Capstone_Module_2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3AKtbV4d1Ka"
      },
      "source": [
        "> # Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WqmwEDlAdxeX"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import folium \n",
        "from wordcloud import WordCloud\n",
        "from folium import Choropleth, Circle, Marker\n",
        "from folium.plugins import HeatMap, MarkerCluster\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5h_p8pWjLbCn"
      },
      "source": [
        "> # Latar Belakang"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aiIM-X62QGdL"
      },
      "source": [
        "**Boston** adalah ibu kota dan kota terbesar di Massachusetts di Amerika Serikat. Kota ini salah satu kota tertua dan terkaya di Amerika, dengan ekonomi berbasis di pendidikan, perawatan kesehatan, keuangan, dan teknologi tinggi (Sumber : [Wikiwand](https://www.wikiwand.com/id/Boston)). Boston merupakan lokasi dari beberapa perguruan tinggi terkemuka, seperti Universitas Harvard dan MIT (Sumber : [idntimes](https://www.idntimes.com/travel/destination/dwi-ayu-silawati/wisata-unik-di-boston-exp-c1c2)).\n",
        "\n",
        "Pada notebook ini, akan dipaparkan analisis dataset tentang kejadian atau insiden yang terjadi di Boston. Dataset bersumber dari Departement Kepolisian Boston yang berisikan rincian awal seputar insiden atau kejahatan yang ditangani oleh Departemen Kepolisian Boston ini (Sumber : [Kaggle](https://www.kaggle.com/datasets/AnalyzeBoston/crimes-in-boston))."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OvEI6DuLekn"
      },
      "source": [
        "> ## Pernyataan Masalah"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afFU-P6eSUF5"
      },
      "source": [
        "Jika kita memposisikan diri sebagai warga yang tinggal disekitaran Boston, tentu kita ingin **mengetahui insiden atau kejadian seperti apa yang terjadi di kota tersebut**. Dan juga **karakteristik seperti apa yang dimiliki oleh setiap insiden atau kejadian**. Informasi ini akan berguna untuk bisa mengidentifikasi kejadian tersebut secara tepat. \n",
        "\n",
        "**Kapan insiden-insiden tersebut sering terjadi**, dan **dimana insiden-insiden tersebut sering terjadi** juga merupakan informasi yang berguna untuk dianalisa. Untuk menjawab pertanyaan-pertanyaan mendasar tersebut, Disini kita akan melakukan analisis terhadap dataset untuk mendapatkan suatu kesimpulan atau wawasan yang berguna terhadap insiden-insiden yang sering terjadi di kota Boston."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOSzgmWZd_fV"
      },
      "source": [
        "> # Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IICber2dd91a"
      },
      "outputs": [],
      "source": [
        "# Memanggil dan menyimpan dataset dalam variabel\n",
        "\n",
        "df_crime = pd.read_csv(\"/content/drive/MyDrive/Purwadhika/crime.csv\", encoding = 'latin')\n",
        "df_offense = pd.read_csv(\"/content/drive/MyDrive/Purwadhika/offense_codes.csv\", encoding = 'latin')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-pF7NTetHid"
      },
      "source": [
        "> ## Content :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3wVkbyitThI"
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      "source": [
        "* **INCIDENT_NUMBER :** unique value untuk setiap kasus. \n",
        "* **OFFENSE_CODE :** Kode untuk tipe kejahatan. Dataset Offens_code menjelaskan arti dari setiap kode.\n",
        "* **OFFENSE_CODE_GROUP :** Nama untuk setiap tipe kejahatan.\n",
        "* **OFFENSE_DESCRIPTION :** Penjelasan lebih spesifik untuk setiap kejahatan.\n",
        "* **DISTRICT :** Kode wilayah dimana kejahatan itu terjadi.\n",
        "* **REPORTING_AREA :** Nomor area TKP(Tempat Kejadian Perkara).\n",
        "* **SHOOTING :** Jika pada kejahatan tersebut terjadi penembakan, maka itu artinya \"Y\".\n",
        "* **OCCURED_ON_DATE :** Waktu kejahatan terjadi (Tahun, bulan, hari dan jam).\n",
        "* **YEAR :** Tahun kejahatan terjadi.\n",
        "* **MONTH :** Bulan kejahatan terjadi.\n",
        "* **DAY OF WEEK :** Hari kejadian.\n",
        "* **HOUR :** Jam kejahatan itu terjadi.\n",
        "* **UCR_PART :** \n",
        "* **STREET :** Nama jalan tempat kejahatan terjadi.\n",
        "* **LAT :** Latitude dari tempat kejahatan terjadi.\n",
        "* **LONG :** Longitude dari tempat kejahatan terjadi.\n",
        "* **LOCATION :** latitude dan longitude dari tempat kejahatan terjadi.\n",
        "\n"
      ]
    },
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              "  INCIDENT_NUMBER  OFFENSE_CODE    OFFENSE_CODE_GROUP   OFFENSE_DESCRIPTION  \\\n",
              "0      I182070945           619               Larceny    LARCENY ALL OTHERS   \n",
              "1      I182070943          1402             Vandalism             VANDALISM   \n",
              "2      I182070941          3410                 Towed   TOWED MOTOR VEHICLE   \n",
              "3      I182070940          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
              "4      I182070938          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
              "\n",
              "  DISTRICT REPORTING_AREA SHOOTING     OCCURRED_ON_DATE  YEAR  MONTH  \\\n",
              "0      D14            808      NaN  2018-09-02 13:00:00  2018      9   \n",
              "1      C11            347      NaN  2018-08-21 00:00:00  2018      8   \n",
              "2       D4            151      NaN  2018-09-03 19:27:00  2018      9   \n",
              "3       D4            272      NaN  2018-09-03 21:16:00  2018      9   \n",
              "4       B3            421      NaN  2018-09-03 21:05:00  2018      9   \n",
              "\n",
              "  DAY_OF_WEEK  HOUR    UCR_PART       STREET        Lat       Long  \\\n",
              "0      Sunday    13    Part One   LINCOLN ST  42.357791 -71.139371   \n",
              "1     Tuesday     0    Part Two     HECLA ST  42.306821 -71.060300   \n",
              "2      Monday    19  Part Three  CAZENOVE ST  42.346589 -71.072429   \n",
              "3      Monday    21  Part Three   NEWCOMB ST  42.334182 -71.078664   \n",
              "4      Monday    21  Part Three     DELHI ST  42.275365 -71.090361   \n",
              "\n",
              "                      Location  \n",
              "0  (42.35779134, -71.13937053)  \n",
              "1  (42.30682138, -71.06030035)  \n",
              "2  (42.34658879, -71.07242943)  \n",
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              "      <td>I182070945</td>\n",
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              "      <td>2018-08-21 00:00:00</td>\n",
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              "      <td>Part Two</td>\n",
              "      <td>HECLA ST</td>\n",
              "      <td>42.306821</td>\n",
              "      <td>-71.060300</td>\n",
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              "      <th>2</th>\n",
              "      <td>I182070941</td>\n",
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              "      <td>D4</td>\n",
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              "      <td>42.346589</td>\n",
              "      <td>-71.072429</td>\n",
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              "      <th>3</th>\n",
