000\n",
    "                else:\n",
    "                    meters = itemsplits[0]\n",
    "                    \n",
    "                if meters.isdigit():\n",
    "                    df.at[index,arr[ind-1]] = meters\n",
    "                    #print(meters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sy94YjgsjthO",
    "outputId": "19081a4d-c48a-4d8f-cf2c-62d2549554f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'hotel', 'star', 'rating', 'reviews', 'harga', 'places_nearby',\n",
      "       'facil_+_akomod', 'food_and_drinks_list', 'hotel_services_list',\n",
      "       'in-room_facilities_list', 'business_facilities_list',\n",
      "       'nearby_facilities_list', 'public_facilities_list', 'general_list',\n",
      "       'things_to_do_list', 'accessibilty_list', 'connectivity_list',\n",
      "       'transportation_list', 'kids_and_pets_list',\n",
      "       'sports_and_recreations_list', 'shuttle_service_list', 'description',\n",
      "       'Shuttle Service', 'Sports and Recreations', 'Kids and Pets',\n",
      "       'Transportation', 'Connectivity', 'Accessibilty', 'Things to Do',\n",
      "       'General', 'Public Facilities', 'Nearby Facilities',\n",
      "       'Business Facilities', 'In-room Facilities', 'Hotel Services',\n",
      "       'Food and Drinks', 'Fast Food', 'Shop & Gifts', 'Business',\n",
      "       'Transportation Hub', 'Casual Dining', 'Nightlife', 'Park & Zoo',\n",
      "       'Public Service', 'Arts & Sciences', 'Fine Dining', 'Sport',\n",
      "       'Quick Bites', 'Education', 'Street Food', 'Activity & Games', 'Cafe',\n",
      "       'Entertainment', 'Food Court', 'Sight & Landmark'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jWw94ZOLjvSs"
   },
   "outputs": [],
   "source": [
    "df.harga = df['harga'].str.replace('.','', regex = True)\n",
    "df.harga = df['harga'].str.replace(',','.', regex = True)\n",
    "df.harga = df['harga'].astype(float).astype(int)\n",
    "\n",
    "df.reviews = df['reviews'].str.replace('.','', regex = True)\n",
    "df.reviews = df['reviews'].str.replace(',','.', regex = True)\n",
    "df.reviews = df['reviews'].astype(float).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HlVmxDSZjwRi"
   },
   "outputs": [],
   "source": [
    "# Replace Place Nearby\n",
    "df = df.fillna(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7xI1Mc2CjyEH"
   },
   "outputs": [],
   "source": [
    "c = df.select_dtypes(object).columns\n",
    "df[c] = df[c].apply(pd.to_numeric,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZuJtxo4fj2Ih",
    "outputId": "db2ae4cf-1265-4023-cf96-4119d3b43911"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                               int64\n",
       "hotel                          float64\n",
       "star                           float64\n",
       "rating                         float64\n",
       "reviews                          int64\n",
       "harga                            int64\n",
       "places_nearby                  float64\n",
       "facil_+_akomod                 float64\n",
       "food_and_drinks_list           float64\n",
       "hotel_services_list            float64\n",
       "in-room_facilities_list        float64\n",
       "business_facilities_list       float64\n",
       "nearby_facilities_list         float64\n",
       "public_facilities_list         float64\n",
       "general_list                   float64\n",
       "things_to_do_list              float64\n",
       "accessibilty_list              float64\n",
       "connectivity_list              float64\n",
       "transportation_list            float64\n",
       "kids_and_pets_list             float64\n",
       "sports_and_recreations_list    float64\n",
       "shuttle_service_list           float64\n",
       "description                    float64\n",
       "Shuttle Service                float64\n",
       "Sports and Recreations         float64\n",
       "Kids and Pets                  float64\n",
       "Transportation                 float64\n",
       "Connectivity                   float64\n",
       "Accessibilty                   float64\n",
       "Things to Do                   float64\n",
       "General                        float64\n",
       "Public Facilities              float64\n",
       "Nearby Facilities              float64\n",
       "Business Facilities            float64\n",
       "In-room Facilities             float64\n",
       "Hotel Services                 float64\n",
       "Food and Drinks                float64\n",
       "Fast Food                        int64\n",
       "Shop & Gifts                     int64\n",
       "Business                         int64\n",
       "Transportation Hub               int64\n",
       "Casual Dining                    int64\n",
       "Nightlife                        int64\n",
       "Park & Zoo                       int64\n",
       "Public Service                   int64\n",
       "Arts & Sciences                  int64\n",
       "Fine Dining                      int64\n",
       "Sport                            int64\n",
       "Quick Bites                      int64\n",
       "Education                        int64\n",
       "Street Food                      int64\n",
       "Activity & Games                 int64\n",
       "Cafe                             int64\n",
       "Entertainment                    int64\n",
       "Food Court                       int64\n",
       "Sight & Landmark                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "sn0XPnUQj3V3",
    "outputId": "b707cf9f-4f47-4382-b039-e887dc2ec8aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-2e373ac8-2086-496d-a4da-5e36706af1af\">\n",
