Toronto_Police_MCI_with_pyCaret.ipynb

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            "_model_module": "@jupyter-widgets/controls",
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              "Pipeline Plot",
              "t-SNE (3d) Dimension Plot",
              "Cluster t-SNE (3d)",
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              "Silhouette Plot",
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            "style": "IPY_MODEL_f2d78dfa2b584c618b44c7a873031d2c",
            "tooltips": []
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            "_view_name": "OutputView",
            "layout": "IPY_MODEL_e093b1a72bda4769b3b5f9584e263abb",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1700x600 with 1 Axes>",
                  "image/png": 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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pycaret in /usr/local/lib/python3.9/dist-packages (3.0.0rc9)\n",
            "Requirement already satisfied: plotly>=5.0.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (5.5.0)\n",
            "Requirement already satisfied: pandas<1.6.0,>=1.3.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (1.4.4)\n",
            "Requirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (7.9.0)\n",
            "Requirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (0.56.4)\n",
            "Requirement already satisfied: deprecation>=2.1.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (2.1.0)\n",
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            "Requirement already satisfied: lightgbm>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (3.3.5)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (5.7.3)\n",
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            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (3.7.1)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.9/dist-packages (from pycaret) (7.7.1)\n",
            "Requirement already satisfied: kaleido>=0.2.1 in /usr/local/lib/python3.9/dist-packages (from pycaret) (0.2.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.12.0 in /usr/local/lib/python3.9/dist-packages (from pycaret) (6.0.0)\n",
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          ]
        }
      ],
      "source": [
        "### Toronto Police / MCI indicators\n",
        "### Created on March 16, 2023 / Last update March 17, 2023\n",
        "### Pycaret version\n",
        "### https://github.com/pycaret/pycaret/blob/master/tutorials/Tutorial%20-%20Clustering.ipynb\n",
        "###\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import date\n",
        "import warnings                    #These 2 lines remove all the warnigns in the code  \n",
        "warnings.filterwarnings(\"ignore\")  #These 2 lines remove all the warnigns in the code  \n",
        "from google.colab import drive\n",
        "from google.colab import data_table        #Mar 16, 2023 - Interactive Tables for DataFrames \n",
        "data_table.enable_dataframe_formatter()    #Mar 16, 2023 - Interactive Tables for Dataframes \n",
        "\n",
        "#!pip install pycaret\n",
        "!pip install --pre pycaret\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check installed version\n",
        "import pycaret\n",
        "pycaret.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ITP-QFniaUHK",
        "outputId": "1b304b86-1e39-4dc4-a412-952c6208d7a5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.0.0.rc9'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Change the code here, depending on your dataset location\n",
        "### data = pd.read_csv('Major_Crime_Indicators.csv', header=0)\n",
        "drive.mount('/content/drive')\n",
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Major_Crime_Indicators.csv', header=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B38B5qNtbNpV",
        "outputId": "b63503fa-3f05-45c2-eeb7-b4456b500474"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhXUIIOQb0iO",
        "outputId": "e4b1c3b4-17e4-4e27-b9f7-4bd20e5dbd3e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(301233, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "AKpR5ncIb2SQ",
        "outputId": "87290a52-dc14-4a6c-add2-552a95da4288"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (30) exceeds max_columns (20). Falling back to pandas display.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X             Y  Index_ event_unique_id Division  \\\n",
              "0 -8.850398e+06  5.429359e+06     201  GO-20141273318      D31   \n",
              "1 -8.818405e+06  5.436836e+06     202  GO-20141274349      D42   \n",
              "2 -8.853791e+06  5.402071e+06     203  GO-20141274052      D22   \n",
              "3 -8.832822e+06  5.419628e+06     204  GO-20141276966      D53   \n",
              "4 -8.853452e+06  5.410706e+06     205  GO-20141274457      D22   \n",
              "\n",
              "           occurrencedate            reporteddate  \\\n",
              "0  2014/01/03 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "1  2014/01/03 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "2  2014/01/03 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "3  2014/01/03 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "4  2014/01/03 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "\n",
              "                                       location_type premises_type  ucr_code  \\\n",
              "0                   Apartment (Rooming House, Condo)     Apartment      1430   \n",
              "1  Single Home, House (Attach Garage, Cottage, Mo...         House      2120   \n",
              "2                  Open Areas (Lakes, Parks, Rivers)       Outside      1430   \n",
              "3  Other Commercial / Corporate Places (For Profi...    Commercial      2130   \n",
              "4                                 Convenience Stores    Commercial      1610   \n",
              "\n",
              "   ...  occurrenceday occurrencedayofyear  occurrencedayofweek occurrencehour  \\\n",
              "0  ...            3.0                 3.0           Friday                 11   \n",
              "1  ...            3.0                 3.0           Friday                 14   \n",
              "2  ...            3.0                 3.0           Friday                 13   \n",
              "3  ...            3.0                 3.0           Friday                 12   \n",
              "4  ...            3.0                 3.0           Friday                 14   \n",
              "\n",
              "      mci_category  Hood_ID               Neighbourhood  Longitude   Latitude  \\\n",
              "0          Assault       27     York University Heights -79.504475  43.766371   \n",
              "1  Break and Enter      132                     Malvern -79.217083  43.814861   \n",
              "2          Assault       19                 Long Branch -79.534957  43.589085   \n",
              "3       Theft Over       55            Thorncliffe Park -79.346588  43.703212   \n",
              "4          Robbery       14  Islington-City Centre West -79.531916  43.645247   \n",
              "\n",
              "  ObjectId  \n",
              "0        1  \n",
              "1        2  \n",
              "2        3  \n",
              "3        4  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Exploring the data to gain insigths\n",
        "### Defining our scope with years with complete data year = 2013 ONLY\n",
        "\n",
        "### ocurrencedate min = 9/6/1966 and year = NULL AND ocurrencedata max = 30/06/2022\n",
        "### Clearning the dataset to have only the occurences for 2013 to save computing\n",
        "data = data[data.occurrenceyear == 2013]"
      ],
      "metadata": {
        "id": "it7YYJdTfc2G"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import pycaret clustering and init setup\n",
        "from pycaret.clustering import *\n",
        "s = setup(data, session_id = 123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "O_nimvLpdleF",
        "outputId": "a05a6dea-fc21-4fc9-e0aa-08b82d7214bf"
      },
      "execution_count": 54,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f901bbb6250>"
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              "#T_97eaf_row5_col1 {\n",
              "  background-color: lightgreen;\n",
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              "</style>\n",
              "<table id=\"T_97eaf\" class=\"dataframe\">\n",
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              "      <th id=\"T_97eaf_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_97eaf_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_97eaf_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_97eaf_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_97eaf_row1_col1\" class=\"data row1 col1\" >(596, 30)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_97eaf_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_97eaf_row2_col1\" class=\"data row2 col1\" >(596, 1371)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_97eaf_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_97eaf_row3_col1\" class=\"data row3 col1\" >16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_97eaf_row4_col0\" class=\"data row4 col0\" >Categorical features</td>\n",
              "      <td id=\"T_97eaf_row4_col1\" class=\"data row4 col1\" >14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_97eaf_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n",
              "      <td id=\"T_97eaf_row5_col1\" class=\"data row5 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_97eaf_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n",
              "      <td id=\"T_97eaf_row6_col1\" class=\"data row6 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_97eaf_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_97eaf_row7_col1\" class=\"data row7 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_97eaf_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_97eaf_row8_col1\" class=\"data row8 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_97eaf_row9_col0\" class=\"data row9 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_97eaf_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_97eaf_row10_col0\" class=\"data row10 col0\" >Encoding method</td>\n",
              "      <td id=\"T_97eaf_row10_col1\" class=\"data row10 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_97eaf_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_97eaf_row11_col1\" class=\"data row11 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_97eaf_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n",
              "      <td id=\"T_97eaf_row12_col1\" class=\"data row12 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_97eaf_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_97eaf_row13_col1\" class=\"data row13 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_97eaf_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_97eaf_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_97eaf_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_97eaf_row15_col0\" class=\"data row15 col0\" >USI</td>\n",
              "      <td id=\"T_97eaf_row15_col1\" class=\"data row15 col1\" >64d5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import ClusteringExperiment and init the class\n",
        "from pycaret.clustering import ClusteringExperiment\n",
        "exp = ClusteringExperiment()"
      ],
      "metadata": {
        "id": "aKkMqS7MgTkb"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the type of exp\n",
        "type(exp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zVrqcGprgXnQ",
        "outputId": "e4fd9fe6-9c2c-4013-88fb-16fd087933f6"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pycaret.clustering.oop.ClusteringExperiment"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# init setup on exp\n",
        "exp.setup(data, session_id = 123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "ZWDDkaZggbPN",
        "outputId": "0285a739-1b35-4e22-bfb9-dcad99789993"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f901b8c2fd0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b2ab3_row5_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b2ab3\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b2ab3_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_b2ab3_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_b2ab3_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_b2ab3_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_b2ab3_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_b2ab3_row1_col1\" class=\"data row1 col1\" >(596, 30)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_b2ab3_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_b2ab3_row2_col1\" class=\"data row2 col1\" >(596, 1371)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_b2ab3_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_b2ab3_row3_col1\" class=\"data row3 col1\" >16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_b2ab3_row4_col0\" class=\"data row4 col0\" >Categorical features</td>\n",
              "      <td id=\"T_b2ab3_row4_col1\" class=\"data row4 col1\" >14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_b2ab3_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n",
              "      <td id=\"T_b2ab3_row5_col1\" class=\"data row5 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_b2ab3_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n",
              "      <td id=\"T_b2ab3_row6_col1\" class=\"data row6 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_b2ab3_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_b2ab3_row7_col1\" class=\"data row7 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_b2ab3_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_b2ab3_row8_col1\" class=\"data row8 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_b2ab3_row9_col0\" class=\"data row9 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_b2ab3_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_b2ab3_row10_col0\" class=\"data row10 col0\" >Encoding method</td>\n",
              "      <td id=\"T_b2ab3_row10_col1\" class=\"data row10 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_b2ab3_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_b2ab3_row11_col1\" class=\"data row11 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_b2ab3_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n",
              "      <td id=\"T_b2ab3_row12_col1\" class=\"data row12 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_b2ab3_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_b2ab3_row13_col1\" class=\"data row13 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_b2ab3_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_b2ab3_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b2ab3_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_b2ab3_row15_col0\" class=\"data row15 col0\" >USI</td>\n",
              "      <td id=\"T_b2ab3_row15_col1\" class=\"data row15 col1\" >6283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pycaret.clustering.oop.ClusteringExperiment at 0x7f902c343d30>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## CREATE MODEL\n"
      ],
      "metadata": {
        "id": "E1Onv_Drgk_0"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train kmeans model\n",
        "kmeans = create_model('kmeans')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "dd00845266c24df0ab566e97c1a3bcb1",
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            "7339955092884a2c8571c1d64536e138",
            "c0db3910ad28467292dfd86ac4eddb40",
            "3c613b7f767d4a0b8fd618888cdffe9d",
            "d191aaa8c87c4009be8e18d82a5b7711",
            "86e02e2f4c0a48a0b026b3489454b9ae",
            "1e91f37f1454423c8e747c4ab28e8341",
            "ccc80138b2dd430d9b9bb1782f376f16",
            "5af358ab0f554bb19ce83b535c1d7bdb"
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        },
        "id": "2oHqSSscgpjJ",
        "outputId": "9902763d-4680-43dd-b23f-6cb6aeef88c3"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f902c1d1dc0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_85867\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_85867_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n",
