code.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dependencies"
      ],
      "metadata": {
        "id": "WTt8vNqDSgmb",
        "cell_id": "00001-fb46e4e9-2e83-4a20-b228-f8f4951d927e",
        "deepnote_cell_type": "markdown",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "hBIKyJLKRkPD",
        "cell_id": "00002-ab5d2929-b7df-4896-b4de-3122bd9ad9d6",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "f9633b4b",
        "execution_start": 1656166062236,
        "execution_millis": 2088,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 405
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "from matplotlib import pyplot\n",
        "import numpy as np\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "import matplotlib.patches as mpatches\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, ConfusionMatrixDisplay, precision_score, recall_score, f1_score, classification_report, roc_curve, plot_roc_curve, auc, precision_recall_curve, plot_precision_recall_curve, average_precision_score\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Dataset"
      ],
      "metadata": {
        "id": "4b7Y6I8FSjrj",
        "cell_id": "00003-0f530eb7-f7b1-404b-97a4-bd04a39c3d0d",
        "deepnote_cell_type": "markdown",
        "deepnote_cell_height": 82
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Data acquired from https://www.kaggle.com/datasets/aprabowo/indonesia-tourism-destination?resource=download"
      ],
      "metadata": {
        "id": "XXjF6eJDWSeE",
        "cell_id": "00004-8f148010-c0f2-4e5b-9857-fc4bb8ed5a47",
        "deepnote_cell_type": "markdown",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50Vng9TWL0tR",
        "outputId": "81ec89da-9ffe-4c14-8d47-730867dab58a",
        "cell_id": "00005-299da514-caaf-45e9-a5f6-67d502138544",
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        "source_hash": "a4721514",
        "execution_start": 1656166064324,
        "execution_millis": 644,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 130.1875
      },
      "source": [
        "# Clone Github\n",
        "!git clone https://github.com/dunasi4139/dataset.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "fatal: destination path 'dataset' already exists and is not an empty directory.\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "b348dd7a783142f8a46e63dd13e0b4cd",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "ad8f3ca4",
        "execution_start": 1656166064973,
        "execution_millis": 650,
        "deepnote_cell_type": "code",
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        "id": "aew1AW472m6O",
        "outputId": "2793121f-0968-41aa-d501-0902c6aa9d7b"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "dataset\ndeepnote_exports\nindonesia-tourism-destination.xlsx\ninit.ipynb\nProject_Akhir_Visualisasi_Data_Kelompok_8.ipynb\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xoz-HqGEWrDS",
        "cell_id": "00006-d258d67a-d2b7-47f1-8ce6-11aa825f8f99",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "e74cf884",
        "execution_start": 1656166065624,
        "execution_millis": 15,
        "deepnote_cell_type": "code",
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      },
      "source": [
        "df = pd.read_csv(\"dataset/tourism_with_id.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
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          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "cell_id": "00007-416d13f1-fa43-40ca-bcb7-83cf1510d354",
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        "deepnote_table_state": {
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          "pageIndex": 2,
          "filters": [],
          "sortBy": []
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        "deepnote_table_loading": false,
        "deepnote_cell_type": "code",
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      "source": [
        "df.head(5)"
      ],
      "execution_count": null,
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          "output_type": "execute_result",
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                  "Description": "Dunia Fantasi atau disebut juga Dufan adalah tempat hiburan yang terletak di kawasan Taman Impian Jaya Ancol, Jakarta Utara, Indonesia. Dufan diresmikan dan dibuka pada tanggal 29 Agustus 1985.",
                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 270000,
                  "Rating": 4.6,
                  "Time_Minutes": 360,
                  "Coordinate": "{'lat': -6.125312399999999, 'lng': 106.8335377}",
                  "Lat": -6.1253124,
                  "Long": 106.8335377,
                  "Unnamed: 11": "nan",
                  "Unnamed: 12": 3,
                  "_deepnote_index_column": 2
                },
                {
                  "Place_Id": 4,
                  "Place_Name": "Taman Mini Indonesia Indah (TMII)",
                  "Description": "Taman Mini Indonesia Indah merupakan suatu kawasan taman wisata bertema budaya Indonesia di Jakarta Timur. Area seluas kurang lebih 150 hektare atau 1,5 kilometer persegi ini terletak pada koordinat 6°18′6.8″LS,106°53′47.2″BT",
                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 10000,
                  "Rating": 4.5,
                  "Time_Minutes": "nan",
                  "Coordinate": "{'lat': -6.302445899999999, 'lng': 106.8951559}",
                  "Lat": -6.3024459,
                  "Long": 106.8951559,
                  "Unnamed: 11": "nan",
                  "Unnamed: 12": 4,
                  "_deepnote_index_column": 3
                },
                {
                  "Place_Id": 5,
                  "Place_Name": "Atlantis Water Adventure",
                  "Description": "Atlantis Water Adventure atau dikenal dengan Atlantis Ancol akan menyuguhkan petualangan wisata air tak terlupakan. Tempat Wisata bertemakan permainan air dengan luas 5 hektar ini memberi sensasi petualangan di 8 kolam utama. Yaitu kolam Antila, Plaza Atlas, Poseidon, Aquarius, Octopus, Kiddy Pool, dan Atlantean. Berlokasi di kawasan Ancol Jakarta Baycity, Atlantis bisa menjadi pilihan destinasi yang pas untuk wisata berenang.",
                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 94000,
                  "Rating": 4.5,
                  "Time_Minutes": 60,
                  "Coordinate": "{'lat': -6.12419, 'lng': 106.839134}",
                  "Lat": -6.12419,
                  "Long": 106.839134,
                  "Unnamed: 11": "nan",
                  "Unnamed: 12": 5,
                  "_deepnote_index_column": 4
                }
              ]
            },
            "text/plain": "   Place_Id                         Place_Name  \\\n0         1                   Monumen Nasional   \n1         2                           Kota Tua   \n2         3                      Dunia Fantasi   \n3         4  Taman Mini Indonesia Indah (TMII)   \n4         5           Atlantis Water Adventure   \n\n                                         Description       Category     City  \\\n0  Monumen Nasional atau yang populer disingkat d...         Budaya  Jakarta   \n1  Kota tua di Jakarta, yang juga bernama Kota Tu...         Budaya  Jakarta   \n2  Dunia Fantasi atau disebut juga Dufan adalah t...  Taman Hiburan  Jakarta   \n3  Taman Mini Indonesia Indah merupakan suatu kaw...  Taman Hiburan  Jakarta   \n4  Atlantis Water Adventure atau dikenal dengan A...  Taman Hiburan  Jakarta   \n\n    Price  Rating  Time_Minutes  \\\n0   20000     4.6          15.0   \n1       0     4.6          90.0   \n2  270000     4.6         360.0   \n3   10000     4.5           NaN   \n4   94000     4.5          60.0   \n\n                                        Coordinate       Lat        Long  \\\n0          {'lat': -6.1753924, 'lng': 106.8271528} -6.175392  106.827153   \n1  {'lat': -6.137644799999999, 'lng': 106.8171245} -6.137645  106.817125   \n2  {'lat': -6.125312399999999, 'lng': 106.8335377} -6.125312  106.833538   \n3  {'lat': -6.302445899999999, 'lng': 106.8951559} -6.302446  106.895156   \n4             {'lat': -6.12419, 'lng': 106.839134} -6.124190  106.839134   \n\n   Unnamed: 11  Unnamed: 12  \n0          NaN            1  \n1          NaN            2  \n2          NaN            3  \n3          NaN            4  \n4          NaN            5  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Place_Id</th>\n      <th>Place_Name</th>\n      <th>Description</th>\n      <th>Category</th>\n      <th>City</th>\n      <th>Price</th>\n      <th>Rating</th>\n      <th>Time_Minutes</th>\n      <th>Coordinate</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>Unnamed: 11</th>\n      <th>Unnamed: 12</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Monumen Nasional</td>\n      <td>Monumen Nasional atau yang populer disingkat d...</td>\n      <td>Budaya</td>\n      <td>Jakarta</td>\n      <td>20000</td>\n      <td>4.6</td>\n      <td>15.0</td>\n      <td>{'lat': -6.1753924, 'lng': 106.8271528}</td>\n      <td>-6.175392</td>\n      <td>106.827153</td>\n      <td>NaN</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Kota Tua</td>\n      <td>Kota tua di Jakarta, yang juga bernama Kota Tu...</td>\n      <td>Budaya</td>\n      <td>Jakarta</td>\n      <td>0</td>\n      <td>4.6</td>\n      <td>90.0</td>\n      <td>{'lat': -6.137644799999999, 'lng': 106.8171245}</td>\n      <td>-6.137645</td>\n      <td>106.817125</td>\n      <td>NaN</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Dunia Fantasi</td>\n      <td>Dunia Fantasi atau disebut juga Dufan adalah t...</td>\n      <td>Taman Hiburan</td>\n      <td>Jakarta</td>\n      <td>270000</td>\n      <td>4.6</td>\n      <td>360.0</td>\n      <td>{'lat': -6.125312399999999, 'lng': 106.8335377}</td>\n      <td>-6.125312</td>\n      <td>106.833538</td>\n      <td>NaN</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Taman Mini Indonesia Indah (TMII)</td>\n      <td>Taman Mini Indonesia Indah merupakan suatu kaw...</td>\n      <td>Taman Hiburan</td>\n      <td>Jakarta</td>\n      <td>10000</td>\n      <td>4.5</td>\n      <td>NaN</td>\n      <td>{'lat': -6.302445899999999, 'lng': 106.8951559}</td>\n      <td>-6.302446</td>\n      <td>106.895156</td>\n      <td>NaN</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Atlantis Water Adventure</td>\n      <td>Atlantis Water Adventure atau dikenal dengan A...</td>\n      <td>Taman Hiburan</td>\n      <td>Jakarta</td>\n      <td>94000</td>\n      <td>4.5</td>\n      <td>60.0</td>\n      <td>{'lat': -6.12419, 'lng': 106.839134}</td>\n      <td>-6.124190</td>\n      <td>106.839134</td>\n      <td>NaN</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILyWrvRqYhXb",
        "outputId": "db1fa05f-6cbe-4b4b-e228-f20277ebbde0",
        "cell_id": "00008-72734523-f4d6-438c-bdbd-ca502090dc5f",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "de1e323c",
        "execution_start": 1656166065713,
        "execution_millis": 14,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 496
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 437 entries, 0 to 436\nData columns (total 13 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   Place_Id      437 non-null    int64  \n 1   Place_Name    437 non-null    object \n 2   Description   437 non-null    object \n 3   Category      437 non-null    object \n 4   City          437 non-null    object \n 5   Price         437 non-null    int64  \n 6   Rating        437 non-null    float64\n 7   Time_Minutes  205 non-null    float64\n 8   Coordinate    437 non-null    object \n 9   Lat           437 non-null    float64\n 10  Long          437 non-null    float64\n 11  Unnamed: 11   0 non-null      float64\n 12  Unnamed: 12   437 non-null    int64  \ndtypes: float64(5), int64(3), object(5)\nmemory usage: 44.5+ KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "153ee6ba62a649e4893120c2d8e36b2c",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h3",
        "id": "aaq8611H2m6S"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuyEn-d1YlZU",
        "cell_id": "00009-fffd4241-4c24-4147-aeb6-e8f924336e1c",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "940e4c71",
        "execution_start": 1656166065731,
        "execution_millis": 3,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81
      },
      "source": [
        "df.drop(['Unnamed: 11', 'Unnamed: 12'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan transformasi data pada kolom Time_Minutes dengan menggunakan nilai rata-rata pada objek wisata per masing-masing kota"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "f680b51f88b747f29f5e77597cd07998",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "PQlkIwpy2m6V"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "de0da0ed6ed34fcfbb235b5d3b9fcf30",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c732f9c0",
        "execution_start": 1656166065777,
        "execution_millis": 1,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "6Hm9PIOH2m6V"
      },
      "source": [
        "df['Time_Minutes'] = round(df['Time_Minutes'].fillna(df.groupby(df['City'])['Time_Minutes'].transform('mean')))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merubah nilai 0 pada kolom price menjadi NaN untuk mempermudah proses transformasi"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "85e8844c48df46fab4391a2d612dbda8",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "2cdedtkr2m6X"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "fb292d2ecba3418ea11a77ff33d75850",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "85e6141e",