       "    <div 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>id</th>\n",
       "      <th>hotel</th>\n",
       "      <th>star</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviews</th>\n",
       "      <th>harga</th>\n",
       "      <th>places_nearby</th>\n",
       "      <th>facil_+_akomod</th>\n",
       "      <th>food_and_drinks_list</th>\n",
       "      <th>hotel_services_list</th>\n",
       "      <th>...</th>\n",
       "      <th>Fine Dining</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Quick Bites</th>\n",
       "      <th>Education</th>\n",
       "      <th>Street Food</th>\n",
       "      <th>Activity &amp; Games</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Entertainment</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>NaN</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>238</td>\n",
       "      <td>3823600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8405</td>\n",
       "      <td>6231500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>424</td>\n",
       "      <td>7623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>393</td>\n",
       "      <td>1756254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>11026</td>\n",
       "      <td>385200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1511</td>\n",
       "      <td>2420000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>517</td>\n",
       "      <td>688000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2717</td>\n",
       "      <td>431250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8445</td>\n",
       "      <td>446400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 56 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e373ac8-2086-496d-a4da-5e36706af1af')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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",
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       "        const buttonEl =\n",
       "          document.querySelector('#df-2e373ac8-2086-496d-a4da-5e36706af1af 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-2e373ac8-2086-496d-a4da-5e36706af1af');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    id  hotel  star  rating  reviews    harga  places_nearby  facil_+_akomod  \\\n",
       "0    0    NaN   5.0     8.9     4363  2480500            NaN             NaN   \n",
       "1    1    NaN   5.0     8.8      238  3823600            NaN             NaN   \n",
       "2    2    NaN   5.0     8.5     8405  6231500            NaN             NaN   \n",
       "3    3    NaN   4.0     8.9      424     7623            NaN             NaN   \n",
       "4    4    NaN   5.0     9.0      393  1756254            NaN             NaN   \n",
       "..  ..    ...   ...     ...      ...      ...            ...             ...   \n",
       "84  84    NaN   2.0     7.9    11026   385200            NaN             NaN   \n",
       "85  85    NaN   5.0     8.9     1511  2420000            NaN             NaN   \n",
       "86  86    NaN   4.0     7.8      517   688000            NaN             NaN   \n",
       "87  87    NaN   4.0     8.4     2717   431250            NaN             NaN   \n",
       "88  88    NaN   2.0     8.3     8445   446400            NaN             NaN   \n",
       "\n",
       "    food_and_drinks_list  hotel_services_list  ...  Fine Dining  Sport  \\\n",
       "0                    NaN                  NaN  ...        10000  10000   \n",
       "1                    NaN                  NaN  ...        10000  10000   \n",
       "2                    NaN                  NaN  ...        10000  10000   \n",
       "3                    NaN                  NaN  ...        10000  10000   \n",
       "4                    NaN                  NaN  ...        10000  10000   \n",
       "..                   ...                  ...  ...          ...    ...   \n",
       "84                   0.0                  NaN  ...        10000  10000   \n",
       "85                   NaN                  NaN  ...        10000  10000   \n",
       "86                   NaN                  NaN  ...        10000  10000   \n",
       "87                   NaN                  NaN  ...        10000  10000   \n",
       "88                   0.0                  NaN  ...        10000  10000   \n",
       "\n",
       "    Quick Bites  Education  Street Food  Activity & Games   Cafe  \\\n",
       "0         10000      10000        10000             10000  10000   \n",
       "1         10000      10000        10000             10000  10000   \n",
       "2         10000      10000        10000             10000  10000   \n",
       "3         10000      10000        10000             10000  10000   \n",
       "4         10000      10000        10000             10000  10000   \n",
       "..          ...        ...          ...               ...    ...   \n",
       "84        10000      10000        10000             10000  10000   \n",
       "85        10000      10000        10000             10000  10000   \n",
       "86        10000      10000        10000             10000  10000   \n",
       "87        10000      10000        10000             10000  10000   \n",
       "88        10000      10000        10000             10000  10000   \n",
       "\n",
       "    Entertainment  Food Court  Sight & Landmark  \n",
       "0           10000       10000             10000  \n",
       "1           10000       10000             10000  \n",
       "2           10000       10000             10000  \n",
       "3           10000       10000             10000  \n",
       "4           10000       10000             10000  \n",
