ML_for_Banking Basemodel Pycaret.ipynb

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        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
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          "height": 1000
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        "outputId": "0e07209f-a61d-4001-bcd0-ad1cf52dbd64"
      },
      "source": [
        "! pip install pycaret"
      ],
      "execution_count": 37,
      "outputs": [
        {
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            "Collecting pycaret\n",
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            "\u001b[?25hRequirement already satisfied: IPython in /usr/local/lib/python3.6/dist-packages (from pycaret) (5.5.0)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2c/2f/aae19e2173c10a9bb7fee5f5cad35dbe53a393960fc91abc477dcc4661e8/pandas-profiling-2.3.0.tar.gz (127kB)\n",
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            "\u001b[?25hRequirement already satisfied: umap-learn in /usr/local/lib/python3.6/dist-packages (from pycaret) (0.4.3)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pycaret) (1.18.4)\n",
            "Collecting awscli\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/26/2bf03cd24e42550e0487b2f78202cd4aa8b9e2fe859f09335c9c2ab1b2c5/awscli-1.18.69-py2.py3-none-any.whl (3.1MB)\n",
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            "\u001b[?25hRequirement already satisfied: seaborn in /usr/local/lib/python3.6/dist-packages (from pycaret) (0.10.1)\n",
            "Collecting scikit-learn==0.22\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2e/d0/860c4f6a7027e00acff373d9f5327f4ae3ed5872234b3cbdd7bcb52e5eff/scikit_learn-0.22-cp36-cp36m-manylinux1_x86_64.whl (7.0MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/cf/6d6ab47c0759d246262f9bdb53e89be3814bf1774bc51fffff995f5859f9/yellowbrick-1.0.1-py3-none-any.whl (378kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/16/2b/af8efaee30c0ba4238cb4d0645a07100d33d11d20a8783c443ed8b813eb9/datefinder-0.7.0-py2.py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/22/a5297f3a1f92468cc737f8ce7ba6e5f245fcfafeae810ba37bd1039ea01c/DateTime-4.3-py2.py3-none-any.whl (60kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 6.2MB/s \n",
            "\u001b[?25hCollecting lightgbm==2.3.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0b/9d/ddcb2f43aca194987f1a99e27edf41cf9bc39ea750c3371c2a62698c509a/lightgbm-2.3.1-py2.py3-none-manylinux1_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 39.9MB/s \n",
            "\u001b[?25hCollecting shap==0.32.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/57/43/08f152a59a1d60f0328b476bdd58c791498989981ab9c6d595ec5448a86a/shap-0.32.1.tar.gz (259kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/79/c0/f7d8a0eb41ac6f302b4bc100f91b6e0f2558425ccfefaa0ec0430f77ee97/kmodes-0.10.1-py2.py3-none-any.whl\n",
            "Collecting chart-studio>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/ce/330794a6b6ca4b9182c38fc69dd2a9cbff60fd49421cb8648ee5fee352dc/chart_studio-1.1.0-py3-none-any.whl (64kB)\n",
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            "Collecting funcy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/4b/6ffa76544e46614123de31574ad95758c421aae391a1764921b8a81e1eae/funcy-1.14.tar.gz (548kB)\n",
            "\u001b[K     |████████████████████████████████| 552kB 44.4MB/s \n",
            "\u001b[?25hCollecting combo\n",
            "  Downloading https://files.pythonhosted.org/packages/78/52/e880bd923eba122515307d29ab43c1c356bad60610c27bed2cdec25d0240/combo-0.1.0.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a1/87/9170cabe1b5e10a7d095c0e28f2e30e7c1886a13f063de85d3cfacc06f4b/suod-0.0.4.tar.gz (2.1MB)\n",
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            "Collecting htmlmin>=0.1.12\n",
            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
            "Collecting phik>=0.9.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/01/5a/7ef1c04ce62cd72f900c06298dc2385840550d5c653a0dbc19109a5477e6/phik-0.10.0-py3-none-any.whl (599kB)\n",
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            "\u001b[?25hCollecting confuse>=1.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/64/85dbcea372efee5cba13eaa10a3bfa7019b8fe0c3c8314d8e189116e477a/confuse-1.1.0.tar.gz\n",
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            "Collecting rsa<=3.5.0,>=3.1.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/ae/baedc9cb175552e95f3395c43055a6a5e125ae4d48a1d7a924baca83e92e/rsa-3.4.2-py2.py3-none-any.whl (46kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.9MB/s \n",
            "\u001b[?25hCollecting botocore==1.16.19\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/8c/b8d7e77d9e5a373124dc5817901e454fdde03efcfeadab519dc7be74e3ae/botocore-1.16.19-py2.py3-none-any.whl (6.2MB)\n",
            "\u001b[K     |████████████████████████████████| 6.2MB 44.2MB/s \n",
            "\u001b[?25hCollecting colorama<0.4.4,>=0.2.5; python_version != \"3.4\"\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n",