        "execution_start": 1656166065778,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "mwDCFf382m6Y"
      },
      "source": [
        "df['Price'] = df['Price'].replace(0, np.NaN)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan transformasi data pada kolom Price dengan menggunakan nilai rata-rata harga berdasarkan Kota"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "13aa9e3735764b5b9e2a1f83063461fd",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "3oMnZdFK2m6Z"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "e55b366ea1cb4f7eabeaed00ab6b86e5",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "b5c1b227",
        "execution_start": 1656166065779,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "hrZmzaKR2m6Z"
      },
      "source": [
        "df['Price'] = round(df['Price'].fillna(df.groupby([df['City']])['Price'].transform('mean')))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "6a6a2807251c49fa9a59461f1664010f",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "ddb29315",
        "execution_start": 1656166065779,
        "execution_millis": 5,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 118.1875,
        "deepnote_output_heights": [
          21.1875
        ],
        "id": "7oiHsfQU2m6a",
        "outputId": "06d4d749-94e0-4fdf-bf78-b5ff55312793"
      },
      "source": [
        "df['Price'].isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan info dibawah dapat kita simpulkan bahwa data telah bersih dan tidak ada nilai NaN atau Null"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "f5d2d5e38691405ca528e478f12fd203",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "lnNjw36U2m6b"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "81a55cc7bc1440218570a1d96d83d408",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "de1e323c",
        "execution_start": 1656166065784,
        "execution_millis": 34,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 455.375,
        "id": "n7SOEinP2m6c",
        "outputId": "77ca2992-3c80-465b-9b06-53f1a78e6e27"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 437 entries, 0 to 436\nData columns (total 11 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   Place_Id      437 non-null    int64  \n 1   Place_Name    437 non-null    object \n 2   Description   437 non-null    object \n 3   Category      437 non-null    object \n 4   City          437 non-null    object \n 5   Price         437 non-null    float64\n 6   Rating        437 non-null    float64\n 7   Time_Minutes  437 non-null    float64\n 8   Coordinate    437 non-null    object \n 9   Lat           437 non-null    float64\n 10  Long          437 non-null    float64\ndtypes: float64(5), int64(1), object(5)\nmemory usage: 37.7+ KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "875372fa44034e93b327ddbc008f46f6",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "14f60b8f",
        "execution_start": 1656166065847,
        "execution_millis": 4,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 118.1875,
        "deepnote_output_heights": [
          21.1875
        ],
        "id": "RS38Ot-U2m6d",
        "outputId": "7786ab0f-8bba-4457-a210-dbb7c2861bbc"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "(437, 11)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memeriksa duplikat data"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "3ee1eca8c90149abb9ae5f763b861faa",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "D59dPPg22m6e"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "a50d4d948b024ab586dfd00e84002ac6",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "5ce2c336",
        "execution_start": 1656166065848,
        "execution_millis": 3,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 118.1875,
        "deepnote_output_heights": [
          21.1875
        ],
        "id": "IeWVZUqk2m6e",
        "outputId": "e49d320e-8025-4f4e-9bd7-0b76e614b118"
      },
      "source": [
        "df.duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "cef0d2f126c04e539dc1322e576d1d4e",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h1",
        "id": "vmWYfX6T2m6f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Rata-Rata Rating Berdasarkan Kota"
      ],
      "metadata": {
        "id": "vR1F4V_VfwqR",
        "cell_id": "00012-4ce71411-5845-447e-a598-f4e3df445b71",
        "deepnote_cell_type": "markdown",
        "deepnote_cell_height": 70
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOGggWHCf3QH",
        "outputId": "373f0e2f-025e-423d-96ed-3b29bca01e6e",
        "cell_id": "00013-9365454c-815b-4b5a-b182-814a037fc2ec",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "2d9b3421",
        "execution_start": 1656166065849,
        "execution_millis": 44,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 231.125
      },
      "source": [
        "                                                                                        rating_per_city = df.loc[:, ['City', 'Rating']].groupby('City', as_index=False).mean().sort_values(by='Rating', ascending=False, ignore_index=True)\n",
        "print(rating_per_city)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "         City    Rating\n0     Jakarta  4.488095\n1  Yogyakarta  4.466667\n2    Surabaya  4.454348\n3     Bandung  4.420968\n4    Semarang  4.361404\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "NFFJxfiFh65T",
        "outputId": "fba0c29f-befe-4a6b-b58c-e81c8148be9a",
        "cell_id": "00014-96c30775-0af2-4fa2-91cb-1b0d59f2203d",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "3f3aa78",
        "execution_start": 1656166065892,
        "execution_millis": 1056,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 644.1875,
        "deepnote_output_heights": [
          21.1875,
          351
        ]
      },
      "source": [
        "city = rating_per_city['City']\n",
        "rating = rating_per_city['Rating']\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([1,1,1,1])\n",
        "ax.bar(city, rating, color=['blue','red', 'orange', 'green', 'purple'])\n",
        "plt.xlabel('City')\n",
        "plt.ylabel('Rating')\n",
        "plt.title('Perbandingan Rata-Rata Rating Berdasarkan Kota')\n",
        "plt.ylim((4, 4.6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "(4.0, 4.6)"
          },
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 483,
              "height": 349
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil visualisasi dapat dilihat bahwa rata-rata rating tertinggi terdapat pada kota Jakarta sebesar 4,48 dan terendah didapat kota semarang sebesar 4,36.  Perbedaan rata-rata rating antar kota tidak terlalu signifikan sehingga dapat disimpulkan bahwa pada masing-masing kota banyak objek wisata yang mendapat rating yang bagus secara merata."
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "61f8b192810b4e4290d8e9b9289488fe",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "H4VWlPPA2m6j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hubungan Antara Rating dan Rata-rata Harga"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "b4f4149ba37040b1b2fe4b6afaa87c85",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "MdNHJI9u2m6j"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "cd6de36b859c433e867291b3bf0c0fbc",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "29d5c72c",
        "execution_start": 1656166066203,
        "execution_millis": 755,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 772.1875,
        "deepnote_output_heights": [
          21.1875,
          335
        ],
        "id": "XJxr9fJe2m6k",
        "outputId": "f45ef623-8086-4ff3-9976-f2dc872f7d60"
      },
      "source": [
        "get_rating_price =  round(df.groupby(df['Rating'])['Price'].mean())\n",
        "df1 = get_rating_price.reset_index(name = 'Price')\n",
        "rating = list(df1['Rating'])\n",
        "price = list(df1['Price'])\n",
        "fig = plt.figure()\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "# creating the bar plot\n",
        "cmap = plt.cm.tab10\n",
        "colors = cmap(np.arange(len(rating)) % cmap.N)\n",
        "plt.bar(rating, price, color=colors, width=0.08)\n",
        "plt.xlabel(\"Rating\")\n",
        "plt.ylabel(\"Rata-rata Harga\")\n",
        "plt.title(\"Hubungan Antara Rating dan Rata-rata Harga\")\n",
        "# plt.ylim(1000.0, 125000.0)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 720x360 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 631,
              "height": 333
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "baaba178b98346cea9096e25e23f0e3e",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "674dd8a8",
        "execution_start": 1656166066428,
        "execution_millis": 531,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 394.8125,
        "id": "kYtWPFHR2m6l",
        "outputId": "62f0a586-0544-41f2-da76-a3ee891a76cd"
      },
      "source": [
        "print(df1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "    Rating     Price\n0      3.4    5000.0\n1      3.8   37500.0\n2      3.9   21853.0\n3      4.0   42219.0\n4      4.1   14409.0\n5      4.2   24784.0\n6      4.3   33833.0\n7      4.4   36394.0\n8      4.5   34288.0\n9      4.6   41641.0\n10     4.7   34084.0\n11     4.8  122422.0\n12     4.9   33548.0\n13     5.0   48588.0\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil visualisasi diatas harga tidak terlalu mempengaruhi rating. Tempat wisata yang mendapat rating 4.5 keatas harga tiket masuknya rata-rata dari angka 35000 sampai 123000. Objek wisata tersebut kemungkinan meningkatkan kualitas pelayanan yang sebanding dengan harga yang ditetapkan"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "6bdcb8edab744e798f58128042ddaab2",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "5JqeddLZ2m6o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Jumlah Berdasarkan Kategori Objek  Wisata"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "bf45dafdf91444bf9e7a615df40cb2f9",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "imBZ-BXM2m6q"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "d1933389a03c4a29ae13191535405738",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "5a9a272b",
        "execution_start": 1656166066475,
        "execution_millis": 486,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 626.1875,
        "deepnote_output_heights": [
          21.1875,
          351
        ],
        "id": "vUosL0TX2m6q",
        "outputId": "acc2b5e4-b594-48fe-e925-1b4c52c989e1"
      },
      "source": [
        "category = df.groupby('Category')['Category'].count()\n",
        "df2 = category.reset_index(name = 'Count')\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([1,1,1.5,1])\n",
        "ax.bar(df2['Category'],df2['Count'], color=['blue','red', 'orange', 'green', 'purple', 'grey'])\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Perbandingan Jumlah Berdasarkan Kategori Objek Wisata')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Perbandingan Jumlah Berdasarkan Kategori Objek Wisata')"
          },
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 702,
              "height": 349
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Jumlah Berdasarkan Kategori Objek Wisata"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "9b42d9c3b39c48e6b6c3586aab68c544",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "8RTPxO9h2m6r"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "156e0afefe6c442f9666535dad337fcb",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "226dc00c",
        "execution_start": 1656166066668,
        "execution_millis": 294,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 233.3125,
        "id": "aJRVf5iZ2m6s",
        "outputId": "0589f3b6-6809-4737-920f-6ea0c8e242cf"
      },
      "source": [
        "print(df2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "             Category  Count\n0              Bahari     47\n1              Budaya    117\n2          Cagar Alam    106\n3  Pusat Perbelanjaan     15\n4       Taman Hiburan    135\n5       Tempat Ibadah     17\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "4d1860e38b4140e99322cfc86fc86e98",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "88ded0eb",
        "execution_start": 1656166066678,
        "execution_millis": 988,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 563,
        "deepnote_output_heights": [
          412
        ],
        "id": "EhcDI4ux2m6t",
        "outputId": "3e5982bf-d6e6-4912-abdf-0c13c5d9d207"
      },
      "source": [
        "df2['Count'].plot(kind='pie', autopct='%1.1f%%', startangle=180, figsize =(14, 7), labels=None)\n",
        "plt.legend(df2['Category'])\n",
        "plt.title('Perbandingan Jumlah Berdasarkan Kategori Objek Wisata')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 1008x504 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 412,
              "height": 410
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada dataset yang digunakan terdapat 6 kategori objek wisata yaitu Budaya, Taman Hiburan, Cagar Alam, Bahari, Pusat Perbelanjaan, Tempat Ibadah. Dilihat dari presentase jumlah objek wisata Taman Hiburan yang paling banyak yaitu 135 (30.9%) dari seluruh data. Yang paling sedikit yaitu kategori Tempat Ibadah sebanyak 17(3.9%) dan Pusat Perbelanjaan sebanyak 15(3.4%)"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "56c815b4654246ec9899b38c00c2ba2d",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "zTJo1AIa2m6u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Objek Wisata dari Rating (4.8 sampai 5.0) Berdasarkan Harga"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "ff6488edb0a74507b4ee34cc067d743a",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "SAe9SXr52m6u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengambil Objek wisata yang memiliki Rating 4.7 - 5.0 diambil sebanyak 18 data"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "b8fe2d455c6146b2b645079d0d82e217",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "78G6NDl42m6w"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "dce389a7a052419090ba0af21a53d826",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "d86ddc7",