       "..            ...         ...               ...  \n",
       "84          10000       10000             10000  \n",
       "85          10000       10000             10000  \n",
       "86          10000       10000             10000  \n",
       "87          10000       10000             10000  \n",
       "88          10000       10000             10000  \n",
       "\n",
       "[89 rows x 56 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xnFKCAzf1hqA"
   },
   "outputs": [],
   "source": [
    "# df.to_csv('data_preprocess.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XARHCvJ6kQNz"
   },
   "source": [
    "## Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CO5OS2gioSNI",
    "outputId": "c39b7f91-c9f2-4e11-bb22-916945a11fd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'hotel', 'star', 'rating', 'reviews', 'harga', 'places_nearby',\n",
       "       'facil_+_akomod', 'food_and_drinks_list', 'hotel_services_list',\n",
       "       'in-room_facilities_list', 'business_facilities_list',\n",
       "       'nearby_facilities_list', 'public_facilities_list', 'general_list',\n",
       "       'things_to_do_list', 'accessibilty_list', 'connectivity_list',\n",
       "       'transportation_list', 'kids_and_pets_list',\n",
       "       'sports_and_recreations_list', 'shuttle_service_list', 'description',\n",
       "       'Shuttle Service', 'Sports and Recreations', 'Kids and Pets',\n",
       "       'Transportation', 'Connectivity', 'Accessibilty', 'Things to Do',\n",
       "       'General', 'Public Facilities', 'Nearby Facilities',\n",
       "       'Business Facilities', 'In-room Facilities', 'Hotel Services',\n",
       "       'Food and Drinks', 'Fast Food', 'Shop & Gifts', 'Business',\n",
       "       'Transportation Hub', 'Casual Dining', 'Nightlife', 'Park & Zoo',\n",
       "       'Public Service', 'Arts & Sciences', 'Fine Dining', 'Sport',\n",
       "       'Quick Bites', 'Education', 'Street Food', 'Activity & Games', 'Cafe',\n",
       "       'Entertainment', 'Food Court', 'Sight & Landmark'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "h8wxjpIUquvy",
    "outputId": "c4afbe7e-e651-4658-917d-4a3f675d687e"
   },
   "outputs": [
    {
     "data": {
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       "      background-color: #3B4455;\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      ],
      "text/plain": [
       "   star  rating  reviews    harga  Shuttle Service  Sports and Recreations  \\\n",
       "0   5.0     8.9     4363  2480500              1.0                     1.0   \n",
       "1   5.0     8.8      238  3823600              0.0                     1.0   \n",
       "2   5.0     8.5     8405  6231500              1.0                     1.0   \n",
       "3   4.0     8.9      424     7623              0.0                     1.0   \n",
       "4   5.0     9.0      393  1756254              1.0                     1.0   \n",
       "\n",
       "   Kids and Pets  Transportation  Connectivity  Accessibilty  ...  \\\n",
       "0            2.0             2.0           3.0           4.0  ...   \n",
       "1            1.0             2.0           1.0           0.0  ...   \n",
       "2            1.0             5.0           4.0           6.0  ...   \n",
       "3            0.0             1.0           0.0           1.0  ...   \n",
       "4            4.0             4.0           1.0           0.0  ...   \n",
       "\n",
       "   Fine Dining  Sport  Quick Bites  Education  Street Food  Activity & Games  \\\n",
       "0        10000  10000        10000      10000        10000             10000   \n",
       "1        10000  10000        10000      10000        10000             10000   \n",
       "2        10000  10000        10000      10000        10000             10000   \n",
       "3        10000  10000        10000      10000        10000             10000   \n",
       "4        10000  10000        10000      10000        10000             10000   \n",
       "\n",
       "    Cafe  Entertainment  Food Court  Sight & Landmark  \n",
       "0  10000          10000       10000             10000  \n",
       "1  10000          10000       10000             10000  \n",
       "2  10000          10000       10000             10000  \n",
       "3  10000          10000       10000             10000  \n",
       "4  10000          10000       10000             10000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(df.columns[[0, 1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D5vAsdKhkPsp"
   },
   "outputs": [],
   "source": [
    "X = df[['star','reviews','harga', 'Shuttle Service', 'Sports and Recreations', 'Kids and Pets', 'Transportation', 'Connectivity', 'Accessibilty', 'Things to Do', 'General', 'Public Facilities', 'Nearby Facilities', 'Business Facilities', 'In-room Facilities', 'Hotel Services', 'Food and Drinks', 'Fast Food', 'Shop & Gifts', 'Business', 'Transportation Hub', 'Casual Dining', 'Nightlife', 'Park & Zoo', 'Public Service', 'Arts & Sciences', 'Fine Dining', 'Sport', 'Quick Bites', 'Education', 'Street Food', 'Activity & Games', 'Cafe', 'Entertainment', 'Food Court', 'Sight & Landmark' ]]\n",
    "y = df['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zi5FtO0Bsfkt",
    "outputId": "70eca5b1-8447-49c5-eea2-9c22df8e2062"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['star', 'rating', 'reviews', 'harga', 'Shuttle Service',\n",
       "       'Sports and Recreations', 'Kids and Pets', 'Transportation',\n",
       "       'Connectivity', 'Accessibilty', 'Things to Do', 'General',\n",
       "       'Public Facilities', 'Nearby Facilities', 'Business Facilities',\n",
