ShopCampaing_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/muntakim1/data-science/blob/main/ShopCampaing_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "metadata": {
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      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('marketing_campaign.csv',delimiter=\";\")"
      ],
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      },
      "execution_count": null,
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              "     ID  Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
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              "\n",
              "  Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  AcceptedCmp3  \\\n",
              "0  2012-09-04       58       635  ...                  7             0   \n",
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              "2  2013-08-21       26       426  ...                  4             0   \n",
              "3  2014-02-10       26        11  ...                  6             0   \n",
              "4  2014-01-19       94       173  ...                  5             0   \n",
              "\n",
              "   AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  \\\n",
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              "   Z_CostContact  Z_Revenue  Response  \n",
              "0              3         11         1  \n",
              "1              3         11         0  \n",
              "2              3         11         0  \n",
              "3              3         11         0  \n",
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              "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
              "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
              "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
              "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
              "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
              "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
              "       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],\n",
              "      dtype='object')"
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        {
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          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d0b62190>"
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          },
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          "execution_count": 285
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Income.isna().count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqVtjJcCVMKN",
        "outputId": "f3032cf4-965a-478f-a31f-1f96e1de7f3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2240"
            ]
          },
          "metadata": {},
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df.Income)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "7hykVSFHVYhN",
        "outputId": "dc0f64f8-e02c-4b32-cf1b-e830361e924a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0cf8baa50>"
            ]
          },
          "metadata": {},
          "execution_count": 287
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Income.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEV35cnjVnDt",
        "outputId": "6a94154d-34aa-4cbf-b546-b2b240553bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      2216.000000\n",
              "mean      52247.251354\n",
              "std       25173.076661\n",
              "min        1730.000000\n",
              "25%       35303.000000\n",
              "50%       51381.500000\n",
              "75%       68522.000000\n",
              "max      666666.000000\n",
              "Name: Income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Income.fillna(df.Income.median(),inplace=True)"
      ],
      "metadata": {
        "id": "e444Wvc7Vt10"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Income.isna().count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGtT1To9WVBm",
        "outputId": "ac18b572-7dea-4a3f-c8d2-45db04a2359a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2240"
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "bR0ns9dvV3A7",
        "outputId": "007af267-5fc3-4491-e59d-ad11002b062e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0cf8689d0>"
            ]
          },
          "metadata": {},
          "execution_count": 291
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iro0v3ClWBSk",
        "outputId": "0769a6d3-2c42-42d0-adf8-718f66d68b81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                     2240\n",
              "Year_Birth             2240\n",
              "Education              2240\n",
              "Marital_Status         2240\n",
              "Income                 2240\n",
              "Kidhome                2240\n",
              "Teenhome               2240\n",
              "Dt_Customer            2240\n",
              "Recency                2240\n",
              "MntWines               2240\n",
              "MntFruits              2240\n",
              "MntMeatProducts        2240\n",
              "MntFishProducts        2240\n",
              "MntSweetProducts       2240\n",
              "MntGoldProds           2240\n",
              "NumDealsPurchases      2240\n",
              "NumWebPurchases        2240\n",
              "NumCatalogPurchases    2240\n",
              "NumStorePurchases      2240\n",
              "NumWebVisitsMonth      2240\n",
              "AcceptedCmp3           2240\n",
              "AcceptedCmp4           2240\n",
              "AcceptedCmp5           2240\n",
              "AcceptedCmp1           2240\n",
              "AcceptedCmp2           2240\n",
              "Complain               2240\n",
              "Z_CostContact          2240\n",
              "Z_Revenue              2240\n",
              "Response               2240\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "T6Ty_E9yXSPT",
        "outputId": "dea913a4-9c57-4213-f90e-2d192d1d5bb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
              "0      5524        1957  Graduation         Single  58138.0        0   \n",
              "1      2174        1954  Graduation         Single  46344.0        1   \n",
              "2      4141        1965  Graduation       Together  71613.0        0   \n",
              "3      6182        1984  Graduation       Together  26646.0        1   \n",
              "4      5324        1981         PhD        Married  58293.0        1   \n",
              "...     ...         ...         ...            ...      ...      ...   \n",
              "2235  10870        1967  Graduation        Married  61223.0        0   \n",
              "2236   4001        1946         PhD       Together  64014.0        2   \n",
              "2237   7270        1981  Graduation       Divorced  56981.0        0   \n",
              "2238   8235        1956      Master       Together  69245.0        0   \n",
              "2239   9405        1954         PhD        Married  52869.0        1   \n",
              "\n",
              "      Teenhome Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  \\\n",
              "0            0  2012-09-04       58       635  ...                  7   \n",
              "1            1  2014-03-08       38        11  ...                  5   \n",
              "2            0  2013-08-21       26       426  ...                  4   \n",
              "3            0  2014-02-10       26        11  ...                  6   \n",
              "4            0  2014-01-19       94       173  ...                  5   \n",
              "...        ...         ...      ...       ...  ...                ...   \n",
              "2235         1  2013-06-13       46       709  ...                  5   \n",
              "2236         1  2014-06-10       56       406  ...                  7   \n",
              "2237         0  2014-01-25       91       908  ...                  6   \n",
              "2238         1  2014-01-24        8       428  ...                  3   \n",
              "2239         1  2012-10-15       40        84  ...                  7   \n",
              "\n",
              "      AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
              "0                0             0             0             0             0   \n",
              "1                0             0             0             0             0   \n",
              "2                0             0             0             0             0   \n",
              "3                0             0             0             0             0   \n",
              "4                0             0             0             0             0   \n",
              "...            ...           ...           ...           ...           ...   \n",
              "2235             0             0             0             0             0   \n",
              "2236             0             0             0             1             0   \n",
              "2237             0             1             0             0             0   \n",