              "      <td>I182070940</td>\n",
              "      <td>3114</td>\n",
              "      <td>Investigate Property</td>\n",
              "      <td>INVESTIGATE PROPERTY</td>\n",
              "      <td>D4</td>\n",
              "      <td>272</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-09-03 21:16:00</td>\n",
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              "      <td>NEWCOMB ST</td>\n",
              "      <td>42.334182</td>\n",
              "      <td>-71.078664</td>\n",
              "      <td>(42.33418175, -71.07866441)</td>\n",
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              "      <td>INVESTIGATE PROPERTY</td>\n",
              "      <td>B3</td>\n",
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              "       INCIDENT_NUMBER  OFFENSE_CODE OFFENSE_CODE_GROUP  \\\n",
              "319068   I050310906-00          3125    Warrant Arrests   \n",
              "319069   I030217815-08           111           Homicide   \n",
              "319070   I030217815-08          3125    Warrant Arrests   \n",
              "319071   I010370257-00          3125    Warrant Arrests   \n",
              "319072       142052550          3125    Warrant Arrests   \n",
              "\n",
              "                        OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
              "319068                       WARRANT ARREST       D4            285      NaN   \n",
              "319069  MURDER, NON-NEGLIGIENT MANSLAUGHTER      E18            520      NaN   \n",
              "319070                       WARRANT ARREST      E18            520      NaN   \n",
              "319071                       WARRANT ARREST      E13            569      NaN   \n",
              "319072                       WARRANT ARREST       D4            903      NaN   \n",
              "\n",
              "           OCCURRED_ON_DATE  YEAR  MONTH DAY_OF_WEEK  HOUR    UCR_PART  \\\n",
              "319068  2016-06-05 17:25:00  2016      6      Sunday    17  Part Three   \n",
              "319069  2015-07-09 13:38:00  2015      7    Thursday    13    Part One   \n",
              "319070  2015-07-09 13:38:00  2015      7    Thursday    13  Part Three   \n",
              "319071  2016-05-31 19:35:00  2016      5     Tuesday    19  Part Three   \n",
              "319072  2015-06-22 00:12:00  2015      6      Monday     0  Part Three   \n",
              "\n",
              "                   STREET        Lat       Long                     Location  \n",
              "319068        COVENTRY ST  42.336951 -71.085748  (42.33695098, -71.08574813)  \n",
              "319069           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
              "319070           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
              "319071  NEW WASHINGTON ST  42.302333 -71.111565  (42.30233307, -71.11156487)  \n",
              "319072      WASHINGTON ST  42.333839 -71.080290  (42.33383935, -71.08029038)  "
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              "      <td>D4</td>\n",
              "      <td>285</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-06-05 17:25:00</td>\n",
              "      <td>2016</td>\n",
              "      <td>6</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>17</td>\n",
              "      <td>Part Three</td>\n",
              "      <td>COVENTRY ST</td>\n",
              "      <td>42.336951</td>\n",
              "      <td>-71.085748</td>\n",
              "      <td>(42.33695098, -71.08574813)</td>\n",
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              "    <tr>\n",
              "      <th>319069</th>\n",
              "      <td>I030217815-08</td>\n",
              "      <td>111</td>\n",
              "      <td>Homicide</td>\n",
              "      <td>MURDER, NON-NEGLIGIENT MANSLAUGHTER</td>\n",
              "      <td>E18</td>\n",
              "      <td>520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-07-09 13:38:00</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>13</td>\n",
              "      <td>Part One</td>\n",
              "      <td>RIVER ST</td>\n",
              "      <td>42.255926</td>\n",
              "      <td>-71.123172</td>\n",
              "      <td>(42.25592648, -71.12317207)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319070</th>\n",
              "      <td>I030217815-08</td>\n",
              "      <td>3125</td>\n",
              "      <td>Warrant Arrests</td>\n",
              "      <td>WARRANT ARREST</td>\n",
              "      <td>E18</td>\n",
              "      <td>520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-07-09 13:38:00</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>13</td>\n",
              "      <td>Part Three</td>\n",
              "      <td>RIVER ST</td>\n",
              "      <td>42.255926</td>\n",
              "      <td>-71.123172</td>\n",
              "      <td>(42.25592648, -71.12317207)</td>\n",
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              "    <tr>\n",
              "      <th>319071</th>\n",
              "      <td>I010370257-00</td>\n",
              "      <td>3125</td>\n",
              "      <td>Warrant Arrests</td>\n",
              "      <td>WARRANT ARREST</td>\n",
              "      <td>E13</td>\n",
              "      <td>569</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-05-31 19:35:00</td>\n",
              "      <td>2016</td>\n",
              "      <td>5</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>19</td>\n",
              "      <td>Part Three</td>\n",
              "      <td>NEW WASHINGTON ST</td>\n",
              "      <td>42.302333</td>\n",
              "      <td>-71.111565</td>\n",
              "      <td>(42.30233307, -71.11156487)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319072</th>\n",
              "      <td>142052550</td>\n",
              "      <td>3125</td>\n",
              "      <td>Warrant Arrests</td>\n",
              "      <td>WARRANT ARREST</td>\n",
              "      <td>D4</td>\n",
              "      <td>903</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015-06-22 00:12:00</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>Monday</td>\n",
              "      <td>0</td>\n",
              "      <td>Part Three</td>\n",
              "      <td>WASHINGTON ST</td>\n",
              "      <td>42.333839</td>\n",
              "      <td>-71.080290</td>\n",
              "      <td>(42.33383935, -71.08029038)</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-4ce9a9d0-9372-4438-82bb-62e022ce7eed');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Melihat 5 data teratas dan terbawah dataset crime\n",
        "\n",
        "display(df_crime.head(), df_crime.tail())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "sZx5vveRiE7L",
        "outputId": "068dc8f5-3876-4ff3-9605-7459b3a4d9fd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   CODE                                     NAME\n",
              "0   612         LARCENY PURSE SNATCH - NO FORCE \n",
              "1   613                      LARCENY SHOPLIFTING\n",
              "2   615  LARCENY THEFT OF MV PARTS & ACCESSORIES\n",
              "3  1731                                   INCEST\n",