              "      <th id=\"T_85867_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n",
              "      <th id=\"T_85867_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n",
              "      <th id=\"T_85867_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n",
              "      <th id=\"T_85867_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n",
              "      <th id=\"T_85867_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_85867_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_85867_row0_col0\" class=\"data row0 col0\" >0.7135</td>\n",
              "      <td id=\"T_85867_row0_col1\" class=\"data row0 col1\" >453601.5549</td>\n",
              "      <td id=\"T_85867_row0_col2\" class=\"data row0 col2\" >0.4025</td>\n",
              "      <td id=\"T_85867_row0_col3\" class=\"data row0 col3\" >0</td>\n",
              "      <td id=\"T_85867_row0_col4\" class=\"data row0 col4\" >0</td>\n",
              "      <td id=\"T_85867_row0_col5\" class=\"data row0 col5\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd00845266c24df0ab566e97c1a3bcb1"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to check all the available models\n",
        "models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "D6Kfw75MgvmS",
        "outputId": "152f5ab3-ec2f-42c4-e1e0-f3ba5564d1c0"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Name  \\\n",
              "ID                                            \n",
              "kmeans                   K-Means Clustering   \n",
              "ap                     Affinity Propagation   \n",
              "meanshift             Mean Shift Clustering   \n",
              "sc                      Spectral Clustering   \n",
              "hclust             Agglomerative Clustering   \n",
              "dbscan     Density-Based Spatial Clustering   \n",
              "optics                    OPTICS Clustering   \n",
              "birch                      Birch Clustering   \n",
              "\n",
              "                                                   Reference  \n",
              "ID                                                            \n",
              "kmeans                        sklearn.cluster._kmeans.KMeans  \n",
              "ap         sklearn.cluster._affinity_propagation.Affinity...  \n",
              "meanshift              sklearn.cluster._mean_shift.MeanShift  \n",
              "sc              sklearn.cluster._spectral.SpectralClustering  \n",
              "hclust     sklearn.cluster._agglomerative.AgglomerativeCl...  \n",
              "dbscan                        sklearn.cluster._dbscan.DBSCAN  \n",
              "optics                        sklearn.cluster._optics.OPTICS  \n",
              "birch                           sklearn.cluster._birch.Birch  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-011629ce-c1f6-43bd-a2ab-01de468b5c78\">\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>Name</th>\n",
              "      <th>Reference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>kmeans</th>\n",
              "      <td>K-Means Clustering</td>\n",
              "      <td>sklearn.cluster._kmeans.KMeans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap</th>\n",
              "      <td>Affinity Propagation</td>\n",
              "      <td>sklearn.cluster._affinity_propagation.Affinity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meanshift</th>\n",
              "      <td>Mean Shift Clustering</td>\n",
              "      <td>sklearn.cluster._mean_shift.MeanShift</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sc</th>\n",
              "      <td>Spectral Clustering</td>\n",
              "      <td>sklearn.cluster._spectral.SpectralClustering</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hclust</th>\n",
              "      <td>Agglomerative Clustering</td>\n",
              "      <td>sklearn.cluster._agglomerative.AgglomerativeCl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbscan</th>\n",
              "      <td>Density-Based Spatial Clustering</td>\n",
              "      <td>sklearn.cluster._dbscan.DBSCAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>optics</th>\n",
              "      <td>OPTICS Clustering</td>\n",
              "      <td>sklearn.cluster._optics.OPTICS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>birch</th>\n",
              "      <td>Birch Clustering</td>\n",
              "      <td>sklearn.cluster._birch.Birch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-011629ce-c1f6-43bd-a2ab-01de468b5c78')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-011629ce-c1f6-43bd-a2ab-01de468b5c78 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-011629ce-c1f6-43bd-a2ab-01de468b5c78');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/99dac6621f6ae8c4/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"kmeans\",\n\"K-Means Clustering\",\n\"sklearn.cluster._kmeans.KMeans\"],\n [\"ap\",\n\"Affinity Propagation\",\n\"sklearn.cluster._affinity_propagation.AffinityPropagation\"],\n [\"meanshift\",\n\"Mean Shift Clustering\",\n\"sklearn.cluster._mean_shift.MeanShift\"],\n [\"sc\",\n\"Spectral Clustering\",\n\"sklearn.cluster._spectral.SpectralClustering\"],\n [\"hclust\",\n\"Agglomerative Clustering\",\n\"sklearn.cluster._agglomerative.AgglomerativeClustering\"],\n [\"dbscan\",\n\"Density-Based Spatial Clustering\",\n\"sklearn.cluster._dbscan.DBSCAN\"],\n [\"optics\",\n\"OPTICS Clustering\",\n\"sklearn.cluster._optics.OPTICS\"],\n [\"birch\",\n\"Birch Clustering\",\n\"sklearn.cluster._birch.Birch\"]],\n        columns: [[\"string\", \"ID\"], [\"string\", \"Name\"], [\"string\", \"Reference\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    "
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train meanshift model\n",
        "meanshift = create_model('meanshift')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "3b5a9b1f6d8c45bb892c3f9c21ec1a07",
            "42f6b70f6338401880e18b8c32143a88",
            "d5f4c0b51fca4964a939a961e2d126c5",
            "bc60266895b9491d9aa7be82d5936dd7",
            "5b196486bfe14f17847cbcf0bcd0e2c2",
            "619bc0dcb1944af5a93ee7104ec125be",
            "3a74289e9bd94367ba8db767091f3e16",
            "13ecc462354a4fea9331b767fe1514bb",
            "f225e4faaf4b4a7fb20cf2fedfbe0e3d",
            "d00d5c659098499baaedff6fe8e072ff",
            "2956bdd74dc84da28d811452f76fb24d"
          ]
        },
        "id": "uhVZ43hog0b6",
        "outputId": "dd27f867-6e4d-4fba-fba3-8872a9b1f257"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f901b631d30>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_31519\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_31519_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n",
              "      <th id=\"T_31519_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n",
              "      <th id=\"T_31519_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n",
              "      <th id=\"T_31519_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n",
              "      <th id=\"T_31519_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n",
              "      <th id=\"T_31519_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_31519_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_31519_row0_col0\" class=\"data row0 col0\" >0.9915</td>\n",
              "      <td id=\"T_31519_row0_col1\" class=\"data row0 col1\" >187705.0731</td>\n",
              "      <td id=\"T_31519_row0_col2\" class=\"data row0 col2\" >0.0131</td>\n",
              "      <td id=\"T_31519_row0_col3\" class=\"data row0 col3\" >0</td>\n",
              "      <td id=\"T_31519_row0_col4\" class=\"data row0 col4\" >0</td>\n",
              "      <td id=\"T_31519_row0_col5\" class=\"data row0 col5\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b5a9b1f6d8c45bb892c3f9c21ec1a07"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": []
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## ASSIGN MODEL\n"
      ],
      "metadata": {
        "id": "x0cNAoaRg3MV"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans_cluster = assign_model(kmeans)\n",
        "kmeans_cluster"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "Hv2gtVyohC12",
        "outputId": "49ea1e15-e402-4a47-9959-be19775d8a5a"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (31) exceeds max_columns (20). Falling back to pandas display.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             X          Y  Index_ event_unique_id Division  \\\n",
              "0   -8840854.0  5412123.5     208  GO-20141275836      D14   \n",
              "1   -8840317.0  5409695.0     216  GO-20141275533      D14   \n",
              "2   -8822241.0  5426445.0     229  GO-20141275619      D43   \n",
              "3   -8841433.0  5420008.0     243  GO-20141279054      D53   \n",
              "4   -8841075.0  5423336.5     254  GO-20141279637      D32   \n",
              "..         ...        ...     ...             ...      ...   \n",
              "591 -8842066.0  5433651.0  271385  GO-20211819377      D32   \n",
              "592 -8828027.0  5420954.5  272165  GO-20211873191      D55   \n",
              "593 -8825024.0  5417692.5  276174  GO-20212138598      D41   \n",
              "594 -8834612.0  5412888.5  289495   GO-2022526645      D51   \n",
              "595 -8860511.0  5425955.0  301070  GO-20221238028      D23   \n",
              "\n",
              "             occurrencedate            reporteddate  \\\n",
              "0    2013/12/31 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "1    2013/12/18 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "2    2013/12/20 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "3    2013/11/27 05:00:00+00  2014/01/04 05:00:00+00   \n",
              "4    2013/12/25 05:00:00+00  2014/01/04 05:00:00+00   \n",
              "..                      ...                     ...   \n",
              "591  2013/09/01 04:00:00+00  2021/09/22 04:00:00+00   \n",
              "592  2013/01/01 05:00:00+00  2021/09/29 04:00:00+00   \n",
              "593  2013/10/01 04:00:00+00  2021/11/06 04:00:00+00   \n",
              "594  2013/01/01 05:00:00+00  2022/03/21 04:00:00+00   \n",
              "595  2013/12/01 05:00:00+00  2022/06/29 04:00:00+00   \n",
              "\n",
              "                                         location_type premises_type  \\\n",
              "0    Parking Lots (Apt., Commercial Or Non-Commercial)       Outside   \n",
              "1                     Apartment (Rooming House, Condo)     Apartment   \n",
              "2    Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "3                     Apartment (Rooming House, Condo)     Apartment   \n",
              "4    Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "..                                                 ...           ...   \n",
              "591  Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "592  Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "593  Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "594                   Apartment (Rooming House, Condo)     Apartment   \n",
              "595                   Apartment (Rooming House, Condo)     Apartment   \n",
              "\n",
              "     ucr_code  ...  occurrencedayofyear occurrencedayofweek  occurrencehour  \\\n",
              "0        2132  ...                365.0          Tuesday                 11   \n",
              "1        2120  ...                352.0          Wednesday               19   \n",
              "2        2135  ...                354.0          Friday                   3   \n",
              "3        1420  ...                331.0          Wednesday               20   \n",
              "4        2120  ...                359.0          Wednesday               18   \n",
              "..        ...  ...                  ...                 ...             ...   \n",
              "591      1430  ...                244.0          Sunday                   0   \n",
              "592      1430  ...                  1.0          Tuesday                  0   \n",
              "593      1430  ...                274.0          Tuesday                 14   \n",
              "594      1430  ...                  1.0          Tuesday                  0   \n",
              "595      1430  ...                335.0          Sunday                   8   \n",
              "\n",
              "        mci_category  Hood_ID                      Neighbourhood  Longitude  \\\n",
              "0         Theft Over       81                  Trinity-Bellwoods -79.418747   \n",
              "1    Break and Enter       82                            Niagara -79.413918   \n",
              "2         Auto Theft      127                            Bendale -79.251541   \n",
              "3            Assault      102                  Forest Hill North -79.423950   \n",
              "4    Break and Enter       39               Bedford Park-Nortown -79.420731   \n",
              "..               ...      ...                                ...        ...   \n",
              "591          Assault       36                   Newtonbrook West -79.429634   \n",
              "592          Assault       54                   OConnor-Parkview -79.303520   \n",
              "593          Assault      122              Birchcliffe-Cliffside -79.276543   \n",
              "594          Assault       72                        Regent Park -79.362671   \n",
              "595          Assault        2  Mount Olive-Silverstone-Jamestown -79.595329   \n",
              "\n",
              "      Latitude  ObjectId    Cluster  \n",
              "0    43.654457         8  Cluster 0  \n",
              "1    43.638672        16  Cluster 0  \n",
              "2    43.747467        29  Cluster 0  \n",
              "3    43.705681        43  Cluster 0  \n",
              "4    43.727291        54  Cluster 0  \n",
              "..         ...       ...        ...  \n",
              "591  43.794212    271441  Cluster 3  \n",
              "592  43.711826    272884  Cluster 3  \n",
              "593  43.690643    276210  Cluster 3  \n",
              "594  43.659428    289995  Cluster 3  \n",
              "595  43.744289    301186  Cluster 3  \n",
              "\n",
              "[596 rows x 31 columns]"
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              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Index_</th>\n",
              "      <th>event_unique_id</th>\n",
              "      <th>Division</th>\n",
              "      <th>occurrencedate</th>\n",
              "      <th>reporteddate</th>\n",
              "      <th>location_type</th>\n",
              "      <th>premises_type</th>\n",
              "      <th>ucr_code</th>\n",
              "      <th>...</th>\n",
              "      <th>occurrencedayofyear</th>\n",
              "      <th>occurrencedayofweek</th>\n",
              "      <th>occurrencehour</th>\n",
              "      <th>mci_category</th>\n",
              "      <th>Hood_ID</th>\n",
              "      <th>Neighbourhood</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>ObjectId</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-8840854.0</td>\n",
              "      <td>5412123.5</td>\n",
              "      <td>208</td>\n",
              "      <td>GO-20141275836</td>\n",
              "      <td>D14</td>\n",
              "      <td>2013/12/31 05:00:00+00</td>\n",
              "      <td>2014/01/03 05:00:00+00</td>\n",
              "      <td>Parking Lots (Apt., Commercial Or Non-Commercial)</td>\n",
              "      <td>Outside</td>\n",
              "      <td>2132</td>\n",
              "      <td>...</td>\n",
              "      <td>365.0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>11</td>\n",
              "      <td>Theft Over</td>\n",
              "      <td>81</td>\n",
              "      <td>Trinity-Bellwoods</td>\n",
              "      <td>-79.418747</td>\n",
              "      <td>43.654457</td>\n",