              "2238             0             0             0             0             0   \n",
              "2239             0             0             0             0             0   \n",
              "\n",
              "      Complain  Z_CostContact  Z_Revenue  Response  \n",
              "0            0              3         11         1  \n",
              "1            0              3         11         0  \n",
              "2            0              3         11         0  \n",
              "3            0              3         11         0  \n",
              "4            0              3         11         0  \n",
              "...        ...            ...        ...       ...  \n",
              "2235         0              3         11         0  \n",
              "2236         0              3         11         0  \n",
              "2237         0              3         11         0  \n",
              "2238         0              3         11         0  \n",
              "2239         0              3         11         1  \n",
              "\n",
              "[2240 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ab150c5-508a-492d-a569-05e9bd2e6f55\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Year_Birth</th>\n",
              "      <th>Education</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income</th>\n",
              "      <th>Kidhome</th>\n",
              "      <th>Teenhome</th>\n",
              "      <th>Dt_Customer</th>\n",
              "      <th>Recency</th>\n",
              "      <th>MntWines</th>\n",
              "      <th>...</th>\n",
              "      <th>NumWebVisitsMonth</th>\n",
              "      <th>AcceptedCmp3</th>\n",
              "      <th>AcceptedCmp4</th>\n",
              "      <th>AcceptedCmp5</th>\n",
              "      <th>AcceptedCmp1</th>\n",
              "      <th>AcceptedCmp2</th>\n",
              "      <th>Complain</th>\n",
              "      <th>Z_CostContact</th>\n",
              "      <th>Z_Revenue</th>\n",
              "      <th>Response</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5524</td>\n",
              "      <td>1957</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Single</td>\n",
              "      <td>58138.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2012-09-04</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2174</td>\n",
              "      <td>1954</td>\n",
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              "      <td>46344.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2014-03-08</td>\n",
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              "      <th>2</th>\n",
              "      <td>4141</td>\n",
              "      <td>1965</td>\n",
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              "      <td>71613.0</td>\n",
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              "      <td>2013-08-21</td>\n",
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              "      <th>3</th>\n",
              "      <td>6182</td>\n",
              "      <td>1984</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Together</td>\n",
              "      <td>26646.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2014-02-10</td>\n",
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              "      <td>6</td>\n",
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              "      <th>4</th>\n",
              "      <td>5324</td>\n",
              "      <td>1981</td>\n",
              "      <td>PhD</td>\n",
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              "      <td>58293.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-19</td>\n",
              "      <td>94</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2235</th>\n",
              "      <td>10870</td>\n",
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              "    <tr>\n",
              "      <th>2236</th>\n",
              "      <td>4001</td>\n",
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              "      <td>64014.0</td>\n",
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              "      <td>11</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2237</th>\n",
              "      <td>7270</td>\n",
              "      <td>1981</td>\n",
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              "      <td>56981.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>91</td>\n",
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              "      <td>6</td>\n",
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              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2238</th>\n",
              "      <td>8235</td>\n",
              "      <td>1956</td>\n",
              "      <td>Master</td>\n",
              "      <td>Together</td>\n",
              "      <td>69245.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-01-24</td>\n",
              "      <td>8</td>\n",
              "      <td>428</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2239</th>\n",
              "      <td>9405</td>\n",
              "      <td>1954</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Married</td>\n",
              "      <td>52869.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2012-10-15</td>\n",
              "      <td>40</td>\n",
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              "      <td>11</td>\n",
              "      <td>1</td>\n",
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              "</table>\n",
              "<p>2240 rows × 29 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Dt_Customer']=pd.to_datetime(df['Dt_Customer'])"
      ],
      "metadata": {
        "id": "5Q3JyIq_XV_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
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        "id": "n2x00TFSXs66",
        "outputId": "b741e347-e0d2-4faf-861b-8918bbf25278"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \\\n",
              "0      5524        1957  Graduation         Single  58138.0        0   \n",
              "1      2174        1954  Graduation         Single  46344.0        1   \n",
              "2      4141        1965  Graduation       Together  71613.0        0   \n",
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              "4      5324        1981         PhD        Married  58293.0        1   \n",
              "...     ...         ...         ...            ...      ...      ...   \n",
              "2235  10870        1967  Graduation        Married  61223.0        0   \n",
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              "2238   8235        1956      Master       Together  69245.0        0   \n",
              "2239   9405        1954         PhD        Married  52869.0        1   \n",
              "\n",
              "      Teenhome Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  \\\n",
              "0            0  2012-09-04       58       635  ...                  7   \n",
              "1            1  2014-03-08       38        11  ...                  5   \n",
              "2            0  2013-08-21       26       426  ...                  4   \n",
              "3            0  2014-02-10       26        11  ...                  6   \n",
              "4            0  2014-01-19       94       173  ...                  5   \n",
              "...        ...         ...      ...       ...  ...                ...   \n",
              "2235         1  2013-06-13       46       709  ...                  5   \n",
              "2236         1  2014-06-10       56       406  ...                  7   \n",
              "2237         0  2014-01-25       91       908  ...                  6   \n",
              "2238         1  2014-01-24        8       428  ...                  3   \n",
              "2239         1  2012-10-15       40        84  ...                  7   \n",
              "\n",
              "      AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
              "0                0             0             0             0             0   \n",
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              "2238             0             0             0             0             0   \n",
              "2239             0             0             0             0             0   \n",
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              "      Complain  Z_CostContact  Z_Revenue  Response  \n",
              "0            0              3         11         1  \n",
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              "2235         0              3         11         0  \n",
              "2236         0              3         11         0  \n",
              "2237         0              3         11         0  \n",
              "2238         0              3         11         0  \n",
              "2239         0              3         11         1  \n",
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              "      <td>0</td>\n",
              "      <td>2013-08-21</td>\n",
