Pycaret.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
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              "widget-interact"
            ],
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            "box_style": "",
            "children": [
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            "layout": "IPY_MODEL_311374342e044687b70577d4e07b72c2"
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            "_options_labels": [
              "Pipeline Plot",
              "Hyperparameters",
              "AUC",
              "Confusion Matrix",
              "Threshold",
              "Precision Recall",
              "Prediction Error",
              "Class Report",
              "Feature Selection",
              "Learning Curve",
              "Manifold Learning",
              "Calibration Curve",
              "Validation Curve",
              "Dimensions",
              "Feature Importance",
              "Feature Importance (All)",
              "Decision Boundary",
              "Lift Chart",
              "Gain Chart",
              "Decision Tree",
              "KS Statistic Plot"
            ],
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            "description": "Plot Type:",
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            "icons": [
              ""
            ],
            "index": 3,
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            "style": "IPY_MODEL_46a45cef022f427f98bdcfc3b917c07e",
            "tooltips": []
          }
        },
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            "_view_name": "OutputView",
            "layout": "IPY_MODEL_3bd0b5d5bd0649b39a09118f828bc485",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 800x550 with 1 Axes>",
                  "image/png": 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            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.4)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.3.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.8.4)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.10.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.5.1)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.3.0)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (21.2.0)\n",
            "Requirement already satisfied: jupyter-server<3,>=1.8 in /usr/local/lib/python3.10/dist-packages (from notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.24.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.16.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.5)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.22)\n",
            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (3.7.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.8.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.2)\n",
            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-2.0.1-py3-none-any.whl size=193267 sha256=c6c51591b50b6a35a4866f1a9f3bd892f12d40720315a2fa7ceb97aec5c6475a\n",
            "  Stored in directory: /root/.cache/pip/wheels/94/75/88/b853cf33b0053b0a001dca55b74d515048b7656e736364eb57\n",
            "Successfully built pyod\n",
            "Installing collected packages: kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, tsdownsample, scikit-base, schemdraw, retrying, orjson, joblib, jedi, deprecation, scikit-learn, sktime, scikit-plot, pyod, imbalanced-learn, dash, pmdarima, plotly-resampler, category-encoders, tbats, pycaret\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.4.2\n",
            "    Uninstalling joblib-1.4.2:\n",
            "      Successfully uninstalled joblib-1.4.2\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: imbalanced-learn\n",
            "    Found existing installation: imbalanced-learn 0.10.1\n",
            "    Uninstalling imbalanced-learn-0.10.1:\n",
            "      Successfully uninstalled imbalanced-learn-0.10.1\n",
            "Successfully installed category-encoders-2.6.3 dash-2.17.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecation-2.1.0 imbalanced-learn-0.12.3 jedi-0.19.1 joblib-1.3.2 kaleido-0.2.1 orjson-3.10.6 plotly-resampler-0.10.0 pmdarima-2.0.4 pycaret-3.3.2 pyod-2.0.1 retrying-1.3.4 schemdraw-0.15 scikit-base-0.7.8 scikit-learn-1.4.2 scikit-plot-0.3.7 sktime-0.26.0 tbats-1.1.3 tsdownsample-0.1.3 wurlitzer-3.1.1 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install --pre pycaret"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "0wJ7fkq3ng4O",
        "outputId": "43b5ecf6-5eca-4f15-c91d-51d1d6cd2427"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "Error: credential propagation was unsuccessful",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-d5df0069828e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    131\u001b[0m   )\n\u001b[1;32m    132\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    134\u001b[0m         \u001b[0;34m'request_auth'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'authType'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'dfs_ephemeral'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    174\u001b[0m       \u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpect_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m   )\n\u001b[0;32m--> 176\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    101\u001b[0m     ):\n\u001b[1;32m    102\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/Cleaned_Preprocessed_CKD.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "EGc_pXMFYXJv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "2ea90925-5c4a-42f1-ac55-f9c63cf2edc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  age  blood_pressure  specific_gravity  albumin  sugar  \\\n",
              "0           0   48              80             1.020        1      0   \n",
              "1           1    7              50             1.020        4      0   \n",
              "2           2   62              80             1.010        2      3   \n",
              "3           3   48              70             1.005        4      0   \n",
              "4           4   51              80             1.010        2      0   \n",
              "\n",
              "   red_blood_cells  pus_cell  pus_cell_clumps  bacteria  ...  \\\n",
              "0                1         1                0         0  ...   \n",
              "1                1         1                0         0  ...   \n",
              "2                1         1                0         0  ...   \n",
              "3                1         0                1         0  ...   \n",
              "4                1         1                0         0  ...   \n",
              "\n",
              "   packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n",
              "0                  44                    7800                   5.2   \n",
              "1                  38                    6000                   5.3   \n",
              "2                  31                    7500                   5.7   \n",
              "3                  32                    6700                   3.9   \n",
              "4                  35                    7300                   4.6   \n",
              "\n",
              "   hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n",
              "0             1                  1                        0         0   \n",
              "1             0                  0                        0         0   \n",
              "2             0                  1                        0         1   \n",
              "3             1                  0                        0         1   \n",
              "4             0                  0                        0         0   \n",
              "\n",
              "   peda_edema  aanemia  class  \n",
              "0           0        0      0  \n",
              "1           0        0      0  \n",
              "2           0        1      0  \n",
              "3           1        1      0  \n",
              "4           0        0      0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a9ccb65-4137-40e5-886a-c1626889dd89\" 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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>age</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>specific_gravity</th>\n",
              "      <th>albumin</th>\n",
              "      <th>sugar</th>\n",
              "      <th>red_blood_cells</th>\n",
              "      <th>pus_cell</th>\n",