        "execution_start": 1656166067038,
        "execution_millis": 6,
        "deepnote_table_state": {
          "pageSize": 10,
          "pageIndex": 0,
          "filters": [],
          "sortBy": []
        },
        "deepnote_table_loading": false,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "RH8E-w4Q2m6w"
      },
      "source": [
        "df3 = df.sort_values('Rating', ascending =False).iloc[1:1+18][['Rating','Place_Name','Category','Price', 'Time_Minutes']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mencari Objek wisata dari rating 4.7-5.0 dengan harga yang terjangkau/rendah. Data yang diambil sebanyak 10 objek wisata."
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "d150019d73854d94862acba79af1f52b",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "yZga4Xvv2m6x"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "f542567187a944d2a906e3e2d6dfd313",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "5af8df27",
        "execution_start": 1656166067049,
        "execution_millis": 33,
        "deepnote_table_state": {
          "pageSize": 10,
          "pageIndex": 0,
          "filters": [],
          "sortBy": []
        },
        "deepnote_table_loading": false,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "YzIhLpti2m6x"
      },
      "source": [
        "df4 = df3.sort_values('Price', ascending =True).iloc[1:1+10][['Rating','Place_Name','Category','Price', 'Time_Minutes']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mencari Objek wisata dengan Rating tinggi, harga terjangkau dan waktu tempuh yang singkat. Data yang diambil sebanyak 5 Objek Wisata"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "51520c24146d453c9dc4a74ce8f38ad1",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "hOImwdev2m6y"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "7c2d022786b84f2389e03ac61f7e1db8",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c9eeefff",
        "execution_start": 1656166067083,
        "execution_millis": 7,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "JfIUR50Q2m6z"
      },
      "source": [
        "df5 = df4.sort_values('Time_Minutes', ascending =True).iloc[1:1+5][['Rating','Place_Name','Category','Price', 'Time_Minutes']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Objek diurutkan berdasarkan Rating rendah-tinggi agar memudahkan dalam mengamati"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "d293202cdfb741a09af92e51d68d4e97",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "2fzK--sw2m6z"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "a8575b8ff6e1494783dcf4f0efd7dafd",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "13c330f3",
        "execution_start": 1656166067091,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "e5oSEiNi2m60"
      },
      "source": [
        "df5 = df5.sort_values('Rating', ascending =True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "9c1b72f00654494eba57d431f90c964f",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "186b6e3a",
        "execution_start": 1656166067091,
        "execution_millis": 575,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 622.328125,
        "deepnote_output_heights": [
          21.1875,
          383.140625
        ],
        "id": "YCEdr5lX2m61",
        "outputId": "3b4015e4-487e-49c5-e3d7-8b48deaf2993"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([1,2,2,1.5])\n",
        "ax.bar(df5['Place_Name'],df5['Price'], color=['blue','red', 'orange', 'green', 'purple'], width=0.5)\n",
        "plt.xlabel('Nama Objek')\n",
        "plt.ylabel('Harga')\n",
        "plt.title('Perbandingan Objek Wisata dari Rating (4.8 sampai 5.0) Berdasarkan Harga')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Perbandingan Objek Wisata dari Rating (4.8 sampai 5.0) Berdasarkan Harga')"
          },
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 930,
              "height": 493
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada visualisasi diatas dapat diamati bahwa terdapat 5 Objek Wisata yang telah difilter berdasarkan Rating, Harga dan Waktu tempuh yang paling tertinggi/ mudah dijangkau oleh pengunjung. Urutan datanya telah disesuaikan berdasarkan dari rating terendah-tertinggi yaitu Gua Maria Kerep Ambarawa, Kawasan Malioboro, Masjid Nasional Al-Akbar, Desa Wisata Sungai Code Jogja Kota, Kauman Pakualaman Yogjakarta. Berdasarkan harganya yang paling terjangkau yaitu Gua Maria Kerep Ambarawa(Rp. 2000) dan yang paling mahal yaitu Masjid Nasional Al-Akbar (Rp. 27500)."
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "4446ce1d582c4f60bed0702189f89205",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "hSzC5AiA2m63"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Jumlah 5 Rating Tertinggi dengan Waffle Chart"
      ],
      "metadata": {
        "cell_id": "a7cc65d6fec348b1a98b64602cf01fdb",
        "tags": [],
        "is_collapsed": false,
        "formattedRanges": [],
        "deepnote_cell_type": "text-cell-h2",
        "id": "YjZEyEB82m64"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "7f30cae9e3d04cbd81a99a6cf19fdbe0",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "ee243ed2",
        "execution_start": 1656166067375,
        "execution_millis": 1,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 117,
        "id": "_4Vsht-U2m65"
      },
      "source": [
        "df_rating = df['Rating'].value_counts()\n",
        "df_rating = df_rating.rename_axis('rating').reset_index(name='counts')\n",
        "df_rating.set_index('rating', inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "efe2a98818464ec3a3f345704fc09747",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "f7e196ee",
        "execution_start": 1656166067376,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 1179,
        "id": "gBsnA8Gf2m66"
      },
      "source": [
        "def create_waffle_chart(kategori, nilai, height, width, colormap, value_sign=''):\n",
        "\n",
        "    total_nilai = sum(nilai)\n",
        "    proporsi_kategori = [(value/ total_nilai) for value in nilai]\n",
        "\n",
        "    total_num_tiles = width * height\n",
        "    print ('Total number of tiles is', total_num_tiles)\n",
        "\n",
        "    tiles_per_category = [round(proportion * total_num_tiles) for proportion in proporsi_kategori]\n",
        "\n",
        "    for i, tiles in enumerate(tiles_per_category):\n",
        "        print (df_rating.index.values[i] + ': ' + str(tiles))\n",
        "\n",
        "    waffle_chart = np.zeros((height, width))\n",
        "\n",
        "    category_index = 0\n",
        "    tile_index = 0\n",
        "\n",
        "    for col in range(width):\n",
        "        for row in range(height):\n",
        "            tile_index += 1\n",
        "\n",
        "            if tile_index > sum(tiles_per_category[0:category_index]):\n",
        "                category_index += 1\n",
        "\n",
        "            waffle_chart[row, col] = category_index\n",
        "\n",
        "    fig = plt.figure()\n",
        "\n",
        "    colormap = plt.cm.coolwarm\n",
        "    plt.matshow(waffle_chart, cmap=colormap)\n",
        "    plt.colorbar()\n",
        "\n",
        "    ax = plt.gca()\n",
        "\n",
        "    ax.set_xticks(np.arange(-.5, (width), 1), minor=True)\n",
        "    ax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n",
        "\n",
        "    ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n",
        "\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    values_cumsum = np.cumsum(nilai)\n",
        "    total_values = values_cumsum[len(values_cumsum) - 1]\n",
        "\n",
        "    legend_handles = []\n",
        "    for i, category in enumerate(kategori):\n",
        "        if value_sign == '%':\n",
        "            label_str = category + ' (' + str(nilai[i]) + value_sign + ')'\n",
        "        else:\n",
        "            label_str = category + ' (' + value_sign + str(nilai[i]) + ')'\n",
        "\n",
        "        color_val = colormap(float(values_cumsum[i])/total_values)\n",
        "        legend_handles.append(mpatches.Patch(color=color_val, label=label_str))\n",
        "\n",
        "    plt.legend(\n",
        "        handles=legend_handles,\n",
        "        loc='lower center',\n",
        "        ncol=len(kategori),\n",
        "        bbox_to_anchor=(0., -0.2, 0.95, .1)\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "00b606c7fa6c46438b994b8d229c97dc",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "db8d1574",
        "execution_start": 1656166067377,
        "execution_millis": 289,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 600,
        "id": "u98IcBZ92m67",
        "outputId": "1e83380e-a43c-476f-b750-65e6ca779823"
      },
      "source": [
        "df_rating"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "application/vnd.deepnote.dataframe.v3+json": {
              "column_count": 1,
              "row_count": 14,
              "columns": [
                {
                  "name": "counts",
                  "dtype": "int64",
                  "stats": {
                    "unique_count": 11,
                    "nan_count": 0,
                    "min": "1",
                    "max": "98",
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                {
                  "name": "_deepnote_index_column",
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                }
              ],
              "rows": [
                {
                  "counts": 98,
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                {
                  "counts": 98,
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                {
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                },
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                },
                {
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                },
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                },
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                },
                {
                  "counts": 4,
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                }
              ]
            },
            "text/plain": "        counts\nrating        \n4.4         98\n4.5         98\n4.6         75\n4.3         47\n4.7         37\n4.2         20\n4.1         20\n4.0         18\n4.8         10\n4.9          4\n5.0          4\n3.9          3\n3.8          2\n3.4          1",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>counts</th>\n    </tr>\n    <tr>\n      <th>rating</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4.4</th>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>4.5</th>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>4.6</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4.3</th>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>4.7</th>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>4.2</th>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>4.1</th>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>4.0</th>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>4.8</th>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4.9</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5.0</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3.9</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3.8</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3.4</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_rating.index = df_rating.index.map(str)"
      ],
      "metadata": {
        "cell_id": "77ee0f426e2e4b4ebd8f9a661e2b274f",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "118f2193",
        "execution_start": 1656166067397,
        "execution_millis": 1,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "CLFKLsUt2m68"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "df_rating_sort = df_rating.sort_index()\n",
        "df_rating_top5 = df_rating_sort.tail(5)\n",
        "df_rating_top5"
      ],
      "metadata": {
        "cell_id": "07fb1ecc0515407aaf0478950ed2b434",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "b9fa9736",
        "execution_start": 1656166067398,
        "execution_millis": 268,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 431,
        "id": "QW66gvVw2m69",
        "outputId": "3ad7b068-69e2-4925-8143-576aeafa0289"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "application/vnd.deepnote.dataframe.v3+json": {
              "column_count": 1,
              "row_count": 5,
              "columns": [
                {
                  "name": "counts",
                  "dtype": "int64",
                  "stats": {
                    "unique_count": 4,
                    "nan_count": 0,
                    "min": "4",
                    "max": "75",
                    "histogram": [
                      {
                        "bin_start": 4,
                        "bin_end": 11.1,
                        "count": 3
                      },
                      {