              "4  3111                LICENSE PREMISE VIOLATION"
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              "     CODE                                       NAME\n",
              "571  1806  DRUGS - CLASS B TRAFFICKING OVER 18 GRAMS\n",
              "572  1807  DRUGS - CLASS D TRAFFICKING OVER 50 GRAMS\n",
              "573  1610    HUMAN TRAFFICKING - COMMERCIAL SEX ACTS\n",
              "574  2010                              HOME INVASION\n",
              "575  1620  HUMAN TRAFFICKING - INVOLUNTARY SERVITUDE"
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Melihat 5 data teratas dan terbawah dataset Offense_codes\n",
        "\n",
        "display(df_offense.head(), df_offense.tail())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbw6XwSdzt7O"
      },
      "source": [
        "> # Data Understanding and Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0ZVpMP2iHwj",
        "outputId": "1f02c804-ce7d-4c77-dd31-5e830436b4df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 319073 entries, 0 to 319072\n",
            "Data columns (total 17 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   INCIDENT_NUMBER      319073 non-null  object \n",
            " 1   OFFENSE_CODE         319073 non-null  int64  \n",
            " 2   OFFENSE_CODE_GROUP   319073 non-null  object \n",
            " 3   OFFENSE_DESCRIPTION  319073 non-null  object \n",
            " 4   DISTRICT             317308 non-null  object \n",
            " 5   REPORTING_AREA       319073 non-null  object \n",
            " 6   SHOOTING             1019 non-null    object \n",
            " 7   OCCURRED_ON_DATE     319073 non-null  object \n",
            " 8   YEAR                 319073 non-null  int64  \n",
            " 9   MONTH                319073 non-null  int64  \n",
            " 10  DAY_OF_WEEK          319073 non-null  object \n",
            " 11  HOUR                 319073 non-null  int64  \n",
            " 12  UCR_PART             318983 non-null  object \n",
            " 13  STREET               308202 non-null  object \n",
            " 14  Lat                  299074 non-null  float64\n",
            " 15  Long                 299074 non-null  float64\n",
            " 16  Location             319073 non-null  object \n",
            "dtypes: float64(2), int64(4), object(11)\n",
            "memory usage: 41.4+ MB\n"
          ]
        }
      ],
      "source": [
        "# informasi dasar mengenai data\n",
        "\n",
        "df_crime.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f2VKW8vdynUb",
        "outputId": "baef94b7-ec5f-4460-a1de-8bc0bb51db2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(319073, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Dimensi dataset sebelum di cleaning\n",
        "\n",
        "sebelum = df_crime.shape\n",
        "sebelum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "yZvL5jwap2Wi",
        "outputId": "e21c1307-24ee-486b-8dec-41aaded44c7c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       INCIDENT_NUMBER               OFFENSE_CODE_GROUP  \\\n",
              "count           319073                           319073   \n",
              "unique          282517                               67   \n",
              "top         I162030584  Motor Vehicle Accident Response   \n",
              "freq                13                            37132   \n",
              "\n",
              "                  OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
              "count                          319073   317308         319073     1019   \n",
              "unique                            244       12            879        1   \n",
              "top     SICK/INJURED/MEDICAL - PERSON       B2                       Y   \n",
              "freq                            18783    49945          20250     1019   \n",
              "\n",
              "           OCCURRED_ON_DATE DAY_OF_WEEK    UCR_PART         STREET  \\\n",
              "count                319073      319073      318983         308202   \n",
              "unique               233229           7           4           4657   \n",
              "top     2017-06-01 00:00:00      Friday  Part Three  WASHINGTON ST   \n",
              "freq                     29       48495      158553          14194   \n",
              "\n",
              "                        Location  \n",
              "count                     319073  \n",
              "unique                     18194  \n",
              "top     (0.00000000, 0.00000000)  \n",
              "freq                       19999  "
            ],
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              "      <th>top</th>\n",
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              "      <td>B2</td>\n",
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              "      <td>Part Three</td>\n",
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              "      <td>(0.00000000, 0.00000000)</td>\n",
              "    </tr>\n",
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              "      <th>freq</th>\n",
              "      <td>13</td>\n",
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            "text/plain": [
              "        OFFENSE_CODE           YEAR          MONTH           HOUR  \\\n",
              "count  319073.000000  319073.000000  319073.000000  319073.000000   \n",
              "mean     2317.546956    2016.560586       6.609719      13.118205   \n",
              "std      1185.285543       0.996344       3.273691       6.294205   \n",
              "min       111.000000    2015.000000       1.000000       0.000000   \n",
              "25%      1001.000000    2016.000000       4.000000       9.000000   \n",
              "50%      2907.000000    2017.000000       7.000000      14.000000   \n",
              "75%      3201.000000    2017.000000       9.000000      18.000000   \n",
              "max      3831.000000    2018.000000      12.000000      23.000000   \n",
              "\n",
              "                 Lat           Long  \n",
              "count  299074.000000  299074.000000  \n",
              "mean       42.214381     -70.908272  \n",
              "std         2.159766       3.493618  \n",
              "min        -1.000000     -71.178674  \n",
              "25%        42.297442     -71.097135  \n",
              "50%        42.325538     -71.077524  \n",
              "75%        42.348624     -71.062467  \n",
              "max        42.395042      -1.000000  "
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              "\n",
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              "      <th>25%</th>\n",
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              "      <td>3201.000000</td>\n",
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              "      <td>18.000000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>42.395042</td>\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# statistik deskriptif\n",
        "display(df_crime.describe(include = \"O\"))\n",
        "display(df_crime.describe())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46KgEDMIq_cU"
      },
      "source": [
