Clustering: GMM(Gaussian_Mixture_Model).ipynb

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      "name": "02: Clustering_GMM(Gaussian_Mixture_Model).ipynb",
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      "source": [
        "Help is available on https://www.youtube.com/watch?v=WsGNJYCxwNM"
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        "!pip install pmlb"
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            "Collecting pmlb\n",
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            "\u001b[K     |████████████████████████████████| 645kB 8.3MB/s \n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0.5->pmlb) (1.15.0)\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: pyyaml, requests, pmlb\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
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            "  Found existing installation: requests 2.23.0\n",
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            "      Successfully uninstalled requests-2.23.0\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ns1Idye4X8-P"
      },
      "source": [
        "#For plotting\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from plotnine import *\n",
        "\n",
        "import scipy\n",
        "from scipy import stats as st\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import time\n",
        "from pmlb import fetch_data\n",
        "import matplotlib.pyplot as plt\n",
        "#for matrix math\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "#for plotting\n",
        "import seaborn as sns\n",
        "sns.set_style(\"white\")\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "gvHvMXGPaREB",
        "outputId": "15da9eb6-5811-4c96-b3b4-f421af35c4c9"
      },
      "source": [
        "bk = pd.read_csv('/content/burger-king-items.txt', sep='\\t')\n",
        "bk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item</th>\n",
              "      <th>Serving.size</th>\n",
              "      <th>Calories</th>\n",
              "      <th>Fat.Cal</th>\n",
              "      <th>Protein(g)</th>\n",
              "      <th>Fat(g)</th>\n",
              "      <th>Sat.Fat(g)</th>\n",
              "      <th>Trans.fat(g)</th>\n",
              "      <th>Chol(mg)</th>\n",
              "      <th>Sodium(mg)</th>\n",
              "      <th>Carbs(g)</th>\n",
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              "      <th>Sugar(g)</th>\n",
              "      <th>Meat</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hamburger</td>\n",
              "      <td>109.0</td>\n",
              "      <td>260</td>\n",
              "      <td>90.0</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cheeseburger</td>\n",
              "      <td>121.0</td>\n",
              "      <td>300</td>\n",
              "      <td>130.0</td>\n",
              "      <td>16</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45</td>\n",
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              "      <th>2</th>\n",
              "      <td>Double_Hamburger</td>\n",
              "      <td>146.0</td>\n",
              "      <td>360</td>\n",
              "      <td>160.0</td>\n",
              "      <td>22</td>\n",
              "      <td>18.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Double_Cheeseburger</td>\n",
              "      <td>171.0</td>\n",
              "      <td>450</td>\n",
              "      <td>230.0</td>\n",
              "      <td>26</td>\n",
              "      <td>26.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>95</td>\n",
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              "      <td>29</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Buck_Double</td>\n",
              "      <td>158.0</td>\n",
              "      <td>410</td>\n",
              "      <td>200.0</td>\n",
              "      <td>24</td>\n",
              "      <td>22.0</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>28</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>FatFREEMilk(8oz)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>118</th>\n",
              "      <td>1%LowFatChocolateMilk(8oz)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>160</td>\n",
              "      <td>25.0</td>\n",
              "      <td>8</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>15</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>ChocolateMilkShake12fl oz</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>StrawberryMilkShake12fl oz</td>\n",
              "      <td>NaN</td>\n",
              "      <td>390</td>\n",
              "      <td>100.0</td>\n",
              "      <td>8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40</td>\n",
              "      <td>290</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>122 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Item  Serving.size  ...  Not Breakfast  CarbsxMeat\n",
              "0                     Hamburger         109.0  ...              1          28\n",
              "1                  Cheeseburger         121.0  ...              1          28\n",
              "2              Double_Hamburger         146.0  ...              1          28\n",
              "3           Double_Cheeseburger         171.0  ...              1          29\n",
              "4                   Buck_Double         158.0  ...              1          28\n",
              "..                          ...           ...  ...            ...         ...\n",
              "117            FatFREEMilk(8oz)           NaN  ...              1           0\n",
              "118  1%LowFatChocolateMilk(8oz)           NaN  ...              1           0\n",
              "119     VanillaMilkShake12fl oz           NaN  ...              1           0\n",
              "120   ChocolateMilkShake12fl oz           NaN  ...              1           0\n",
              "121  StrawberryMilkShake12fl oz           NaN  ...              1           0\n",
              "\n",
              "[122 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "8-Zfmj5tjQ7v",
        "outputId": "0348703a-c5fb-45e5-8a9d-fbbefb4f784b"
      },
      "source": [
        "features = ['Calories', 'Sodium(mg)']\n",