                        "bin_start": 11.1,
                        "bin_end": 18.2,
                        "count": 0
                      },
                      {
                        "bin_start": 18.2,
                        "bin_end": 25.299999999999997,
                        "count": 0
                      },
                      {
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                        "bin_end": 32.4,
                        "count": 0
                      },
                      {
                        "bin_start": 32.4,
                        "bin_end": 39.5,
                        "count": 1
                      },
                      {
                        "bin_start": 39.5,
                        "bin_end": 46.599999999999994,
                        "count": 0
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                      {
                        "bin_start": 46.599999999999994,
                        "bin_end": 53.699999999999996,
                        "count": 0
                      },
                      {
                        "bin_start": 53.699999999999996,
                        "bin_end": 60.8,
                        "count": 0
                      },
                      {
                        "bin_start": 60.8,
                        "bin_end": 67.9,
                        "count": 0
                      },
                      {
                        "bin_start": 67.9,
                        "bin_end": 75,
                        "count": 1
                      }
                    ]
                  }
                },
                {
                  "name": "_deepnote_index_column",
                  "dtype": "object"
                }
              ],
              "rows": [
                {
                  "counts": 75,
                  "_deepnote_index_column": "4.6"
                },
                {
                  "counts": 37,
                  "_deepnote_index_column": "4.7"
                },
                {
                  "counts": 10,
                  "_deepnote_index_column": "4.8"
                },
                {
                  "counts": 4,
                  "_deepnote_index_column": "4.9"
                },
                {
                  "counts": 4,
                  "_deepnote_index_column": "5.0"
                }
              ]
            },
            "text/plain": "        counts\nrating        \n4.6         75\n4.7         37\n4.8         10\n4.9          4\n5.0          4",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>counts</th>\n    </tr>\n    <tr>\n      <th>rating</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4.6</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4.7</th>\n      <td>37</td>\n    </tr>\n    <tr>\n      <th>4.8</th>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4.9</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5.0</th>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "625e623df8c34c06be9421865fbc979c",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "81e66a8c",
        "execution_start": 1656166067433,
        "execution_millis": 1,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 207,
        "id": "1Qg5Py9W2m6-"
      },
      "source": [
        "width = 100\n",
        "height = 30\n",
        "\n",
        "kategori = df_rating_top5.index.values\n",
        "nilai = df_rating_top5['counts']\n",
        "# df_rating.index = df_rating.index.map(str)\n",
        "\n",
        "colormap = plt.cm.coolwarm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "801ed6436f4049918e989a174bc8f70b",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "36aba19e",
        "execution_start": 1656166067509,
        "execution_millis": 716,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 547.3125,
        "deepnote_output_heights": [
          null,
          21.1875,
          251,
          243
        ],
        "id": "nzknCNUb2m6_",
        "outputId": "895c2761-5080-4404-d04e-7e0b66957c90"
      },
      "source": [
        "create_waffle_chart(kategori, nilai, height, width, colormap)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Total number of tiles is 3000\n4.4: 1731\n4.5: 854\n4.6: 231\n4.3: 92\n4.7: 92\n",
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 0 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "<Figure size 960x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 684,
              "height": 249
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "pada visualisasi diatas berisikan informasi proporsi setiap rating. Rating yang memiliki jumlah tertinggi adalah 4.6 sebanyak 75. Rating yang memiliki jumlah terkecil adalah rating 4.9 dan 5 sebanyak 4 objek wisata"
      ],
      "metadata": {
        "cell_id": "fce680d5124247cbb6da02cd5aaf9e18",
        "tags": [],
        "deepnote_cell_type": "markdown",
        "deepnote_cell_height": 97.171875,
        "id": "fBeRE5_s2m7A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Jumlah Semua Rating dengan Regression Plot"
      ],
      "metadata": {
        "cell_id": "2832554f796e4c5a86ba1f22c8afa852",
        "tags": [],
        "is_collapsed": false,
        "formattedRanges": [],
        "owner_user_id": "87fbebec-7185-4fc6-9677-941ad7cdb251",
        "deepnote_cell_type": "text-cell-h2",
        "id": "VYvGy3Uc2m7A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_rp = pd.DataFrame(df['Rating'].value_counts())\n",
        "df_rp.index = map(float, df_rp.index)\n",
        "df_rp.reset_index(inplace=True)\n",
        "df_rp.columns = ['rating', 'total']"
      ],
      "metadata": {
        "cell_id": "5a3f370f28d14cd18eba997f31058053",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "db25ba26",
        "execution_start": 1656167919692,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 135,
        "id": "gruSrPRW2m7B"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "df_rp.head()"
      ],
      "metadata": {
        "cell_id": "6a070e4adbe244b49e0f40ddcbc84e6c",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "1650e1b8",
        "execution_start": 1656167924882,
        "execution_millis": 16,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 395,
        "deepnote_output_heights": [
          117.125
        ],
        "id": "fty2aPvo2m7B",
        "outputId": "aaf041bd-28e8-410f-ebbe-ee936e92b055"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 88,
          "data": {
            "application/vnd.deepnote.dataframe.v3+json": {
              "column_count": 2,
              "row_count": 5,
              "columns": [
                {
                  "name": "rating",
                  "dtype": "float64",
                  "stats": {
                    "unique_count": 5,
                    "nan_count": 0,
                    "min": "4.3",
                    "max": "4.7",
                    "histogram": [
                      {
                        "bin_start": 4.3,
                        "bin_end": 4.34,
                        "count": 1
                      },
                      {
                        "bin_start": 4.34,
                        "bin_end": 4.38,
                        "count": 0
                      },
                      {
                        "bin_start": 4.38,
                        "bin_end": 4.42,
                        "count": 1
                      },
                      {
                        "bin_start": 4.42,
                        "bin_end": 4.46,
                        "count": 0
                      },
                      {
                        "bin_start": 4.46,
                        "bin_end": 4.5,
                        "count": 0
                      },
                      {
                        "bin_start": 4.5,
                        "bin_end": 4.54,
                        "count": 1
                      },
                      {
                        "bin_start": 4.54,
                        "bin_end": 4.58,
                        "count": 0
                      },
                      {
                        "bin_start": 4.58,
                        "bin_end": 4.62,
                        "count": 1
                      },
                      {
                        "bin_start": 4.62,
                        "bin_end": 4.66,
                        "count": 0
                      },
                      {
                        "bin_start": 4.66,
                        "bin_end": 4.7,
                        "count": 1
                      }
                    ]
                  }
                },
                {
                  "name": "total",
                  "dtype": "int64",
                  "stats": {
                    "unique_count": 4,
                    "nan_count": 0,
                    "min": "37",
                    "max": "98",
                    "histogram": [
                      {
                        "bin_start": 37,
                        "bin_end": 43.1,
                        "count": 1
                      },
                      {
                        "bin_start": 43.1,
                        "bin_end": 49.2,
                        "count": 1
                      },
                      {
                        "bin_start": 49.2,
                        "bin_end": 55.3,
                        "count": 0
                      },
                      {
                        "bin_start": 55.3,
                        "bin_end": 61.4,
                        "count": 0
                      },
                      {
                        "bin_start": 61.4,
                        "bin_end": 67.5,
                        "count": 0
                      },
                      {
                        "bin_start": 67.5,
                        "bin_end": 73.6,
                        "count": 0
                      },
                      {
                        "bin_start": 73.6,
                        "bin_end": 79.69999999999999,
                        "count": 1
                      },
                      {
                        "bin_start": 79.69999999999999,
                        "bin_end": 85.8,
                        "count": 0
                      },
                      {
                        "bin_start": 85.8,
                        "bin_end": 91.9,
                        "count": 0
                      },
                      {
                        "bin_start": 91.9,
                        "bin_end": 98,
                        "count": 2
                      }
                    ]
                  }
                },
                {
                  "name": "_deepnote_index_column",
                  "dtype": "int64"
                }
              ],
              "rows": [
                {
                  "rating": 4.4,
                  "total": 98,
                  "_deepnote_index_column": 0
                },
                {
                  "rating": 4.5,
                  "total": 98,
                  "_deepnote_index_column": 1
                },
                {
                  "rating": 4.6,
                  "total": 75,
                  "_deepnote_index_column": 2
                },
                {
                  "rating": 4.3,
                  "total": 47,
                  "_deepnote_index_column": 3
                },
                {
                  "rating": 4.7,
                  "total": 37,
                  "_deepnote_index_column": 4
                }
              ]
            },
            "text/plain": "   rating  total\n0     4.4     98\n1     4.5     98\n2     4.6     75\n3     4.3     47\n4     4.7     37",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>rating</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4.4</td>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.5</td>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.6</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.3</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.7</td>\n      <td>37</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.regplot(x='rating', y='total', data=df_rp, color='red')"
      ],
      "metadata": {
        "cell_id": "8d5c5125f9264841803d200d49dfd591",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "37099e2",
        "execution_start": 1656167959315,
        "execution_millis": 308,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 361,
        "deepnote_output_heights": [
          264
        ],
        "id": "-NWQlFjn2m7C",
        "outputId": "eddb828a-37af-4fb9-e468-006d78a47980"
      },
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 391,
              "height": 262
            }
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "c93c0b66e91b4f878663ffb9a144ebd1",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h1",
        "id": "Ipwxhg_V2m7D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "5d21d927ad504fa08ef9786023f2d82a",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "pKI34x7L2m7E"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "69d27088920d484ba9b068f72779cf29",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "9143dae2",
        "execution_start": 1656166068222,
        "execution_millis": 5,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 430,
        "id": "KFcoIq3s2m7E",
        "outputId": "276e856c-31cf-40be-864e-354b899c35ef"
      },
      "source": [
        "data = df.copy()\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "application/vnd.deepnote.dataframe.v3+json": {