              "      <th>pus_cell_clumps</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>packed_cell_volume</th>\n",
              "      <th>white_blood_cell_count</th>\n",
              "      <th>red_blood_cell_count</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>coronary_artery_disease</th>\n",
              "      <th>appetite</th>\n",
              "      <th>peda_edema</th>\n",
              "      <th>aanemia</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>80</td>\n",
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              "      <td>5.2</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>5.3</td>\n",
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              "      <td>1.010</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>5.7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48</td>\n",
              "      <td>70</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51</td>\n",
              "      <td>80</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a9ccb65-4137-40e5-886a-c1626889dd89')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4a9ccb65-4137-40e5-886a-c1626889dd89 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-4a9ccb65-4137-40e5-886a-c1626889dd89');\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",
              "\n",
              "\n",
              "<div id=\"df-74545169-4fa2-42f3-b7d2-9ceb93346b13\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74545169-4fa2-42f3-b7d2-9ceb93346b13')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74545169-4fa2-42f3-b7d2-9ceb93346b13 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gofOQpuTpWn5",
        "outputId": "6a959dd3-5aed-453b-c35b-ca15eb4bbc1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns.values.tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXpejYe9ksdB",
        "outputId": "5523e39f-3b2e-4bb2-ee92-b2a89c2c3869"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Unnamed: 0',\n",
              " 'age',\n",
              " 'blood_pressure',\n",
              " 'specific_gravity',\n",
              " 'albumin',\n",
              " 'sugar',\n",
              " 'red_blood_cells',\n",
              " 'pus_cell',\n",
              " 'pus_cell_clumps',\n",
              " 'bacteria',\n",
              " 'blood_glucose_random',\n",
              " 'blood_urea',\n",
              " 'serum_creatinine',\n",
              " 'sodium',\n",
              " 'potassium',\n",
              " 'haemoglobin',\n",
              " 'packed_cell_volume',\n",
              " 'white_blood_cell_count',\n",
              " 'red_blood_cell_count',\n",
              " 'hypertension',\n",
              " 'diabetes_mellitus',\n",
              " 'coronary_artery_disease',\n",
              " 'appetite',\n",
              " 'peda_edema',\n",
              " 'aanemia',\n",
              " 'class']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data= df.interpolate();\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mtJXSW7kvRZ",
        "outputId": "33827fed-0541-41e4-8f77-3d997e5a63ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Unnamed: 0  age  blood_pressure  specific_gravity  albumin  sugar  \\\n",
            "0             0   48              80             1.020        1      0   \n",
            "1             1    7              50             1.020        4      0   \n",
            "2             2   62              80             1.010        2      3   \n",
            "3             3   48              70             1.005        4      0   \n",
            "4             4   51              80             1.010        2      0   \n",
            "..          ...  ...             ...               ...      ...    ...   \n",
            "395         395   55              80             1.020        0      0   \n",
            "396         396   42              70             1.025        0      0   \n",
            "397         397   12              80             1.020        0      0   \n",
            "398         398   17              60             1.025        0      0   \n",
            "399         399   58              80             1.025        0      0   \n",
            "\n",
            "     red_blood_cells  pus_cell  pus_cell_clumps  bacteria  ...  \\\n",
            "0                  1         1                0         0  ...   \n",
            "1                  1         1                0         0  ...   \n",
            "2                  1         1                0         0  ...   \n",
            "3                  1         0                1         0  ...   \n",
            "4                  1         1                0         0  ...   \n",
            "..               ...       ...              ...       ...  ...   \n",
            "395                1         1                0         0  ...   \n",
            "396                1         1                0         0  ...   \n",
            "397                1         1                0         0  ...   \n",
            "398                1         1                0         0  ...   \n",
            "399                1         1                0         0  ...   \n",
            "\n",
            "     packed_cell_volume  white_blood_cell_count  red_blood_cell_count  \\\n",
            "0                    44                    7800                   5.2   \n",
            "1                    38                    6000                   5.3   \n",
            "2                    31                    7500                   5.7   \n",
            "3                    32                    6700                   3.9   \n",
            "4                    35                    7300                   4.6   \n",
            "..                  ...                     ...                   ...   \n",
            "395                  47                    6700                   4.9   \n",
            "396                  54                    7800                   6.2   \n",
            "397                  49                    6600                   5.4   \n",
            "398                  51                    7200                   5.9   \n",
            "399                  53                    6800                   6.1   \n",
            "\n",
            "     hypertension  diabetes_mellitus  coronary_artery_disease  appetite  \\\n",
            "0               1                  1                        0         0   \n",
            "1               0                  0                        0         0   \n",
            "2               0                  1                        0         1   \n",
            "3               1                  0                        0         1   \n",
            "4               0                  0                        0         0   \n",
            "..            ...                ...                      ...       ...   \n",
            "395             0                  0                        0         0   \n",
            "396             0                  0                        0         0   \n",
            "397             0                  0                        0         0   \n",
            "398             0                  0                        0         0   \n",
            "399             0                  0                        0         0   \n",
            "\n",
            "     peda_edema  aanemia  class  \n",
            "0             0        0      0  \n",
            "1             0        0      0  \n",
            "2             0        1      0  \n",
            "3             1        1      0  \n",
            "4             0        0      0  \n",
            "..          ...      ...    ...  \n",
            "395           0        0      1  \n",
            "396           0        0      1  \n",
            "397           0        0      1  \n",
            "398           0        0      1  \n",
            "399           0        0      1  \n",
            "\n",
            "[400 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install markupsafe==2.0.1"
      ],
      "metadata": {
        "id": "5fL4EB3YV5Wq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Convert the 'Timestamp' column to a numeric datetime representation\n",
        "data['Timestamp'] = pd.to_datetime(data['Timestamp'], format='%Y-%m-%d %H:%M:%S.%f%z')  # Include milliseconds in the format\n",
        "data['Timestamp'] = data['Timestamp'].astype(np.int64) // 10**9  # Convert to Unix timestamp (seconds)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "F4cJUhDIeJTS",