        "X = bk[features]\n",
        "X.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Calories</th>\n",
              "      <th>Sodium(mg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>87</th>\n",
              "      <td>160</td>\n",
              "      <td>320</td>\n",
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              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>300</td>\n",
              "      <td>690</td>\n",
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              "      <th>98</th>\n",
              "      <td>630</td>\n",
              "      <td>1110</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>590</td>\n",
              "      <td>1450</td>\n",
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              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>320</td>\n",
              "      <td>900</td>\n",
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              "      <th>93</th>\n",
              "      <td>400</td>\n",
              "      <td>380</td>\n",
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              "      <th>21</th>\n",
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              "      <td>1640</td>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
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            ],
            "text/plain": [
              "     Calories  Sodium(mg)\n",
              "87        160         320\n",
              "69        300         690\n",
              "98        630        1110\n",
              "22        590        1450\n",
              "105       320         900\n",
              "93        400         380\n",
              "50        310         490\n",
              "21        800        1640\n",
              "96        550         650\n",
              "64        240         550"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vH4KNWwOkQRl"
      },
      "source": [
        "##Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpunQxhPkPey"
      },
      "source": [
        "z = StandardScaler()\n",
        "X[features] = z.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eLq40nkkTvp"
      },
      "source": [
        "##Define Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiQ21WJ7jqDD",
        "outputId": "93a5216d-63ef-4e2f-fcca-dbe86369cb92"
      },
      "source": [
        "EM = GaussianMixture(n_components=3) #number of clusters\n",
        "EM.fit(X)\n",
        "cluster = EM.predict(X)\n",
        "cluster  #hard assignment"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 0, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1,\n",
              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 2, 1, 2,\n",
              "       2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSwH4TkJkkAT"
      },
      "source": [
        "# cluster = EM.predict_proba(X)\n",
        "# cluster  #Soft assignment"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "2KXe7mplkx-u",
        "outputId": "819c2ced-7789-4141-8593-b969a61548d7"
      },
      "source": [
        "print(\"SILHOUETTE: \", silhouette_score(X, cluster))\n",
        "X['cluster'] = cluster\n",
        "\n",
        "(ggplot(X, aes(x = \"Calories\", y = \"Sodium(mg)\", color = 'cluster'))+geom_point())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SILHOUETTE:  0.357211963389611\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363277886466216)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPQItkHkmb8Y"
      },
      "source": [
        "#Multiple Dimension"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQApQlX6mAB6"
      },
      "source": [
        "features = ['Sugar(g)', 'Protein(g)', 'Fat(g)']\n",
        "X = bk[features]\n",
        "z = StandardScaler()\n",
        "X[features] = z.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKLZDT4km7zT",
        "outputId": "3eaa5ba8-7c5b-4386-d731-192f1aeab0d5"
      },
      "source": [
        "EM = GaussianMixture(n_components=3)\n",
        "EM.fit(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
              "                means_init=None, n_components=3, n_init=1, precisions_init=None,\n",
              "                random_state=None, reg_covar=1e-06, tol=0.001, verbose=0,\n",
              "                verbose_interval=10, warm_start=False, weights_init=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7RdKmXhnMwo",
        "outputId": "c953917b-04a0-4bf5-bcbe-d289ab8b9893"
      },
      "source": [
        "cluster = EM.predict(X)\n",
        "cluster  #Hard clustering"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0,\n",
              "       0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-tA0B8DnW0R"
      },
      "source": [
        "# cluster = EM.predict_proba(X)\n",
        "# cluster  #soft clustering"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJWYrRsanrg2",
        "outputId": "b98a1769-bd32-4e73-8264-5c8901586d0a"
      },
      "source": [
        "print(\"SILHOUETTE: \", silhouette_score(X, cluster))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SILHOUETTE:  0.5250510147137473\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dml5F0xgoAnE"
      },
      "source": [
        "X['cluster'] = cluster"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "KGOfUNzinDSE",
        "outputId": "9c1056bd-ad87-4028-8322-f69716664e03"
      },
      "source": [
        "(ggplot(X, aes(x = \"Sugar(g)\", y = \"Fat(g)\", color = 'cluster'))+geom_point())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363277895948302)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "wcQessd_n80T",
        "outputId": "46101ab3-184a-406f-af22-4c5d97a52bae"
      },
      "source": [
        "(ggplot(X, aes(x = \"Sugar(g)\", y = \"Protein(g)\", color = 'cluster'))+geom_point())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8758958800837)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "R_u2WZnbn9tT",
        "outputId": "9776babc-679a-43cb-ad03-2821df5b5685"
      },
      "source": [
        "(ggplot(X, aes(x = \"Protein(g)\", y = \"Fat(g)\", color = 'cluster')) + geom_point())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8758958831862)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    }
  ]
}