        "Dari beberapa informasi diatas, kita bisa tahu bahwa beberapa kolom memiliki missing value. Pada kolom `INCIDENT_NUMBER` jumlah data dan jumlah unique valuenya berbeda, padahal setiap kasus harusnya hanya memiliki satu unique value, sehingga ada indikasi data yang terduplikasi. Kita juga perlu mengetahui dataset OFFENSE_CODE perlu juga untuk dianalisis. Dalam setiap data yang memiliki data tahunan, hal yang penting untuk diperiksa juga adalah kelengkapan bulan untuk setiap tahun, supaya analisa kita tidak bias karena ada data yang tidak lengkap."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXL2zxAmcPMR"
      },
      "source": [
        "> ## Apa fungsi dataset `Offense_Code`?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5ksmcNejWJA"
      },
      "source": [
        "Dataset OFFENSE_CODE adalah penjelasan dari kode kejahatan pada dataset CRIME. Namun pada dataset CRIME terdapat kolom `offense_description`. Kita akan mencoba menggabungkan kedua tabel ini dan melihat apakah kolom `offense_description` sama dengan kolom `Name` pada dataset OFFENSE_CODE."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58gTEwqvhjq9",
        "outputId": "61272ed3-94a4-4d85-f5f2-c24869a12d2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['CODE', 'NAME'], dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['OFFENSE_CODE', 'NAME'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Mengubah nama kolom CODE menjadi OFFENSE_CODE\n",
        "print(df_offense.columns)\n",
        "df_offense.rename(columns = {\"CODE\" :  \"OFFENSE_CODE\"}, inplace = True)\n",
        "df_offense.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "rTo26OHGie_0",
        "outputId": "093feace-91cd-4a0a-9e36-40eeb900f939"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        OFFENSE_CODE                      OFFENSE_DESCRIPTION  \\\n",
              "120876          3301                           VERBAL DISPUTE   \n",
              "75328           3114                     INVESTIGATE PROPERTY   \n",
              "403741          3115                       INVESTIGATE PERSON   \n",
              "568571          1415                                 GRAFFITI   \n",
              "398192          3115                       INVESTIGATE PERSON   \n",
              "299049          3207                         PROPERTY - FOUND   \n",
              "105541          3301                           VERBAL DISPUTE   \n",
              "116742          3301                           VERBAL DISPUTE   \n",
              "363445           617              LARCENY THEFT FROM BUILDING   \n",
              "340135          3125                           WARRANT ARREST   \n",
              "369484           617              LARCENY THEFT FROM BUILDING   \n",
              "553762          3503  MISSING PERSON - NOT REPORTED - LOCATED   \n",
              "431958          3807        M/V ACCIDENT - OTHER CITY VEHICLE   \n",
              "110411          3301                           VERBAL DISPUTE   \n",
              "260982           802                 ASSAULT SIMPLE - BATTERY   \n",
              "\n",
              "                                           NAME  \n",
              "120876                           VERBAL DISPUTE  \n",
              "75328                      INVESTIGATE PROPERTY  \n",
              "403741                       INVESTIGATE PERSON  \n",
              "568571                     VANDALISM - GRAFFITI  \n",
              "398192                       INVESTIGATE PERSON  \n",
              "299049                         PROPERTY - FOUND  \n",
              "105541                           VERBAL DISPUTE  \n",
              "116742                           VERBAL DISPUTE  \n",
              "363445              LARCENY THEFT FROM BUILDING  \n",
              "340135                           WARRANT ARREST  \n",
              "369484        LARCENY IN A BUILDING $200 & OVER  \n",
              "553762  MISSING PERSON - NOT REPORTED - LOCATED  \n",
              "431958        M/V ACCIDENT - OTHER CITY VEHICLE  \n",
              "110411                           VERBAL DISPUTE  \n",
              "260982                        ASSAULT & BATTERY  "
            ],
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              "\n",
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              "      <th>340135</th>\n",
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              "      <th>431958</th>\n",
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              "          const element = document.querySelector('#df-7bd8fa1f-60e5-4800-a556-f06f8b838780');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "pd.merge(df_crime, df_offense, on = \"OFFENSE_CODE\")[[\"OFFENSE_CODE\", \"OFFENSE_DESCRIPTION\", \"NAME\"]].sample(15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OAdb2cdxmDtQ"
      },
      "source": [
        "Terlihat isi kedua kolom itu sama, yang berarti dataset OFFENSE_CODE tidak akan digunakan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5ni8IxZb3CC"
      },
      "source": [
        "> ## Handling Duplicate Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xCpmd_pnDFe"
      },
      "source": [
        "Kolom `INCIDENT_NUMBER` adalah unique value untuk setiap kejahatan, yang berarti kalau ada `INCIDENT_NUMBER` yang sama kemungkinan besar data tersebut adalah duplikat."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsYzicOoWkBS",
        "outputId": "69699fc2-d259-4a33-dd33-49a30519e10f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36556"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Menghitung jumlah data yang terindikasi merupakan data duplicate\n",
        "\n",
        "df_crime.duplicated(subset = \"INCIDENT_NUMBER\", keep = \"first\").sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "QnWCdMQjZcY4",
        "outputId": "ccbf166d-4861-40e8-fd3f-90da36202485"
      },
      "outputs": [
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          "data": {
            "text/plain": [
              "   INCIDENT_NUMBER  OFFENSE_CODE OFFENSE_CODE_GROUP  \\\n",
              "96      I182070829          1810     Drug Violation   \n",
              "97      I182070829          1842     Drug Violation   \n",
              "\n",
              "                     OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
              "96          DRUGS - SALE / MANUFACTURING       B2            325      NaN   \n",
              "97  DRUGS - POSS CLASS A - HEROIN, ETC.        B2            325      NaN   \n",
              "\n",
              "       OCCURRED_ON_DATE  YEAR  MONTH DAY_OF_WEEK  HOUR  UCR_PART  \\\n",
              "96  2018-09-03 13:45:00  2018      9      Monday    13  Part Two   \n",
              "97  2018-09-03 13:45:00  2018      9      Monday    13  Part Two   \n",
              "\n",
              "          STREET        Lat       Long                     Location  \n",
              "96  WOODCLIFF ST  42.315809 -71.076187  (42.31580934, -71.07618683)  \n",
              "97  WOODCLIFF ST  42.315809 -71.076187  (42.31580934, -71.07618683)  "
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4e09eff8-74f3-442c-a5c1-cee0580c5330 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4e09eff8-74f3-442c-a5c1-cee0580c5330');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Check salah satu INCIDENT_NUMBER yang terindikasai duplikat\n",