        "outputId": "11706ac2-0445-4200-fc11-e9ab7d604be5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "time data \"2022-10-26 08:17:21\" doesn't match format \"%Y-%m-%d %H:%M:%S.%f%z\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-838156a1cc39>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Convert the 'Timestamp' column to a numeric datetime representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Timestamp'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Timestamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%Y-%m-%d %H:%M:%S.%f%z'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Include milliseconds in the format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Timestamp'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Timestamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m9\u001b[0m  \u001b[0;31m# Convert to Unix timestamp (seconds)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1049\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1050\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1051\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1052\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"mixed\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_array_strptime_with_fallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     result, tz_parsed = objects_to_datetime64ns(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m    482\u001b[0m     \u001b[0mCall\u001b[0m \u001b[0marray_strptime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mfallback\u001b[0m \u001b[0mbehavior\u001b[0m \u001b[0mdepending\u001b[0m \u001b[0mon\u001b[0m \u001b[0;34m'errors'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m     \"\"\"\n\u001b[0;32m--> 484\u001b[0;31m     \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimezones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_strptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mutc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    485\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtz\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtz\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimezones\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_return_parsed_timezone_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimezones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/tslibs/strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/tslibs/strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: time data \"2022-10-26 08:17:21\" doesn't match format \"%Y-%m-%d %H:%M:%S.%f%z\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dBMsQXfTi-U6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "s = setup(data, target = 'class')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "dE5al3BiczQN",
        "outputId": "ddfdaa1a-6158-41bf-d161-47747b9737b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x783aa8640070>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_cb5f3_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_cb5f3\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_cb5f3_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_cb5f3_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_cb5f3_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_cb5f3_row0_col1\" class=\"data row0 col1\" >5861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_cb5f3_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_cb5f3_row1_col1\" class=\"data row1 col1\" >class</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_cb5f3_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_cb5f3_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_cb5f3_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_cb5f3_row3_col1\" class=\"data row3 col1\" >(400, 26)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_cb5f3_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_cb5f3_row4_col1\" class=\"data row4 col1\" >(400, 26)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_cb5f3_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_cb5f3_row5_col1\" class=\"data row5 col1\" >(280, 26)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_cb5f3_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_cb5f3_row6_col1\" class=\"data row6 col1\" >(120, 26)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_cb5f3_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_cb5f3_row7_col1\" class=\"data row7 col1\" >25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_cb5f3_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_cb5f3_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_cb5f3_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_cb5f3_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_cb5f3_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_cb5f3_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_cb5f3_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_cb5f3_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_cb5f3_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_cb5f3_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_cb5f3_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_cb5f3_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_cb5f3_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_cb5f3_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_cb5f3_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_cb5f3_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_cb5f3_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_cb5f3_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_cb5f3_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_cb5f3_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_cb5f3_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_cb5f3_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_cb5f3_row18_col1\" class=\"data row18 col1\" >cd16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load your data (adjust the file path as necessary)\n",
        "df = pd.read_csv('/content/cleaned_data.csv')\n",
        "\n",
        "# Assuming the timestamp column is named 'timestamp' and the truncated timestamp is 'truncated_timestamp'\n",
        "# If not, replace 'timestamp' with your column name\n",
        "df['truncated_timestamp'] = pd.to_datetime(df['Timestamp'])\n",
        "\n",
        "# Now set the datetime column as the index\n",
        "df.set_index('truncated_timestamp', inplace=True)\n",
        "\n",
        "# Convert any numerical columns to numeric types if necessary\n",
        "for col in df.columns:\n",
        "    df[col] = pd.to_numeric(df[col], errors='coerce')"
      ],
      "metadata": {
        "id": "KopdvYmHh_vR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yv4h2dCWiQ9R",
        "outputId": "4281a4c5-8bbd-46dd-cba5-c1f217c32aa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num                       int64\n",
            "Timestamp               float64\n",
            "Current_J0              float64\n",
            "Temperature_T0          float64\n",
            "Current_J1              float64\n",
            "Temperature_J1          float64\n",
            "Current_J2              float64\n",
            "Temperature_J2          float64\n",
            "Current_J3              float64\n",
            "Temperature_J3          float64\n",
            "Current_J4              float64\n",
            "Temperature_J4          float64\n",
            "Current_J5              float64\n",
            "Temperature_J5          float64\n",
            "Speed_J0                float64\n",
            "Speed_J1                float64\n",
            "Speed_J2                float64\n",
            "Speed_J3                float64\n",
            "Speed_J4                float64\n",
            "Speed_J5                float64\n",
            "Tool_current            float64\n",
            "cycle                     int64\n",
            "Robot_ProtectiveStop      int64\n",
            "grip_lost                  bool\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "WJulkrbbiXXM",
        "outputId": "61563c5e-07cf-483b-867b-0b388146a133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Num  Current_J0  Temperature_T0  Current_J1  \\\n",
              "Timestamp                                                          \n",
              "2022-10-26 08:17:21    1    0.109628          27.875   -2.024669   \n",
              "2022-10-26 08:17:22    2    0.595605          27.875   -2.278456   \n",
              "2022-10-26 08:17:23    3   -0.229474          27.875   -2.800408   \n",
              "2022-10-26 08:17:24    4    0.065053          27.875   -3.687768   \n",