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            "Collecting zope.interface\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/57/33/565274c28a11af60b7cfc0519d46bde4125fcd7d32ebc0a81b480d0e8da6/zope.interface-5.1.0-cp36-cp36m-manylinux2010_x86_64.whl (234kB)\n",
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            "Building wheels for collected packages: cufflinks, pyLDAvis, pyod, pandas-profiling, shap, funcy, combo, suod, htmlmin, confuse\n",
            "  Building wheel for cufflinks (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cufflinks: filename=cufflinks-0.17.0-cp36-none-any.whl size=67744 sha256=80b0a5d342c9fa0a9c1f5e88b6aea1c5e5be6ca4f69b875b17d96a19fc48a035\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/d7/dc/e830ab00bc2dd3b2731295103baa070f8cbdda8891f71a7a8d\n",
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            "  Stored in directory: /root/.cache/pip/wheels/ba/a6/81/2dd042e240090f3603a686b897d03402219a86e3f61bc71184\n",
            "  Building wheel for pandas-profiling (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/ce/c7/f1/dbfef4848ebb048cb1d4a22d1ed0c62d8ff2523747235e19fe\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.32.1-cp36-cp36m-linux_x86_64.whl size=376810 sha256=a5ae7105d7b4b1e1a094443b36b5fdd2fd8c9a01e456f5a8202490caed726d8b\n",
            "  Stored in directory: /root/.cache/pip/wheels/8e/b2/50/8fadb5a59789cb5bdeb01b800223be540651ae92915172050b\n",
            "  Building wheel for funcy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for funcy: filename=funcy-1.14-py2.py3-none-any.whl size=32042 sha256=164e9771a577a54bf675dec84a1de7f353a650c9ee2c31e4577a776df6705e07\n",
            "  Stored in directory: /root/.cache/pip/wheels/20/5a/d8/1d875df03deae6f178dfdf70238cca33f948ef8a6f5209f2eb\n",
            "  Building wheel for combo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for combo: filename=combo-0.1.0-cp36-none-any.whl size=42043 sha256=f45a469ae8402000c8f891316b7aeeb749c80a88ddffb239196040e6957c989d\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/fd/6c/8da495ef08ce61844a646df2423c2b8ecda377a89c90ecd88e\n",
            "  Building wheel for suod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for suod: filename=suod-0.0.4-cp36-none-any.whl size=2167157 sha256=749be5fcc7eb58d1c5609447c1def0229d245f657cf7899fed33120cfe474a61\n",
            "  Stored in directory: /root/.cache/pip/wheels/57/55/e5/a4fca65bba231f6d0115059b589148774b41faea25b3f2aa27\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-cp36-none-any.whl size=27084 sha256=0e334df6bee16c03840aeb01d9b33b5dc9ae2350d27ad0bdcb09a56168b5cb5d\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n",
            "  Building wheel for confuse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for confuse: filename=confuse-1.1.0-cp36-none-any.whl size=17574 sha256=6a93b5a9a888552f715aacc9ee48cdf255f67e443bc4b6cf51a01136d4a289f7\n",
            "  Stored in directory: /root/.cache/pip/wheels/f6/8b/23/41a1b516f6d8d4cc81f5bdb55394a47cdbe9659c53668d3c9e\n",
            "Successfully built cufflinks pyLDAvis pyod pandas-profiling shap funcy combo suod htmlmin confuse\n",
            "Installing collected packages: chart-studio, cufflinks, catboost, funcy, pyLDAvis, scikit-learn, combo, suod, pyod, htmlmin, phik, confuse, pandas-profiling, rsa, botocore, colorama, awscli, yellowbrick, datefinder, zope.interface, DateTime, lightgbm, shap, kmodes, pycaret\n",
            "  Found existing installation: cufflinks 0.17.3\n",
            "    Uninstalling cufflinks-0.17.3:\n",
            "      Successfully uninstalled cufflinks-0.17.3\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
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            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
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            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "  Found existing installation: rsa 4.0\n",
            "    Uninstalling rsa-4.0:\n",
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            "  Found existing installation: botocore 1.16.13\n",
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            "      Successfully uninstalled botocore-1.16.13\n",
            "  Found existing installation: yellowbrick 0.9.1\n",
            "    Uninstalling yellowbrick-0.9.1:\n",
            "      Successfully uninstalled yellowbrick-0.9.1\n",
            "  Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "Successfully installed DateTime-4.3 awscli-1.18.69 botocore-1.16.19 catboost-0.20.2 chart-studio-1.1.0 colorama-0.4.3 combo-0.1.0 confuse-1.1.0 cufflinks-0.17.0 datefinder-0.7.0 funcy-1.14 htmlmin-0.1.12 kmodes-0.10.1 lightgbm-2.3.1 pandas-profiling-2.3.0 phik-0.10.0 pyLDAvis-2.1.2 pycaret-1.0.0 pyod-0.8.0 rsa-3.4.2 scikit-learn-0.22 shap-0.32.1 suod-0.0.4 yellowbrick-1.0.1 zope.interface-5.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "rsa"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjjlWeyfkOts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xz2cU5hXkhi6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv('/content/drive/My Drive/ML for Banking/train_fNxu4vz.csv')\n",