        "\n",
        "df_crime[df_crime[\"INCIDENT_NUMBER\"] == \"I182070829\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ppx0H2bztbi"
      },
      "source": [
        "Terlihat bahwa kedua data diatas hampir sama persis, kecuali pada kolom `OFFENSE_CODE` dan `OFFENSE_DESCRIPTION`. Jika ditinjau dari waktu dan tempat kejadian, bisa dikatakan mustahil dua kejadian yang mirip terjadi pada waktu dan tempat yang sama. Sehingga asumsinya kedua data tersebut adalah data yang sama namun terjadi double counting karena pilihan deskripsi yang tidak konsisten dengan tipe kejahatan tesebut."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "zhZ-m7EJyiO-"
      },
      "outputs": [],
      "source": [
        "# Menghapus baris data yang terindikasi duplikat.\n",
        "\n",
        "df_crime.drop_duplicates(subset = \"INCIDENT_NUMBER\", keep = \"first\", inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKZrTkc9vXKx"
      },
      "source": [
        "> ## Apakah data setiap tahun lengkap?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOlTqT2nw1g6"
      },
      "source": [
        "Dari penampakan datset yang akan dianalisis, bisa dikatakan bahwa analisis berdasarkan waktu adalah salah satu analisis yang penting. Jadi kita akan memeriksa apakah data pada setiap tahun itu lengkap atau tidak."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FqHEG-3Mvcqg",
        "outputId": "517d1c29-8088-473c-f860-ae66459d30ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Min_Month  Max_Month\n",
              "YEAR                      \n",
              "2015          6         12\n",
              "2016          1         12\n",
              "2017          1         12\n",
              "2018          1          9"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Min_Month</th>\n",
              "      <th>Max_Month</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YEAR</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44bbc0bc-ae8d-49f7-8745-f1f90c46dcfe')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-44bbc0bc-ae8d-49f7-8745-f1f90c46dcfe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44bbc0bc-ae8d-49f7-8745-f1f90c46dcfe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Memeriksa kelengkapan bulan pada setiap tahun.\n",
        "\n",
        "check_year = df_crime.groupby(by = \"YEAR\")[[\"MONTH\"]].min().rename(columns = {\"MONTH\" : \"Min_Month\"})\n",
        "check_year[\"Max_Month\"] = df_crime.groupby(by = \"YEAR\")[\"MONTH\"].max()\n",
        "check_year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6YvXjtsxq4q"
      },
      "source": [
        "Data tahun 2016 dan 2017 memiliki data yang lengkap untuk setiap bulannya. Jika data dianalisa berdasarkan tahun, maka data pada tahun 2015 dan 2018 akan selalu lebih sedikit dibandingkan 2016 dan 2017. Analisis bisa menjadi bias jika kita menganalisa berdasarkan tahun. Sehingga kita tidak akan menganalisa berdasarkan tahun 2015 - 2018, akan tetapi kita akan menganalisa dari bulan Juni 2015 sampai Juni 2018. Dan data pada bulan Juli, Agustus, dan September akan dibuang."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus bulan juli, agustus, dan september tahun 2018\n",
        "\n",
        "list_1 = (df_crime[(df_crime[\"YEAR\"] == 2018) & (df_crime[\"MONTH\"] == 7)]).index.tolist()\n",
        "list_2 = (df_crime[(df_crime[\"YEAR\"] == 2018) & (df_crime[\"MONTH\"] == 8)]).index.tolist()\n",
        "list_3 = (df_crime[(df_crime[\"YEAR\"] == 2018) & (df_crime[\"MONTH\"] == 9)]).index.tolist()\n",
        "\n",
        "index_drop = list_1 + list_2 + list_3\n",
        "df_crime.drop(index_drop, inplace = True)"
      ],
      "metadata": {
        "id": "K7j2HzfLDHT_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_P18YOFIMttn"
      },
      "source": [
        "> ## Handling Missing Value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "jB3Gxr_I2EBb",
        "outputId": "c98bec6e-e094-41a3-9524-f43b583a3cb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Total Missing Value  % Missing Value\n",
              "DISTRICT                 1392         0.521723\n",
              "SHOOTING               266216        99.778118\n",
              "UCR_PART                   53         0.019864\n",
              "STREET                   8954         3.355971\n",
              "Lat                     16224         6.080777\n",
              "Long                    16224         6.080777"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1cc6d0d3-5d95-40a3-8a2a-63e3ff5ba888\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DISTRICT</th>\n",
              "      <td>1392</td>\n",
              "      <td>0.521723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SHOOTING</th>\n",
              "      <td>266216</td>\n",
              "      <td>99.778118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UCR_PART</th>\n",
              "      <td>53</td>\n",
              "      <td>0.019864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STREET</th>\n",
              "      <td>8954</td>\n",
              "      <td>3.355971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lat</th>\n",
              "      <td>16224</td>\n",
              "      <td>6.080777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Long</th>\n",
              "      <td>16224</td>\n",
              "      <td>6.080777</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        \n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1cc6d0d3-5d95-40a3-8a2a-63e3ff5ba888 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1cc6d0d3-5d95-40a3-8a2a-63e3ff5ba888');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Memeriksa kolom dengan missing value\n",
        "\n",
        "null = df_crime.isna().sum()      # Check jumlah NaN setiap kolom.\n",
        "null = null[null != 0]            # Mengambil hanya kolom yang punya NaN.\n",
        "null_percent = null[null != 0]/df_crime.shape[0] * 100      # Menghitung % NaN\n",
        "\n",
        "pd.DataFrame({\n",
        "    \"Total Missing Value\" : null.to_numpy(),\n",
        "    \"% Missing Value\" : null_percent.to_numpy()\n",
        "}, index = null.index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_cWBEk76B-k"
      },
      "source": [
        "> ### Kolom `SHOOTING`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uctTsWrx0esm"
      },
      "source": [
        "Hampir seluruh nilai dari kolom `SHOOTING` adalah Missing Value, untuk menghindari bias, kolom ini akan dihapus daripada melakukan imputasi."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "3vY1X5mK4sdx"
      },
      "outputs": [],
      "source": [
        "# Menghapus kolom shooting\n",
        "df_crime.drop([\"SHOOTING\"], axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kolom sisa yang memiliki missing value adalah kolom yang berkaitan dengan posisi. Terutama nilai latitude dan longitude, nilai ini harus cukup presisi untuk setiap lokasi, begitu pula untuk kolom-kolom penanda lokasi yang lain. Akan sulit melakukan metode imputasi karena menggunakan nilai pendekatan untuk nilai ini bisa membuat kejadian-kejadian bisa terpusat didaerah lain. Sehingga dalam kasus ini, kita akan menghapus seluruh baris data yang memiliki missing value."