              "column_count": 11,
              "row_count": 5,
              "columns": [
                {
                  "name": "Place_Id",
                  "dtype": "int64",
                  "stats": {
                    "unique_count": 5,
                    "nan_count": 0,
                    "min": "1",
                    "max": "5",
                    "histogram": [
                      {
                        "bin_start": 1,
                        "bin_end": 1.4,
                        "count": 1
                      },
                      {
                        "bin_start": 1.4,
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                        "count": 0
                      },
                      {
                        "bin_start": 1.8,
                        "bin_end": 2.2,
                        "count": 1
                      },
                      {
                        "bin_start": 2.2,
                        "bin_end": 2.6,
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                      },
                      {
                        "bin_start": 2.6,
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                      {
                        "bin_start": 3,
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                      },
                      {
                        "bin_start": 3.4000000000000004,
                        "bin_end": 3.8000000000000003,
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                      {
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                        "bin_end": 4.2,
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                      },
                      {
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                      },
                      {
                        "bin_start": 4.6,
                        "bin_end": 5,
                        "count": 1
                      }
                    ]
                  }
                },
                {
                  "name": "Place_Name",
                  "dtype": "object",
                  "stats": {
                    "unique_count": 5,
                    "nan_count": 0,
                    "categories": [
                      {
                        "name": "Monumen Nasional",
                        "count": 1
                      },
                      {
                        "name": "Kota Tua",
                        "count": 1
                      },
                      {
                        "name": "3 others",
                        "count": 3
                      }
                    ]
                  }
                },
                {
                  "name": "Description",
                  "dtype": "object",
                  "stats": {
                    "unique_count": 5,
                    "nan_count": 0,
                    "categories": [
                      {
                        "name": "Monumen Nasional atau yang populer disingkat dengan Monas atau Tugu Monas adalah monumen peringatan setinggi 132 meter (433 kaki) yang didirikan untuk mengenang perlawanan dan perjuangan rakyat Indonesia untuk merebut kemerdekaan dari pemerintahan kolonial Hindia Belanda. Pembangunan monumen ini dimulai pada tanggal 17 Agustus 1961 di bawah perintah presiden Soekarno dan dibuka untuk umum pada tanggal 12 Juli 1975. Tugu ini dimahkotai lidah api yang dilapisi lembaran emas yang melambangkan semangat perjuangan yang menyala-nyala. Monumen Nasional terletak tepat di tengah Lapangan Medan Merdeka, Jakarta Pusat.",
                        "count": 1
                      },
                      {
                        "name": "Kota tua di Jakarta, yang juga bernama Kota Tua, berpusat di Alun-Alun Fatahillah, yaitu alun-alun yang ramai dengan pertunjukan rutin tarian tradisional. Museum Sejarah Jakarta adalah bangunan era Belanda dengan lukisan dan barang antik, sedangkan Museum Wayang memamerkan boneka kayu khas Jawa. Desa Glodok, atau Chinatown, terkenal dengan makanan kaki lima, seperti pangsit dan mi goreng. Di dekatnya, terdapat sekunar dan kapal penangkap ikan di pelabuhan Sunda Kelapa yang kuno",
                        "count": 1
                      },
                      {
                        "name": "3 others",
                        "count": 3
                      }
                    ]
                  }
                },
                {
                  "name": "Category",
                  "dtype": "object",
                  "stats": {
                    "unique_count": 2,
                    "nan_count": 0,
                    "categories": [
                      {
                        "name": "Taman Hiburan",
                        "count": 3
                      },
                      {
                        "name": "Budaya",
                        "count": 2
                      }
                    ]
                  }
                },
                {
                  "name": "City",
                  "dtype": "object",
                  "stats": {
                    "unique_count": 1,
                    "nan_count": 0,
                    "categories": [
                      {
                        "name": "Jakarta",
                        "count": 5
                      }
                    ]
                  }
                },
                {
                  "name": "Price",
                  "dtype": "float64",
                  "stats": {
                    "unique_count": 5,
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                    ]
                  }
                },
                {
                  "name": "Rating",
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                  "stats": {
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                    "histogram": [
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                  "Description": "Dunia Fantasi atau disebut juga Dufan adalah tempat hiburan yang terletak di kawasan Taman Impian Jaya Ancol, Jakarta Utara, Indonesia. Dufan diresmikan dan dibuka pada tanggal 29 Agustus 1985.",
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                  "Description": "Atlantis Water Adventure atau dikenal dengan Atlantis Ancol akan menyuguhkan petualangan wisata air tak terlupakan. Tempat Wisata bertemakan permainan air dengan luas 5 hektar ini memberi sensasi petualangan di 8 kolam utama. Yaitu kolam Antila, Plaza Atlas, Poseidon, Aquarius, Octopus, Kiddy Pool, dan Atlantean. Berlokasi di kawasan Ancol Jakarta Baycity, Atlantis bisa menjadi pilihan destinasi yang pas untuk wisata berenang.",
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            "text/plain": "   Place_Id                         Place_Name  \\\n0         1                   Monumen Nasional   \n1         2                           Kota Tua   \n2         3                      Dunia Fantasi   \n3         4  Taman Mini Indonesia Indah (TMII)   \n4         5           Atlantis Water Adventure   \n\n                                         Description       Category     City  \\\n0  Monumen Nasional atau yang populer disingkat d...         Budaya  Jakarta   \n1  Kota tua di Jakarta, yang juga bernama Kota Tu...         Budaya  Jakarta   \n2  Dunia Fantasi atau disebut juga Dufan adalah t...  Taman Hiburan  Jakarta   \n3  Taman Mini Indonesia Indah merupakan suatu kaw...  Taman Hiburan  Jakarta   \n4  Atlantis Water Adventure atau dikenal dengan A...  Taman Hiburan  Jakarta   \n\n      Price  Rating  Time_Minutes  \\\n0   20000.0     4.6          15.0   \n1   72904.0     4.6          90.0   \n2  270000.0     4.6         360.0   \n3   10000.0     4.5          99.0   \n4   94000.0     4.5          60.0   \n\n                                        Coordinate       Lat        Long  \n0          {'lat': -6.1753924, 'lng': 106.8271528} -6.175392  106.827153  \n1  {'lat': -6.137644799999999, 'lng': 106.8171245} -6.137645  106.817125  \n2  {'lat': -6.125312399999999, 'lng': 106.8335377} -6.125312  106.833538  \n3  {'lat': -6.302445899999999, 'lng': 106.8951559} -6.302446  106.895156  \n4             {'lat': -6.12419, 'lng': 106.839134} -6.124190  106.839134  ",
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    {
      "cell_type": "markdown",
      "source": [
        "Menghapus kolom yang tidak digunakan dalam proses machine learning. Disini kita menghapus 6 kolom yaitu Place_Id,&nbsp;Place_Name,&nbsp;Description, Coordinate, Lat, Long"
      ],
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      },
      "source": [
        "data.drop(['Place_Id', 'Place_Name', 'Description','Coordinate','Lat','Long'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kolom yang digunakan dalam proses machine learning yaitu Category, City, Price, Rating, Time_Minutes"
      ],
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                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 10000,
                  "Rating": 4.5,
                  "Time_Minutes": 99,
                  "_deepnote_index_column": 3
                },
                {
                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 94000,
                  "Rating": 4.5,
                  "Time_Minutes": 60,
                  "_deepnote_index_column": 4
                },
                {
                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 25000,
                  "Rating": 4.5,
                  "Time_Minutes": 10,
                  "_deepnote_index_column": 5
                },
                {
                  "Category": "Cagar Alam",
                  "City": "Jakarta",
                  "Price": 4000,
                  "Rating": 4.5,
                  "Time_Minutes": 99,
                  "_deepnote_index_column": 6
                },
                {
                  "Category": "Taman Hiburan",
                  "City": "Jakarta",
                  "Price": 180000,
                  "Rating": 4,
                  "Time_Minutes": 99,
                  "_deepnote_index_column": 7
                },
                {
                  "Category": "Bahari",
                  "City": "Jakarta",
                  "Price": 175000,
                  "Rating": 4.4,
                  "Time_Minutes": 99,
                  "_deepnote_index_column": 8
                },
                {
                  "Category": "Bahari",
                  "City": "Jakarta",
                  "Price": 150000,
                  "Rating": 4.5,
                  "Time_Minutes": 99,
                  "_deepnote_index_column": 9
                }
              ]
            },
            "text/plain": "          Category      City     Price  Rating  Time_Minutes\n0           Budaya   Jakarta   20000.0     4.6          15.0\n1           Budaya   Jakarta   72904.0     4.6          90.0\n2    Taman Hiburan   Jakarta  270000.0     4.6         360.0\n3    Taman Hiburan   Jakarta   10000.0     4.5          99.0\n4    Taman Hiburan   Jakarta   94000.0     4.5          60.0\n..             ...       ...       ...     ...           ...\n432         Budaya  Surabaya    2000.0     4.4          45.0\n433  Taman Hiburan  Surabaya   27588.0     4.6          52.0\n434  Taman Hiburan  Surabaya   27588.0     4.4          45.0\n435  Taman Hiburan  Surabaya   27588.0     4.6          52.0\n436  Tempat Ibadah  Surabaya   10000.0     4.8          52.0\n\n[437 rows x 5 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Category</th>\n      <th>City</th>\n      <th>Price</th>\n      <th>Rating</th>\n      <th>Time_Minutes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Budaya</td>\n      <td>Jakarta</td>\n      <td>20000.0</td>\n      <td>4.6</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Budaya</td>\n      <td>Jakarta</td>\n      <td>72904.0</td>\n      <td>4.6</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Taman Hiburan</td>\n      <td>Jakarta</td>\n      <td>270000.0</td>\n      <td>4.6</td>\n      <td>360.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Taman Hiburan</td>\n      <td>Jakarta</td>\n      <td>10000.0</td>\n      <td>4.5</td>\n      <td>99.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Taman Hiburan</td>\n      <td>Jakarta</td>\n      <td>94000.0</td>\n      <td>4.5</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>432</th>\n      <td>Budaya</td>\n      <td>Surabaya</td>\n      <td>2000.0</td>\n      <td>4.4</td>\n      <td>45.0</td>\n    </tr>\n    <tr>\n      <th>433</th>\n      <td>Taman Hiburan</td>\n      <td>Surabaya</td>\n      <td>27588.0</td>\n      <td>4.6</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>434</th>\n      <td>Taman Hiburan</td>\n      <td>Surabaya</td>\n      <td>27588.0</td>\n      <td>4.4</td>\n      <td>45.0</td>\n    </tr>\n    <tr>\n      <th>435</th>\n      <td>Taman Hiburan</td>\n      <td>Surabaya</td>\n      <td>27588.0</td>\n      <td>4.6</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>436</th>\n      <td>Tempat Ibadah</td>\n      <td>Surabaya</td>\n      <td>10000.0</td>\n      <td>4.8</td>\n      <td>52.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>437 rows × 5 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada proses machine learning kita harus melakukan transformasi atau perubahan bentuk data menjadi numeric, kolom yang akan ditransform Category dan City. Pada Category sendiri terdapat 6 Kategori yaitu Budaya (1), Taman Hiburan (2), Cagar Alam (3), Bahari (4), Pusat Perbelanjaan (5), Tempat Ibadah (6)."