       "       'In-room Facilities', 'Hotel Services', 'Food and Drinks', 'Fast Food',\n",
       "       'Shop & Gifts', 'Business', 'Transportation Hub', 'Casual Dining',\n",
       "       'Nightlife', 'Park & Zoo', 'Public Service', 'Arts & Sciences',\n",
       "       'Fine Dining', 'Sport', 'Quick Bites', 'Education', 'Street Food',\n",
       "       'Activity & Games', 'Cafe', 'Entertainment', 'Food Court',\n",
       "       'Sight & Landmark'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p9aL4n2-VyW0"
   },
   "source": [
    "# Weighted Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z85ISF-0ks72"
   },
   "source": [
    "## JSON Input test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fbUGNbSEkyK5"
   },
   "source": [
    "Flask : API model untuk bisa terima json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3_bLNHDkkvTF",
    "outputId": "fec0ebcd-18ce-41f8-970a-73749a8609a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"key\": [\"Transportation\", \"Connectivity\", \"Shuttle Service\"]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "jsondata = {\"key\": ['Transportation', 'Connectivity', 'Shuttle Service']}\n",
    "input_data = json.dumps(jsondata)\n",
    "print(input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jKw65sdPkwaL"
   },
   "outputs": [],
   "source": [
    "def user (input):\n",
    "  test = pd.read_json(input)\n",
    "  test = test['key']\n",
    "  return test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TbZnw9yfyp-u",
    "outputId": "11329a67-79b0-4b37-eab3-a175615ab8a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Transportation\n",
       "1       Connectivity\n",
       "2    Shuttle Service\n",
       "Name: key, dtype: object"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = user(input_data)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "pjmEg94i2-Vw",
    "outputId": "101f99dd-1a17-4320-ad20-d034f832fac0"
   },
   "outputs": [
    {
     "data": {
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       "      <div>\n",
       "<style scoped>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
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       "      <th>3</th>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1511</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>517</td>\n",
       "      <td>688000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2717</td>\n",
       "      <td>431250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8445</td>\n",
       "      <td>446400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 37 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-643bbe26-69e4-480a-b19a-c5af38c2fd2d')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
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       "    }\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",
       "    [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-643bbe26-69e4-480a-b19a-c5af38c2fd2d 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-643bbe26-69e4-480a-b19a-c5af38c2fd2d');\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",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    star  rating  reviews    harga  Shuttle Service  Sports and Recreations  \\\n",
       "0    5.0     8.9     4363  2480500              1.0                     1.0   \n",
       "1    5.0     8.8      238  3823600              0.0                     1.0   \n",
       "2    5.0     8.5     8405  6231500              1.0                     1.0   \n",
       "3    4.0     8.9      424     7623              0.0                     1.0   \n",
       "4    5.0     9.0      393  1756254              1.0                     1.0   \n",
       "..   ...     ...      ...      ...              ...                     ...   \n",
       "84   2.0     7.9    11026   385200              1.0                     0.0   \n",
       "85   5.0     8.9     1511  2420000              1.0                     0.0   \n",
       "86   4.0     7.8      517   688000              0.0                     0.0   \n",
       "87   4.0     8.4     2717   431250              1.0                     1.0   \n",
       "88   2.0     8.3     8445   446400              1.0                     0.0   \n",
       "\n",
       "    Kids and Pets  Transportation  Connectivity  Accessibilty  ...  \\\n",
       "0             2.0             2.0           3.0           4.0  ...   \n",
       "1             1.0             2.0           1.0           0.0  ...   \n",
       "2             1.0             5.0           4.0           6.0  ...   \n",
       "3             0.0             1.0           0.0           1.0  ...   \n",
       "4             4.0             4.0           1.0           0.0  ...   \n",
       "..            ...             ...           ...           ...  ...   \n",
       "84            0.0             2.0           1.0           1.0  ...   \n",
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       "\n",
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       "0         10000  10000        10000      10000        10000             10000   \n",
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       "3         10000  10000        10000      10000        10000             10000   \n",
       "4         10000  10000        10000      10000        10000             10000   \n",
       "..          ...    ...          ...        ...          ...               ...   \n",
       "84        10000  10000        10000      10000        10000             10000   \n",
       "85        10000  10000        10000      10000        10000             10000   \n",
       "86        10000  10000        10000      10000        10000             10000   \n",