      ],
      "metadata": {
        "id": "fIBNM_Hn_av-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus baris yang memiliki Missing Value\n",
        "df_crime.dropna(inplace = True)"
      ],
      "metadata": {
        "id": "gpWYVK8HAIQ4"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dimensi data sebelum dan sesudah dicleaning\n",
        "print(sebelum)\n",
        "df_crime.shape"
      ],
      "metadata": {
        "id": "ke8tGXSkGQm9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1cb8f01-277a-42ce-997b-ff37a2264ef7"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(319073, 17)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(248693, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_crime.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4OiSwrpIn2G",
        "outputId": "1f373d55-62cd-473f-c181-1d2942366033"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INCIDENT_NUMBER        0\n",
              "OFFENSE_CODE           0\n",
              "OFFENSE_CODE_GROUP     0\n",
              "OFFENSE_DESCRIPTION    0\n",
              "DISTRICT               0\n",
              "REPORTING_AREA         0\n",
              "OCCURRED_ON_DATE       0\n",
              "YEAR                   0\n",
              "MONTH                  0\n",
              "DAY_OF_WEEK            0\n",
              "HOUR                   0\n",
              "UCR_PART               0\n",
              "STREET                 0\n",
              "Lat                    0\n",
              "Long                   0\n",
              "Location               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zMp82G2M1ih"
      },
      "source": [
        "> # Data Analisis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gE2R0khICFNd"
      },
      "source": [
        "> ## Analisis Berdasarkan Waktu Kejadian"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "cPzbkYpCDuGT",
        "outputId": "dcdd7350-dddc-4410-902a-6eeeeefb732e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    MONTH  INCIDENT_NUMBER\n",
              "0       6            25275\n",
              "1       8            21739\n",
              "2       5            21603\n",
              "3       7            21335\n",
              "4       9            21316\n",
              "5      10            21128\n",
              "6       4            19929\n",
              "7       3            19774\n",
              "8       1            19661\n",
              "9      12            19625\n",
              "10     11            19560\n",
              "11      2            17748"
            ],
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              "          const element = document.querySelector('#df-1b8a9f52-04dd-4a23-88aa-af7298940b19');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Jumlah Kejahatan per bulan\n",
        "total_per_month = df_crime.groupby(by = \"MONTH\", as_index = False).count()[[\"MONTH\",\"INCIDENT_NUMBER\"]].sort_values(\"INCIDENT_NUMBER\", ascending=False).reset_index(drop=True)\n",
        "\n",
        "total_per_month"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Jumlah Kejahatan setiap bulan\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "plt.title(\"Jumlah Kejahatan Setiap Bulan\", fontsize = 15)\n",
        "plot = sns.barplot(\n",
        "    data = total_per_month, \n",
        "    x = \"MONTH\",\n",
        "    y = \"INCIDENT_NUMBER\",\n",
        "    order = total_per_month[\"MONTH\"],\n",
        "    palette = \"gray\"\n",
        ")\n",
        "\n",
        "for p in plot.patches:\n",
        "    plot.annotate(int(p.get_height()), (p.get_x()*1.005, p.get_height() * 1.005))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "-KR1pa17vuuy",
        "outputId": "e3b8a8f3-78f9-43e7-fceb-a0fc01d0d7e0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "5fI1T9IqD7wJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "4e3814d3-ed00-4ca6-9025-2c4150ae4d20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             INCIDENT_NUMBER\n",
              "DAY_OF_WEEK                 \n",
              "Friday                 37826\n",
              "Wednesday              36482\n",
              "Thursday               36167\n",
              "Tuesday                36114\n",
              "Monday                 35646\n",
              "Saturday               34870\n",
              "Sunday                 31588"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5cb4010-3020-4498-adab-a42b3effc5be\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>INCIDENT_NUMBER</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAY_OF_WEEK</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Friday</th>\n",
              "      <td>37826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wednesday</th>\n",
              "      <td>36482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thursday</th>\n",
              "      <td>36167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tuesday</th>\n",
              "      <td>36114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monday</th>\n",
              "      <td>35646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saturday</th>\n",
              "      <td>34870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sunday</th>\n",
              "      <td>31588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5cb4010-3020-4498-adab-a42b3effc5be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f5cb4010-3020-4498-adab-a42b3effc5be button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f5cb4010-3020-4498-adab-a42b3effc5be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Jumlah kejahatan per hari\n",
        "total_per_day = df_crime.groupby(by = \"DAY_OF_WEEK\").count()[[\"INCIDENT_NUMBER\"]].sort_values(\"INCIDENT_NUMBER\", ascending=False)\n",
        "total_per_day"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Jumlah Kejahatan per hari\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "plt.title(\"Jumlah Kejahatan Per Hari\", fontsize = 15)\n",
        "plot = sns.barplot(\n",
        "    data = total_per_day, \n",
        "    x = total_per_day.index,\n",
        "    y = \"INCIDENT_NUMBER\",\n",
        "    order = total_per_day.index,\n",
        "    palette = \"gray\"\n",
        ")\n",
        "\n",
        "for p in plot.patches:\n",
        "    plot.annotate(int(p.get_height()), (p.get_x()*1.005, p.get_height() * 1.005))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "EzCainvJY86p",
        "outputId": "aa3a701f-0520-4cf3-eac2-7a213500691e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "dbHmIvVIETKB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "fca93ff6-76fc-4343-f897-94649693aab3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                INCIDENT_NUMBER\n",
              "WAKTU_KEJADIAN                 \n",
              "Dini Hari                 36234\n",
              "Malam                     55583\n",
              "Pagi                      70493\n",
              "Siang                     39724\n",
              "Sore                      46659"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56859524-cd94-4536-87fb-72f7e7195eba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>INCIDENT_NUMBER</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WAKTU_KEJADIAN</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Dini Hari</th>\n",