              "      <td>8</td>\n",
              "      <td>Cluster 0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-8840317.0</td>\n",
              "      <td>5409695.0</td>\n",
              "      <td>216</td>\n",
              "      <td>GO-20141275533</td>\n",
              "      <td>D14</td>\n",
              "      <td>2013/12/18 05:00:00+00</td>\n",
              "      <td>2014/01/03 05:00:00+00</td>\n",
              "      <td>Apartment (Rooming House, Condo)</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>2120</td>\n",
              "      <td>...</td>\n",
              "      <td>352.0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>19</td>\n",
              "      <td>Break and Enter</td>\n",
              "      <td>82</td>\n",
              "      <td>Niagara</td>\n",
              "      <td>-79.413918</td>\n",
              "      <td>43.638672</td>\n",
              "      <td>16</td>\n",
              "      <td>Cluster 0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-8822241.0</td>\n",
              "      <td>5426445.0</td>\n",
              "      <td>229</td>\n",
              "      <td>GO-20141275619</td>\n",
              "      <td>D43</td>\n",
              "      <td>2013/12/20 05:00:00+00</td>\n",
              "      <td>2014/01/03 05:00:00+00</td>\n",
              "      <td>Single Home, House (Attach Garage, Cottage, Mo...</td>\n",
              "      <td>House</td>\n",
              "      <td>2135</td>\n",
              "      <td>...</td>\n",
              "      <td>354.0</td>\n",
              "      <td>Friday</td>\n",
              "      <td>3</td>\n",
              "      <td>Auto Theft</td>\n",
              "      <td>127</td>\n",
              "      <td>Bendale</td>\n",
              "      <td>-79.251541</td>\n",
              "      <td>43.747467</td>\n",
              "      <td>29</td>\n",
              "      <td>Cluster 0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-8841433.0</td>\n",
              "      <td>5420008.0</td>\n",
              "      <td>243</td>\n",
              "      <td>GO-20141279054</td>\n",
              "      <td>D53</td>\n",
              "      <td>2013/11/27 05:00:00+00</td>\n",
              "      <td>2014/01/04 05:00:00+00</td>\n",
              "      <td>Apartment (Rooming House, Condo)</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>1420</td>\n",
              "      <td>...</td>\n",
              "      <td>331.0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>20</td>\n",
              "      <td>Assault</td>\n",
              "      <td>102</td>\n",
              "      <td>Forest Hill North</td>\n",
              "      <td>-79.423950</td>\n",
              "      <td>43.705681</td>\n",
              "      <td>43</td>\n",
              "      <td>Cluster 0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-8841075.0</td>\n",
              "      <td>5423336.5</td>\n",
              "      <td>254</td>\n",
              "      <td>GO-20141279637</td>\n",
              "      <td>D32</td>\n",
              "      <td>2013/12/25 05:00:00+00</td>\n",
              "      <td>2014/01/04 05:00:00+00</td>\n",
              "      <td>Single Home, House (Attach Garage, Cottage, Mo...</td>\n",
              "      <td>House</td>\n",
              "      <td>2120</td>\n",
              "      <td>...</td>\n",
              "      <td>359.0</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>18</td>\n",
              "      <td>Break and Enter</td>\n",
              "      <td>39</td>\n",
              "      <td>Bedford Park-Nortown</td>\n",
              "      <td>-79.420731</td>\n",
              "      <td>43.727291</td>\n",
              "      <td>54</td>\n",
              "      <td>Cluster 0</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>-8842066.0</td>\n",
              "      <td>5433651.0</td>\n",
              "      <td>271385</td>\n",
              "      <td>GO-20211819377</td>\n",
              "      <td>D32</td>\n",
              "      <td>2013/09/01 04:00:00+00</td>\n",
              "      <td>2021/09/22 04:00:00+00</td>\n",
              "      <td>Single Home, House (Attach Garage, Cottage, Mo...</td>\n",
              "      <td>House</td>\n",
              "      <td>1430</td>\n",
              "      <td>...</td>\n",
              "      <td>244.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>0</td>\n",
              "      <td>Assault</td>\n",
              "      <td>36</td>\n",
              "      <td>Newtonbrook West</td>\n",
              "      <td>-79.429634</td>\n",
              "      <td>43.794212</td>\n",
              "      <td>271441</td>\n",
              "      <td>Cluster 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>-8828027.0</td>\n",
              "      <td>5420954.5</td>\n",
              "      <td>272165</td>\n",
              "      <td>GO-20211873191</td>\n",
              "      <td>D55</td>\n",
              "      <td>2013/01/01 05:00:00+00</td>\n",
              "      <td>2021/09/29 04:00:00+00</td>\n",
              "      <td>Single Home, House (Attach Garage, Cottage, Mo...</td>\n",
              "      <td>House</td>\n",
              "      <td>1430</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>0</td>\n",
              "      <td>Assault</td>\n",
              "      <td>54</td>\n",
              "      <td>OConnor-Parkview</td>\n",
              "      <td>-79.303520</td>\n",
              "      <td>43.711826</td>\n",
              "      <td>272884</td>\n",
              "      <td>Cluster 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>-8825024.0</td>\n",
              "      <td>5417692.5</td>\n",
              "      <td>276174</td>\n",
              "      <td>GO-20212138598</td>\n",
              "      <td>D41</td>\n",
              "      <td>2013/10/01 04:00:00+00</td>\n",
              "      <td>2021/11/06 04:00:00+00</td>\n",
              "      <td>Single Home, House (Attach Garage, Cottage, Mo...</td>\n",
              "      <td>House</td>\n",
              "      <td>1430</td>\n",
              "      <td>...</td>\n",
              "      <td>274.0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>14</td>\n",
              "      <td>Assault</td>\n",
              "      <td>122</td>\n",
              "      <td>Birchcliffe-Cliffside</td>\n",
              "      <td>-79.276543</td>\n",
              "      <td>43.690643</td>\n",
              "      <td>276210</td>\n",
              "      <td>Cluster 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>-8834612.0</td>\n",
              "      <td>5412888.5</td>\n",
              "      <td>289495</td>\n",
              "      <td>GO-2022526645</td>\n",
              "      <td>D51</td>\n",
              "      <td>2013/01/01 05:00:00+00</td>\n",
              "      <td>2022/03/21 04:00:00+00</td>\n",
              "      <td>Apartment (Rooming House, Condo)</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>1430</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>0</td>\n",
              "      <td>Assault</td>\n",
              "      <td>72</td>\n",
              "      <td>Regent Park</td>\n",
              "      <td>-79.362671</td>\n",
              "      <td>43.659428</td>\n",
              "      <td>289995</td>\n",
              "      <td>Cluster 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>-8860511.0</td>\n",
              "      <td>5425955.0</td>\n",
              "      <td>301070</td>\n",
              "      <td>GO-20221238028</td>\n",
              "      <td>D23</td>\n",
              "      <td>2013/12/01 05:00:00+00</td>\n",
              "      <td>2022/06/29 04:00:00+00</td>\n",
              "      <td>Apartment (Rooming House, Condo)</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>1430</td>\n",
              "      <td>...</td>\n",
              "      <td>335.0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>8</td>\n",
              "      <td>Assault</td>\n",
              "      <td>2</td>\n",
              "      <td>Mount Olive-Silverstone-Jamestown</td>\n",
              "      <td>-79.595329</td>\n",
              "      <td>43.744289</td>\n",
              "      <td>301186</td>\n",
              "      <td>Cluster 3</td>\n",
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              "<p>596 rows × 31 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## ANALYSE MODEL"
      ],
      "metadata": {
        "id": "WgQr3O2ahEyB"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot pca cluster plot \n",
        "plot_model(kmeans, plot = 'cluster')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "xd1FSPoShNNT",
        "outputId": "fc78f267-f5f6-4ffa-dcab-41fea40d3ff9"
      },
      "execution_count": 65,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "# plot elbow\n",
        "plot_model(kmeans, plot = 'elbow')"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
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        "id": "0TGyGos6hQDg",
        "outputId": "46ddfeed-0538-4a39-b879-99776580efb6"
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      "execution_count": 66,
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot silhouette\n",
        "plot_model(kmeans, plot = 'silhouette')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "O41BrxxohTd0",
        "outputId": "95fb39af-6912-4437-ee6f-a23292e36f0f"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check docstring to see available plots \n",
        "# help(plot_model)\n",
        "evaluate_model(kmeans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
            "8eee5d815e504d95ac9df28ec7271466",
            "27ab1d197a5048889ab73e47688fe160",
            "4675840231c34e1bbe2a373337ee4ee1",
            "3df6ff63372e4900aba6a68964e565d1",
            "8213a2091124488896d7fc7daa36ab68",
            "f2d78dfa2b584c618b44c7a873031d2c",
            "e093b1a72bda4769b3b5f9584e263abb"
          ]
        },
        "id": "fZTyhE5mhf0W",
        "outputId": "9e63b57e-bf6d-4a6c-f6e8-1e6b05ec8d56"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8eee5d815e504d95ac9df28ec7271466"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##PREDICTION"
      ],
      "metadata": {
        "id": "zcgGUA8vhlEx"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# predict on test set\n",
        "kmeans_pred = predict_model(kmeans, data=data)\n",
        "kmeans_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "mnwYg15GhvPl",
        "outputId": "6775ac57-9f44-4ba6-a69a-2d08b34492ac"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (1372) exceeds max_columns (20). Falling back to pandas display.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   X             Y    Index_  event_unique_id_GO-20141275836  \\\n",
              "7      -8.840854e+06  5.412123e+06     208.0                             1.0   \n",
              "15     -8.840317e+06  5.409695e+06     216.0                             0.0   \n",
              "28     -8.822241e+06  5.426445e+06     229.0                             0.0   \n",
              "42     -8.841433e+06  5.420008e+06     243.0                             0.0   \n",
              "53     -8.841075e+06  5.423337e+06     254.0                             0.0   \n",
              "...              ...           ...       ...                             ...   \n",
              "271440 -8.842066e+06  5.433651e+06  271385.0                             0.0   \n",
              "272883 -8.828027e+06  5.420954e+06  272165.0                             0.0   \n",
              "276209 -8.825024e+06  5.417692e+06  276174.0                             0.0   \n",
              "289994 -8.834612e+06  5.412888e+06  289495.0                             0.0   \n",
              "301185 -8.860511e+06  5.425955e+06  301070.0                             0.0   \n",
              "\n",
              "        event_unique_id_GO-20141275533  event_unique_id_GO-20141275619  \\\n",
              "7                                  0.0                             0.0   \n",
              "15                                 1.0                             0.0   \n",
              "28                                 0.0                             1.0   \n",
              "42                                 0.0                             0.0   \n",
              "53                                 0.0                             0.0   \n",
              "...                                ...                             ...   \n",
              "271440                             0.0                             0.0   \n",
              "272883                             0.0                             0.0   \n",
              "276209                             0.0                             0.0   \n",
              "289994                             0.0                             0.0   \n",
              "301185                             0.0                             0.0   \n",
              "\n",
              "        event_unique_id_GO-20141279054  event_unique_id_GO-20141279637  \\\n",
              "7                                  0.0                             0.0   \n",
              "15                                 0.0                             0.0   \n",
              "28                                 0.0                             0.0   \n",
              "42                                 1.0                             0.0   \n",
              "53                                 0.0                             1.0   \n",
              "...                                ...                             ...   \n",
              "271440                             0.0                             0.0   \n",
              "272883                             0.0                             0.0   \n",
              "276209                             0.0                             0.0   \n",
              "289994                             0.0                             0.0   \n",
              "301185                             0.0                             0.0   \n",
              "\n",
              "        event_unique_id_GO-20141281791  event_unique_id_GO-20141275615  ...  \\\n",
              "7                                  0.0                             0.0  ...   \n",
              "15                                 0.0                             0.0  ...   \n",
              "28                                 0.0                             0.0  ...   \n",
              "42                                 0.0                             0.0  ...   \n",
              "53                                 0.0                             0.0  ...   \n",
              "...                                ...                             ...  ...   \n",
              "271440                             0.0                             0.0  ...   \n",
              "272883                             0.0                             0.0  ...   \n",
              "276209                             0.0                             0.0  ...   \n",
              "289994                             0.0                             0.0  ...   \n",
              "301185                             0.0                             0.0  ...   \n",
              "\n",
              "        Neighbourhood_Broadview North  Neighbourhood_Kingsway South  \\\n",
              "7                                 0.0                           0.0   \n",
              "15                                0.0                           0.0   \n",
              "28                                0.0                           0.0   \n",
              "42                                0.0                           0.0   \n",
              "53                                0.0                           0.0   \n",
              "...                               ...                           ...   \n",
              "271440                            0.0                           0.0   \n",
              "272883                            0.0                           0.0   \n",
              "276209                            0.0                           0.0   \n",