       "87        10000  10000        10000      10000        10000             10000   \n",
       "88        10000  10000        10000      10000        10000             10000   \n",
       "\n",
       "     Cafe  Entertainment  Food Court  Sight & Landmark  \n",
       "0   10000          10000       10000             10000  \n",
       "1   10000          10000       10000             10000  \n",
       "2   10000          10000       10000             10000  \n",
       "3   10000          10000       10000             10000  \n",
       "4   10000          10000       10000             10000  \n",
       "..    ...            ...         ...               ...  \n",
       "84  10000          10000       10000             10000  \n",
       "85  10000          10000       10000             10000  \n",
       "86  10000          10000       10000             10000  \n",
       "87  10000          10000       10000             10000  \n",
       "88  10000          10000       10000             10000  \n",
       "\n",
       "[89 rows x 37 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ambil data\n",
    "weights = []\n",
    "for index, row in df.iterrows():\n",
    "  for i in range(len(test)):\n",
    "    weights.append(df['{}'.format(test.iat[i])])\n",
    "\n",
    "len(weights)\n",
    "for i in range(len(test)):\n",
    "  weights[i] = weights[i] * 2\n",
    "\n",
    "weights[0]\n",
    "weights = weights[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test)):\n",
    "  df['{}'.format(test.iat[i])] = weights[i].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "#Ubah jadi fungsi\n",
    "def get_weight(test, df):\n",
    "  weights = []\n",
    "  for index, row in df.iterrows():\n",
    "    for i in range(len(test)):\n",
    "      weights.append(df['{}'.format(test.iat[i])])\n",
    "\n",
    "  len(weights)\n",
    "  for i in range(len(test)):\n",
    "    weights[i] = weights[i] * 2\n",
    "\n",
    "  weights = weights[:3]\n",
    "  return weights\n",
    "  '''"
   ]
  },
  {
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    "id": "tOkTGDTE0nuB",
    "outputId": "ef2e70f4-f633-492e-a877-0945f7cc3851"
   },
   "outputs": [
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       "   star  rating  reviews    harga  Shuttle Service  Sports and Recreations  \\\n",
       "0   5.0     8.9     4363  2480500              1.0                     1.0   \n",
       "1   5.0     8.8      238  3823600              0.0                     1.0   \n",
       "2   5.0     8.5     8405  6231500              1.0                     1.0   \n",
       "3   4.0     8.9      424     7623              0.0                     1.0   \n",
       "4   5.0     9.0      393  1756254              1.0                     1.0   \n",
       "\n",
       "   Kids and Pets  Transportation  Connectivity  Accessibilty  ...  \\\n",
       "0            2.0            10.0           3.0           4.0  ...   \n",
       "1            1.0            10.0           1.0           0.0  ...   \n",
       "2            1.0             8.0           4.0           6.0  ...   \n",
       "3            0.0             4.0           0.0           1.0  ...   \n",
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       "\n",
       "    Cafe  Entertainment  Food Court  Sight & Landmark  \n",
       "0  10000          10000       10000             10000  \n",
       "1  10000          10000       10000             10000  \n",
       "2  10000          10000       10000             10000  \n",
       "3  10000          10000       10000             10000  \n",
       "4  10000          10000       10000             10000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wymdYUNWG9lW"
   },
   "source": [
    "## Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "eH9w9Nehz-BK",
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   "outputs": [
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       "      <td>10000</td>\n",
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       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>424</td>\n",
       "      <td>7623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>393</td>\n",
       "      <td>1756254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>11026</td>\n",
       "      <td>385200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1511</td>\n",
       "      <td>2420000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>517</td>\n",
       "      <td>688000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2717</td>\n",
       "      <td>431250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8445</td>\n",
       "      <td>446400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 37 columns</p>\n",
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      ],
      "text/plain": [
       "    star  rating  reviews    harga  Shuttle Service  Sports and Recreations  \\\n",
       "0    5.0     8.9     4363  2480500              1.0                     1.0   \n",
       "1    5.0     8.8      238  3823600              0.0                     1.0   \n",
       "2    5.0     8.5     8405  6231500              1.0                     1.0   \n",
       "3    4.0     8.9      424     7623              0.0                     1.0   \n",
       "4    5.0     9.0      393  1756254              1.0                     1.0   \n",
       "..   ...     ...      ...      ...              ...                     ...   \n",
       "84   2.0     7.9    11026   385200              1.0                     0.0   \n",
       "85   5.0     8.9     1511  2420000              1.0                     0.0   \n",
       "86   4.0     7.8      517   688000              0.0                     0.0   \n",