              "      <td>36234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malam</th>\n",
              "      <td>55583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pagi</th>\n",
              "      <td>70493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Siang</th>\n",
              "      <td>39724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sore</th>\n",
              "      <td>46659</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56859524-cd94-4536-87fb-72f7e7195eba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-56859524-cd94-4536-87fb-72f7e7195eba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56859524-cd94-4536-87fb-72f7e7195eba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Jumlah Kejahatan berdasarkan jam kejadian\n",
        "\n",
        "def waktu(x):\n",
        "    if x in range(0, 7):\n",
        "        return \"Dini Hari\"\n",
        "    elif x in range(7, 13):\n",
        "        return \"Pagi\"\n",
        "    elif x in range(13, 16):\n",
        "        return \"Siang\"\n",
        "    elif x in range(16, 19):\n",
        "        return \"Sore\"\n",
        "    else:\n",
        "        return \"Malam\"\n",
        "\n",
        "data = df_crime[[\"INCIDENT_NUMBER\", \"HOUR\"]]\n",
        "data[\"WAKTU_KEJADIAN\"] = data[\"HOUR\"].apply(waktu)\n",
        "data = data.groupby(by = \"WAKTU_KEJADIAN\").count()[[\"INCIDENT_NUMBER\"]]\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Jumlah Kejahatan per Pelarihan Hari\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "plt.title(\"Jumlah Kejahatan per Pelarihan Hari\", fontsize = 15)\n",
        "plot = sns.barplot(\n",
        "    data = data, \n",
        "    x = data.index,\n",
        "    y = \"INCIDENT_NUMBER\",\n",
        "    order = data.index,\n",
        "    palette = \"gray\"\n",
        ")\n",
        "\n",
        "for p in plot.patches:\n",
        "    plot.annotate(int(p.get_height()), (p.get_x()*1.005, p.get_height() * 1.005))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "hjbbmgTGbsti",
        "outputId": "cd3226a2-533e-44bc-f681-c4609778d719"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi jam paling sering insiden terjadi\n",
        "plt.figure(figsize=(15, 5))\n",
        "plt.title(\"Heatmap Waktu Kejadian (HOUR)\", fontsize = 15)\n",
        "data = pd.pivot_table(\n",
        "    data = df_crime, \n",
        "    index = \"DAY_OF_WEEK\", \n",
        "    columns = \"HOUR\", \n",
        "    values = \"INCIDENT_NUMBER\", \n",
        "    aggfunc = 'count'\n",
        ")\n",
        "\n",
        "sns.heatmap(data, cmap = 'Reds')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "OIgJsK9Izn8O",
        "outputId": "1365cc95-62b2-43bc-ce12-cd7d86b8ed4b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terlihat kejahatan sering terjadi pada sore hari."
      ],
      "metadata": {
        "id": "D7k5zzWM8nSk"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jiPFQ_uXNSxp"
      },
      "source": [
        "> ## Analisis Berdasarkan Tempat Kejadian."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "K6VuDrJ4lp7H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bde3f7a5-9696-4a7a-d59a-4c7820640dde"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B2     37994\n",
              "C11    33887\n",
              "D4     31718\n",
              "B3     27979\n",
              "A1     26240\n",
              "C6     18401\n",
              "D14    16697\n",
              "E18    14267\n",
              "E13    13956\n",
              "A7     11303\n",
              "Name: DISTRICT, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# District tempat insiden sering terjadi.\n",
        "\n",
        "df_crime[\"DISTRICT\"].value_counts()[:10]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Jalan tempat insiden sering terjadi.\n",
        "\n",
        "df_crime[\"STREET\"].value_counts()[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWWOxczEtRZl",
        "outputId": "9e70e513-d3a1-4d9e-9d40-c14eb9af6400"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WASHINGTON ST        11558\n",
              "BLUE HILL AVE         5798\n",
              "BOYLSTON ST           5587\n",
              "DORCHESTER AVE        4085\n",
              "TREMONT ST            3816\n",
              "CENTRE ST             3589\n",
              "MASSACHUSETTS AVE     3448\n",
              "COMMONWEALTH AVE      3412\n",
              "HARRISON AVE          3272\n",
              "HYDE PARK AVE         2939\n",
              "Name: STREET, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pada jam berapa kejahatan di jalan Washington sering terjadi?\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "plt.title(\"Heatmap Waktu Kejadian (HOUR) di Jalan WASHINGTON ST\", fontsize = 15)\n",
        "data = pd.pivot_table(\n",
        "    data = df_crime[df_crime[\"STREET\"] == \"WASHINGTON ST\"], \n",
        "    index = \"DAY_OF_WEEK\", \n",
        "    columns = \"HOUR\", \n",
        "    values = \"INCIDENT_NUMBER\", \n",
        "    aggfunc = 'count'\n",
        ")\n",
        "\n",
        "sns.heatmap(data, cmap = 'Reds')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "I9FRS3Tuw-Bv",
        "outputId": "cb924966-e45d-40da-fb44-a5457d4aee1a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "our_map = folium.Map(location=[42.361145, -71.057083], zoom_start=13)\n",
        "\n",
        "# Tambahkan Marker\n",
        "cluster = MarkerCluster()\n",
        "\n",
        "# iterasi posisi Latitude dan Longitude pada DataFrame\n",
        "for idx, row in df_crime.iterrows():  \n",
        "    cluster.add_child(Marker([row['Lat'], row['Long']]))\n",
        "\n",
        "our_map.add_child(cluster)"
      ],
      "metadata": {
        "id": "DOV0rem4tyOs"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gj6gNKl2NmdI"
      },
      "source": [
        "> ## Analisis Berdasarkan Jenis Kejahatan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "jJeskyt0mMBP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "1f556bda-2494-4ba8-b277-bcd96c924ebb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                OFFENSE_CODE_GROUP  JUMLAH\n",
              "0  Motor Vehicle Accident Response   27454\n",
              "1                          Larceny   22239\n",
              "2               Medical Assistance   20614\n",
              "3               Investigate Person   16139\n",
              "4                            Other   12577\n",
              "5                        Vandalism   12551\n",
              "6                   Simple Assault   12046\n",
              "7                  Verbal Disputes   11600\n",
              "8                   Drug Violation    9759\n",
              "9                            Towed    9658"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8888f424-745f-4ad5-a804-ebc6f7ee893f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>OFFENSE_CODE_GROUP</th>\n",
              "      <th>JUMLAH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Motor Vehicle Accident Response</td>\n",
              "      <td>27454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Larceny</td>\n",