              "289994                            0.0                           0.0   \n",
              "301185                            0.0                           0.0   \n",
              "\n",
              "        Neighbourhood_High Park North  Neighbourhood_Cliffcrest  \\\n",
              "7                                 0.0                       0.0   \n",
              "15                                0.0                       0.0   \n",
              "28                                0.0                       0.0   \n",
              "42                                0.0                       0.0   \n",
              "53                                0.0                       0.0   \n",
              "...                               ...                       ...   \n",
              "271440                            0.0                       0.0   \n",
              "272883                            0.0                       0.0   \n",
              "276209                            0.0                       0.0   \n",
              "289994                            0.0                       0.0   \n",
              "301185                            0.0                       0.0   \n",
              "\n",
              "        Neighbourhood_St.Andrew-Windfields  Neighbourhood_Alderwood  \\\n",
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              "301185                                 0.0                      0.0   \n",
              "\n",
              "        Longitude   Latitude  ObjectId    Cluster  \n",
              "7      -79.418745  43.654456       8.0  Cluster 0  \n",
              "15     -79.413916  43.638670      16.0  Cluster 0  \n",
              "28     -79.251540  43.747469      29.0  Cluster 0  \n",
              "42     -79.423947  43.705682      43.0  Cluster 0  \n",
              "53     -79.420731  43.727292      54.0  Cluster 0  \n",
              "...           ...        ...       ...        ...  \n",
              "271440 -79.429631  43.794213  271441.0  Cluster 3  \n",
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              "276209 -79.276540  43.690642  276210.0  Cluster 3  \n",
              "289994 -79.362674  43.659429  289995.0  Cluster 3  \n",
              "301185 -79.595329  43.744288  301186.0  Cluster 3  \n",
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              "      <th>Neighbourhood_Cliffcrest</th>\n",
              "      <th>Neighbourhood_St.Andrew-Windfields</th>\n",
              "      <th>Neighbourhood_Alderwood</th>\n",
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              "      <th>7</th>\n",
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              "      <th>15</th>\n",
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              "      <th>28</th>\n",
              "      <td>-8.822241e+06</td>\n",
              "      <td>5.426445e+06</td>\n",
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              "      <th>53</th>\n",
              "      <td>-8.841075e+06</td>\n",
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              "      <th>301185</th>\n",
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              "      <td>Cluster 3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>596 rows × 1372 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## SAVE MODEL"
      ],
      "metadata": {
        "id": "SIOkjX97hvkp"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# save pipeline\n",
        "save_model(kmeans, 'kmeans_pipeline')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFUk9CmOh6Nj",
        "outputId": "716f41bd-9743-4b3e-88b5-4622765fb25a"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "          steps=[('numerical_imputer',\n",
              "                  TransformerWrapper(include=['X', 'Y', 'Index_', 'ucr_code',\n",
              "                                              'ucr_ext', 'reportedyear',\n",
              "                                              'reportedday', 'reporteddayofyear',\n",
              "                                              'reportedhour', 'occurrenceyear',\n",
              "                                              'occurrenceday',\n",
              "                                              'occurrencedayofyear',\n",
              "                                              'occurrencehour', 'Longitude',\n",
              "                                              'Latitude', 'ObjectId'],\n",
              "                                     transformer=SimpleImputer())),\n",
              "                 ('cate...\n",
              "                                     transformer=OneHotEncoder(cols=['event_unique_id',\n",
              "                                                                     'Division',\n",
              "                                                                     'occurrencedate',\n",
              "                                                                     'reporteddate',\n",
              "                                                                     'location_type',\n",
              "                                                                     'premises_type',\n",
              "                                                                     'offence',\n",
              "                                                                     'reportedmonth',\n",
              "                                                                     'reporteddayofweek',\n",
              "                                                                     'occurrencemonth',\n",
              "                                                                     'occurrencedayofweek',\n",
              "                                                                     'mci_category',\n",
              "                                                                     'Hood_ID',\n",
              "                                                                     'Neighbourhood'],\n",
              "                                                               handle_missing='return_nan',\n",
              "                                                               use_cat_names=True))),\n",
              "                 ('trained_model', KMeans(n_clusters=4, random_state=123))]),\n",
              " 'kmeans_pipeline.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load pipeline\n",
        "kmeans_pipeline = load_model('kmeans_pipeline')\n",
        "kmeans_pipeline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "ygMLCrKNh8RK",
        "outputId": "74b60058-c935-44f0-b409-af1a2dd3678f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[('numerical_imputer',\n",
              "                 TransformerWrapper(include=['X', 'Y', 'Index_', 'ucr_code',\n",
              "                                             'ucr_ext', 'reportedyear',\n",
              "                                             'reportedday', 'reporteddayofyear',\n",
              "                                             'reportedhour', 'occurrenceyear',\n",
              "                                             'occurrenceday',\n",
              "                                             'occurrencedayofyear',\n",
              "                                             'occurrencehour', 'Longitude',\n",
              "                                             'Latitude', 'ObjectId'],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                ('cate...\n",
              "                                    transformer=OneHotEncoder(cols=['event_unique_id',\n",
              "                                                                    'Division',\n",
              "                                                                    'occurrencedate',\n",
              "                                                                    'reporteddate',\n",
              "                                                                    'location_type',\n",
              "                                                                    'premises_type',\n",
              "                                                                    'offence',\n",
              "                                                                    'reportedmonth',\n",
              "                                                                    'reporteddayofweek',\n",
              "                                                                    'occurrencemonth',\n",
              "                                                                    'occurrencedayofweek',\n",
              "                                                                    'mci_category',\n",
              "                                                                    'Hood_ID',\n",
              "                                                                    'Neighbourhood'],\n",
              "                                                              handle_missing='return_nan',\n",
              "                                                              use_cat_names=True))),\n",
              "                ('trained_model', KMeans(n_clusters=4, random_state=123))])"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[(&#x27;numerical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Index_&#x27;, &#x27;ucr_code&#x27;,\n",
              "                                             &#x27;ucr_ext&#x27;, &#x27;reportedyear&#x27;,\n",
              "                                             &#x27;reportedday&#x27;, &#x27;reporteddayofyear&#x27;,\n",
              "                                             &#x27;reportedhour&#x27;, &#x27;occurrenceyear&#x27;,\n",
              "                                             &#x27;occurrenceday&#x27;,\n",
              "                                             &#x27;occurrencedayofyear&#x27;,\n",
              "                                             &#x27;occurrencehour&#x27;, &#x27;Longitude&#x27;,\n",
              "                                             &#x27;Latitude&#x27;, &#x27;ObjectId&#x27;],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                (&#x27;cate...\n",
              "                                    transformer=OneHotEncoder(cols=[&#x27;event_unique_id&#x27;,\n",
              "                                                                    &#x27;Division&#x27;,\n",
              "                                                                    &#x27;occurrencedate&#x27;,\n",
              "                                                                    &#x27;reporteddate&#x27;,\n",
              "                                                                    &#x27;location_type&#x27;,\n",
              "                                                                    &#x27;premises_type&#x27;,\n",
              "                                                                    &#x27;offence&#x27;,\n",
              "                                                                    &#x27;reportedmonth&#x27;,\n",
              "                                                                    &#x27;reporteddayofweek&#x27;,\n",
              "                                                                    &#x27;occurrencemonth&#x27;,\n",
              "                                                                    &#x27;occurrencedayofweek&#x27;,\n",
              "                                                                    &#x27;mci_category&#x27;,\n",
              "                                                                    &#x27;Hood_ID&#x27;,\n",
              "                                                                    &#x27;Neighbourhood&#x27;],\n",
              "                                                              handle_missing=&#x27;return_nan&#x27;,\n",
              "                                                              use_cat_names=True))),\n",
              "                (&#x27;trained_model&#x27;, KMeans(n_clusters=4, random_state=123))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[(&#x27;numerical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Index_&#x27;, &#x27;ucr_code&#x27;,\n",
              "                                             &#x27;ucr_ext&#x27;, &#x27;reportedyear&#x27;,\n",
              "                                             &#x27;reportedday&#x27;, &#x27;reporteddayofyear&#x27;,\n",
              "                                             &#x27;reportedhour&#x27;, &#x27;occurrenceyear&#x27;,\n",
              "                                             &#x27;occurrenceday&#x27;,\n",
              "                                             &#x27;occurrencedayofyear&#x27;,\n",
              "                                             &#x27;occurrencehour&#x27;, &#x27;Longitude&#x27;,\n",
              "                                             &#x27;Latitude&#x27;, &#x27;ObjectId&#x27;],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                (&#x27;cate...\n",
              "                                    transformer=OneHotEncoder(cols=[&#x27;event_unique_id&#x27;,\n",
              "                                                                    &#x27;Division&#x27;,\n",
              "                                                                    &#x27;occurrencedate&#x27;,\n",
              "                                                                    &#x27;reporteddate&#x27;,\n",
              "                                                                    &#x27;location_type&#x27;,\n",
              "                                                                    &#x27;premises_type&#x27;,\n",
              "                                                                    &#x27;offence&#x27;,\n",
              "                                                                    &#x27;reportedmonth&#x27;,\n",
              "                                                                    &#x27;reporteddayofweek&#x27;,\n",
              "                                                                    &#x27;occurrencemonth&#x27;,\n",
              "                                                                    &#x27;occurrencedayofweek&#x27;,\n",
              "                                                                    &#x27;mci_category&#x27;,\n",
              "                                                                    &#x27;Hood_ID&#x27;,\n",
              "                                                                    &#x27;Neighbourhood&#x27;],\n",
              "                                                              handle_missing=&#x27;return_nan&#x27;,\n",
              "                                                              use_cat_names=True))),\n",
              "                (&#x27;trained_model&#x27;, KMeans(n_clusters=4, random_state=123))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerical_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Index_&#x27;, &#x27;ucr_code&#x27;, &#x27;ucr_ext&#x27;,\n",
              "                            &#x27;reportedyear&#x27;, &#x27;reportedday&#x27;, &#x27;reporteddayofyear&#x27;,\n",
              "                            &#x27;reportedhour&#x27;, &#x27;occurrenceyear&#x27;, &#x27;occurrenceday&#x27;,\n",
              "                            &#x27;occurrencedayofyear&#x27;, &#x27;occurrencehour&#x27;,\n",
              "                            &#x27;Longitude&#x27;, &#x27;Latitude&#x27;, &#x27;ObjectId&#x27;],\n",
              "                   transformer=SimpleImputer())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                            &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;,\n",
              "                            &#x27;offence&#x27;, &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;,\n",
              "                            &#x27;occurrencemonth&#x27;, &#x27;occurrencedayofweek&#x27;,\n",