              "2022-10-26 08:17:25    5    0.884140          27.875   -2.938830   \n",
              "\n",
              "                     Temperature_J1  Current_J2  Temperature_J2  Current_J3  \\\n",
              "Timestamp                                                                     \n",
              "2022-10-26 08:17:21         29.3750   -1.531442         29.3750   -0.998570   \n",
              "2022-10-26 08:17:22         29.3125   -0.866556         29.4375   -0.206097   \n",
              "2022-10-26 08:17:23         29.3125   -2.304336         29.4375   -0.351499   \n",
              "2022-10-26 08:17:24         29.3125   -1.217652         29.4375   -1.209115   \n",
              "2022-10-26 08:17:25         29.3750   -1.794076         29.4375   -2.356471   \n",
              "\n",
              "                     Temperature_J3  Current_J4  ...      Speed_J0  Speed_J1  \\\n",
              "Timestamp                                        ...                           \n",
              "2022-10-26 08:17:21         32.1250   -0.062540  ...  2.955651e-01 -0.000490   \n",
              "2022-10-26 08:17:22         32.1875   -1.062762  ... -7.390000e-30 -0.000304   \n",
              "2022-10-26 08:17:23         32.1250   -0.668869  ...  1.369386e-01  0.007795   \n",
              "2022-10-26 08:17:24         32.1250   -0.819755  ... -9.030032e-02 -0.004911   \n",
              "2022-10-26 08:17:25         32.1875   -0.966427  ...  1.268088e-01  0.005567   \n",
              "\n",
              "                     Speed_J2  Speed_J3  Speed_J4  Speed_J5  Tool_current  \\\n",
              "Timestamp                                                                   \n",
              "2022-10-26 08:17:21  0.001310 -0.132836 -0.007479 -0.152962      0.082732   \n",
              "2022-10-26 08:17:22  0.002185  0.001668 -0.000767  0.000417      0.505895   \n",
              "2022-10-26 08:17:23 -2.535874  0.379867  0.000455 -0.496856      0.079420   \n",
              "2022-10-26 08:17:24 -0.009096 -0.384196  0.018411  0.425559      0.083325   \n",
              "2022-10-26 08:17:25  0.001138 -0.353284  0.014994  0.180989      0.086379   \n",
              "\n",
              "                     cycle   Robot_ProtectiveStop  grip_lost  \n",
              "Timestamp                                                     \n",
              "2022-10-26 08:17:21       1                     0      False  \n",
              "2022-10-26 08:17:22       1                     0      False  \n",
              "2022-10-26 08:17:23       1                     0      False  \n",
              "2022-10-26 08:17:24       1                     0      False  \n",
              "2022-10-26 08:17:25       1                     0      False  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
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              "\n",
              "  <div id=\"df-46e9a99d-6562-4345-be06-6a54b49d1383\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Num</th>\n",
              "      <th>Current_J0</th>\n",
              "      <th>Temperature_T0</th>\n",
              "      <th>Current_J1</th>\n",
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              "      <th>Temperature_J3</th>\n",
              "      <th>Current_J4</th>\n",
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              "      <th>Speed_J1</th>\n",
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              "      <th>cycle</th>\n",
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              "      <th>Timestamp</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-10-26 08:17:21</th>\n",
              "      <td>1</td>\n",
              "      <td>0.109628</td>\n",
              "      <td>27.875</td>\n",
              "      <td>-2.024669</td>\n",
              "      <td>29.3750</td>\n",
              "      <td>-1.531442</td>\n",
              "      <td>29.3750</td>\n",
              "      <td>-0.998570</td>\n",
              "      <td>32.1250</td>\n",
              "      <td>-0.062540</td>\n",
              "      <td>...</td>\n",
              "      <td>2.955651e-01</td>\n",
              "      <td>-0.000490</td>\n",
              "      <td>0.001310</td>\n",
              "      <td>-0.132836</td>\n",
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              "      <td>-0.152962</td>\n",
              "      <td>0.082732</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26 08:17:22</th>\n",
              "      <td>2</td>\n",
              "      <td>0.595605</td>\n",
              "      <td>27.875</td>\n",
              "      <td>-2.278456</td>\n",
              "      <td>29.3125</td>\n",
              "      <td>-0.866556</td>\n",
              "      <td>29.4375</td>\n",
              "      <td>-0.206097</td>\n",
              "      <td>32.1875</td>\n",
              "      <td>-1.062762</td>\n",
              "      <td>...</td>\n",
              "      <td>-7.390000e-30</td>\n",
              "      <td>-0.000304</td>\n",
              "      <td>0.002185</td>\n",
              "      <td>0.001668</td>\n",
              "      <td>-0.000767</td>\n",
              "      <td>0.000417</td>\n",
              "      <td>0.505895</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-26 08:17:23</th>\n",
              "      <td>3</td>\n",
              "      <td>-0.229474</td>\n",
              "      <td>27.875</td>\n",
              "      <td>-2.800408</td>\n",
              "      <td>29.3125</td>\n",
              "      <td>-2.304336</td>\n",
              "      <td>29.4375</td>\n",
              "      <td>-0.351499</td>\n",
              "      <td>32.1250</td>\n",
              "      <td>-0.668869</td>\n",
              "      <td>...</td>\n",
              "      <td>1.369386e-01</td>\n",
              "      <td>0.007795</td>\n",
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              "      <td>0.379867</td>\n",
              "      <td>0.000455</td>\n",
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              "      <td>0.079420</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-26 08:17:24</th>\n",
              "      <td>4</td>\n",
              "      <td>0.065053</td>\n",
              "      <td>27.875</td>\n",
              "      <td>-3.687768</td>\n",
              "      <td>29.3125</td>\n",
              "      <td>-1.217652</td>\n",
              "      <td>29.4375</td>\n",
              "      <td>-1.209115</td>\n",
              "      <td>32.1250</td>\n",
              "      <td>-0.819755</td>\n",
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              "      <td>-9.030032e-02</td>\n",
              "      <td>-0.004911</td>\n",
              "      <td>-0.009096</td>\n",
              "      <td>-0.384196</td>\n",
              "      <td>0.018411</td>\n",
              "      <td>0.425559</td>\n",
              "      <td>0.083325</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-26 08:17:25</th>\n",
              "      <td>5</td>\n",
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              "      <td>32.1875</td>\n",
              "      <td>-0.966427</td>\n",
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              "      <td>0.005567</td>\n",
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              "      <td>-0.353284</td>\n",
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              "      <td>0.086379</td>\n",
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              "      <td>0</td>\n",
              "      <td>False</td>\n",
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              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-96528640-c48e-478e-8bc0-d4cfaa68e570 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models()"
      ],
      "metadata": {
        "id": "HJheh1sjc149",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519,
          "referenced_widgets": [
            "686c20f4833b4d1592e87331983f21ec",
            "c6d19e82dff442d28718e1f7161d6b26",
            "2d14ed6ab1fd46cc88d0a599286dac33",
            "b900e6ef07e8439b9ea6d2b94cc8aa0b",
            "b9f01f496e124c0d9825a40017051da1",
            "1bcb45c5aa4c4b4cb9d0ca5632fe8dad",
            "7b796e332f1f458c8a9f9cccf25a3ac7",
            "4e9c882b5dd245169b18a83f63347827",
            "56bb2ae153f043109134da0c1a8ecf36",
            "603faae4560a42fa8370fdacce257986",
            "c502a8189cf146c3a9c98471a64bab96"
          ]
        },