              "      <td>26</td>\n",
              "      <td>426</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6182</td>\n",
              "      <td>1984</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Together</td>\n",
              "      <td>26646.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014-02-10</td>\n",
              "      <td>26</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5324</td>\n",
              "      <td>1981</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Married</td>\n",
              "      <td>58293.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-19</td>\n",
              "      <td>94</td>\n",
              "      <td>173</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2235</th>\n",
              "      <td>10870</td>\n",
              "      <td>1967</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Married</td>\n",
              "      <td>61223.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-06-13</td>\n",
              "      <td>46</td>\n",
              "      <td>709</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2236</th>\n",
              "      <td>4001</td>\n",
              "      <td>1946</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Together</td>\n",
              "      <td>64014.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-06-10</td>\n",
              "      <td>56</td>\n",
              "      <td>406</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2237</th>\n",
              "      <td>7270</td>\n",
              "      <td>1981</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>56981.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-25</td>\n",
              "      <td>91</td>\n",
              "      <td>908</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2238</th>\n",
              "      <td>8235</td>\n",
              "      <td>1956</td>\n",
              "      <td>Master</td>\n",
              "      <td>Together</td>\n",
              "      <td>69245.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-01-24</td>\n",
              "      <td>8</td>\n",
              "      <td>428</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2239</th>\n",
              "      <td>9405</td>\n",
              "      <td>1954</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Married</td>\n",
              "      <td>52869.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2012-10-15</td>\n",
              "      <td>40</td>\n",
              "      <td>84</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2240 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c777a359-97a5-470a-b761-fc2eddf3d281')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \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",
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              "      height: 32px;\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c777a359-97a5-470a-b761-fc2eddf3d281');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 295
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['total_spending']=df[\"MntWines\"] + df[\"MntFruits\"]+ df[\"MntMeatProducts\"]+ df[\"MntFishProducts\"]+ df[\"MntSweetProducts\"]+ df[\"MntGoldProds\"]\n",
        "df['total_visits']=df[\"NumWebPurchases\"] + df[\"NumStorePurchases\"]+df[\"NumDealsPurchases\"] + df[\"NumCatalogPurchases\"]"
      ],
      "metadata": {
        "id": "1Be1PQ3ZX1xr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWJukbfgbkTJ",
        "outputId": "10049517-7ccb-40f6-bd9d-f642eb633de1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0cf408290>"
            ]
          },
          "metadata": {},
          "execution_count": 328
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "px.pie(df,'Education','Income', hole=.3,color_discrete_sequence=px.colors.sequential.RdBu)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZqtrVrwEp5rN",
        "outputId": "48fc4408-07a1-4ea7-83ec-7056071fd101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "px.pie(df,'Marital_Status','Income', hole=.3,color_discrete_sequence=px.colors.sequential.Magma)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "id": "cm12AoDWq3Ht",
        "outputId": "44e5f11c-b40b-4ca9-e6d4-0348e21f3595"
      },
      "execution_count": null,
      "outputs": [
        {
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              "        income  spending  seniority\n",
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              "        income  spending  seniority\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>income</th>\n",
              "      <th>spending</th>\n",
              "      <th>seniority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>974</th>\n",
              "      <td>51381.5</td>\n",
              "      <td>11709</td>\n",
              "      <td>315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>7500.0</td>\n",
              "      <td>740</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1452</th>\n",
              "      <td>67445.0</td>\n",
              "      <td>3522</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>77766.0</td>\n",
              "      <td>3058</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1332</th>\n",
              "      <td>63841.0</td>\n",
              "      <td>2724</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>162397.0</td>\n",
              "      <td>107</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1967</th>\n",
              "      <td>153924.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1968</th>\n",
              "      <td>156924.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4861.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3502.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1975 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77270f50-2863-4005-aa57-bbda2bc3d2f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-77270f50-2863-4005-aa57-bbda2bc3d2f9 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-77270f50-2863-4005-aa57-bbda2bc3d2f9');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyCaret markupsafe==2.0.1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uUp1Kttgj70",
        "outputId": "63f6775b-da9e-474e-9d13-8b8a6b9b85f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.clustering import *"
      ],
      "metadata": {
        "id": "BPbb8MH9g15b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "setup_df=setup(df_1,normalize=True,silent=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1617
        },
        "id": "gcjoRB5ShQfT",
        "outputId": "ccedee9e-1a09-4ea0-a3f4-c395b4dff1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               Description                 Value\n",
              "0                               session_id                  3752\n",
              "1                            Original Data             (1975, 3)\n",
              "2                           Missing Values                 False\n",
              "3                         Numeric Features                     3\n",
              "4                     Categorical Features                     0\n",
              "5                         Ordinal Features                 False\n",
              "6                High Cardinality Features                 False\n",
              "7                  High Cardinality Method                  None\n",
              "8                         Transformed Data             (1975, 3)\n",
              "9                                 CPU Jobs                    -1\n",
              "10                                 Use GPU                 False\n",
              "11                          Log Experiment                 False\n",
              "12                         Experiment Name  cluster-default-name\n",
              "13                                     USI                  b24c\n",
              "14                         Imputation Type                simple\n",
              "15          Iterative Imputation Iteration                  None\n",
              "16                         Numeric Imputer                  mean\n",
              "17      Iterative Imputation Numeric Model                  None\n",
              "18                     Categorical Imputer                  mode\n",
              "19  Iterative Imputation Categorical Model                  None\n",
              "20           Unknown Categoricals Handling        least_frequent\n",
              "21                               Normalize                  True\n",
              "22                        Normalize Method                zscore\n",
              "23                          Transformation                 False\n",
              "24                   Transformation Method                  None\n",
              "25                                     PCA                 False\n",
              "26                              PCA Method                  None\n",
              "27                          PCA Components                  None\n",
              "28                     Ignore Low Variance                 False\n",