              "                            &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;, &#x27;Neighbourhood&#x27;],\n",
              "                   transformer=SimpleImputer(strategy=&#x27;most_frequent&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehot_encoding: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                            &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;,\n",
              "                            &#x27;offence&#x27;, &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;,\n",
              "                            &#x27;occurrencemonth&#x27;, &#x27;occurrencedayofweek&#x27;,\n",
              "                            &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;, &#x27;Neighbourhood&#x27;],\n",
              "                   transformer=OneHotEncoder(cols=[&#x27;event_unique_id&#x27;,\n",
              "                                                   &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                                                   &#x27;reporteddate&#x27;,\n",
              "                                                   &#x27;location_type&#x27;,\n",
              "                                                   &#x27;premises_type&#x27;, &#x27;offence&#x27;,\n",
              "                                                   &#x27;reportedmonth&#x27;,\n",
              "                                                   &#x27;reporteddayofweek&#x27;,\n",
              "                                                   &#x27;occurrencemonth&#x27;,\n",
              "                                                   &#x27;occurrencedayofweek&#x27;,\n",
              "                                                   &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;,\n",
              "                                                   &#x27;Neighbourhood&#x27;],\n",
              "                                             handle_missing=&#x27;return_nan&#x27;,\n",
              "                                             use_cat_names=True))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(cols=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                    &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;, &#x27;offence&#x27;,\n",
              "                    &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;, &#x27;occurrencemonth&#x27;,\n",
              "                    &#x27;occurrencedayofweek&#x27;, &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;,\n",
              "                    &#x27;Neighbourhood&#x27;],\n",
              "              handle_missing=&#x27;return_nan&#x27;, use_cat_names=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(cols=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                    &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;, &#x27;offence&#x27;,\n",
              "                    &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;, &#x27;occurrencemonth&#x27;,\n",
              "                    &#x27;occurrencedayofweek&#x27;, &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;,\n",
              "                    &#x27;Neighbourhood&#x27;],\n",
              "              handle_missing=&#x27;return_nan&#x27;, use_cat_names=True)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, random_state=123)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Detailed function-by-function overview"
      ],
      "metadata": {
        "id": "GyDK1YociMVC"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# init setup\n",
        "s = setup(data, session_id = 123)"
      ],
      "metadata": {
        "id": "-SMPN4sQibsr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "3a292a52-fd6f-4924-ad69-216e780c8877"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f90177a3430>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_0c960_row5_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_0c960\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_0c960_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_0c960_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_0c960_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_0c960_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_0c960_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_0c960_row1_col1\" class=\"data row1 col1\" >(596, 30)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_0c960_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_0c960_row2_col1\" class=\"data row2 col1\" >(596, 1371)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_0c960_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_0c960_row3_col1\" class=\"data row3 col1\" >16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_0c960_row4_col0\" class=\"data row4 col0\" >Categorical features</td>\n",
              "      <td id=\"T_0c960_row4_col1\" class=\"data row4 col1\" >14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_0c960_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n",
              "      <td id=\"T_0c960_row5_col1\" class=\"data row5 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_0c960_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n",
              "      <td id=\"T_0c960_row6_col1\" class=\"data row6 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_0c960_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_0c960_row7_col1\" class=\"data row7 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_0c960_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_0c960_row8_col1\" class=\"data row8 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_0c960_row9_col0\" class=\"data row9 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_0c960_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_0c960_row10_col0\" class=\"data row10 col0\" >Encoding method</td>\n",
              "      <td id=\"T_0c960_row10_col1\" class=\"data row10 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_0c960_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_0c960_row11_col1\" class=\"data row11 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_0c960_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n",
              "      <td id=\"T_0c960_row12_col1\" class=\"data row12 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_0c960_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_0c960_row13_col1\" class=\"data row13 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_0c960_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_0c960_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0c960_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_0c960_row15_col0\" class=\"data row15 col0\" >USI</td>\n",
              "      <td id=\"T_0c960_row15_col1\" class=\"data row15 col1\" >e809</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check all available config\n",
        "get_config()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_TBlwxzMF4I",
        "outputId": "2eafeffb-f107-440f-8f6d-fa570f561125"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'USI',\n",
              " 'X',\n",
              " 'X_train',\n",
              " 'X_train_transformed',\n",
              " 'X_transformed',\n",
              " '_available_plots',\n",
              " '_ml_usecase',\n",
              " 'data',\n",
              " 'dataset',\n",
              " 'dataset_transformed',\n",
              " 'exp_id',\n",
              " 'exp_name_log',\n",
              " 'gpu_n_jobs_param',\n",
              " 'gpu_param',\n",
              " 'html_param',\n",
              " 'idx',\n",
              " 'is_multiclass',\n",
              " 'log_plots_param',\n",
              " 'logging_param',\n",
              " 'memory',\n",
              " 'n_jobs_param',\n",
              " 'pipeline',\n",
              " 'seed',\n",
              " 'train',\n",
              " 'train_transformed',\n",
              " 'variable_and_property_keys',\n",
              " 'variables'}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets access X_train_transformed\n",
        "get_config('X_train_transformed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "l74A2TlKMIOG",
        "outputId": "5393415b-555c-462a-8c9d-8542fb8c4552"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (1371) exceeds max_columns (20). Falling back to pandas display.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             X          Y    Index_  event_unique_id_GO-20141275836  \\\n",
              "0   -8840854.0  5412123.5     208.0                             1.0   \n",
              "1   -8840317.0  5409695.0     216.0                             0.0   \n",
              "2   -8822241.0  5426445.0     229.0                             0.0   \n",
              "3   -8841433.0  5420008.0     243.0                             0.0   \n",
              "4   -8841075.0  5423336.5     254.0                             0.0   \n",
              "..         ...        ...       ...                             ...   \n",
              "591 -8842066.0  5433651.0  271385.0                             0.0   \n",
              "592 -8828027.0  5420954.5  272165.0                             0.0   \n",
              "593 -8825024.0  5417692.5  276174.0                             0.0   \n",
              "594 -8834612.0  5412888.5  289495.0                             0.0   \n",
              "595 -8860511.0  5425955.0  301070.0                             0.0   \n",
              "\n",
              "     event_unique_id_GO-20141275533  event_unique_id_GO-20141275619  \\\n",
              "0                               0.0                             0.0   \n",
              "1                               1.0                             0.0   \n",
              "2                               0.0                             1.0   \n",
              "3                               0.0                             0.0   \n",
              "4                               0.0                             0.0   \n",
              "..                              ...                             ...   \n",
              "591                             0.0                             0.0   \n",
              "592                             0.0                             0.0   \n",
              "593                             0.0                             0.0   \n",
              "594                             0.0                             0.0   \n",
              "595                             0.0                             0.0   \n",
              "\n",
              "     event_unique_id_GO-20141279054  event_unique_id_GO-20141279637  \\\n",
              "0                               0.0                             0.0   \n",
              "1                               0.0                             0.0   \n",
              "2                               0.0                             0.0   \n",
              "3                               1.0                             0.0   \n",
              "4                               0.0                             1.0   \n",
              "..                              ...                             ...   \n",
              "591                             0.0                             0.0   \n",
              "592                             0.0                             0.0   \n",
              "593                             0.0                             0.0   \n",
              "594                             0.0                             0.0   \n",
              "595                             0.0                             0.0   \n",
              "\n",
              "     event_unique_id_GO-20141281791  event_unique_id_GO-20141275615  ...  \\\n",
              "0                               0.0                             0.0  ...   \n",
              "1                               0.0                             0.0  ...   \n",
              "2                               0.0                             0.0  ...   \n",
              "3                               0.0                             0.0  ...   \n",
              "4                               0.0                             0.0  ...   \n",
              "..                              ...                             ...  ...   \n",
              "591                             0.0                             0.0  ...   \n",
              "592                             0.0                             0.0  ...   \n",
              "593                             0.0                             0.0  ...   \n",
              "594                             0.0                             0.0  ...   \n",
              "595                             0.0                             0.0  ...   \n",
              "\n",
              "     Neighbourhood_Yonge-Eglinton  Neighbourhood_Broadview North  \\\n",
              "0                             0.0                            0.0   \n",
              "1                             0.0                            0.0   \n",
              "2                             0.0                            0.0   \n",
              "3                             0.0                            0.0   \n",
              "4                             0.0                            0.0   \n",
              "..                            ...                            ...   \n",
              "591                           0.0                            0.0   \n",
              "592                           0.0                            0.0   \n",
              "593                           0.0                            0.0   \n",
              "594                           0.0                            0.0   \n",
              "595                           0.0                            0.0   \n",
              "\n",
              "     Neighbourhood_Kingsway South  Neighbourhood_High Park North  \\\n",
              "0                             0.0                            0.0   \n",
              "1                             0.0                            0.0   \n",
              "2                             0.0                            0.0   \n",
              "3                             0.0                            0.0   \n",
              "4                             0.0                            0.0   \n",
              "..                            ...                            ...   \n",
              "591                           0.0                            0.0   \n",
              "592                           0.0                            0.0   \n",
              "593                           0.0                            0.0   \n",
              "594                           0.0                            0.0   \n",
              "595                           0.0                            0.0   \n",
              "\n",
              "     Neighbourhood_Cliffcrest  Neighbourhood_St.Andrew-Windfields  \\\n",
              "0                         0.0                                 0.0   \n",
              "1                         0.0                                 0.0   \n",
              "2                         0.0                                 0.0   \n",
              "3                         0.0                                 0.0   \n",
              "4                         0.0                                 0.0   \n",
              "..                        ...                                 ...   \n",
              "591                       0.0                                 0.0   \n",
              "592                       0.0                                 0.0   \n",
              "593                       0.0                                 0.0   \n",
              "594                       0.0                                 0.0   \n",
              "595                       0.0                                 0.0   \n",
              "\n",
              "     Neighbourhood_Alderwood  Longitude   Latitude  ObjectId  \n",
              "0                        0.0 -79.418747  43.654457       8.0  \n",
              "1                        0.0 -79.413918  43.638672      16.0  \n",