        "outputId": "1c26a256-f4a2-4371-f8d7-15259c90ea98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x783aa86ffe50>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b6bd8 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_b6bd8_row0_col0, #T_b6bd8_row1_col0, #T_b6bd8_row2_col0, #T_b6bd8_row3_col0, #T_b6bd8_row4_col0, #T_b6bd8_row4_col1, #T_b6bd8_row4_col2, #T_b6bd8_row4_col3, #T_b6bd8_row4_col5, #T_b6bd8_row4_col6, #T_b6bd8_row4_col7, #T_b6bd8_row5_col0, #T_b6bd8_row5_col1, #T_b6bd8_row5_col3, #T_b6bd8_row5_col5, #T_b6bd8_row5_col6, #T_b6bd8_row5_col7, #T_b6bd8_row6_col0, #T_b6bd8_row6_col1, #T_b6bd8_row6_col3, #T_b6bd8_row6_col5, #T_b6bd8_row6_col6, #T_b6bd8_row6_col7, #T_b6bd8_row7_col0, #T_b6bd8_row7_col1, #T_b6bd8_row7_col2, #T_b6bd8_row7_col3, #T_b6bd8_row7_col4, #T_b6bd8_row7_col5, #T_b6bd8_row7_col6, #T_b6bd8_row7_col7, #T_b6bd8_row8_col0, #T_b6bd8_row8_col1, #T_b6bd8_row8_col2, #T_b6bd8_row8_col3, #T_b6bd8_row8_col4, #T_b6bd8_row8_col5, #T_b6bd8_row8_col6, #T_b6bd8_row8_col7, #T_b6bd8_row9_col0, #T_b6bd8_row9_col1, #T_b6bd8_row9_col2, #T_b6bd8_row9_col3, #T_b6bd8_row9_col4, #T_b6bd8_row9_col5, #T_b6bd8_row9_col6, #T_b6bd8_row9_col7, #T_b6bd8_row10_col0, #T_b6bd8_row10_col1, #T_b6bd8_row10_col2, #T_b6bd8_row10_col3, #T_b6bd8_row10_col4, #T_b6bd8_row10_col5, #T_b6bd8_row10_col6, #T_b6bd8_row10_col7, #T_b6bd8_row11_col0, #T_b6bd8_row11_col1, #T_b6bd8_row11_col2, #T_b6bd8_row11_col3, #T_b6bd8_row11_col4, #T_b6bd8_row11_col5, #T_b6bd8_row11_col6, #T_b6bd8_row11_col7, #T_b6bd8_row12_col0, #T_b6bd8_row12_col1, #T_b6bd8_row12_col2, #T_b6bd8_row12_col3, #T_b6bd8_row12_col4, #T_b6bd8_row12_col5, #T_b6bd8_row12_col6, #T_b6bd8_row12_col7, #T_b6bd8_row13_col0, #T_b6bd8_row13_col1, #T_b6bd8_row13_col2, #T_b6bd8_row13_col3, #T_b6bd8_row13_col4, #T_b6bd8_row13_col5, #T_b6bd8_row13_col6, #T_b6bd8_row13_col7, #T_b6bd8_row14_col0, #T_b6bd8_row14_col1, #T_b6bd8_row14_col2, #T_b6bd8_row14_col4, #T_b6bd8_row14_col5, #T_b6bd8_row14_col6, #T_b6bd8_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_b6bd8_row0_col1, #T_b6bd8_row0_col2, #T_b6bd8_row0_col3, #T_b6bd8_row0_col4, #T_b6bd8_row0_col5, #T_b6bd8_row0_col6, #T_b6bd8_row0_col7, #T_b6bd8_row1_col1, #T_b6bd8_row1_col2, #T_b6bd8_row1_col3, #T_b6bd8_row1_col4, #T_b6bd8_row1_col5, #T_b6bd8_row1_col6, #T_b6bd8_row1_col7, #T_b6bd8_row2_col1, #T_b6bd8_row2_col2, #T_b6bd8_row2_col3, #T_b6bd8_row2_col4, #T_b6bd8_row2_col5, #T_b6bd8_row2_col6, #T_b6bd8_row2_col7, #T_b6bd8_row3_col1, #T_b6bd8_row3_col2, #T_b6bd8_row3_col3, #T_b6bd8_row3_col4, #T_b6bd8_row3_col5, #T_b6bd8_row3_col6, #T_b6bd8_row3_col7, #T_b6bd8_row4_col4, #T_b6bd8_row5_col2, #T_b6bd8_row5_col4, #T_b6bd8_row6_col2, #T_b6bd8_row6_col4, #T_b6bd8_row14_col3 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_b6bd8_row0_col8, #T_b6bd8_row1_col8, #T_b6bd8_row2_col8, #T_b6bd8_row3_col8, #T_b6bd8_row4_col8, #T_b6bd8_row5_col8, #T_b6bd8_row6_col8, #T_b6bd8_row7_col8, #T_b6bd8_row8_col8, #T_b6bd8_row9_col8, #T_b6bd8_row10_col8, #T_b6bd8_row11_col8, #T_b6bd8_row13_col8, #T_b6bd8_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_b6bd8_row12_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b6bd8\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b6bd8_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_b6bd8_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_b6bd8_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_b6bd8_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_b6bd8_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_b6bd8_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_b6bd8_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_b6bd8_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_b6bd8_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row0\" class=\"row_heading level0 row0\" >dt</th>\n",
              "      <td id=\"T_b6bd8_row0_col0\" class=\"data row0 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_b6bd8_row0_col1\" class=\"data row0 col1\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col2\" class=\"data row0 col2\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col6\" class=\"data row0 col6\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col7\" class=\"data row0 col7\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row0_col8\" class=\"data row0 col8\" >0.0410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
              "      <td id=\"T_b6bd8_row1_col0\" class=\"data row1 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_b6bd8_row1_col1\" class=\"data row1 col1\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col7\" class=\"data row1 col7\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row1_col8\" class=\"data row1 col8\" >0.3700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row2\" class=\"row_heading level0 row2\" >ada</th>\n",
              "      <td id=\"T_b6bd8_row2_col0\" class=\"data row2 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_b6bd8_row2_col1\" class=\"data row2 col1\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col2\" class=\"data row2 col2\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col3\" class=\"data row2 col3\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col4\" class=\"data row2 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col5\" class=\"data row2 col5\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col6\" class=\"data row2 col6\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col7\" class=\"data row2 col7\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row2_col8\" class=\"data row2 col8\" >0.0440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row3\" class=\"row_heading level0 row3\" >gbc</th>\n",
              "      <td id=\"T_b6bd8_row3_col0\" class=\"data row3 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_b6bd8_row3_col1\" class=\"data row3 col1\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col2\" class=\"data row3 col2\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col3\" class=\"data row3 col3\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col4\" class=\"data row3 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col5\" class=\"data row3 col5\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col6\" class=\"data row3 col6\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col7\" class=\"data row3 col7\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row3_col8\" class=\"data row3 col8\" >0.1240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
              "      <td id=\"T_b6bd8_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_b6bd8_row4_col1\" class=\"data row4 col1\" >0.9964</td>\n",
              "      <td id=\"T_b6bd8_row4_col2\" class=\"data row4 col2\" >0.9950</td>\n",
              "      <td id=\"T_b6bd8_row4_col3\" class=\"data row4 col3\" >0.9900</td>\n",
              "      <td id=\"T_b6bd8_row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row4_col5\" class=\"data row4 col5\" >0.9947</td>\n",
              "      <td id=\"T_b6bd8_row4_col6\" class=\"data row4 col6\" >0.9920</td>\n",
              "      <td id=\"T_b6bd8_row4_col7\" class=\"data row4 col7\" >0.9923</td>\n",
              "      <td id=\"T_b6bd8_row4_col8\" class=\"data row4 col8\" >0.0910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row5\" class=\"row_heading level0 row5\" >lightgbm</th>\n",
              "      <td id=\"T_b6bd8_row5_col0\" class=\"data row5 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_b6bd8_row5_col1\" class=\"data row5 col1\" >0.9964</td>\n",
              "      <td id=\"T_b6bd8_row5_col2\" class=\"data row5 col2\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row5_col3\" class=\"data row5 col3\" >0.9909</td>\n",
              "      <td id=\"T_b6bd8_row5_col4\" class=\"data row5 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row5_col5\" class=\"data row5 col5\" >0.9952</td>\n",
              "      <td id=\"T_b6bd8_row5_col6\" class=\"data row5 col6\" >0.9924</td>\n",
              "      <td id=\"T_b6bd8_row5_col7\" class=\"data row5 col7\" >0.9927</td>\n",
              "      <td id=\"T_b6bd8_row5_col8\" class=\"data row5 col8\" >0.1710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row6\" class=\"row_heading level0 row6\" >et</th>\n",
              "      <td id=\"T_b6bd8_row6_col0\" class=\"data row6 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_b6bd8_row6_col1\" class=\"data row6 col1\" >0.9929</td>\n",
              "      <td id=\"T_b6bd8_row6_col2\" class=\"data row6 col2\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row6_col3\" class=\"data row6 col3\" >0.9818</td>\n",