              "29                     Combine Rare Levels                 False\n",
              "30                    Rare Level Threshold                  None\n",
              "31                         Numeric Binning                 False\n",
              "32                         Remove Outliers                 False\n",
              "33                      Outliers Threshold                  None\n",
              "34                Remove Multicollinearity                 False\n",
              "35             Multicollinearity Threshold                  None\n",
              "36             Remove Perfect Collinearity                 False\n",
              "37                              Clustering                 False\n",
              "38                    Clustering Iteration                  None\n",
              "39                     Polynomial Features                 False\n",
              "40                       Polynomial Degree                  None\n",
              "41                    Trignometry Features                 False\n",
              "42                    Polynomial Threshold                  None\n",
              "43                          Group Features                 False\n",
              "44                       Feature Selection                 False\n",
              "45                Feature Selection Method               classic\n",
              "46            Features Selection Threshold                  None\n",
              "47                     Feature Interaction                 False\n",
              "48                           Feature Ratio                 False\n",
              "49                   Interaction Threshold                  None"
            ],
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              "      <td>Iterative Imputation Categorical Model</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
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              "      <th>27</th>\n",
              "      <td>PCA Components</td>\n",
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              "      <th>28</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
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              "      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",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c46d3b60-a738-4744-bfe3-cf1e9b45251f 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-c46d3b60-a738-4744-bfe3-cf1e9b45251f');\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans =create_model('kmeans')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "kyRqX1B0hu0C",
        "outputId": "fc1ca4c6-bc7e-4f36-8c5e-1de86ecee1b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Silhouette  Calinski-Harabasz  Davies-Bouldin  Homogeneity  Rand Index  \\\n",
              "0      0.5236          1344.3918           0.605            0           0   \n",
              "\n",
              "   Completeness  \n",
              "0             0  "
            ],
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              "      <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",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8c5acef-7a63-4b07-b3ec-48641b5db302')\"\n",
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              "      \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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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8c5acef-7a63-4b07-b3ec-48641b5db302 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-f8c5acef-7a63-4b07-b3ec-48641b5db302');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQOVLHHIh5oC",
        "outputId": "c72288d0-0416-478c-a055-304ae6d0a32c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
            "       n_clusters=4, n_init=10, n_jobs=-1, precompute_distances='deprecated',\n",
            "       random_state=3752, tol=0.0001, verbose=0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install autoviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpfS97cg1Bhc",
        "outputId": "19cc193b-8c95-4c66-af18-f27b5461e6b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: qtpy>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from qtconsole->jupyter->autoviz) (2.1.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->panel~=0.12.6->autoviz) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->panel~=0.12.6->autoviz) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->panel~=0.12.6->autoviz) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->panel~=0.12.6->autoviz) (2021.10.8)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->autoviz) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->autoviz) (1.0.1)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->autoviz) (0.5.2)\n"
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    {
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        "\n",
        "evaluate_model(kmeans)\n"
      ],
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        "outputId": "095b91a3-02f2-4a0f-8673-87e1bc2dbe95"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Cluster PCA Plot (2d)', …"
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              "version_major": 2,
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    {
      "cell_type": "code",
      "source": [
        "result=assign_model(kmeans)"
      ],
      "metadata": {
        "id": "_Pj5c8LJiBz6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cluster(val):\n",
        "  if val=='Cluster 0':\n",
        "    return 0\n",
        "  elif val=='Cluster 1':\n",
        "    return 1\n",
        "  elif val=='Cluster 2':\n",
        "    return 2\n",
        "  elif val=='Cluster 3':\n",
        "    return 3"
      ],
      "metadata": {
        "id": "F9UIN60zlAwh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "result.Cluster=result.Cluster.apply(cluster)"
      ],
      "metadata": {
        "id": "zdVMQZ8OiViq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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          "height": 424
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        "id": "CFUU_kEHiWua",
        "outputId": "e28e4309-faf1-42ad-f344-52d3d42310df"
      },
      "execution_count": null,
      "outputs": [
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              "        income  spending  seniority  Cluster\n",
              "0       1730.0         8         15        2\n",
              "1       2447.0      1730         43        1\n",
              "2       3502.0         5          0        2\n",
              "3       4023.0         9         15        2\n",
              "4       4428.0       359         25        2\n",
              "...        ...       ...        ...      ...\n",
              "1970  157243.0      1608         37        0\n",
              "1971  157733.0        59          2        0\n",
              "1972  160803.0      1717         44        1\n",
              "1973  162397.0       107          1        0\n",
              "1974  666666.0        62         11        0\n",
              "\n",
              "[1975 rows x 4 columns]"
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              "Cluster                      0              1             2        3\n",
              "income    count     845.000000      93.000000   1036.000000      1.0\n",
              "          mean    70630.365680   70553.881720  36297.795367  51381.5\n",
              "          std     24893.841316   17207.046266  12326.012649      NaN\n",
              "          min     34445.000000    2447.000000   1730.000000  51381.5\n",
              "          25%     60896.000000   63693.000000  27210.500000  51381.5\n",
              "          50%     69674.000000   70179.000000  36588.500000  51381.5\n",
              "          75%     78331.000000   80124.000000  45149.500000  51381.5\n",
              "          max    666666.000000  160803.000000  68805.000000  51381.5\n",
              "spending  count     845.000000      93.000000   1036.000000      1.0\n",
              "          mean     1136.898225    2502.000000    146.635135  11709.0\n",
              "          std       456.223169     873.834628    142.334233      NaN\n",
              "          min         6.000000     740.000000      5.000000  11709.0\n",
              "          25%       793.000000    1919.000000     45.000000  11709.0\n",
              "          50%      1084.000000    2356.000000     80.500000  11709.0\n",
              "          75%      1471.000000    3058.000000    222.250000  11709.0\n",
              "          max      2352.000000    5050.000000    835.000000  11709.0\n",
              "seniority count     845.000000      93.000000   1036.000000      1.0\n",
              "          mean       21.952663      46.021505      9.793436    315.0\n",
              "          std         5.494300      11.902665      4.865713      NaN\n",
              "          min         0.000000      25.000000      0.000000    315.0\n",