              "2                        0.0 -79.251541  43.747467      29.0  \n",
              "3                        0.0 -79.423950  43.705681      43.0  \n",
              "4                        0.0 -79.420731  43.727291      54.0  \n",
              "..                       ...        ...        ...       ...  \n",
              "591                      0.0 -79.429634  43.794212  271441.0  \n",
              "592                      0.0 -79.303520  43.711826  272884.0  \n",
              "593                      0.0 -79.276543  43.690643  276210.0  \n",
              "594                      0.0 -79.362671  43.659428  289995.0  \n",
              "595                      0.0 -79.595329  43.744289  301186.0  \n",
              "\n",
              "[596 rows x 1371 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55b6dd09-2e38-4dc4-8560-b54702b67de5\">\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>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Index_</th>\n",
              "      <th>event_unique_id_GO-20141275836</th>\n",
              "      <th>event_unique_id_GO-20141275533</th>\n",
              "      <th>event_unique_id_GO-20141275619</th>\n",
              "      <th>event_unique_id_GO-20141279054</th>\n",
              "      <th>event_unique_id_GO-20141279637</th>\n",
              "      <th>event_unique_id_GO-20141281791</th>\n",
              "      <th>event_unique_id_GO-20141275615</th>\n",
              "      <th>...</th>\n",
              "      <th>Neighbourhood_Yonge-Eglinton</th>\n",
              "      <th>Neighbourhood_Broadview North</th>\n",
              "      <th>Neighbourhood_Kingsway South</th>\n",
              "      <th>Neighbourhood_High Park North</th>\n",
              "      <th>Neighbourhood_Cliffcrest</th>\n",
              "      <th>Neighbourhood_St.Andrew-Windfields</th>\n",
              "      <th>Neighbourhood_Alderwood</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>ObjectId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-8840854.0</td>\n",
              "      <td>5412123.5</td>\n",
              "      <td>208.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.418747</td>\n",
              "      <td>43.654457</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-8840317.0</td>\n",
              "      <td>5409695.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.413918</td>\n",
              "      <td>43.638672</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-8822241.0</td>\n",
              "      <td>5426445.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.251541</td>\n",
              "      <td>43.747467</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-8841433.0</td>\n",
              "      <td>5420008.0</td>\n",
              "      <td>243.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.423950</td>\n",
              "      <td>43.705681</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-8841075.0</td>\n",
              "      <td>5423336.5</td>\n",
              "      <td>254.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.420731</td>\n",
              "      <td>43.727291</td>\n",
              "      <td>54.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>591</th>\n",
              "      <td>-8842066.0</td>\n",
              "      <td>5433651.0</td>\n",
              "      <td>271385.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.429634</td>\n",
              "      <td>43.794212</td>\n",
              "      <td>271441.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>-8828027.0</td>\n",
              "      <td>5420954.5</td>\n",
              "      <td>272165.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.303520</td>\n",
              "      <td>43.711826</td>\n",
              "      <td>272884.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>-8825024.0</td>\n",
              "      <td>5417692.5</td>\n",
              "      <td>276174.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.276543</td>\n",
              "      <td>43.690643</td>\n",
              "      <td>276210.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>-8834612.0</td>\n",
              "      <td>5412888.5</td>\n",
              "      <td>289495.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.362671</td>\n",
              "      <td>43.659428</td>\n",
              "      <td>289995.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>-8860511.0</td>\n",
              "      <td>5425955.0</td>\n",
              "      <td>301070.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-79.595329</td>\n",
              "      <td>43.744289</td>\n",
              "      <td>301186.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>596 rows × 1371 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55b6dd09-2e38-4dc4-8560-b54702b67de5')\"\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-55b6dd09-2e38-4dc4-8560-b54702b67de5 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-55b6dd09-2e38-4dc4-8560-b54702b67de5');\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": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# another example: let's access seed\n",
        "print(\"The current seed is: {}\".format(get_config('seed')))\n",
        "\n",
        "# now lets change it using set_config\n",
        "set_config('seed', 786)\n",
        "print(\"The new seed is: {}\".format(get_config('seed')))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K21WNEA2MU3d",
        "outputId": "ecdb2516-2826-4cb2-cb01-187be255878a"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The current seed is: 123\n",
            "The new seed is: 786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# init setup with normalize = True\n",
        "\n",
        "s = setup(data, session_id = 123,\n",
        "          normalize = True, normalize_method = 'minmax')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "JtS6-NjZMaMz",
        "outputId": "e6d78e97-eac6-46b6-d4b9-dcebd6c0ffff"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f90177b33a0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b6d8b_row5_col1, #T_b6d8b_row11_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b6d8b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b6d8b_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_b6d8b_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_b6d8b_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_b6d8b_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_b6d8b_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_b6d8b_row1_col1\" class=\"data row1 col1\" >(596, 30)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_b6d8b_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_b6d8b_row2_col1\" class=\"data row2 col1\" >(596, 1371)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_b6d8b_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_b6d8b_row3_col1\" class=\"data row3 col1\" >16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_b6d8b_row4_col0\" class=\"data row4 col0\" >Categorical features</td>\n",
              "      <td id=\"T_b6d8b_row4_col1\" class=\"data row4 col1\" >14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_b6d8b_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n",
              "      <td id=\"T_b6d8b_row5_col1\" class=\"data row5 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_b6d8b_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n",
              "      <td id=\"T_b6d8b_row6_col1\" class=\"data row6 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_b6d8b_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_b6d8b_row7_col1\" class=\"data row7 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_b6d8b_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_b6d8b_row8_col1\" class=\"data row8 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_b6d8b_row9_col0\" class=\"data row9 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_b6d8b_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_b6d8b_row10_col0\" class=\"data row10 col0\" >Encoding method</td>\n",
              "      <td id=\"T_b6d8b_row10_col1\" class=\"data row10 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_b6d8b_row11_col0\" class=\"data row11 col0\" >Normalize</td>\n",
              "      <td id=\"T_b6d8b_row11_col1\" class=\"data row11 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_b6d8b_row12_col0\" class=\"data row12 col0\" >Normalize method</td>\n",
              "      <td id=\"T_b6d8b_row12_col1\" class=\"data row12 col1\" >minmax</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_b6d8b_row13_col0\" class=\"data row13 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_b6d8b_row13_col1\" class=\"data row13 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_b6d8b_row14_col0\" class=\"data row14 col0\" >Use GPU</td>\n",
              "      <td id=\"T_b6d8b_row14_col1\" class=\"data row14 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_b6d8b_row15_col0\" class=\"data row15 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_b6d8b_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_b6d8b_row16_col0\" class=\"data row16 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_b6d8b_row16_col1\" class=\"data row16 col1\" >cluster-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6d8b_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_b6d8b_row17_col0\" class=\"data row17 col0\" >USI</td>\n",
              "      <td id=\"T_b6d8b_row17_col1\" class=\"data row17 col1\" >de68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets check the X_train_transformed to see effect of params passed\n",
        "get_config('X_train_transformed')['occurrenceday'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "PMSjHkxxMeK4",
        "outputId": "5befe646-d661-47c4-dbdb-663201ab590d"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_config('X_train')['occurrenceday'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6tNeF-0UNDcG",
        "outputId": "8ff4813d-eb27-47e8-9631-34c0e9157842"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check all the available models\n",
        "models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "CweNDNj_NOnB",
        "outputId": "f19cf309-4b0a-4975-af12-a3effbd8fa1d"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Name  \\\n",
              "ID                                            \n",
              "kmeans                   K-Means Clustering   \n",
              "ap                     Affinity Propagation   \n",
              "meanshift             Mean Shift Clustering   \n",
              "sc                      Spectral Clustering   \n",
              "hclust             Agglomerative Clustering   \n",
              "dbscan     Density-Based Spatial Clustering   \n",
              "optics                    OPTICS Clustering   \n",
              "birch                      Birch Clustering   \n",
              "\n",
              "                                                   Reference  \n",
              "ID                                                            \n",
              "kmeans                        sklearn.cluster._kmeans.KMeans  \n",
              "ap         sklearn.cluster._affinity_propagation.Affinity...  \n",
              "meanshift              sklearn.cluster._mean_shift.MeanShift  \n",
              "sc              sklearn.cluster._spectral.SpectralClustering  \n",
              "hclust     sklearn.cluster._agglomerative.AgglomerativeCl...  \n",
              "dbscan                        sklearn.cluster._dbscan.DBSCAN  \n",
              "optics                        sklearn.cluster._optics.OPTICS  \n",
              "birch                           sklearn.cluster._birch.Birch  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f6323b0-e71d-440e-863e-7260a4703084\">\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>Name</th>\n",
              "      <th>Reference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>kmeans</th>\n",
              "      <td>K-Means Clustering</td>\n",
              "      <td>sklearn.cluster._kmeans.KMeans</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ap</th>\n",
              "      <td>Affinity Propagation</td>\n",
              "      <td>sklearn.cluster._affinity_propagation.Affinity...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meanshift</th>\n",
              "      <td>Mean Shift Clustering</td>\n",
              "      <td>sklearn.cluster._mean_shift.MeanShift</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sc</th>\n",
              "      <td>Spectral Clustering</td>\n",
              "      <td>sklearn.cluster._spectral.SpectralClustering</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hclust</th>\n",
              "      <td>Agglomerative Clustering</td>\n",
              "      <td>sklearn.cluster._agglomerative.AgglomerativeCl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbscan</th>\n",
              "      <td>Density-Based Spatial Clustering</td>\n",
              "      <td>sklearn.cluster._dbscan.DBSCAN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>optics</th>\n",
              "      <td>OPTICS Clustering</td>\n",
              "      <td>sklearn.cluster._optics.OPTICS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>birch</th>\n",
              "      <td>Birch Clustering</td>\n",
              "      <td>sklearn.cluster._birch.Birch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f6323b0-e71d-440e-863e-7260a4703084')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9f6323b0-e71d-440e-863e-7260a4703084 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-9f6323b0-e71d-440e-863e-7260a4703084');\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",
              "  "
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/99dac6621f6ae8c4/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"kmeans\",\n\"K-Means Clustering\",\n\"sklearn.cluster._kmeans.KMeans\"],\n [\"ap\",\n\"Affinity Propagation\",\n\"sklearn.cluster._affinity_propagation.AffinityPropagation\"],\n [\"meanshift\",\n\"Mean Shift Clustering\",\n\"sklearn.cluster._mean_shift.MeanShift\"],\n [\"sc\",\n\"Spectral Clustering\",\n\"sklearn.cluster._spectral.SpectralClustering\"],\n [\"hclust\",\n\"Agglomerative Clustering\",\n\"sklearn.cluster._agglomerative.AgglomerativeClustering\"],\n [\"dbscan\",\n\"Density-Based Spatial Clustering\",\n\"sklearn.cluster._dbscan.DBSCAN\"],\n [\"optics\",\n\"OPTICS Clustering\",\n\"sklearn.cluster._optics.OPTICS\"],\n [\"birch\",\n\"Birch Clustering\",\n\"sklearn.cluster._birch.Birch\"]],\n        columns: [[\"string\", \"ID\"], [\"string\", \"Name\"], [\"string\", \"Reference\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    "
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train kmeans\n",
        "kmeans = create_model('kmeans')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "b4cd0ea59b744b72b4d9cc011e693bc3",
            "69f2695bff934c6188735daf6573c713",
            "7780f15036c24870997a3ec11fdc57ab",
            "6278ebd6eb9f4bd883ba22ca75c507ed",
            "e937bbd6112d4cb487b7be4b9184c84d",
            "dcda2c9a7efb413cabcb0a50668db0f0",
            "0d6555d1fb484bf8a1d95cff0b37800e",
            "5f1c0e4446bd4b6fa13c9ad4774def30",
            "48cc1933501e48848d24c22eaa8809d8",
            "cac17f3d641944438bb65dc42cab7685",
            "3786292a29814541a538370280d0c564"
          ]
        },
        "id": "ENtzZhBXNSaj",
        "outputId": "dfaebc41-8b2f-48c4-f7bb-97675a4a1a40"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f901bfc0580>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_23f2a\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_23f2a_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n",
              "      <th id=\"T_23f2a_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n",
              "      <th id=\"T_23f2a_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n",
              "      <th id=\"T_23f2a_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n",
              "      <th id=\"T_23f2a_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n",