              "      <td>22239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Medical Assistance</td>\n",
              "      <td>20614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Investigate Person</td>\n",
              "      <td>16139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Other</td>\n",
              "      <td>12577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Vandalism</td>\n",
              "      <td>12551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Simple Assault</td>\n",
              "      <td>12046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Verbal Disputes</td>\n",
              "      <td>11600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Drug Violation</td>\n",
              "      <td>9759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Towed</td>\n",
              "      <td>9658</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# Insiden yang paling sering terjadi\n",
        "\n",
        "total_per_crime = df_crime[\"OFFENSE_CODE_GROUP\"].value_counts().rename_axis(\"OFFENSE_CODE_GROUP\").reset_index(name= \"JUMLAH\")\n",
        "total_per_crime.head(10)"
      ]
    },
    {
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      "execution_count": 36,
      "metadata": {
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          "height": 717
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        "outputId": "38f80e8f-ca9e-45cf-a2d2-66d563131120"
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Fungsi untuk menampilkan grafik treemap\n",
        "\n",
        "def treemap(categories, title, path, values):\n",
        "    fig = px.treemap(categories, \n",
        "                     path = path, \n",
        "                     values = values, \n",
        "                     height = 700, \n",
        "                     title = title, \n",
        "                     color_discrete_sequence = px.colors.sequential.RdBu)\n",
        "    fig.data[0].textinfo = 'label+text+value'\n",
        "    fig.show()\n",
        "\n",
        "treemap(total_per_crime, \"Mayoritas Insiden Kota Boston\", [\"OFFENSE_CODE_GROUP\"], total_per_crime[\"JUMLAH\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Jam berapa dan Di jalan mana Kejadian Motor Vehicle Accident Respons paling sering terjadi?**"
      ],
      "metadata": {
        "id": "YMjd6Jyw3uoE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_crime[df_crime[\"OFFENSE_CODE_GROUP\"] == \"Motor Vehicle Accident Response\"].groupby([\"STREET\", \"HOUR\"]).count()[[\"OFFENSE_CODE_GROUP\"]].sort_values(\"OFFENSE_CODE_GROUP\", ascending=False)\n",
        "df.head(10)"
      ],
      "metadata": {
        "id": "2AKx2Q_t3Ajc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "a9bad332-b778-4788-d0a3-c6487b2c485a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    OFFENSE_CODE_GROUP\n",
              "STREET        HOUR                    \n",
              "WASHINGTON ST 16                    80\n",
              "              15                    66\n",
              "BLUE HILL AVE 17                    64\n",
              "WASHINGTON ST 18                    63\n",
              "              10                    61\n",
              "              13                    60\n",
              "              17                    59\n",
              "BLUE HILL AVE 14                    56\n",
              "              18                    56\n",
              "              16                    54"
            ],
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              "\n",
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              "      <th rowspan=\"2\" valign=\"top\">WASHINGTON ST</th>\n",
              "      <th>16</th>\n",
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              "      <th>17</th>\n",
              "      <td>64</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-031fc7f4-6051-4c04-8d3b-1322931d2de2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-031fc7f4-6051-4c04-8d3b-1322931d2de2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pada jam berapa dan dimana Larceny paling sering terjadi?**"
      ],
      "metadata": {
        "id": "TuCWdXCh4MH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_crime[df_crime[\"OFFENSE_CODE_GROUP\"] == \"Larceny\"].groupby([\"STREET\", \"HOUR\"]).count()[[\"OFFENSE_CODE_GROUP\"]].sort_values(\"OFFENSE_CODE_GROUP\", ascending=False)\n",
        "df.head(10)"
      ],
      "metadata": {
        "id": "xriOYHm25s8k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "db9850d8-7e13-4566-cff9-fa755e800d67"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    OFFENSE_CODE_GROUP\n",
              "STREET        HOUR                    \n",
              "BOYLSTON ST   17                   202\n",
              "              16                   197\n",
              "              14                   184\n",
              "              15                   182\n",
              "              18                   176\n",
              "WASHINGTON ST 12                   168\n",
              "BOYLSTON ST   13                   157\n",
              "WASHINGTON ST 15                   144\n",
              "BOYLSTON ST   19                   142\n",
              "              12                   140"
            ],
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    }\n",
              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3c8c13c0-fd9d-4ad3-ad2d-99240938d819 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c8c13c0-fd9d-4ad3-ad2d-99240938d819');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> # Kesimpulan dan Rekomendasi."
      ],
      "metadata": {
        "id": "oNem994i1UPm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Bulan Agustus adalah bulan yang paling sering terjadi suatu insiden atau kejahatan. Hari jumat adalah hari yang paling sering tejadi suatu insiden atau kejahatan. Dan waktu pagi (pukul 6 - 12 am) adalah waktu yang paling sering terjadi kejahatan. Dari data ini kita bisa tahu kapan harus lebih berwaspada lagi.\n",
        "- Kejahatan yang paling sering terjadi adalah karena kendaraan bermotor (Motor Vehicle Accident Respons). Dan yang paling sering kedua adalah Larceny (Pencurian).\n",
        "  - Kejadian Motor Vehicle Accident Respons sederhananya adalah kecelakaan kendaraan bermotor. Data ini merupakan yang paling banyak, berarti lebih membutuhkan perhatian khusus. Solusinya bisa dengan memperbaiki lagi regulasi pengawasan di jalan raya ketika berkendara. Harus lebih berhati-hati ketika dijalan BLUE HILLAVE dan WASHINGTON pada waktu siang hari (zuhur sampai maghrib) karena merupakan tempat dan waktu insiden tersebut terjadi.\n",
        "  - Larceny (Pencurian) adalah kasus kedua yang paling sering terjadi. Sebagai warga, kita bisa lebih berhati-hati dalam menyimpan dan membawa barang berharga, dan sebagai penegak hukum harus lebih sering berpatroli atau cara pencegahan yang lain terutama pada jam siang dan sore di jalan BOYLSTON dan WASHINGTON karena merupakan waktu dan tempat sering terjadi pencurian.\n",
        "- District tempat paling sering terjadi kejahatan adalah B2, C11, D4 dst..., yang berarti insiden kejadian sering terjadi dipusat kota."
      ],
      "metadata": {
        "id": "cGOnyh5C172X"
      }
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