              "          25%        18.000000      40.000000      6.000000    315.0\n",
              "          50%        22.000000      44.000000      9.000000    315.0\n",
              "          75%        25.000000      52.000000     13.000000    315.0\n",
              "          max        46.000000     104.000000     32.000000    315.0"
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              "      <td>1730.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>63693.000000</td>\n",
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              "      <td>51381.5</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>70179.000000</td>\n",
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              "      <td>80124.000000</td>\n",
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              "      <td>2502.000000</td>\n",
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              "      <td>11709.0</td>\n",
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              "      <td>142.334233</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>793.000000</td>\n",
              "      <td>1919.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>11709.0</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>2356.000000</td>\n",
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              "      <td>11709.0</td>\n",
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              "      <th>75%</th>\n",
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              "      <th rowspan=\"8\" valign=\"top\">seniority</th>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>21.952663</td>\n",
              "      <td>46.021505</td>\n",
              "      <td>9.793436</td>\n",
              "      <td>315.0</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.494300</td>\n",
              "      <td>11.902665</td>\n",
              "      <td>4.865713</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-b0f1f241-ed5f-4d7b-8597-fb93222f5ad0');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result.Cluster=result.Cluster.replace({0:'Stars',1:'Need attention',2:'High potential',3:'Leaky bucket'})"
      ],
      "metadata": {
        "id": "QMxnm8yXkJlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "px.scatter_3d(result,x='income',y='seniority',z='spending',color='Cluster')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "eh-AhxxBldBf",
        "outputId": "4266f1a9-65c2-4507-d9e0-4d313c7c4e2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "px.bar(result, x=\"Cluster\", y=\"spending\",color=\"Cluster\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Lv4OlNGRnxX3",
        "outputId": "d3d0d328-7349-4f2e-8be3-f0ab1901a100"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *"
      ],
      "metadata": {
        "id": "Y0ki-4YooRZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classificaiton = setup(df,target='Response')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1931,
          "referenced_widgets": [
            "95e5fca36aea49de863f1b87dee7c75c",
            "ee6ed613bb304c27989f05a5ad3f3e9f",
            "9337917407e84daf8956b9d3d148f4f2",
            "b200ffd6ce8a4da48f3dc416dcd69a33",
            "c497122f590546b98590cf2ca60b3e44",
            "488b1111b50741cc9771bc10eac0940e"
          ]
        },
        "id": "M4ExIqe4vbTP",
        "outputId": "c304ebaa-d1bf-493e-f892-94b9183c1996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id               986\n",
              "1                                   Target          Response\n",
              "2                              Target Type            Binary\n",
              "3                            Label Encoded              None\n",
              "4                            Original Data        (2240, 31)\n",
              "5                           Missing Values             False\n",
              "6                         Numeric Features                12\n",
              "7                     Categorical Features                17\n",
              "8                         Ordinal Features             False\n",
              "9                High Cardinality Features             False\n",
              "10                 High Cardinality Method              None\n",
              "11                   Transformed Train Set       (1567, 134)\n",
              "12                    Transformed Test Set        (673, 134)\n",
              "13                      Shuffle Train-Test              True\n",
              "14                     Stratify Train-Test             False\n",
              "15                          Fold Generator   StratifiedKFold\n",
              "16                             Fold Number                10\n",
              "17                                CPU Jobs                -1\n",
              "18                                 Use GPU             False\n",
              "19                          Log Experiment             False\n",
              "20                         Experiment Name  clf-default-name\n",
              "21                                     USI              0711\n",
              "22                         Imputation Type            simple\n",
              "23          Iterative Imputation Iteration              None\n",
              "24                         Numeric Imputer              mean\n",
              "25      Iterative Imputation Numeric Model              None\n",
              "26                     Categorical Imputer          constant\n",
              "27  Iterative Imputation Categorical Model              None\n",
              "28           Unknown Categoricals Handling    least_frequent\n",
              "29                               Normalize             False\n",
              "30                        Normalize Method              None\n",
              "31                          Transformation             False\n",
              "32                   Transformation Method              None\n",
              "33                                     PCA             False\n",
              "34                              PCA Method              None\n",
              "35                          PCA Components              None\n",
              "36                     Ignore Low Variance             False\n",
              "37                     Combine Rare Levels             False\n",
              "38                    Rare Level Threshold              None\n",
              "39                         Numeric Binning             False\n",
              "40                         Remove Outliers             False\n",
              "41                      Outliers Threshold              None\n",
              "42                Remove Multicollinearity             False\n",
              "43             Multicollinearity Threshold              None\n",
              "44             Remove Perfect Collinearity              True\n",
              "45                              Clustering             False\n",
              "46                    Clustering Iteration              None\n",
              "47                     Polynomial Features             False\n",
              "48                       Polynomial Degree              None\n",
              "49                    Trignometry Features             False\n",
              "50                    Polynomial Threshold              None\n",
              "51                          Group Features             False\n",
              "52                       Feature Selection             False\n",
              "53                Feature Selection Method           classic\n",
              "54            Features Selection Threshold              None\n",
              "55                     Feature Interaction             False\n",
              "56                           Feature Ratio             False\n",
              "57                   Interaction Threshold              None\n",
              "58                           Fix Imbalance             False\n",
              "59                    Fix Imbalance Method             SMOTE"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-68493614-0f20-42cd-8d51-0872aab945ee\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Response</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Target Type</td>\n",
              "      <td>Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Label Encoded</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(2240, 31)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(1567, 134)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(673, 134)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>USI</td>\n",
              "      <td>0711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Fix Imbalance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Fix Imbalance Method</td>\n",
              "      <td>SMOTE</td>\n",
              "    </tr>\n",
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    {
      "cell_type": "code",
      "source": [
        "best=compare_models(n_select=3)"