      ],
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        "execution_start": 1656166068273,
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      },
      "source": [
        "data['Category'] = data['Category'].replace('Budaya', 1)\n",
        "data['Category'] = data['Category'].replace('Taman Hiburan', 2)\n",
        "data['Category'] = data['Category'].replace('Cagar Alam', 3)\n",
        "data['Category'] = data['Category'].replace('Bahari', 4)\n",
        "data['Category'] = data['Category'].replace('Pusat Perbelanjaan', 5)\n",
        "data['Category'] = data['Category'].replace('Tempat Ibadah', 6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada kolom City sendiri terdiri dari 5 Kota yaitu Jakarta (1), Yogyakarta (2), Bandung (3), Semarang (4) dan Surabaya (5)"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "ed1c331c4c86411da45a53aa7aaeba9b",
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        "execution_start": 1656166068293,
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        "deepnote_cell_type": "code",
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      },
      "source": [
        "data['City'] = data['City'].replace('Jakarta', 1)\n",
        "data['City'] = data['City'].replace('Yogyakarta', 2)\n",
        "data['City'] = data['City'].replace('Bandung', 3)\n",
        "data['City'] = data['City'].replace('Semarang', 4)\n",
        "data['City'] = data['City'].replace('Surabaya', 5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "cell_id": "a361d8c7ab0a4fa590055bff06cf445f",
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        "id": "m1dlJVfX2m7V"
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      "source": [
        "data_copy = data.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "data.head()"
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          "output_type": "execute_result",
          "execution_count": 41,
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                {
                  "name": "Rating",
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              "rows": [
                {
                  "Category": 1,
                  "City": 1,
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                {
                  "Category": 1,
                  "City": 1,
                  "Price": 72904,
                  "Rating": 4.6,
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                  "_deepnote_index_column": 1
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                {
                  "Category": 2,
                  "City": 1,
                  "Price": 270000,
                  "Rating": 4.6,
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                {
                  "Category": 2,
                  "City": 1,
                  "Price": 10000,
                  "Rating": 4.5,
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                {
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                  "City": 1,
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                  "Rating": 4.5,
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                  "_deepnote_index_column": 4
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            },
            "text/plain": "   Category  City     Price  Rating  Time_Minutes\n0         1     1   20000.0     4.6          15.0\n1         1     1   72904.0     4.6          90.0\n2         2     1  270000.0     4.6         360.0\n3         2     1   10000.0     4.5          99.0\n4         2     1   94000.0     4.5          60.0",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Category</th>\n      <th>City</th>\n      <th>Price</th>\n      <th>Rating</th>\n      <th>Time_Minutes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>20000.0</td>\n      <td>4.6</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>72904.0</td>\n      <td>4.6</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1</td>\n      <td>270000.0</td>\n      <td>4.6</td>\n      <td>360.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>1</td>\n      <td>10000.0</td>\n      <td>4.5</td>\n      <td>99.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>1</td>\n      <td>94000.0</td>\n      <td>4.5</td>\n      <td>60.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clustering K-Means"
      ],
      "metadata": {
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        "cell_id": "4350589698964321af89c5eb699bf61c",
        "tags": [],
        "is_collapsed": false,
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        "id": "L9shSUqq2m7Y"
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    },
    {
      "cell_type": "code",
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        "cell_id": "f5190e225e5446009e2c57de27494f11",
        "tags": [],
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        "source_hash": "cd3ceadb",
        "execution_start": 1656166068322,
        "execution_millis": 4,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 99,
        "id": "aXENFDrj2m7a"
      },
      "source": [
        "ms = MinMaxScaler()\n",
        "feature_mms = ms.fit_transform(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "bbb66e3c6270410f9b4cf0923168a364",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
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        "execution_start": 1656166068331,
        "execution_millis": 5,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "8zYubo9p2m7b"
      },
      "source": [
        "data = pd.DataFrame(feature_mms, columns=['Category', 'City', 'Price', 'Rating', 'Time_Minutes'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "c364c0e401574043b6a51037aa91ee40",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "2d4cec7e",
        "execution_start": 1656166068338,
        "execution_millis": 59,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 537.9375,
        "deepnote_output_heights": [
          404.9375
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        "id": "OhpB1JwN2m7b",
        "outputId": "44934024-a4b4-4eae-a7f8-73fef2e50b89"
      },
      "source": [
        "kmeans = KMeans(n_clusters = 5)\n",
        "kmeans.fit(feature_mms)\n",
        "kmeans.labels_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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                  "name": "Time_Minutes",
                  "dtype": "float64",
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                    "histogram": [
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                    ]
                  }
                },
                {
                  "name": "cluster",
                  "dtype": "int32",
                  "stats": {
                    "unique_count": 5,
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                    "max": "4",
                    "histogram": [
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                    ]
                  }
                },
                {
                  "name": "_deepnote_index_column",
                  "dtype": "int64"
                }
              ],
              "rows": [
                {
                  "Category": 4,
                  "City": 1,
                  "Price": 5000,
                  "Rating": 4.6,
                  "Time_Minutes": 99,
                  "cluster": 3,
                  "_deepnote_index_column": 10
                },
                {
                  "Category": 4,
                  "City": 1,
                  "Price": 150000,
                  "Rating": 4,
                  "Time_Minutes": 99,
                  "cluster": 3,
                  "_deepnote_index_column": 11
                },
                {
                  "Category": 4,
                  "City": 1,
                  "Price": 5000,
                  "Rating": 4.2,
                  "Time_Minutes": 99,
                  "cluster": 3,
                  "_deepnote_index_column": 12
                },
                {
                  "Category": 4,
                  "City": 1,
                  "Price": 900000,
                  "Rating": 4.8,
                  "Time_Minutes": 99,
                  "cluster": 3,
                  "_deepnote_index_column": 13
                },
                {
                  "Category": 5,
                  "City": 1,
                  "Price": 72904,
                  "Rating": 4.4,
                  "Time_Minutes": 90,
                  "cluster": 3,
                  "_deepnote_index_column": 14
                },
                {
                  "Category": 1,
                  "City": 1,
                  "Price": 72904,
                  "Rating": 4.3,
                  "Time_Minutes": 99,
                  "cluster": 0,
                  "_deepnote_index_column": 15
                },
                {
                  "Category": 1,
                  "City": 1,
                  "Price": 5000,
                  "Rating": 4.4,
                  "Time_Minutes": 99,
                  "cluster": 0,
                  "_deepnote_index_column": 16
                },
                {
                  "Category": 1,
                  "City": 1,
                  "Price": 2000,
                  "Rating": 4.7,
                  "Time_Minutes": 99,
                  "cluster": 0,
                  "_deepnote_index_column": 17
                },
                {
                  "Category": 2,
                  "City": 1,
                  "Price": 185000,
                  "Rating": 4.6,
                  "Time_Minutes": 300,
                  "cluster": 0,
                  "_deepnote_index_column": 18
                },
                {
                  "Category": 1,
                  "City": 1,
                  "Price": 2000,
                  "Rating": 4.5,
                  "Time_Minutes": 90,
                  "cluster": 0,
                  "_deepnote_index_column": 19
                }
              ]
            },
            "text/plain": "     Category  City     Price  Rating  Time_Minutes  cluster\n0           1     1   20000.0     4.6          15.0        0\n1           1     1   72904.0     4.6          90.0        0\n2           2     1  270000.0     4.6         360.0        0\n3           2     1   10000.0     4.5          99.0        0\n4           2     1   94000.0     4.5          60.0        0\n..        ...   ...       ...     ...           ...      ...\n432         1     5    2000.0     4.4          45.0        1\n433         2     5   27588.0     4.6          52.0        1\n434         2     5   27588.0     4.4          45.0        1\n435         2     5   27588.0     4.6          52.0        1\n436         6     5   10000.0     4.8          52.0        4\n\n[437 rows x 6 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Category</th>\n      <th>City</th>\n      <th>Price</th>\n      <th>Rating</th>\n      <th>Time_Minutes</th>\n      <th>cluster</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>20000.0</td>\n      <td>4.6</td>\n      <td>15.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>72904.0</td>\n      <td>4.6</td>\n      <td>90.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>1</td>\n      <td>270000.0</td>\n      <td>4.6</td>\n      <td>360.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>1</td>\n      <td>10000.0</td>\n      <td>4.5</td>\n      <td>99.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>1</td>\n      <td>94000.0</td>\n      <td>4.5</td>\n      <td>60.0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>432</th>\n      <td>1</td>\n      <td>5</td>\n      <td>2000.0</td>\n      <td>4.4</td>\n      <td>45.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>433</th>\n      <td>2</td>\n      <td>5</td>\n      <td>27588.0</td>\n      <td>4.6</td>\n      <td>52.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>434</th>\n      <td>2</td>\n      <td>5</td>\n      <td>27588.0</td>\n      <td>4.4</td>\n      <td>45.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>435</th>\n      <td>2</td>\n      <td>5</td>\n      <td>27588.0</td>\n      <td>4.6</td>\n      <td>52.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>436</th>\n      <td>6</td>\n      <td>5</td>\n      <td>10000.0</td>\n      <td>4.8</td>\n      <td>52.0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>437 rows × 6 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "105caeb811604ba58e68c7efb2b000ac",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "5b072377",
        "execution_start": 1656166068539,
        "execution_millis": 340,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 646.375,
        "deepnote_output_heights": [
          null,
          389
        ],
        "id": "CAWlUGqP2m7e",
        "outputId": "49a0af31-c57b-44d8-cd41-d03e7c046fc4"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data_copy['Category'], data_copy['City'], hue=data_copy['cluster'],\n",
        "                palette=sns.color_palette('hls', 5))\n",
        "plt.title('KMeans with 5 Clusters')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n",
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 609,
              "height": 387
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "3bada2dbaaf24e14aa42ffb074bdc0bf",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "fa7a8e6c",
        "execution_start": 1656166068883,
        "execution_millis": 862,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 664.375,
        "deepnote_output_heights": [
          null,
          389
        ],
        "id": "7HekTpAD2m7f",
        "outputId": "38fcc79d-bd6c-464b-b8cd-52c761cf3783"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data_copy['Price'], data_copy['Rating'], hue=data_copy['cluster'],\n",
        "                palette=sns.color_palette('hls', 5))\n",
        "plt.title('KMeans with 5 Clusters')\n",
        "plt.xlim((0, 200000))\n",
        "plt.ylim((3.8, 5))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n",
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 628,
              "height": 387
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "16b91dcc7d99409c94ecfe1274556d69",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "2e6e235d",
        "execution_start": 1656166069254,
        "execution_millis": 495,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 629.375,
        "deepnote_output_heights": [
          null,
          390
        ],
        "id": "9_F-zCrQ2m7g",
        "outputId": "4bc97523-4488-4f90-aba5-251571b2e93c"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data_copy['Time_Minutes'], data_copy['City'], hue=data_copy['cluster'],\n",
        "                palette=sns.color_palette('hls', 5))\n",
        "plt.title('KMeans with 5 Clusters')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "text": "/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n",
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 609,
              "height": 388
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dalam Proses Clustering didapatkan 5 Cluster yaitu Cluster 0,1,2,3, dan 4.  "
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "ce1d424559ce4910b50a152066a50706",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "JTQ6ffb02m7h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster 0 City nya adalah Bandung, Semarang dengan Category  Budaya, Taman Hiburan, Cagar Alam, Bahari yang Price nya 0k sampai 75k dan Rating= 3.8 sampai 4,8 serta Time Minutes antara 50 sampai 200."