              "      <th id=\"T_23f2a_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_23f2a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_23f2a_row0_col0\" class=\"data row0 col0\" >0.0524</td>\n",
              "      <td id=\"T_23f2a_row0_col1\" class=\"data row0 col1\" >29.0783</td>\n",
              "      <td id=\"T_23f2a_row0_col2\" class=\"data row0 col2\" >3.5929</td>\n",
              "      <td id=\"T_23f2a_row0_col3\" class=\"data row0 col3\" >0</td>\n",
              "      <td id=\"T_23f2a_row0_col4\" class=\"data row0 col4\" >0</td>\n",
              "      <td id=\"T_23f2a_row0_col5\" class=\"data row0 col5\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b4cd0ea59b744b72b4d9cc011e693bc3"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans_results = pull()\n",
        "print(type(kmeans_results))\n",
        "kmeans_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "g7lJsiCaNV2d",
        "outputId": "21125935-c511-485e-dd62-7a4e0d2ffb8d"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Silhouette  Calinski-Harabasz  Davies-Bouldin  Homogeneity  Rand Index  \\\n",
              "0      0.0524            29.0783          3.5929            0           0   \n",
              "\n",
              "   Completeness  \n",
              "0             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34e68509-92ea-449f-adb7-79f4e812671b\">\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>Silhouette</th>\n",
              "      <th>Calinski-Harabasz</th>\n",
              "      <th>Davies-Bouldin</th>\n",
              "      <th>Homogeneity</th>\n",
              "      <th>Rand Index</th>\n",
              "      <th>Completeness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0524</td>\n",
              "      <td>29.0783</td>\n",
              "      <td>3.5929</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34e68509-92ea-449f-adb7-79f4e812671b')\"\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-34e68509-92ea-449f-adb7-79f4e812671b 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-34e68509-92ea-449f-adb7-79f4e812671b');\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",
              "  "
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/99dac6621f6ae8c4/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0.0524,\n            'f': \"0.0524\",\n        },\n{\n            'v': 29.0783,\n            'f': \"29.0783\",\n        },\n{\n            'v': 3.5929,\n            'f': \"3.5929\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"number\", \"Silhouette\"], [\"number\", \"Calinski-Harabasz\"], [\"number\", \"Davies-Bouldin\"], [\"number\", \"Homogeneity\"], [\"number\", \"Rand Index\"], [\"number\", \"Completeness\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    "
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train kmeans with 10 clusters\n",
        "create_model('kmeans', num_clusters = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "cc415b8a1fc34379a3fc4a3f6ae1696d",
            "f4dcdf40df754f34a2a4e855614182d0",
            "6977b0fc44a9404198c12e5a649c3acf",
            "6338f99d682b46148cc4586463205ee7",
            "bd1efb22595e4a748c3811bc76014147",
            "8ad366a4803042f38539d240805de3c9",
            "722ac301c0b14477a2a09648272f5975",
            "642b64e219e04ac7b60c86bc9bb80466",
            "139ed8b1e7884842ad16ca3543d2d8c8",
            "592c27cdd1fc445aaa72cb47c76017ec",
            "5116986ac4054289a3665d1bc6343184"
          ]
        },
        "id": "NZ-MKlUtNaMq",
        "outputId": "796f4df0-e5b5-4731-c67c-a63f448ed348"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f901b97f820>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_93c43\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_93c43_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n",
              "      <th id=\"T_93c43_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n",
              "      <th id=\"T_93c43_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n",
              "      <th id=\"T_93c43_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n",
              "      <th id=\"T_93c43_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n",
              "      <th id=\"T_93c43_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_93c43_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_93c43_row0_col0\" class=\"data row0 col0\" >0.0588</td>\n",
              "      <td id=\"T_93c43_row0_col1\" class=\"data row0 col1\" >17.2126</td>\n",
              "      <td id=\"T_93c43_row0_col2\" class=\"data row0 col2\" >3.4629</td>\n",
              "      <td id=\"T_93c43_row0_col3\" class=\"data row0 col3\" >0</td>\n",
              "      <td id=\"T_93c43_row0_col4\" class=\"data row0 col4\" >0</td>\n",
              "      <td id=\"T_93c43_row0_col5\" class=\"data row0 col5\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc415b8a1fc34379a3fc4a3f6ae1696d"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=10, random_state=123)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=10, random_state=123)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" checked><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=10, random_state=123)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assign_model(kmeans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "ZkA55TXpNhax",
        "outputId": "72aba586-4d58-424a-fd79-8ed80ce582e7"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (31) exceeds max_columns (20). Falling back to pandas display.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             X          Y  Index_ event_unique_id Division  \\\n",
              "0   -8840854.0  5412123.5     208  GO-20141275836      D14   \n",
              "1   -8840317.0  5409695.0     216  GO-20141275533      D14   \n",
              "2   -8822241.0  5426445.0     229  GO-20141275619      D43   \n",
              "3   -8841433.0  5420008.0     243  GO-20141279054      D53   \n",
              "4   -8841075.0  5423336.5     254  GO-20141279637      D32   \n",
              "..         ...        ...     ...             ...      ...   \n",
              "591 -8842066.0  5433651.0  271385  GO-20211819377      D32   \n",
              "592 -8828027.0  5420954.5  272165  GO-20211873191      D55   \n",
              "593 -8825024.0  5417692.5  276174  GO-20212138598      D41   \n",
              "594 -8834612.0  5412888.5  289495   GO-2022526645      D51   \n",
              "595 -8860511.0  5425955.0  301070  GO-20221238028      D23   \n",
              "\n",
              "             occurrencedate            reporteddate  \\\n",
              "0    2013/12/31 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "1    2013/12/18 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "2    2013/12/20 05:00:00+00  2014/01/03 05:00:00+00   \n",
              "3    2013/11/27 05:00:00+00  2014/01/04 05:00:00+00   \n",
              "4    2013/12/25 05:00:00+00  2014/01/04 05:00:00+00   \n",
              "..                      ...                     ...   \n",
              "591  2013/09/01 04:00:00+00  2021/09/22 04:00:00+00   \n",
              "592  2013/01/01 05:00:00+00  2021/09/29 04:00:00+00   \n",
              "593  2013/10/01 04:00:00+00  2021/11/06 04:00:00+00   \n",
              "594  2013/01/01 05:00:00+00  2022/03/21 04:00:00+00   \n",
              "595  2013/12/01 05:00:00+00  2022/06/29 04:00:00+00   \n",
              "\n",
              "                                         location_type premises_type  \\\n",
              "0    Parking Lots (Apt., Commercial Or Non-Commercial)       Outside   \n",
              "1                     Apartment (Rooming House, Condo)     Apartment   \n",
              "2    Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "3                     Apartment (Rooming House, Condo)     Apartment   \n",
              "4    Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "..                                                 ...           ...   \n",
              "591  Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "592  Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "593  Single Home, House (Attach Garage, Cottage, Mo...         House   \n",
              "594                   Apartment (Rooming House, Condo)     Apartment   \n",
              "595                   Apartment (Rooming House, Condo)     Apartment   \n",
              "\n",
              "     ucr_code  ...  occurrencedayofyear occurrencedayofweek  occurrencehour  \\\n",
              "0        2132  ...                365.0          Tuesday                 11   \n",
              "1        2120  ...                352.0          Wednesday               19   \n",
              "2        2135  ...                354.0          Friday                   3   \n",
              "3        1420  ...                331.0          Wednesday               20   \n",
              "4        2120  ...                359.0          Wednesday               18   \n",
              "..        ...  ...                  ...                 ...             ...   \n",
              "591      1430  ...                244.0          Sunday                   0   \n",
              "592      1430  ...                  1.0          Tuesday                  0   \n",
              "593      1430  ...                274.0          Tuesday                 14   \n",
              "594      1430  ...                  1.0          Tuesday                  0   \n",
              "595      1430  ...                335.0          Sunday                   8   \n",
              "\n",
              "        mci_category  Hood_ID                      Neighbourhood  Longitude  \\\n",
              "0         Theft Over       81                  Trinity-Bellwoods -79.418747   \n",
              "1    Break and Enter       82                            Niagara -79.413918   \n",
              "2         Auto Theft      127                            Bendale -79.251541   \n",
              "3            Assault      102                  Forest Hill North -79.423950   \n",
              "4    Break and Enter       39               Bedford Park-Nortown -79.420731   \n",
              "..               ...      ...                                ...        ...   \n",
              "591          Assault       36                   Newtonbrook West -79.429634   \n",
              "592          Assault       54                   OConnor-Parkview -79.303520   \n",
              "593          Assault      122              Birchcliffe-Cliffside -79.276543   \n",
              "594          Assault       72                        Regent Park -79.362671   \n",
              "595          Assault        2  Mount Olive-Silverstone-Jamestown -79.595329   \n",
              "\n",
              "      Latitude  ObjectId    Cluster  \n",
              "0    43.654457         8  Cluster 2  \n",
              "1    43.638672        16  Cluster 2  \n",
              "2    43.747467        29  Cluster 2  \n",
              "3    43.705681        43  Cluster 3  \n",
              "4    43.727291        54  Cluster 2  \n",
              "..         ...       ...        ...  \n",
              "591  43.794212    271441  Cluster 1  \n",
              "592  43.711826    272884  Cluster 0  \n",
              "593  43.690643    276210  Cluster 1  \n",
              "594  43.659428    289995  Cluster 0  \n",
              "595  43.744289    301186  Cluster 3  \n",
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              "      <td>-79.595329</td>\n",
              "      <td>43.744289</td>\n",
              "      <td>301186</td>\n",
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          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to control the scale of plot\n",
        "plot_model(kmeans, plot = 'elbow', scale = 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "RJJf617BNmN7",
        "outputId": "2b5c47d3-8bb4-4cf6-b857-49ddde8a298d"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to save the plot\n",
        "plot_model(kmeans, plot = 'elbow', save=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "SOH80i82Ntje",
        "outputId": "60f2541b-353f-4abb-9dcc-57e899da9c81"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Elbow Plot.png'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save model\n",
        "save_model(kmeans, 'my_first_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pR9CFwpN1RJ",
        "outputId": "cf184666-d20c-4679-fabb-05d981d3fa95"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "          steps=[('numerical_imputer',\n",
              "                  TransformerWrapper(include=['X', 'Y', 'Index_', 'ucr_code',\n",
              "                                              'ucr_ext', 'reportedyear',\n",
              "                                              'reportedday', 'reporteddayofyear',\n",
              "                                              'reportedhour', 'occurrenceyear',\n",
              "                                              'occurrenceday',\n",
              "                                              'occurrencedayofyear',\n",
              "                                              'occurrencehour', 'Longitude',\n",
              "                                              'Latitude', 'ObjectId'],\n",
              "                                     transformer=SimpleImputer())),\n",
              "                 ('cate...\n",
              "                                                                     'occurrencedate',\n",
              "                                                                     'reporteddate',\n",
              "                                                                     'location_type',\n",
              "                                                                     'premises_type',\n",
              "                                                                     'offence',\n",
              "                                                                     'reportedmonth',\n",
              "                                                                     'reporteddayofweek',\n",
              "                                                                     'occurrencemonth',\n",
              "                                                                     'occurrencedayofweek',\n",
              "                                                                     'mci_category',\n",
              "                                                                     'Hood_ID',\n",
              "                                                                     'Neighbourhood'],\n",
              "                                                               handle_missing='return_nan',\n",
              "                                                               use_cat_names=True))),\n",
              "                 ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n",
              "                 ('trained_model', KMeans(n_clusters=4, random_state=123))]),\n",
              " 'my_first_model.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load model\n",
        "loaded_from_disk = load_model('my_first_model')\n",
        "loaded_from_disk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "uDcEPwByN5UO",
        "outputId": "95ebf949-0f59-4132-8622-ca3f904eb003"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[('numerical_imputer',\n",
              "                 TransformerWrapper(include=['X', 'Y', 'Index_', 'ucr_code',\n",
              "                                             'ucr_ext', 'reportedyear',\n",
              "                                             'reportedday', 'reporteddayofyear',\n",
              "                                             'reportedhour', 'occurrenceyear',\n",
              "                                             'occurrenceday',\n",
              "                                             'occurrencedayofyear',\n",
              "                                             'occurrencehour', 'Longitude',\n",
              "                                             'Latitude', 'ObjectId'],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                ('cate...\n",