              "      <td id=\"T_b6bd8_row6_col4\" class=\"data row6 col4\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row6_col5\" class=\"data row6 col5\" >0.9905</td>\n",
              "      <td id=\"T_b6bd8_row6_col6\" class=\"data row6 col6\" >0.9848</td>\n",
              "      <td id=\"T_b6bd8_row6_col7\" class=\"data row6 col7\" >0.9853</td>\n",
              "      <td id=\"T_b6bd8_row6_col8\" class=\"data row6 col8\" >0.1680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row7\" class=\"row_heading level0 row7\" >lr</th>\n",
              "      <td id=\"T_b6bd8_row7_col0\" class=\"data row7 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_b6bd8_row7_col1\" class=\"data row7 col1\" >0.9821</td>\n",
              "      <td id=\"T_b6bd8_row7_col2\" class=\"data row7 col2\" >0.9979</td>\n",
              "      <td id=\"T_b6bd8_row7_col3\" class=\"data row7 col3\" >0.9818</td>\n",
              "      <td id=\"T_b6bd8_row7_col4\" class=\"data row7 col4\" >0.9755</td>\n",
              "      <td id=\"T_b6bd8_row7_col5\" class=\"data row7 col5\" >0.9778</td>\n",
              "      <td id=\"T_b6bd8_row7_col6\" class=\"data row7 col6\" >0.9629</td>\n",
              "      <td id=\"T_b6bd8_row7_col7\" class=\"data row7 col7\" >0.9641</td>\n",
              "      <td id=\"T_b6bd8_row7_col8\" class=\"data row7 col8\" >0.6180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row8\" class=\"row_heading level0 row8\" >lda</th>\n",
              "      <td id=\"T_b6bd8_row8_col0\" class=\"data row8 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_b6bd8_row8_col1\" class=\"data row8 col1\" >0.9821</td>\n",
              "      <td id=\"T_b6bd8_row8_col2\" class=\"data row8 col2\" >0.9989</td>\n",
              "      <td id=\"T_b6bd8_row8_col3\" class=\"data row8 col3\" >0.9818</td>\n",
              "      <td id=\"T_b6bd8_row8_col4\" class=\"data row8 col4\" >0.9735</td>\n",
              "      <td id=\"T_b6bd8_row8_col5\" class=\"data row8 col5\" >0.9770</td>\n",
              "      <td id=\"T_b6bd8_row8_col6\" class=\"data row8 col6\" >0.9624</td>\n",
              "      <td id=\"T_b6bd8_row8_col7\" class=\"data row8 col7\" >0.9632</td>\n",
              "      <td id=\"T_b6bd8_row8_col8\" class=\"data row8 col8\" >0.0410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row9\" class=\"row_heading level0 row9\" >ridge</th>\n",
              "      <td id=\"T_b6bd8_row9_col0\" class=\"data row9 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_b6bd8_row9_col1\" class=\"data row9 col1\" >0.9714</td>\n",
              "      <td id=\"T_b6bd8_row9_col2\" class=\"data row9 col2\" >0.9995</td>\n",
              "      <td id=\"T_b6bd8_row9_col3\" class=\"data row9 col3\" >0.9909</td>\n",
              "      <td id=\"T_b6bd8_row9_col4\" class=\"data row9 col4\" >0.9435</td>\n",
              "      <td id=\"T_b6bd8_row9_col5\" class=\"data row9 col5\" >0.9647</td>\n",
              "      <td id=\"T_b6bd8_row9_col6\" class=\"data row9 col6\" >0.9409</td>\n",
              "      <td id=\"T_b6bd8_row9_col7\" class=\"data row9 col7\" >0.9442</td>\n",
              "      <td id=\"T_b6bd8_row9_col8\" class=\"data row9 col8\" >0.0580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row10\" class=\"row_heading level0 row10\" >nb</th>\n",
              "      <td id=\"T_b6bd8_row10_col0\" class=\"data row10 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_b6bd8_row10_col1\" class=\"data row10 col1\" >0.9429</td>\n",
              "      <td id=\"T_b6bd8_row10_col2\" class=\"data row10 col2\" >0.9788</td>\n",
              "      <td id=\"T_b6bd8_row10_col3\" class=\"data row10 col3\" >0.9536</td>\n",
              "      <td id=\"T_b6bd8_row10_col4\" class=\"data row10 col4\" >0.9048</td>\n",
              "      <td id=\"T_b6bd8_row10_col5\" class=\"data row10 col5\" >0.9263</td>\n",
              "      <td id=\"T_b6bd8_row10_col6\" class=\"data row10 col6\" >0.8799</td>\n",
              "      <td id=\"T_b6bd8_row10_col7\" class=\"data row10 col7\" >0.8836</td>\n",
              "      <td id=\"T_b6bd8_row10_col8\" class=\"data row10 col8\" >0.0380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
              "      <td id=\"T_b6bd8_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_b6bd8_row11_col1\" class=\"data row11 col1\" >0.8786</td>\n",
              "      <td id=\"T_b6bd8_row11_col2\" class=\"data row11 col2\" >0.9500</td>\n",
              "      <td id=\"T_b6bd8_row11_col3\" class=\"data row11 col3\" >0.8755</td>\n",
              "      <td id=\"T_b6bd8_row11_col4\" class=\"data row11 col4\" >0.8306</td>\n",
              "      <td id=\"T_b6bd8_row11_col5\" class=\"data row11 col5\" >0.8460</td>\n",
              "      <td id=\"T_b6bd8_row11_col6\" class=\"data row11 col6\" >0.7462</td>\n",
              "      <td id=\"T_b6bd8_row11_col7\" class=\"data row11 col7\" >0.7554</td>\n",
              "      <td id=\"T_b6bd8_row11_col8\" class=\"data row11 col8\" >0.0470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row12\" class=\"row_heading level0 row12\" >dummy</th>\n",
              "      <td id=\"T_b6bd8_row12_col0\" class=\"data row12 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_b6bd8_row12_col1\" class=\"data row12 col1\" >0.6250</td>\n",
              "      <td id=\"T_b6bd8_row12_col2\" class=\"data row12 col2\" >0.5000</td>\n",
              "      <td id=\"T_b6bd8_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row12_col4\" class=\"data row12 col4\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row12_col6\" class=\"data row12 col6\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row12_col7\" class=\"data row12 col7\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row12_col8\" class=\"data row12 col8\" >0.0360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row13\" class=\"row_heading level0 row13\" >svm</th>\n",
              "      <td id=\"T_b6bd8_row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_b6bd8_row13_col1\" class=\"data row13 col1\" >0.6107</td>\n",
              "      <td id=\"T_b6bd8_row13_col2\" class=\"data row13 col2\" >0.9775</td>\n",
              "      <td id=\"T_b6bd8_row13_col3\" class=\"data row13 col3\" >0.6973</td>\n",
              "      <td id=\"T_b6bd8_row13_col4\" class=\"data row13 col4\" >0.5624</td>\n",
              "      <td id=\"T_b6bd8_row13_col5\" class=\"data row13 col5\" >0.5254</td>\n",
              "      <td id=\"T_b6bd8_row13_col6\" class=\"data row13 col6\" >0.2616</td>\n",
              "      <td id=\"T_b6bd8_row13_col7\" class=\"data row13 col7\" >0.3040</td>\n",
              "      <td id=\"T_b6bd8_row13_col8\" class=\"data row13 col8\" >0.0380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b6bd8_level0_row14\" class=\"row_heading level0 row14\" >qda</th>\n",
              "      <td id=\"T_b6bd8_row14_col0\" class=\"data row14 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_b6bd8_row14_col1\" class=\"data row14 col1\" >0.3750</td>\n",
              "      <td id=\"T_b6bd8_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row14_col3\" class=\"data row14 col3\" >1.0000</td>\n",
              "      <td id=\"T_b6bd8_row14_col4\" class=\"data row14 col4\" >0.3750</td>\n",
              "      <td id=\"T_b6bd8_row14_col5\" class=\"data row14 col5\" >0.5452</td>\n",
              "      <td id=\"T_b6bd8_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_b6bd8_row14_col8\" class=\"data row14 col8\" >0.0680</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "686c20f4833b4d1592e87331983f21ec"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(best)"
      ],
      "metadata": {