        "test = pd.read_csv('/content/drive/My Drive/ML for Banking/test_fjtUOL8.csv')\n",
        "ss = pd.read_csv('/content/drive/My Drive/ML for Banking/sample_submission_HSqiq1Q.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "virxbxoImPxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['Loan_Amount_Requested'] = train['Loan_Amount_Requested'].str.replace(',','').astype(float)\n",
        "test['Loan_Amount_Requested'] = test['Loan_Amount_Requested'].str.replace(',','').astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVf2p5VDnsmb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pycaret.classification import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQoVpMuKoB-Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956,
          "referenced_widgets": [
            "c193504655384038984981e30fd56680",
            "bb715dbaa34f4ca3adcde3807ba445c8",
            "c6d8dba97b704327b3cd1ba1b5b949d8",
            "1ff0f5f886cf47209f14bbb3c8655e17",
            "038144b9d1d045a48afdb2fbaff155b7",
            "4b6e3552bc5b4e2f963c26c114d3a814"
          ]
        },
        "outputId": "ef359530-55e4-4a1d-d86c-b8669ccd5723"
      },
      "source": [
        "exp_mclf101 = setup(data = train, target = 'Interest_Rate', session_id=123)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " \n",
            "Setup Succesfully Completed!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "    #T_2c4913e8_a29b_11ea_adab_0242ac1c0002row4_col1 {\n",
              "            background-color:  lightgreen;\n",
              "        }</style><table id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row1_col0\" class=\"data row1 col0\" >Target Type</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row1_col1\" class=\"data row1 col1\" >Multiclass</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row2_col1\" class=\"data row2 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row3_col1\" class=\"data row3 col1\" >(164309, 14)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row4_col1\" class=\"data row4 col1\" >True</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row5_col1\" class=\"data row5 col1\" >7</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row6_col1\" class=\"data row6 col1\" >6</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row7_col1\" class=\"data row7 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row8_col1\" class=\"data row8 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row9_col1\" class=\"data row9 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row10_col1\" class=\"data row10 col1\" >(49292, 14)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row11_col1\" class=\"data row11 col1\" >(34504, 51)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row12_col1\" class=\"data row12 col1\" >(14788, 51)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row13_col1\" class=\"data row13 col1\" >mean</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row14_col1\" class=\"data row14 col1\" >constant</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row16_col1\" class=\"data row16 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row17_col1\" class=\"data row17 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row18_col1\" class=\"data row18 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row19_col1\" class=\"data row19 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row20_col1\" class=\"data row20 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row21_col1\" class=\"data row21 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row22_col1\" class=\"data row22 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row23_col1\" class=\"data row23 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row24_col1\" class=\"data row24 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row25_col1\" class=\"data row25 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row26_col1\" class=\"data row26 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row27_col1\" class=\"data row27 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row28_col1\" class=\"data row28 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row29_col1\" class=\"data row29 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row30_col1\" class=\"data row30 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row31_col1\" class=\"data row31 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row32_col1\" class=\"data row32 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row33_col1\" class=\"data row33 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row34_col1\" class=\"data row34 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row35_col1\" class=\"data row35 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row36_col1\" class=\"data row36 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row37_col1\" class=\"data row37 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row38_col1\" class=\"data row38 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row39_col1\" class=\"data row39 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row40_col1\" class=\"data row40 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