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "36b27b029bd54c4bb4d35ba8c41d44d8",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "NvTrgWJQ2m7h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster 1 City nya adalah Jakarta dan Yogyakarta dengan Category  Cagar Alam, Bahari, Pusat Perbelanjaan yang Price nya 25k sampai 75k dan Rating= 4.0 sampai 4,7 serta Time Minutes antara 25 sampai 200."
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "62441a93f7d24e49bea5b06e36de0ea2",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "5wPI7DCG2m7i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster 2 City nya adalah Semarang, Surabaya dengan Category  Budaya, Taman Hiburan, Cagar Alam, Bahari yang Price nya 0k sampai 50k dan Rating= 4.1 sampai 4,7 serta Time Minutes antara 25 sampai 200.\n"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "2dad5e49358e40d89db72cae3010e447",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "WfMsIp8M2m7i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster 3 City nya adalah Jakarta dan Yogyakarta dengan Category  Cagar Alam, Bahari, Pusat Pembelanjaan yang Price nya 25k sampai 75k dan Rating= 4.0 sampai 4,7 serta Time Minutes antara 25 sampai 150."
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "17b95b2e05b742d0a1cc77a5db6065a3",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "fDZv_A132m7i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cluster 4 City nya adalah Semarang, Surabaya dengan Category  Pusat Perbelanjaan, Tempat Ibadah yang Price nya 0k sampai 40k dan Rating= 4.1 sampai 4,9 serta Time Minutes antara 0 sampai 50.\n"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "9d2a38fb9e474ad5a75f96fdbe9ad0dc",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "7hdXBlHi2m7j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Menyiapkan data"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "2ea1e321dee84e71958da3560301e56e",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "BeJKAKje2m7j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Membagi data latih dan data uji"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "d2cf372cf6cc4bf89cf656e83a30ea27",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "YxFfmC5o2m7j"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "6effc158aded4fb0bef78031a9ec8ee8",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "24014b88",
        "execution_start": 1656166069726,
        "execution_millis": 24,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "KjsJvraq2m7k"
      },
      "source": [
        "data_latih, data_uji = train_test_split(data, test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "bb618faa1bd14788b813b8e987be83b5",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "fb86718e",
        "execution_start": 1656166069758,
        "execution_millis": 11,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 150.375,
        "id": "ZofiBUXA2m7k",
        "outputId": "a6c99fb9-8190-401d-c42d-5283f8111106"
      },
      "source": [
        "print(data_latih.shape[0])\n",
        "print(data_uji.shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "349\n88\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "168b581591824543a754fa8dd952d124",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c83797d",
        "execution_start": 1656166069760,
        "execution_millis": 1,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "J_6NHLt42m7l"
      },
      "source": [
        "label_uji = data_uji.pop('cluster')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mencari Hyperparameter"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "22e8552e47204a40bf6484a37708acbf",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "k9G2KBc32m7l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Melakukan Train-Test Split (Misal 80% Training Data - 20% Testing Data)\n",
        "* Gunakan Training data untuk membuat k-Fold\n",
        "  * Disarankan 5-cross fold Validation\n",
        "  * Jika data > 1000 baris bisa menggunakan 10-cross validation\n",
        "  * Nilai parameter terbaik ditentukan dari nilai rata-rata n-fold\n",
        "* Training kembali 80% training data dengan menggunakan hyperparam terbaik yang didapatkan dari tahapan sebelumnya\n",
        "* Ujikan model dengan 20% testing data\n"
      ],
      "metadata": {
        "cell_id": "47c174b5b59a4c39877c137af9cdf7c3",
        "tags": [],
        "deepnote_cell_type": "markdown",
        "deepnote_cell_height": 255.125,
        "id": "Heh1Nc4W2m7m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menggunakan&nbsp;Grid&nbsp;Search&nbsp;CV&nbsp;untuk&nbsp;melakukan&nbsp;cross&nbsp;validation&nbsp;dan&nbsp;mencari&nbsp;Hyperparameter&nbsp;terbaik"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "4f034da2b25242e288fd6e6424b1c6f7",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "2E-fmSi52m7n"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "29a8a2c9301d47ae8741fc69b85c3c0d",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "156edd41",
        "execution_start": 1656166069761,
        "execution_millis": 3,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 117,
        "id": "pKHnjoXX2m7o"
      },
      "source": [
        "grid_models = [(GaussianNB(),[{'var_smoothing': [1e-09]}]),\n",
        "               (KNeighborsClassifier(), [{'n_neighbors': [3,5,7], 'weights': ['uniform', 'distance'], 'metric': ['euclidean', 'manhattan', 'minkowski']}])\n",
        "               ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "884db4ebb15c414e99fa7628e7cc1512",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "4636b8ab",
        "execution_start": 1656166069765,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 153,
        "id": "Xu_qrxEr2m7p"
      },
      "source": [
        "n_column = len(data_latih.columns)-1\n",
        "data_latih_array = data_latih.values\n",
        "X = data_latih_array[:, 0:n_column] #data\n",
        "y = data_latih_array[:, n_column] #label\n",
        "X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.2, random_state=1, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "3a6000ac52954420a67ccaeec314dc13",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "95ab4cee",
        "execution_start": 1656166069769,
        "execution_millis": 330,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 863,
        "id": "AbLluBHK2m7p",
        "outputId": "a8129156-693a-45d9-e728-71e6cd6b7af6"
      },
      "source": [
        "for i,j in grid_models:\n",
        "    grid = GridSearchCV(estimator=i,param_grid = j, scoring = 'accuracy',cv=5, verbose=2)\n",
        "    grid.fit(X_train, y_train)\n",
        "    best_accuracy = grid.best_score_\n",
        "    best_param = grid.best_params_\n",
        "    print('{}:\\nBest Accuracy : {:.2f}%'.format(i,best_accuracy*100))\n",
        "    print('Hyperparameter terbaik : ',best_param)\n",
        "    print('')\n",
        "    print('----------------')\n",
        "    print('')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n[CV] END ................................var_smoothing=1e-09; total time=   0.0s\n[CV] END ................................var_smoothing=1e-09; total time=   0.0s\n[CV] END ................................var_smoothing=1e-09; total time=   0.0s\n[CV] END ................................var_smoothing=1e-09; total time=   0.0s\n[CV] END ................................var_smoothing=1e-09; total time=   0.0s\nGaussianNB():\nBest Accuracy : 95.34%\nHyperparameter terbaik :  {'var_smoothing': 1e-09}\n\n----------------\n\nFitting 5 folds for each of 18 candidates, totalling 90 fits\n[CV] END ...metric=euclidean, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=euclidean, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=euclidean, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=manhattan, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=manhattan, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=3, weights=uniform; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=3, weights=distance; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=5, weights=uniform; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=5, weights=distance; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ...metric=minkowski, n_neighbors=7, weights=uniform; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=7, weights=distance; total time=   0.0s\n[CV] END ..metric=minkowski, n_neighbors=7, weights=distance; total time=   0.0s\nKNeighborsClassifier():\nBest Accuracy : 99.64%\nHyperparameter terbaik :  {'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'distance'}\n\n----------------\n\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training menggunakan 2 Model"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "085f2dc9d5b448ee9cc67dcedc041a7e",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "GQ3T4pOf2m7q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Naive Bayes Gaussian"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "46dde9ba587e48a68cee4054767f96cb",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h3",
        "id": "gUrg-Ljc2m7q"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "cd103334af27418ea4763c469b364019",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "2154c4bc",
        "execution_start": 1656166070104,
        "execution_millis": 6,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 117,
        "id": "sqnSgB5F2m7r"
      },
      "source": [
        "NBclassifier = GaussianNB(var_smoothing= 1e-09)\n",
        "NBclassifier.fit(X_train, y_train)\n",
        "y_predNB = NBclassifier.predict(X_validation)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "7bde79af62cb4f63b67ee6482bff8ef8",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "a34ed239",
        "execution_start": 1656166070159,
        "execution_millis": 2,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 352.25,
        "id": "LphpvkoX2m7r",
        "outputId": "b5cff496-abf0-4750-93aa-85d1e2c607ff"
      },
      "source": [
        "cm_NB = confusion_matrix(y_validation, y_predNB)\n",
        "print(classification_report(y_validation, y_predNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "              precision    recall  f1-score   support\n\n         0.0       0.93      1.00      0.96        26\n         1.0       0.75      1.00      0.86         9\n         2.0       1.00      0.86      0.92        21\n         3.0       1.00      0.83      0.91        12\n         4.0       1.00      1.00      1.00         2\n\n    accuracy                           0.93        70\n   macro avg       0.94      0.94      0.93        70\nweighted avg       0.94      0.93      0.93        70\n\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist((y_validation, y_predNB), bins=10)\n",
        "plt.xlabel(\"Topik Faktual\")\n",
        "plt.ylabel(\"Topik Predicted\")\n",
        "plt.show()"
      ],
      "metadata": {
        "cell_id": "8696e47c84f046f992c7ce40ff154260",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c149f890",
        "execution_start": 1656166070160,
        "execution_millis": 225,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 415,
        "deepnote_output_heights": [
          264
        ],
        "id": "Dmyck2qh2m7s",
        "outputId": "e42ac2f5-75b6-4116-df6a-497dab6d8f0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 382,
              "height": 262
            }