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      "metadata": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "gbc          Gradient Boosting Classifier    0.8794  0.8567  0.4005  0.6729   \n",
              "ridge                    Ridge Classifier    0.8775  0.0000  0.3281  0.7108   \n",
              "lightgbm  Light Gradient Boosting Machine    0.8762  0.8671  0.3665  0.6622   \n",
              "et                 Extra Trees Classifier    0.8749  0.8488  0.2572  0.7360   \n",
              "lda          Linear Discriminant Analysis    0.8705  0.8657  0.4592  0.5906   \n",
              "rf               Random Forest Classifier    0.8685  0.8577  0.2112  0.7351   \n",
              "ada                  Ada Boost Classifier    0.8673  0.8351  0.3788  0.5869   \n",
              "lr                    Logistic Regression    0.8500  0.7558  0.1225  0.4855   \n",
              "dummy                    Dummy Classifier    0.8488  0.5000  0.0000  0.0000   \n",
              "knn                K Neighbors Classifier    0.8309  0.6200  0.0929  0.3133   \n",
              "dt               Decision Tree Classifier    0.8226  0.6615  0.4304  0.4099   \n",
              "nb                            Naive Bayes    0.7639  0.7165  0.4891  0.3199   \n",
              "svm                   SVM - Linear Kernel    0.7609  0.0000  0.2083  0.1013   \n",
              "qda       Quadratic Discriminant Analysis    0.5080  0.5057  0.5024  0.1553   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "gbc       0.4976  0.4347  0.4558     0.850  \n",
              "ridge     0.4412  0.3841  0.4229     0.035  \n",
              "lightgbm  0.4632  0.4019  0.4277     0.158  \n",
              "et        0.3744  0.3253  0.3825     0.500  \n",
              "lda       0.5121  0.4399  0.4466     0.070  \n",
              "rf        0.3188  0.2721  0.3387     0.686  \n",
              "ada       0.4539  0.3845  0.3986     0.319  \n",
              "lr        0.1912  0.1443  0.1853     0.461  \n",
              "dummy     0.0000  0.0000  0.0000     0.014  \n",
              "knn       0.1413  0.0774  0.0962     0.127  \n",
              "dt        0.4163  0.3124  0.3147     0.041  \n",
              "nb        0.3855  0.2480  0.2571     0.016  \n",
              "svm       0.1181  0.0457  0.0554     0.043  \n",
              "qda       0.2363  0.0071  0.0086     0.057  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.8794</td>\n",
              "      <td>0.8567</td>\n",
              "      <td>0.4005</td>\n",
              "      <td>0.6729</td>\n",
              "      <td>0.4976</td>\n",
              "      <td>0.4347</td>\n",
              "      <td>0.4558</td>\n",
              "      <td>0.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.8775</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.3281</td>\n",
              "      <td>0.7108</td>\n",
              "      <td>0.4412</td>\n",
              "      <td>0.3841</td>\n",
              "      <td>0.4229</td>\n",
              "      <td>0.035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.8762</td>\n",
              "      <td>0.8671</td>\n",
              "      <td>0.3665</td>\n",
              "      <td>0.6622</td>\n",
              "      <td>0.4632</td>\n",
              "      <td>0.4019</td>\n",
              "      <td>0.4277</td>\n",
              "      <td>0.158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.8749</td>\n",
              "      <td>0.8488</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.7360</td>\n",
              "      <td>0.3744</td>\n",
              "      <td>0.3253</td>\n",
              "      <td>0.3825</td>\n",
              "      <td>0.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.8705</td>\n",
              "      <td>0.8657</td>\n",
              "      <td>0.4592</td>\n",
              "      <td>0.5906</td>\n",
              "      <td>0.5121</td>\n",
              "      <td>0.4399</td>\n",
              "      <td>0.4466</td>\n",
              "      <td>0.070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.8685</td>\n",
              "      <td>0.8577</td>\n",
              "      <td>0.2112</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>0.2721</td>\n",
              "      <td>0.3387</td>\n",
              "      <td>0.686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.8673</td>\n",
              "      <td>0.8351</td>\n",
              "      <td>0.3788</td>\n",
              "      <td>0.5869</td>\n",
              "      <td>0.4539</td>\n",
              "      <td>0.3845</td>\n",
              "      <td>0.3986</td>\n",
              "      <td>0.319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8500</td>\n",
              "      <td>0.7558</td>\n",
              "      <td>0.1225</td>\n",
              "      <td>0.4855</td>\n",
              "      <td>0.1912</td>\n",
              "      <td>0.1443</td>\n",
              "      <td>0.1853</td>\n",
              "      <td>0.461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dummy</th>\n",
              "      <td>Dummy Classifier</td>\n",
              "      <td>0.8488</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.8309</td>\n",
              "      <td>0.6200</td>\n",
              "      <td>0.0929</td>\n",
              "      <td>0.3133</td>\n",
              "      <td>0.1413</td>\n",
              "      <td>0.0774</td>\n",
              "      <td>0.0962</td>\n",
              "      <td>0.127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.8226</td>\n",
              "      <td>0.6615</td>\n",
              "      <td>0.4304</td>\n",
              "      <td>0.4099</td>\n",
              "      <td>0.4163</td>\n",
              "      <td>0.3124</td>\n",
              "      <td>0.3147</td>\n",
              "      <td>0.041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.7165</td>\n",
              "      <td>0.4891</td>\n",
              "      <td>0.3199</td>\n",
              "      <td>0.3855</td>\n",
              "      <td>0.2480</td>\n",
              "      <td>0.2571</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7609</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2083</td>\n",
              "      <td>0.1013</td>\n",
              "      <td>0.1181</td>\n",
              "      <td>0.0457</td>\n",
              "      <td>0.0554</td>\n",
              "      <td>0.043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.5080</td>\n",
              "      <td>0.5057</td>\n",
              "      <td>0.5024</td>\n",
              "      <td>0.1553</td>\n",
              "      <td>0.2363</td>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.0086</td>\n",
              "      <td>0.057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26636714-0b20-4cce-9135-83ecdde739e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-26636714-0b20-4cce-9135-83ecdde739e9');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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    },
    {
      "cell_type": "code",
      "source": [
        "blended=blend_models(best)"
      ],
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          "height": 457,
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        "outputId": "db3857fc-5295-4db3-b8a9-95c80d26a851"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Accuracy  AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                       \n",
              "0       0.8854  0.0  0.4583  0.6875  0.5500  0.4873  0.5005\n",
              "1       0.8854  0.0  0.3333  0.8000  0.4706  0.4183  0.4690\n",
              "2       0.8917  0.0  0.4583  0.7333  0.5641  0.5060  0.5243\n",
              "3       0.8790  0.0  0.2917  0.7778  0.4242  0.3719  0.4282\n",
              "4       0.9108  0.0  0.5000  0.8571  0.6316  0.5848  0.6124\n",
              "5       0.8726  0.0  0.4167  0.6250  0.5000  0.4303  0.4420\n",
              "6       0.8726  0.0  0.3333  0.6667  0.4444  0.3814  0.4107\n",
              "7       0.8462  0.0  0.1739  0.4444  0.2500  0.1822  0.2073\n",
              "8       0.9038  0.0  0.4348  0.8333  0.5714  0.5232  0.5585\n",
              "9       0.8590  0.0  0.2174  0.5556  0.3125  0.2503  0.2848\n",
              "Mean    0.8806  0.0  0.3618  0.6981  0.4719  0.4136  0.4438\n",
              "Std     0.0185  0.0  0.1043  0.1236  0.1133  0.1177  0.1159"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e343c4b8-49a0-4680-9e89-d0eff230ce1d\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8854</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4583</td>\n",
              "      <td>0.6875</td>\n",
              "      <td>0.5500</td>\n",
              "      <td>0.4873</td>\n",
              "      <td>0.5005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8854</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.8000</td>\n",
              "      <td>0.4706</td>\n",
              "      <td>0.4183</td>\n",
              "      <td>0.4690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8917</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4583</td>\n",
              "      <td>0.7333</td>\n",
              "      <td>0.5641</td>\n",