       "87   4.0     8.4     2717   431250              1.0                     1.0   \n",
       "88   2.0     8.3     8445   446400              1.0                     0.0   \n",
       "\n",
       "    Kids and Pets  Transportation  Connectivity  Accessibilty  ...  \\\n",
       "0             2.0            10.0           3.0           4.0  ...   \n",
       "1             1.0            10.0           1.0           0.0  ...   \n",
       "2             1.0             8.0           4.0           6.0  ...   \n",
       "3             0.0             4.0           0.0           1.0  ...   \n",
       "4             4.0             8.0           1.0           0.0  ...   \n",
       "..            ...             ...           ...           ...  ...   \n",
       "84            0.0            10.0           1.0           1.0  ...   \n",
       "85            0.0             6.0           1.0           6.0  ...   \n",
       "86            0.0             4.0           1.0           0.0  ...   \n",
       "87            0.0             2.0           2.0           1.0  ...   \n",
       "88            0.0             4.0           1.0           1.0  ...   \n",
       "\n",
       "    Fine Dining  Sport  Quick Bites  Education  Street Food  Activity & Games  \\\n",
       "0         10000  10000        10000      10000        10000             10000   \n",
       "1         10000  10000        10000      10000        10000             10000   \n",
       "2         10000  10000        10000      10000        10000             10000   \n",
       "3         10000  10000        10000      10000        10000             10000   \n",
       "4         10000  10000        10000      10000        10000             10000   \n",
       "..          ...    ...          ...        ...          ...               ...   \n",
       "84        10000  10000        10000      10000        10000             10000   \n",
       "85        10000  10000        10000      10000        10000             10000   \n",
       "86        10000  10000        10000      10000        10000             10000   \n",
       "87        10000  10000        10000      10000        10000             10000   \n",
       "88        10000  10000        10000      10000        10000             10000   \n",
       "\n",
       "     Cafe  Entertainment  Food Court  Sight & Landmark  \n",
       "0   10000          10000       10000             10000  \n",
       "1   10000          10000       10000             10000  \n",
       "2   10000          10000       10000             10000  \n",
       "3   10000          10000       10000             10000  \n",
       "4   10000          10000       10000             10000  \n",
       "..    ...            ...         ...               ...  \n",
       "84  10000          10000       10000             10000  \n",
       "85  10000          10000       10000             10000  \n",
       "86  10000          10000       10000             10000  \n",
       "87  10000          10000       10000             10000  \n",
       "88  10000          10000       10000             10000  \n",
       "\n",
       "[89 rows x 37 columns]"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aOZWD-0skS7W"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "dev_X, val_X, dev_y, val_y = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GXU3LRM1kn8y"
   },
   "outputs": [],
   "source": [
    "train_dataset = df.loc[:70]\n",
    "test_dataset = df.drop(train_dataset.index)\n",
    "\n",
    "train_features = train_dataset.copy()\n",
    "test_features = test_dataset.copy()\n",
    "\n",
    "train_labels = train_features.pop('rating')\n",
    "test_labels = test_features.pop('rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kvd21LHaj54u"
   },
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jKtecf2tlBmQ"
   },
   "source": [
    "## Hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U73WrJCilDdb",
    "outputId": "6001ffa8-6703-4d6c-9572-95cf3fb478e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 13 calls to <function PreprocessingLayer.make_adapt_function.<locals>.adapt_step at 0x7f5c18135dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "[[4.2816901e+00 2.4317466e+03 1.4929205e+06 7.3239440e-01 1.2957746e+00\n",
      "  5.3521127e-01 5.4647884e+00 1.7746480e+00 2.1549296e+00 4.4788733e+00\n",
      "  5.9718308e+00 9.0000000e+00 4.0985913e+00 4.3380280e+00 7.2816901e+00\n",
      "  9.5915489e+00 9.3661976e+00 9.5789727e+03 2.8839155e+03 3.0320562e+03\n",
      "  3.7190706e+03 2.1346338e+03 9.8607891e+03 9.5860977e+03 7.4591831e+03\n",
      "  9.3296055e+03 9.4377754e+03 9.7351133e+03 9.8595781e+03 8.7858164e+03\n",
      "  9.7189004e+03 9.5949570e+03 9.4380703e+03 9.7323945e+03 9.8594512e+03\n",
      "  9.7262676e+03]]\n"
     ]
    }
   ],
   "source": [
    "normalizer = tf.keras.layers.Normalization(axis=-1)\n",
    "normalizer.adapt(np.array(train_features))\n",
    "print(normalizer.mean.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xk5ILs3AlHZj"
   },
   "outputs": [],
   "source": [
    "regularizer = 0.000001\n",
    "dropout = 0.25\n",
    "schedul = -0.0001\n",
    "lr = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1KusQ0JXlcJk"
   },
   "outputs": [],
   "source": [
    "optimizer = tf.optimizers.Adam(learning_rate=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "j9qId3gJENEl",
    "outputId": "402b717d-e261-44ab-dd92-7c3687f91bf7"
   },
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>238</td>\n",
       "      <td>3823600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8405</td>\n",
       "      <td>6231500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>424</td>\n",