              "                        <td id=\"T_2c4913e8_a29b_11ea_adab_0242ac1c0002row41_col1\" class=\"data row41 col1\" >None</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f4e3f1ec6a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yj75Au6BoQNr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "compare_models(fold=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVxs8g6To2Ol",
        "colab_type": "text"
      },
      "source": [
        "# Top BaseModels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mXA2WNwo2qH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gbc_model = create_model('gbc')\n",
        "ada_model = create_model('ada')\n",
        "#lgbm_model = create_model('lightgbm')\n",
        "xgb_model = create_model('xgboost')\n",
        "cat_model = create_model('catboost')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqtC8ZCMrsHK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "4ae852e7a8b149ce8018f44b77681f7d",
            "22ee8ea0bc164749ad9e3b70a8c12673",
            "84462f5f84a44dc29380511a85262537"
          ]
        },
        "outputId": "df1dd11e-aba2-4feb-88b0-53b4a4858534"
      },
      "source": [
        "#xgb_model = create_model('xgboost')\n",
        "cat_model = create_model('catboost')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.5245</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4728</td>\n",
              "      <td>0.5231</td>\n",
              "      <td>0.5093</td>\n",
              "      <td>0.2259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.5398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4940</td>\n",
              "      <td>0.5432</td>\n",
              "      <td>0.5284</td>\n",
              "      <td>0.2537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.5312</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4831</td>\n",
              "      <td>0.5365</td>\n",
              "      <td>0.5185</td>\n",
              "      <td>0.2369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.5436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4973</td>\n",
              "      <td>0.5477</td>\n",
              "      <td>0.5324</td>\n",
              "      <td>0.2593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.5362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4868</td>\n",
              "      <td>0.5360</td>\n",
              "      <td>0.5233</td>\n",
              "      <td>0.2467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.5255</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4778</td>\n",
              "      <td>0.5299</td>\n",
              "      <td>0.5127</td>\n",
              "      <td>0.2281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.5362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4863</td>\n",
              "      <td>0.5405</td>\n",
              "      <td>0.5232</td>\n",
              "      <td>0.2447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.5365</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4879</td>\n",
              "      <td>0.5362</td>\n",
              "      <td>0.5235</td>\n",
              "      <td>0.2478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.5267</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4826</td>\n",
              "      <td>0.5280</td>\n",
              "      <td>0.5165</td>\n",
              "      <td>0.2335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.5267</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4783</td>\n",
              "      <td>0.5271</td>\n",
              "      <td>0.5136</td>\n",
              "      <td>0.2310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.5327</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4847</td>\n",
              "      <td>0.5348</td>\n",
              "      <td>0.5201</td>\n",
              "      <td>0.2408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.0074</td>\n",
              "      <td>0.0069</td>\n",
              "      <td>0.0107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy  AUC  Recall   Prec.      F1   Kappa\n",
              "0       0.5245  0.0  0.4728  0.5231  0.5093  0.2259\n",
              "1       0.5398  0.0  0.4940  0.5432  0.5284  0.2537\n",
              "2       0.5312  0.0  0.4831  0.5365  0.5185  0.2369\n",
              "3       0.5436  0.0  0.4973  0.5477  0.5324  0.2593\n",
              "4       0.5362  0.0  0.4868  0.5360  0.5233  0.2467\n",
              "5       0.5255  0.0  0.4778  0.5299  0.5127  0.2281\n",
              "6       0.5362  0.0  0.4863  0.5405  0.5232  0.2447\n",
              "7       0.5365  0.0  0.4879  0.5362  0.5235  0.2478\n",
              "8       0.5267  0.0  0.4826  0.5280  0.5165  0.2335\n",
              "9       0.5267  0.0  0.4783  0.5271  0.5136  0.2310\n",
              "Mean    0.5327  0.0  0.4847  0.5348  0.5201  0.2408\n",
              "SD      0.0064  0.0  0.0071  0.0074  0.0069  0.0107"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2KoY75Tp-IH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rdG6BV1moy3y",