              "      <td>0.5060</td>\n",
              "      <td>0.5243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8790</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2917</td>\n",
              "      <td>0.7778</td>\n",
              "      <td>0.4242</td>\n",
              "      <td>0.3719</td>\n",
              "      <td>0.4282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.9108</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.8571</td>\n",
              "      <td>0.6316</td>\n",
              "      <td>0.5848</td>\n",
              "      <td>0.6124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8726</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4167</td>\n",
              "      <td>0.6250</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.4303</td>\n",
              "      <td>0.4420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8726</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>0.3814</td>\n",
              "      <td>0.4107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8462</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1739</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.1822</td>\n",
              "      <td>0.2073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4348</td>\n",
              "      <td>0.8333</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>0.5232</td>\n",
              "      <td>0.5585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8590</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2174</td>\n",
              "      <td>0.5556</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>0.2503</td>\n",
              "      <td>0.2848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8806</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3618</td>\n",
              "      <td>0.6981</td>\n",
              "      <td>0.4719</td>\n",
              "      <td>0.4136</td>\n",
              "      <td>0.4438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0185</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1043</td>\n",
              "      <td>0.1236</td>\n",
              "      <td>0.1133</td>\n",
              "      <td>0.1177</td>\n",
              "      <td>0.1159</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e343c4b8-49a0-4680-9e89-d0eff230ce1d button.colab-df-convert');\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e343c4b8-49a0-4680-9e89-d0eff230ce1d');\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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    {
      "cell_type": "code",
      "source": [
        "stacked_model=stack_models(best)"
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          "height": 457,
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                          \n",
              "0       0.8535  0.7202  0.1667  0.5714  0.2581  0.2030  0.2513\n",
              "1       0.8726  0.8283  0.2500  0.7500  0.3750  0.3233  0.3845\n",
              "2       0.8662  0.7641  0.2083  0.7143  0.3226  0.2723  0.3370\n",
              "3       0.8535  0.8396  0.1250  0.6000  0.2069  0.1628  0.2254\n",
              "4       0.8471  0.8042  0.1250  0.5000  0.2000  0.1479  0.1923\n",
              "5       0.8344  0.6867  0.0833  0.3333  0.1333  0.0769  0.1000\n",
              "6       0.8344  0.7917  0.1250  0.3750  0.1875  0.1203  0.1430\n",
              "7       0.8462  0.7372  0.0435  0.3333  0.0769  0.0444  0.0734\n",
              "8       0.8846  0.8280  0.3043  0.7778  0.4375  0.3866  0.4399\n",
              "9       0.8333  0.7346  0.0435  0.2000  0.0714  0.0198  0.0270\n",
              "Mean    0.8526  0.7735  0.1475  0.5155  0.2269  0.1757  0.2174\n",
              "Std     0.0165  0.0499  0.0815  0.1896  0.1160  0.1147  0.1303"
            ],
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              "\n",
              "  <div id=\"df-7a1a89f5-d6fe-457e-98f1-96f5a33429e3\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8535</td>\n",
              "      <td>0.7202</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>0.2581</td>\n",
              "      <td>0.2030</td>\n",
              "      <td>0.2513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8726</td>\n",
              "      <td>0.8283</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.3233</td>\n",
              "      <td>0.3845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8662</td>\n",
              "      <td>0.7641</td>\n",
              "      <td>0.2083</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.3226</td>\n",
              "      <td>0.2723</td>\n",
              "      <td>0.3370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8535</td>\n",
              "      <td>0.8396</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8471</td>\n",
              "      <td>0.8042</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>0.1479</td>\n",
              "      <td>0.1923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8344</td>\n",
              "      <td>0.6867</td>\n",
              "      <td>0.0833</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.1333</td>\n",
              "      <td>0.0769</td>\n",
              "      <td>0.1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8344</td>\n",
              "      <td>0.7917</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.1875</td>\n",
              "      <td>0.1203</td>\n",
              "      <td>0.1430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8462</td>\n",
              "      <td>0.7372</td>\n",
              "      <td>0.0435</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.0769</td>\n",
              "      <td>0.0444</td>\n",
              "      <td>0.0734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8846</td>\n",
              "      <td>0.8280</td>\n",
              "      <td>0.3043</td>\n",
              "      <td>0.7778</td>\n",
              "      <td>0.4375</td>\n",
              "      <td>0.3866</td>\n",
              "      <td>0.4399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8333</td>\n",
              "      <td>0.7346</td>\n",
              "      <td>0.0435</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>0.0714</td>\n",
              "      <td>0.0198</td>\n",
              "      <td>0.0270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8526</td>\n",
              "      <td>0.7735</td>\n",
              "      <td>0.1475</td>\n",
              "      <td>0.5155</td>\n",
              "      <td>0.2269</td>\n",
              "      <td>0.1757</td>\n",
              "      <td>0.2174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0165</td>\n",
              "      <td>0.0499</td>\n",
              "      <td>0.0815</td>\n",
              "      <td>0.1896</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>0.1147</td>\n",
              "      <td>0.1303</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_stacked=tune_model(stacked_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
            "77949ef3b8b742e79dda505df8cb4d58",
            "2cda2ec290544141938045ff7f3f4500",
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        "id": "6F8u_pDfyML-",
        "outputId": "488f269c-d277-4412-f325-202ca18c3ae1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                          \n",
              "0       0.8854  0.7619  0.3750  0.7500  0.5000  0.4433  0.4774\n",
              "1       0.8726  0.8584  0.2500  0.7500  0.3750  0.3233  0.3845\n",
              "2       0.8662  0.7635  0.2083  0.7143  0.3226  0.2723  0.3370\n",
              "3       0.8471  0.8399  0.0833  0.5000  0.1429  0.1037  0.1560\n",
              "4       0.8471  0.7610  0.1250  0.5000  0.2000  0.1479  0.1923\n",
              "5       0.8280  0.6873  0.0833  0.2857  0.1290  0.0644  0.0797\n",
              "6       0.8344  0.7923  0.1250  0.3750  0.1875  0.1203  0.1430\n",
              "7       0.8462  0.7012  0.0870  0.4000  0.1429  0.0952  0.1296\n",
              "8       0.8846  0.8333  0.3043  0.7778  0.4375  0.3866  0.4399\n",
              "9       0.8397  0.7453  0.0435  0.2500  0.0741  0.0318  0.0469\n",
              "Mean    0.8551  0.7744  0.1685  0.5303  0.2511  0.1989  0.2386\n",
              "Std     0.0195  0.0542  0.1048  0.1931  0.1391  0.1380  0.1485"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8854</td>\n",
              "      <td>0.7619</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.4433</td>\n",
              "      <td>0.4774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8726</td>\n",
              "      <td>0.8584</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.3233</td>\n",
              "      <td>0.3845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8662</td>\n",
              "      <td>0.7635</td>\n",
              "      <td>0.2083</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.3226</td>\n",
              "      <td>0.2723</td>\n",
              "      <td>0.3370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8471</td>\n",
              "      <td>0.8399</td>\n",