              "                                                                    'occurrencedate',\n",
              "                                                                    'reporteddate',\n",
              "                                                                    'location_type',\n",
              "                                                                    'premises_type',\n",
              "                                                                    'offence',\n",
              "                                                                    'reportedmonth',\n",
              "                                                                    'reporteddayofweek',\n",
              "                                                                    'occurrencemonth',\n",
              "                                                                    'occurrencedayofweek',\n",
              "                                                                    'mci_category',\n",
              "                                                                    'Hood_ID',\n",
              "                                                                    'Neighbourhood'],\n",
              "                                                              handle_missing='return_nan',\n",
              "                                                              use_cat_names=True))),\n",
              "                ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n",
              "                ('trained_model', KMeans(n_clusters=4, random_state=123))])"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[(&#x27;numerical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Index_&#x27;, &#x27;ucr_code&#x27;,\n",
              "                                             &#x27;ucr_ext&#x27;, &#x27;reportedyear&#x27;,\n",
              "                                             &#x27;reportedday&#x27;, &#x27;reporteddayofyear&#x27;,\n",
              "                                             &#x27;reportedhour&#x27;, &#x27;occurrenceyear&#x27;,\n",
              "                                             &#x27;occurrenceday&#x27;,\n",
              "                                             &#x27;occurrencedayofyear&#x27;,\n",
              "                                             &#x27;occurrencehour&#x27;, &#x27;Longitude&#x27;,\n",
              "                                             &#x27;Latitude&#x27;, &#x27;ObjectId&#x27;],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                (&#x27;cate...\n",
              "                                                                    &#x27;occurrencedate&#x27;,\n",
              "                                                                    &#x27;reporteddate&#x27;,\n",
              "                                                                    &#x27;location_type&#x27;,\n",
              "                                                                    &#x27;premises_type&#x27;,\n",
              "                                                                    &#x27;offence&#x27;,\n",
              "                                                                    &#x27;reportedmonth&#x27;,\n",
              "                                                                    &#x27;reporteddayofweek&#x27;,\n",
              "                                                                    &#x27;occurrencemonth&#x27;,\n",
              "                                                                    &#x27;occurrencedayofweek&#x27;,\n",
              "                                                                    &#x27;mci_category&#x27;,\n",
              "                                                                    &#x27;Hood_ID&#x27;,\n",
              "                                                                    &#x27;Neighbourhood&#x27;],\n",
              "                                                              handle_missing=&#x27;return_nan&#x27;,\n",
              "                                                              use_cat_names=True))),\n",
              "                (&#x27;normalize&#x27;, TransformerWrapper(transformer=MinMaxScaler())),\n",
              "                (&#x27;trained_model&#x27;, KMeans(n_clusters=4, random_state=123))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=FastMemory(location=/tmp/joblib),\n",
              "         steps=[(&#x27;numerical_imputer&#x27;,\n",
              "                 TransformerWrapper(include=[&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Index_&#x27;, &#x27;ucr_code&#x27;,\n",
              "                                             &#x27;ucr_ext&#x27;, &#x27;reportedyear&#x27;,\n",
              "                                             &#x27;reportedday&#x27;, &#x27;reporteddayofyear&#x27;,\n",
              "                                             &#x27;reportedhour&#x27;, &#x27;occurrenceyear&#x27;,\n",
              "                                             &#x27;occurrenceday&#x27;,\n",
              "                                             &#x27;occurrencedayofyear&#x27;,\n",
              "                                             &#x27;occurrencehour&#x27;, &#x27;Longitude&#x27;,\n",
              "                                             &#x27;Latitude&#x27;, &#x27;ObjectId&#x27;],\n",
              "                                    transformer=SimpleImputer())),\n",
              "                (&#x27;cate...\n",
              "                                                                    &#x27;occurrencedate&#x27;,\n",
              "                                                                    &#x27;reporteddate&#x27;,\n",
              "                                                                    &#x27;location_type&#x27;,\n",
              "                                                                    &#x27;premises_type&#x27;,\n",
              "                                                                    &#x27;offence&#x27;,\n",
              "                                                                    &#x27;reportedmonth&#x27;,\n",
              "                                                                    &#x27;reporteddayofweek&#x27;,\n",
              "                                                                    &#x27;occurrencemonth&#x27;,\n",
              "                                                                    &#x27;occurrencedayofweek&#x27;,\n",
              "                                                                    &#x27;mci_category&#x27;,\n",
              "                                                                    &#x27;Hood_ID&#x27;,\n",
              "                                                                    &#x27;Neighbourhood&#x27;],\n",
              "                                                              handle_missing=&#x27;return_nan&#x27;,\n",
              "                                                              use_cat_names=True))),\n",
              "                (&#x27;normalize&#x27;, TransformerWrapper(transformer=MinMaxScaler())),\n",
              "                (&#x27;trained_model&#x27;, KMeans(n_clusters=4, random_state=123))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerical_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Index_&#x27;, &#x27;ucr_code&#x27;, &#x27;ucr_ext&#x27;,\n",
              "                            &#x27;reportedyear&#x27;, &#x27;reportedday&#x27;, &#x27;reporteddayofyear&#x27;,\n",
              "                            &#x27;reportedhour&#x27;, &#x27;occurrenceyear&#x27;, &#x27;occurrenceday&#x27;,\n",
              "                            &#x27;occurrencedayofyear&#x27;, &#x27;occurrencehour&#x27;,\n",
              "                            &#x27;Longitude&#x27;, &#x27;Latitude&#x27;, &#x27;ObjectId&#x27;],\n",
              "                   transformer=SimpleImputer())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                            &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;,\n",
              "                            &#x27;offence&#x27;, &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;,\n",
              "                            &#x27;occurrencemonth&#x27;, &#x27;occurrencedayofweek&#x27;,\n",
              "                            &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;, &#x27;Neighbourhood&#x27;],\n",
              "                   transformer=SimpleImputer(strategy=&#x27;most_frequent&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehot_encoding: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(include=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                            &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;,\n",
              "                            &#x27;offence&#x27;, &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;,\n",
              "                            &#x27;occurrencemonth&#x27;, &#x27;occurrencedayofweek&#x27;,\n",
              "                            &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;, &#x27;Neighbourhood&#x27;],\n",
              "                   transformer=OneHotEncoder(cols=[&#x27;event_unique_id&#x27;,\n",
              "                                                   &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                                                   &#x27;reporteddate&#x27;,\n",
              "                                                   &#x27;location_type&#x27;,\n",
              "                                                   &#x27;premises_type&#x27;, &#x27;offence&#x27;,\n",
              "                                                   &#x27;reportedmonth&#x27;,\n",
              "                                                   &#x27;reporteddayofweek&#x27;,\n",
              "                                                   &#x27;occurrencemonth&#x27;,\n",
              "                                                   &#x27;occurrencedayofweek&#x27;,\n",
              "                                                   &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;,\n",
              "                                                   &#x27;Neighbourhood&#x27;],\n",
              "                                             handle_missing=&#x27;return_nan&#x27;,\n",
              "                                             use_cat_names=True))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(cols=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                    &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;, &#x27;offence&#x27;,\n",
              "                    &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;, &#x27;occurrencemonth&#x27;,\n",
              "                    &#x27;occurrencedayofweek&#x27;, &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;,\n",
              "                    &#x27;Neighbourhood&#x27;],\n",
              "              handle_missing=&#x27;return_nan&#x27;, use_cat_names=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(cols=[&#x27;event_unique_id&#x27;, &#x27;Division&#x27;, &#x27;occurrencedate&#x27;,\n",
              "                    &#x27;reporteddate&#x27;, &#x27;location_type&#x27;, &#x27;premises_type&#x27;, &#x27;offence&#x27;,\n",
              "                    &#x27;reportedmonth&#x27;, &#x27;reporteddayofweek&#x27;, &#x27;occurrencemonth&#x27;,\n",
              "                    &#x27;occurrencedayofweek&#x27;, &#x27;mci_category&#x27;, &#x27;Hood_ID&#x27;,\n",
              "                    &#x27;Neighbourhood&#x27;],\n",
              "              handle_missing=&#x27;return_nan&#x27;, use_cat_names=True)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">normalize: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(transformer=MinMaxScaler())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, random_state=123)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save experiment\n",
        "save_experiment('my_experiment')"
      ],
      "metadata": {
        "id": "S6rw53GRN9w3"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load experiment from disk\n",
        "exp_from_disk = load_experiment('my_experiment', data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "Mt-yqYu9N_eu",
        "outputId": "b627022a-3ed9-46c3-979f-7c1cd1ee08a2"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f901755d7f0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_47bef_row5_col1, #T_47bef_row11_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_47bef\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_47bef_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_47bef_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_47bef_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_47bef_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_47bef_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_47bef_row1_col1\" class=\"data row1 col1\" >(596, 30)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_47bef_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_47bef_row2_col1\" class=\"data row2 col1\" >(596, 1371)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_47bef_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_47bef_row3_col1\" class=\"data row3 col1\" >16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_47bef_row4_col0\" class=\"data row4 col0\" >Categorical features</td>\n",
              "      <td id=\"T_47bef_row4_col1\" class=\"data row4 col1\" >14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_47bef_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n",
              "      <td id=\"T_47bef_row5_col1\" class=\"data row5 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_47bef_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n",
              "      <td id=\"T_47bef_row6_col1\" class=\"data row6 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_47bef_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_47bef_row7_col1\" class=\"data row7 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_47bef_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_47bef_row8_col1\" class=\"data row8 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_47bef_row9_col0\" class=\"data row9 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_47bef_row9_col1\" class=\"data row9 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_47bef_row10_col0\" class=\"data row10 col0\" >Encoding method</td>\n",
              "      <td id=\"T_47bef_row10_col1\" class=\"data row10 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_47bef_row11_col0\" class=\"data row11 col0\" >Normalize</td>\n",
              "      <td id=\"T_47bef_row11_col1\" class=\"data row11 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_47bef_row12_col0\" class=\"data row12 col0\" >Normalize method</td>\n",
              "      <td id=\"T_47bef_row12_col1\" class=\"data row12 col1\" >minmax</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_47bef_row13_col0\" class=\"data row13 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_47bef_row13_col1\" class=\"data row13 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_47bef_row14_col0\" class=\"data row14 col0\" >Use GPU</td>\n",
              "      <td id=\"T_47bef_row14_col1\" class=\"data row14 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_47bef_row15_col0\" class=\"data row15 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_47bef_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_47bef_row16_col0\" class=\"data row16 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_47bef_row16_col1\" class=\"data row16 col1\" >cluster-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_47bef_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_47bef_row17_col0\" class=\"data row17 col0\" >USI</td>\n",
              "      <td id=\"T_47bef_row17_col1\" class=\"data row17 col1\" >c02f</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}