       "      <td>7623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>393</td>\n",
       "      <td>1756254</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>11026</td>\n",
       "      <td>385200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1511</td>\n",
       "      <td>2420000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>517</td>\n",
       "      <td>688000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2717</td>\n",
       "      <td>431250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8445</td>\n",
       "      <td>446400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 37 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff23dbf0-58e6-4c48-952d-b65f42b8fa53')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-ff23dbf0-58e6-4c48-952d-b65f42b8fa53 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-ff23dbf0-58e6-4c48-952d-b65f42b8fa53');\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",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    star  rating  reviews    harga  Shuttle Service  Sports and Recreations  \\\n",
       "0    5.0     8.9     4363  2480500              1.0                     1.0   \n",
       "1    5.0     8.8      238  3823600              0.0                     1.0   \n",
       "2    5.0     8.5     8405  6231500              1.0                     1.0   \n",
       "3    4.0     8.9      424     7623              0.0                     1.0   \n",
       "4    5.0     9.0      393  1756254              1.0                     1.0   \n",
       "..   ...     ...      ...      ...              ...                     ...   \n",
       "84   2.0     7.9    11026   385200              1.0                     0.0   \n",
       "85   5.0     8.9     1511  2420000              1.0                     0.0   \n",
       "86   4.0     7.8      517   688000              0.0                     0.0   \n",
       "87   4.0     8.4     2717   431250              1.0                     1.0   \n",
       "88   2.0     8.3     8445   446400              1.0                     0.0   \n",
       "\n",
       "    Kids and Pets  Transportation  Connectivity  Accessibilty  ...  \\\n",
       "0             2.0            10.0           3.0           4.0  ...   \n",
       "1             1.0            10.0           1.0           0.0  ...   \n",
       "2             1.0             8.0           4.0           6.0  ...   \n",
       "3             0.0             4.0           0.0           1.0  ...   \n",
       "4             4.0             8.0           1.0           0.0  ...   \n",
       "..            ...             ...           ...           ...  ...   \n",
       "84            0.0            10.0           1.0           1.0  ...   \n",
       "85            0.0             6.0           1.0           6.0  ...   \n",
       "86            0.0             4.0           1.0           0.0  ...   \n",
       "87            0.0             2.0           2.0           1.0  ...   \n",
       "88            0.0             4.0           1.0           1.0  ...   \n",
       "\n",
       "    Fine Dining  Sport  Quick Bites  Education  Street Food  Activity & Games  \\\n",
       "0         10000  10000        10000      10000        10000             10000   \n",
       "1         10000  10000        10000      10000        10000             10000   \n",
       "2         10000  10000        10000      10000        10000             10000   \n",
       "3         10000  10000        10000      10000        10000             10000   \n",
       "4         10000  10000        10000      10000        10000             10000   \n",
       "..          ...    ...          ...        ...          ...               ...   \n",
       "84        10000  10000        10000      10000        10000             10000   \n",
       "85        10000  10000        10000      10000        10000             10000   \n",
       "86        10000  10000        10000      10000        10000             10000   \n",
       "87        10000  10000        10000      10000        10000             10000   \n",
       "88        10000  10000        10000      10000        10000             10000   \n",
       "\n",
       "     Cafe  Entertainment  Food Court  Sight & Landmark  \n",
       "0   10000          10000       10000             10000  \n",
       "1   10000          10000       10000             10000  \n",
       "2   10000          10000       10000             10000  \n",
       "3   10000          10000       10000             10000  \n",
       "4   10000          10000       10000             10000  \n",
       "..    ...            ...         ...               ...  \n",
       "84  10000          10000       10000             10000  \n",
       "85  10000          10000       10000             10000  \n",
       "86  10000          10000       10000             10000  \n",
       "87  10000          10000       10000             10000  \n",
       "88  10000          10000       10000             10000  \n",
       "\n",
       "[89 rows x 37 columns]"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rrtOtVTmkYFU"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bToJD6_YlPFL"
   },
   "outputs": [],
   "source": [
    "def scale_model(norm):\n",
    "      model = keras.Sequential([\n",
    "      norm,\n",
    "      layers.Dense(16, activation='relu', kernel_regularizer=regularizers.l2(regularizer)),\n",
    "      layers.Dropout(dropout),\n",
    "      layers.Dense(32, activation='relu', kernel_regularizer=regularizers.l2(regularizer)),\n",
    "      layers.Dropout(dropout),\n",