        "id": "XBs0WuCBc6Nn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "48a74446-49de-4c2e-9248-65e880c75e78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Estimator [] does not have the required fit() method.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-d26e9b1c0d3c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pycaret/utils/generic.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    962\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mglobals_d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pycaret/regression/functional.py\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, verbose, display_format)\u001b[0m\n\u001b[1;32m   1644\u001b[0m     \"\"\"\n\u001b[1;32m   1645\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1646\u001b[0;31m     return _CURRENT_EXPERIMENT.plot_model(\n\u001b[0m\u001b[1;32m   1647\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m         \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pycaret/regression/oop.py\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(self, estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, verbose, display_format)\u001b[0m\n\u001b[1;32m   1944\u001b[0m         \"\"\"\n\u001b[1;32m   1945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1946\u001b[0;31m         return super().plot_model(\n\u001b[0m\u001b[1;32m   1947\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1948\u001b[0m             \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pycaret/internal/pycaret_experiment/tabular_experiment.py\u001b[0m in \u001b[0;36mplot_model\u001b[0;34m(self, estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, feature_name, label, verbose, display_format)\u001b[0m\n\u001b[1;32m   2043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2044\u001b[0m         \"\"\"\n\u001b[0;32m-> 2045\u001b[0;31m         return self._plot_model(\n\u001b[0m\u001b[1;32m   2046\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2047\u001b[0m             \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pycaret/internal/pycaret_experiment/tabular_experiment.py\u001b[0m in \u001b[0;36m_plot_model\u001b[0;34m(self, estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, feature_name, label, verbose, system, display, display_format)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"fit\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    407\u001b[0m                 \u001b[0;34mf\"Estimator {estimator} does not have the required fit() method.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: Estimator [] does not have the required fit() method."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(best)"
      ],
      "metadata": {
        "id": "NEcL4-LfdEyv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666,
          "referenced_widgets": [
            "53d5ca3e7f244d91b7228aa0458a4cff",
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            "311374342e044687b70577d4e07b72c2",
            "5d354a5d9e8a4904b5675e88435a2918",
            "46a45cef022f427f98bdcfc3b917c07e",
            "3bd0b5d5bd0649b39a09118f828bc485"
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        },
        "outputId": "cfe8e808-50e2-462c-bf24-7a951bfbaf87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53d5ca3e7f244d91b7228aa0458a4cff"
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          "metadata": {
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              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
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    {
      "cell_type": "code",
      "source": [
        "evaluate_model(best)"
      ],
      "metadata": {
        "id": "--EN76dcEaRS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(best)"
      ],
      "metadata": {
        "id": "ZdBqWWJrF5h2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = create_model('et')"
      ],
      "metadata": {
        "id": "NpX15KlyBtPf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "ad212fb394ad481d9407a0ed2d6e9d1d",
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            "61b0e52deb9b492eab02398f54124c82",
            "8d97ff8d1bdd4011a29692875ff0b967",
            "f9f4add9310745ddba56ddcc41231043",
            "25ab99bf7faf410cb40b02ccd432caec",
            "1c758820e14c46c6a1ba6a312cae0dfe",
            "52ba91615c6d4ac99261f67bd5793daf",
            "33ec1d91d6f44183b0e23b998a8000ea",
            "478e010f9bbc4ccda9f9b27623aacd75"
          ]
        },
        "outputId": "b27358c8-b589-4eb9-a511-b3d5d7167cfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x78a83c737f40>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_2ba37_row10_col0, #T_2ba37_row10_col1, #T_2ba37_row10_col2, #T_2ba37_row10_col3, #T_2ba37_row10_col4, #T_2ba37_row10_col5, #T_2ba37_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_2ba37\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_2ba37_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_2ba37_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_2ba37_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_2ba37_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_2ba37_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_2ba37_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_2ba37_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_2ba37_row0_col0\" class=\"data row0 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row0_col1\" class=\"data row0 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row0_col2\" class=\"data row0 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row0_col6\" class=\"data row0 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_2ba37_row1_col0\" class=\"data row1 col0\" >0.9643</td>\n",
              "      <td id=\"T_2ba37_row1_col1\" class=\"data row1 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row1_col2\" class=\"data row1 col2\" >0.9000</td>\n",
              "      <td id=\"T_2ba37_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row1_col4\" class=\"data row1 col4\" >0.9474</td>\n",
              "      <td id=\"T_2ba37_row1_col5\" class=\"data row1 col5\" >0.9205</td>\n",
              "      <td id=\"T_2ba37_row1_col6\" class=\"data row1 col6\" >0.9234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_2ba37_row2_col0\" class=\"data row2 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row2_col1\" class=\"data row2 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row2_col2\" class=\"data row2 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row2_col3\" class=\"data row2 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row2_col4\" class=\"data row2 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row2_col5\" class=\"data row2 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row2_col6\" class=\"data row2 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_2ba37_row3_col0\" class=\"data row3 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row3_col1\" class=\"data row3 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row3_col2\" class=\"data row3 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row3_col3\" class=\"data row3 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row3_col4\" class=\"data row3 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row3_col5\" class=\"data row3 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row3_col6\" class=\"data row3 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_2ba37_row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row4_col1\" class=\"data row4 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_2ba37_row5_col0\" class=\"data row5 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row5_col1\" class=\"data row5 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row5_col2\" class=\"data row5 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row5_col3\" class=\"data row5 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row5_col4\" class=\"data row5 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row5_col5\" class=\"data row5 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row5_col6\" class=\"data row5 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_2ba37_row6_col0\" class=\"data row6 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row6_col1\" class=\"data row6 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row6_col2\" class=\"data row6 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row6_col4\" class=\"data row6 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row6_col5\" class=\"data row6 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row6_col6\" class=\"data row6 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_2ba37_row7_col0\" class=\"data row7 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row7_col1\" class=\"data row7 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row7_col2\" class=\"data row7 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row7_col3\" class=\"data row7 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row7_col4\" class=\"data row7 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row7_col5\" class=\"data row7 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row7_col6\" class=\"data row7 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_2ba37_row8_col0\" class=\"data row8 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row8_col1\" class=\"data row8 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row8_col3\" class=\"data row8 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row8_col4\" class=\"data row8 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row8_col5\" class=\"data row8 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row8_col6\" class=\"data row8 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_2ba37_row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row9_col1\" class=\"data row9 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_2ba37_row10_col0\" class=\"data row10 col0\" >0.9964</td>\n",