        "colab_type": "text"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXLc0DFaofjI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374,
          "referenced_widgets": [
            "e6d1a8dafe894a11840ea636ee2c6eaf"
          ]
        },
        "outputId": "b53615bf-c261-459c-f7b5-77476c37a525"
      },
      "source": [
        "plot_model(gbc_model, plot = 'confusion_matrix')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-xkr3BZrvcF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374,
          "referenced_widgets": [
            "58bd6c85225e4218b6e9367f67a7f307",
            "2bf5c899a0734c068f7777dfe2989154",
            "a6933f70b440447d9b15edf0e4bcc423"
          ]
        },
        "outputId": "857ca037-82e8-4c73-b6de-2d99f84bc7a7"
      },
      "source": [
        "plot_model(ada_model, plot = 'confusion_matrix')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqTtwHEKr1rt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374,
          "referenced_widgets": [
            "2d36fbae155942f5af46097ab39b7147",
            "5ffbd7ca8c764e9499f7a89b82e9e2b4",
            "982e67cabb25445eb1e098370957be4e"
          ]
        },
        "outputId": "e7a892eb-1419-4faf-adec-0fe732470d8f"
      },
      "source": [
        "plot_model(lgbm_model, plot = 'confusion_matrix')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQnoWr5Mr5b8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374,
          "referenced_widgets": [
            "c8fcf57eae284149aa68de3c17db32f2"
          ]
        },
        "outputId": "669bab7c-2e27-47c5-c9cc-d32838b10cbc"
      },
      "source": [
        "plot_model(cat_model, plot = 'confusion_matrix')"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhiVUY9Hr9h1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_model(xgb_model, plot = 'confusion_matrix')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfBab9Q4sD94",
        "colab_type": "text"
      },
      "source": [
        "# Submission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSfEQ_gDsESn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_preds = predict_model(cat_model,test)['Label']\n",
        "ss['Interest_Rate'] = test_preds\n",
        "ss.to_csv('Submission_4_cat_Pycaret.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUHYonMmu9LJ",
        "colab_type": "text"
      },
      "source": [
        "# LGBM with weighted F1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "017ZFj2zsHdt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "import time\n",
        "def evaluate_weightedF1_lgb(truth, predictions):  \n",
        "    # this follows the discussion in https://github.com/Microsoft/LightGBM/issues/1483\n",
        "    pred_labels = predictions.reshape(len(np.unique(truth)),-1).argmax(axis=0)\n",
        "    f1 = f1_score(truth, pred_labels, average='weighted')\n",
        "    return ('weightedF1', f1, True) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diKuPkpisdzm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_iter = 5\n",
        "folds = StratifiedKFold(n_splits = max_iter)\n",
        "\n",
        "oofs = np.zeros(len(train_data))\n",
        "preds_test = np.zeros(len(test_data))\n",
        "preds_test1 = np.zeros(len(test_data))\n",
        "\n",
        "train = train_data.drop(['class'],axis=1)\n",
        "target = train_data['class']\n",
        "\n",
        "feature_importance_df = pd.DataFrame()\n",
        "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train, target.values)):\n",
        "    \n",
        "    print(f'\\n---- Fold {fold_} -----\\n')\n",
        "    \n",
        "    fold_start_time = time.time()\n",
        "    \n",
        "    X_trn, y_trn = train.iloc[trn_idx], target.iloc[trn_idx]\n",
        "    X_val, y_val = train.iloc[val_idx], target.iloc[val_idx]\n",
        "    X_test = test_data #test[features]\n",
        "    \n",
        "    print(X_trn.shape)\n",
        "    \n",
        "    clf = LGBMClassifier( n_estimators = 5000, learning_rate = 0.01, num_leaves=200, max_depth=4, colsample_by_tree =2, reg_alpha=0.5, reg_lambda=0.5, \n",
        "                        bagging_freq=1, bagging_fraction=0.8, max_bin=50)\n",
        "    _ = clf.fit(X_trn, y_trn, eval_set = [(X_trn, y_trn), (X_val, y_val)], eval_metric = 'logloss', verbose = 100, early_stopping_rounds = 100)\n",
        "    \n",
        "    oofs[val_idx] = clf.predict(X_val)\n",
        "    preds_test += clf.predict_proba(test_data)[:,0]/max_iter\n",
        "    preds_test1 += clf.predict_proba(test_data)[:,1]/max_iter\n",
        "\n",
        "\n",
        "    print(f'\\n Logloss of validation : {log_loss(y_val,oofs[val_idx])}')\n",
        "\n",
        "    fold_importance_df = pd.DataFrame({'feature': X_trn.columns.tolist(), 'importance': clf.feature_importances_})\n",
        "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
        "    \n",
        "    fold_end_time = time.time()\n",
        "    total_fold_time = int(fold_end_time - fold_start_time)\n",
        "    \n",
        "    print(f\"\\n->-> Fold ran for {(total_fold_time)//60} minutes {(total_fold_time)%60} seconds\")"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}