              "      <td>0.0833</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.1429</td>\n",
              "      <td>0.1037</td>\n",
              "      <td>0.1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8471</td>\n",
              "      <td>0.7610</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>0.1479</td>\n",
              "      <td>0.1923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8280</td>\n",
              "      <td>0.6873</td>\n",
              "      <td>0.0833</td>\n",
              "      <td>0.2857</td>\n",
              "      <td>0.1290</td>\n",
              "      <td>0.0644</td>\n",
              "      <td>0.0797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8344</td>\n",
              "      <td>0.7923</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.1875</td>\n",
              "      <td>0.1203</td>\n",
              "      <td>0.1430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8462</td>\n",
              "      <td>0.7012</td>\n",
              "      <td>0.0870</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1429</td>\n",
              "      <td>0.0952</td>\n",
              "      <td>0.1296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8846</td>\n",
              "      <td>0.8333</td>\n",
              "      <td>0.3043</td>\n",
              "      <td>0.7778</td>\n",
              "      <td>0.4375</td>\n",
              "      <td>0.3866</td>\n",
              "      <td>0.4399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8397</td>\n",
              "      <td>0.7453</td>\n",
              "      <td>0.0435</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.0741</td>\n",
              "      <td>0.0318</td>\n",
              "      <td>0.0469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8551</td>\n",
              "      <td>0.7744</td>\n",
              "      <td>0.1685</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.2511</td>\n",
              "      <td>0.1989</td>\n",
              "      <td>0.2386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0195</td>\n",
              "      <td>0.0542</td>\n",
              "      <td>0.1048</td>\n",
              "      <td>0.1931</td>\n",
              "      <td>0.1391</td>\n",
              "      <td>0.1380</td>\n",
              "      <td>0.1485</td>\n",
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              "    .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",
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              "\n",
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              "      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",
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              "\n",
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              "          document.querySelector('#df-90c88c4d-3bd7-4b01-94f0-63a6d2cc389a 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-90c88c4d-3bd7-4b01-94f0-63a6d2cc389a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
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      "source": [
        "evaluate_model(tuned_stacked)"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
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      "source": [
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          "data": {
            "text/plain": [
              "      Accuracy  AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                       \n",
              "0       0.8762  0.0  0.4375  0.6364  0.5185  0.4503  0.4607\n",
              "1       0.8857  0.0  0.3750  0.7500  0.5000  0.4435  0.4775\n",
              "2       0.8762  0.0  0.3125  0.7143  0.4348  0.3770  0.4179\n",
              "3       0.8762  0.0  0.3125  0.7143  0.4348  0.3770  0.4179\n",
              "4       0.8857  0.0  0.5000  0.6667  0.5714  0.5070  0.5140\n",
              "5       0.8667  0.0  0.2500  0.6667  0.3636  0.3059  0.3523\n",
              "6       0.9048  0.0  0.5000  0.8000  0.6154  0.5643  0.5846\n",
              "7       0.8750  0.0  0.4667  0.5833  0.5185  0.4477  0.4514\n",
              "8       0.8846  0.0  0.4667  0.6364  0.5385  0.4743  0.4818\n",
              "9       0.8462  0.0  0.3333  0.4545  0.3846  0.2991  0.3038\n",
              "10      0.8654  0.0  0.3750  0.6000  0.4615  0.3893  0.4033\n",
              "11      0.8750  0.0  0.3125  0.7143  0.4348  0.3764  0.4173\n",
              "12      0.9135  0.0  0.5000  0.8889  0.6400  0.5952  0.6271\n",
              "13      0.8750  0.0  0.4375  0.6364  0.5185  0.4495  0.4599\n",
              "14      0.8462  0.0  0.1250  0.5000  0.2000  0.1475  0.1919\n",
              "Mean    0.8768  0.0  0.3803  0.6641  0.4757  0.4136  0.4374\n",
              "Std     0.0173  0.0  0.1041  0.1057  0.1058  0.1074  0.1020"
            ],
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              "\n",
              "  <div id=\"df-44bdc5ef-02eb-4ffd-a213-b745cf5d375c\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
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              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8762</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4375</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.5185</td>\n",
              "      <td>0.4503</td>\n",
              "      <td>0.4607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.8857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.4435</td>\n",
              "      <td>0.4775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8762</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.4348</td>\n",
              "      <td>0.3770</td>\n",
              "      <td>0.4179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8762</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.4348</td>\n",
              "      <td>0.3770</td>\n",
              "      <td>0.4179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>0.5070</td>\n",
              "      <td>0.5140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.8667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.3636</td>\n",
              "      <td>0.3059</td>\n",
              "      <td>0.3523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.9048</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.8000</td>\n",
              "      <td>0.6154</td>\n",
              "      <td>0.5643</td>\n",
              "      <td>0.5846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4667</td>\n",
              "      <td>0.5833</td>\n",
              "      <td>0.5185</td>\n",
              "      <td>0.4477</td>\n",
              "      <td>0.4514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8846</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4667</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.5385</td>\n",
              "      <td>0.4743</td>\n",
              "      <td>0.4818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8462</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3333</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.3846</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.3038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.8654</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.4615</td>\n",
              "      <td>0.3893</td>\n",
              "      <td>0.4033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.8750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3125</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.4348</td>\n",
              "      <td>0.3764</td>\n",
              "      <td>0.4173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.9135</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.8889</td>\n",
              "      <td>0.6400</td>\n",
              "      <td>0.5952</td>\n",
              "      <td>0.6271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.8750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4375</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.5185</td>\n",
              "      <td>0.4495</td>\n",
              "      <td>0.4599</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.8462</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>0.1475</td>\n",
              "      <td>0.1919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8768</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3803</td>\n",
              "      <td>0.6641</td>\n",
              "      <td>0.4757</td>\n",
              "      <td>0.4136</td>\n",
              "      <td>0.4374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0173</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1041</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.1058</td>\n",
              "      <td>0.1074</td>\n",
              "      <td>0.1020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44bdc5ef-02eb-4ffd-a213-b745cf5d375c')\"\n",
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              "                                                     [key], {});\n",
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              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
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}