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "8ea514ed497e4862adeafda3b8bdc421",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h3",
        "id": "qriaRBVJ2m7t"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "15c53f62253848dcb6e9fdf8bebd94cf",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "400d783a",
        "execution_start": 1656166070383,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 117,
        "id": "e4U7z8I82m7t"
      },
      "source": [
        "KNNclassifier = KNeighborsClassifier(metric= 'manhattan', n_neighbors= 5, weights= 'distance')\n",
        "KNNclassifier.fit(X_train, y_train)\n",
        "y_predKNN = KNNclassifier.predict(X_validation)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "1f1410619a08401580cf923e426453f2",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "98b55b81",
        "execution_start": 1656166070383,
        "execution_millis": 3,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 352.25,
        "id": "JIRaZEkX2m7u",
        "outputId": "6e54d1d6-cc1d-433a-fcf0-91a582c1d3d4"
      },
      "source": [
        "cm_KNN = confusion_matrix(y_validation, y_predKNN)\n",
        "print(classification_report(y_validation, y_predKNN))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "              precision    recall  f1-score   support\n\n         0.0       1.00      1.00      1.00        26\n         1.0       1.00      1.00      1.00         9\n         2.0       1.00      1.00      1.00        21\n         3.0       1.00      1.00      1.00        12\n         4.0       1.00      1.00      1.00         2\n\n    accuracy                           1.00        70\n   macro avg       1.00      1.00      1.00        70\nweighted avg       1.00      1.00      1.00        70\n\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist((y_validation, y_predKNN), bins=10)\n",
        "plt.xlabel(\"Topik Faktual\")\n",
        "plt.ylabel(\"Topik Predicted\")\n",
        "plt.show()"
      ],
      "metadata": {
        "cell_id": "68c39f6d42ea4854a2d2b89028ba447b",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c3e34e19",
        "execution_start": 1656166070384,
        "execution_millis": 222,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 415,
        "deepnote_output_heights": [
          264
        ],
        "id": "LXf2-I4t2m7v",
        "outputId": "a241c0b7-5877-4125-bb57-9cfb1eec161d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 382,
              "height": 262
            }
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing menggunakan 2 Model"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "a50d222334ba4b9fbc5cec4d7d1e8d55",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "KW5_0Joj2m7v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memisahkan data uji dengan label uji"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "0e4e82c9f35a4832b8ac4402d806e471",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-p",
        "id": "iCIvz7H-2m7w"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "54c1baf9dd854055a91284e7ae395829",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "c4e0e2a6",
        "execution_start": 1656166070644,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 135,
        "id": "pWcCkiny2m7w"
      },
      "source": [
        "n_column = len(data_latih.columns)-1\n",
        "data_latih_array = data_latih.values\n",
        "A = data_latih_array[:, 0:n_column] #data\n",
        "b = data_latih_array[:, n_column] #label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Naive Bayes Gaussian"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "9b549a91a50f4e8d8e81b4a0974c2948",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h3",
        "id": "1X7qrjOm2m7x"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "3632fff068e54faea38f6deb7da02562",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "e69b441a",
        "execution_start": 1656166070645,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "PMyN9rB22m7x"
      },
      "source": [
        "testing_y_predNB = NBclassifier.predict(A)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "23a236fc4b274437a6e1118515a6f182",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "d280a824",
        "execution_start": 1656166070645,
        "execution_millis": 3,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 334.25,
        "id": "n7EE5il12m7x",
        "outputId": "534ac7a5-63fb-4c99-c163-887cd6bcffaa"
      },
      "source": [
        "print(classification_report(b, testing_y_predNB))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "              precision    recall  f1-score   support\n\n         0.0       0.96      1.00      0.98       102\n         1.0       0.85      0.98      0.91        57\n         2.0       0.98      0.90      0.94       115\n         3.0       0.98      0.95      0.97        66\n         4.0       1.00      0.89      0.94         9\n\n    accuracy                           0.95       349\n   macro avg       0.96      0.94      0.95       349\nweighted avg       0.95      0.95      0.95       349\n\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "2cd3fa43c2774cddba64d5f130e02fbe",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "e9a6d35a",
        "execution_start": 1656166070647,
        "execution_millis": 0,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 130.1875,
        "id": "N1IxApIw2m7y",
        "outputId": "1356f39c-f102-494d-be71-c25c2dc1fed3"
      },
      "source": [
        "akurasi_NB = accuracy_score(b, testing_y_predNB)*100\n",
        "print(akurasi_NB)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "95.12893982808023\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist((b, testing_y_predNB), bins=10)\n",
        "plt.xlabel(\"Topik Faktual\")\n",
        "plt.ylabel(\"Topik Predicted\")\n",
        "plt.show()"
      ],
      "metadata": {
        "cell_id": "7c43afb9a65847eb8768c5d4062a96c9",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "322e7765",
        "execution_start": 1656166070695,
        "execution_millis": 238,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 418,
        "deepnote_output_heights": [
          267
        ],
        "id": "j-d_nn-N2m7z",
        "outputId": "e66ab4ac-5241-43ea-99d7-6b88776941f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 389,
              "height": 265
            }
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "38f38cb638b84535bca60b14b3f757ba",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h3",
        "id": "b-w-7zgH2m70"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "6c6948d59afa4099aeb7f043f560d818",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "d5f3bcd4",
        "execution_start": 1656166070928,
        "execution_millis": 1,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 81,
        "id": "Wimv7q6i2m71"
      },
      "source": [
        "testing_y_predKNN = KNNclassifier.predict(A)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "57af58d01bfa4186858fb9d51fc81b99",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "9469ee24",
        "execution_start": 1656166070929,
        "execution_millis": 6,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 334.25,
        "id": "0mIvL4GU2m74",
        "outputId": "e005077e-9acf-4e40-86f0-abbf43323728"
      },
      "source": [
        "print(classification_report(b, testing_y_predKNN))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "              precision    recall  f1-score   support\n\n         0.0       1.00      1.00      1.00       102\n         1.0       1.00      1.00      1.00        57\n         2.0       1.00      1.00      1.00       115\n         3.0       1.00      1.00      1.00        66\n         4.0       1.00      1.00      1.00         9\n\n    accuracy                           1.00       349\n   macro avg       1.00      1.00      1.00       349\nweighted avg       1.00      1.00      1.00       349\n\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "98274e035912472ca073c76b466876fe",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "493552b6",
        "execution_start": 1656166070937,
        "execution_millis": 4,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 130.1875,
        "id": "ytykbA5T2m76",
        "outputId": "170c9b3d-9aca-4f58-964a-89d75e8f1533"
      },
      "source": [
        "akurasi_KNN = accuracy_score(b, testing_y_predKNN)*100\n",
        "print(akurasi_KNN)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "100.0\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist((b, testing_y_predKNN), bins=10)\n",
        "plt.xlabel(\"Topik Faktual\")\n",
        "plt.ylabel(\"Topik Predicted\")\n",
        "plt.show()"
      ],
      "metadata": {
        "cell_id": "073a1442ca6e4fb9954185d89b688b00",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "d99f1cd4",
        "execution_start": 1656166070991,
        "execution_millis": 227,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 418,
        "deepnote_output_heights": [
          267
        ],
        "id": "iyBBVm3q2m7-",
        "outputId": "d43184a9-1a6e-41ce-ab11-c4e450dfa2d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light",
            "image/png": {
              "width": 389,
              "height": 265
            }
          },
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perbandingan Hasil Uji Model Naive Bayes dan KNN"
      ],
      "metadata": {
        "formattedRanges": [],
        "cell_id": "b90c8f43df6043b6b60047bcbf271fe3",
        "tags": [],
        "is_collapsed": false,
        "deepnote_cell_type": "text-cell-h2",
        "id": "KiXhRvp-2m8A"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "cell_id": "4f74f1e70e7c47c1b6243d50b3fe486d",
        "tags": [],
        "deepnote_to_be_reexecuted": false,
        "source_hash": "fba6e686",
        "execution_start": 1656166071216,
        "execution_millis": 168,
        "deepnote_cell_type": "code",
        "deepnote_cell_height": 644.1875,
        "deepnote_output_heights": [
          21.1875,
          351
        ],
        "id": "ML4Dk4qt2m8B",
        "outputId": "9e08ee1c-4897-4855-9428-75483f810970"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([1,1,1,1])\n",
        "model_name = ['Naive Bayes', 'KNN']\n",
        "akurasi = [akurasi_NB, akurasi_KNN]\n",
        "ax.bar(model_name, akurasi, color=['blue','red'], width=0.25)\n",
        "plt.xlabel('Nama Model')\n",
        "plt.ylabel('Hasil Akurasi')\n",
        "plt.ylim(10, 100)\n",
        "plt.title('Perbandingan Hasil Akurasi Naive Bayes vs KNN')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 73,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Perbandingan Hasil Akurasi Naive Bayes vs KNN')"
          },
          "metadata": {}
        },
        {
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
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            "image/png": {
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        "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=a1e9023f-0fb6-4275-a3b6-3206b6dfe3fb' target=\"_blank\">\n",
        "<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\n",
        "Created in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>"
      ],
      "metadata": {
        "tags": [],
        "created_in_deepnote_cell": true,
        "deepnote_cell_type": "markdown",
        "id": "xnhdRY1s2m8C"
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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}