    "      layers.Dense(64, activation='relu', kernel_regularizer=regularizers.l2(regularizer)),\n",
    "      layers.Dropout(dropout),\n",
    "      layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(regularizer)),\n",
    "      layers.Dropout(dropout),\n",
    "      layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(regularizer)),\n",
    "      layers.Dropout(dropout),\n",
    "      layers.Dense(1)\n",
    "      ])\n",
    "      \n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mboUkle0lY6v"
   },
   "outputs": [],
   "source": [
    "def scheduler(epoch, lr):\n",
    "  if epoch < 10:\n",
    "    return lr\n",
    "  else:\n",
    "    return lr * tf.math.exp(schedul)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jwSlbYdcla6i"
   },
   "outputs": [],
   "source": [
    "def DNN_Pipeline (model):\n",
    "    \n",
    "    model.compile(optimizer= optimizer, loss='mean_absolute_error')\n",
    "    \n",
    "    history = model.fit(\n",
    "              train_features,\n",
    "              train_labels,\n",
    "              validation_split=0.2,\n",
    "              callbacks = tf.keras.callbacks.LearningRateScheduler(scheduler),\n",
    "              verbose=0, epochs=350)\n",
    "    \n",
    "    hist = pd.DataFrame(history.history)\n",
    "    hist['epoch'] = history.epoch\n",
    "    \n",
    "    plt.plot(history.history['loss'], label='loss')\n",
    "    plt.plot(history.history['val_loss'], label='val_loss')\n",
    "    plt.ylim([0, 3])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Error [MPG]')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    print(hist.tail(10))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 474
    },
    "id": "78gnGa8aliNp",
    "outputId": "fb177cbf-90cb-49dc-a207-651dc5a3d315"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         loss  val_loss        lr  epoch\n",
      "340  0.548193  0.211862  0.000967    340\n",
      "341  0.678658  0.132982  0.000967    341\n",
      "342  0.573079  0.143035  0.000967    342\n",
      "343  0.678480  0.231480  0.000967    343\n",
      "344  0.646001  0.395733  0.000967    344\n",
      "345  0.616183  0.558515  0.000967    345\n",
      "346  0.763127  0.573921  0.000967    346\n",
      "347  0.699716  0.402925  0.000967    347\n",
      "348  0.683147  0.202339  0.000967    348\n",
      "349  0.594304  0.157285  0.000967    349\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale = DNN_Pipeline(scale_model(normalizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GDbnlzhEnKFQ"
   },
   "source": [
    "## Rank Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IrZDhiYDlrqX",
    "outputId": "e28830f0-9a75-448b-e7f7-443d2efb65c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.699828 ],\n",
       "       [8.750009 ],\n",
       "       [8.619454 ],\n",
       "       [8.624614 ],\n",
       "       [8.948209 ],\n",
       "       [8.770069 ],\n",
       "       [8.913201 ],\n",
       "       [8.818605 ],\n",
       "       [8.842495 ],\n",
       "       [8.771911 ],\n",
       "       [8.650928 ],\n",
       "       [8.749527 ],\n",
       "       [8.636458 ],\n",
       "       [8.70949  ],\n",
       "       [8.922677 ],\n",
       "       [8.677221 ],\n",
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     "execution_count": 335,
     "metadata": {},
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   ],
   "source": [
    "# Rank train datasets\n",
    "scale.predict(train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "id": "sQR8sOODl2VP",
    "outputId": "da54d4c7-ad22-49b8-e020-bdd1f30b44f7"
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      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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       "46        10000      10000        10000             10000  10000   \n",
       "..          ...        ...          ...               ...    ...   \n",
       "31        10000      10000        10000             10000     16   \n",
       "2         10000      10000        10000             10000  10000   \n",
       "57        10000      10000        10000             10000  10000   \n",
       "53        10000        142        10000             10000  10000   \n",
       "56        10000      10000        10000             10000     10   \n",
       "\n",
       "    Entertainment  Food Court  Sight & Landmark  predict_score  \n",
       "4           10000       10000             10000       8.948209  \n",
       "14          10000       10000             10000       8.922677  \n",
       "6           10000       10000             10000       8.913201  \n",
       "45          10000       10000               193       8.888311  \n",
       "46          10000       10000             10000       8.844734  \n",
       "..            ...         ...               ...            ...  \n",
       "31            158       10000             10000       8.620786  \n",
       "2           10000       10000             10000       8.619454  \n",
       "57          10000       10000             10000       8.615482  \n",
       "53          10000       10000             10000       8.615096  \n",
       "56          10000       10000             10000       8.582860  \n",
       "\n",
       "[71 rows x 38 columns]"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rank = train_dataset\n",
    "df_rank['predict_score'] = scale.predict(train_features)\n",
    "# df_rank\n",
    "\n",
    "new_sorted = df_rank.sort_values(by=['predict_score'], ascending=False)\n",
    "new_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nYCyLE3uw-I8"
   },
   "outputs": [],
   "source": []
  }
 ],
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