              "      <td id=\"T_2ba37_row10_col1\" class=\"data row10 col1\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row10_col2\" class=\"data row10 col2\" >0.9900</td>\n",
              "      <td id=\"T_2ba37_row10_col3\" class=\"data row10 col3\" >1.0000</td>\n",
              "      <td id=\"T_2ba37_row10_col4\" class=\"data row10 col4\" >0.9947</td>\n",
              "      <td id=\"T_2ba37_row10_col5\" class=\"data row10 col5\" >0.9920</td>\n",
              "      <td id=\"T_2ba37_row10_col6\" class=\"data row10 col6\" >0.9923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2ba37_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_2ba37_row11_col0\" class=\"data row11 col0\" >0.0107</td>\n",
              "      <td id=\"T_2ba37_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_2ba37_row11_col2\" class=\"data row11 col2\" >0.0300</td>\n",
              "      <td id=\"T_2ba37_row11_col3\" class=\"data row11 col3\" >0.0000</td>\n",
              "      <td id=\"T_2ba37_row11_col4\" class=\"data row11 col4\" >0.0158</td>\n",
              "      <td id=\"T_2ba37_row11_col5\" class=\"data row11 col5\" >0.0239</td>\n",
              "      <td id=\"T_2ba37_row11_col6\" class=\"data row11 col6\" >0.0230</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad212fb394ad481d9407a0ed2d6e9d1d"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_rf = tune_model(rf)"
      ],
      "metadata": {
        "id": "tuwVrHXmB8ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511,
          "referenced_widgets": [
            "e1d118278f6d49c7965598b3c4ef2cb5",
            "88f95821ad3846a0bf3666a38229d7eb",
            "5eb33c0f4ea143b092185303bc9fe536",
            "4965fa4a357a44a9afacbc8c6b5c5bb5",
            "80042178506c43af907ff1184a596fb9",
            "0cd31fa04c4e44c9be054fca58dba4f2",
            "672da93d46244093a1c89d636791cc98",
            "02e30451f9b04be99230edd9719ea347",
            "bf95eb9d5c43468a82eb21f2cebe76c5",
            "de1c52de23f34b39b81ab1f4e0348c4f",
            "3f30cf511ac24acdadd9c2c0601e9493"
          ]
        },
        "outputId": "5e13a59d-b778-401e-fa8e-ce3b460a5826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x78a83cc128f0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_6f1e6_row10_col0, #T_6f1e6_row10_col1, #T_6f1e6_row10_col2, #T_6f1e6_row10_col3, #T_6f1e6_row10_col4, #T_6f1e6_row10_col5, #T_6f1e6_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_6f1e6\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_6f1e6_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_6f1e6_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_6f1e6_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_6f1e6_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_6f1e6_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_6f1e6_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_6f1e6_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_6f1e6_row0_col0\" class=\"data row0 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row0_col1\" class=\"data row0 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row0_col2\" class=\"data row0 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row0_col6\" class=\"data row0 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_6f1e6_row1_col0\" class=\"data row1 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row1_col1\" class=\"data row1 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_6f1e6_row2_col0\" class=\"data row2 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row2_col1\" class=\"data row2 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row2_col2\" class=\"data row2 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row2_col3\" class=\"data row2 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row2_col4\" class=\"data row2 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row2_col5\" class=\"data row2 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row2_col6\" class=\"data row2 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_6f1e6_row3_col0\" class=\"data row3 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row3_col1\" class=\"data row3 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row3_col2\" class=\"data row3 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row3_col3\" class=\"data row3 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row3_col4\" class=\"data row3 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row3_col5\" class=\"data row3 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row3_col6\" class=\"data row3 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_6f1e6_row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row4_col1\" class=\"data row4 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_6f1e6_row5_col0\" class=\"data row5 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row5_col1\" class=\"data row5 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row5_col2\" class=\"data row5 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row5_col3\" class=\"data row5 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row5_col4\" class=\"data row5 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row5_col5\" class=\"data row5 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row5_col6\" class=\"data row5 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_6f1e6_row6_col0\" class=\"data row6 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row6_col1\" class=\"data row6 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row6_col2\" class=\"data row6 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row6_col4\" class=\"data row6 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row6_col5\" class=\"data row6 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row6_col6\" class=\"data row6 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_6f1e6_row7_col0\" class=\"data row7 col0\" >0.9643</td>\n",
              "      <td id=\"T_6f1e6_row7_col1\" class=\"data row7 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row7_col2\" class=\"data row7 col2\" >0.9091</td>\n",
              "      <td id=\"T_6f1e6_row7_col3\" class=\"data row7 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row7_col4\" class=\"data row7 col4\" >0.9524</td>\n",
              "      <td id=\"T_6f1e6_row7_col5\" class=\"data row7 col5\" >0.9239</td>\n",
              "      <td id=\"T_6f1e6_row7_col6\" class=\"data row7 col6\" >0.9266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_6f1e6_row8_col0\" class=\"data row8 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row8_col1\" class=\"data row8 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row8_col3\" class=\"data row8 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row8_col4\" class=\"data row8 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row8_col5\" class=\"data row8 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row8_col6\" class=\"data row8 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_6f1e6_row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row9_col1\" class=\"data row9 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_6f1e6_row10_col0\" class=\"data row10 col0\" >0.9964</td>\n",
              "      <td id=\"T_6f1e6_row10_col1\" class=\"data row10 col1\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row10_col2\" class=\"data row10 col2\" >0.9909</td>\n",
              "      <td id=\"T_6f1e6_row10_col3\" class=\"data row10 col3\" >1.0000</td>\n",
              "      <td id=\"T_6f1e6_row10_col4\" class=\"data row10 col4\" >0.9952</td>\n",
              "      <td id=\"T_6f1e6_row10_col5\" class=\"data row10 col5\" >0.9924</td>\n",
              "      <td id=\"T_6f1e6_row10_col6\" class=\"data row10 col6\" >0.9927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6f1e6_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_6f1e6_row11_col0\" class=\"data row11 col0\" >0.0107</td>\n",
              "      <td id=\"T_6f1e6_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_6f1e6_row11_col2\" class=\"data row11 col2\" >0.0273</td>\n",
              "      <td id=\"T_6f1e6_row11_col3\" class=\"data row11 col3\" >0.0000</td>\n",
              "      <td id=\"T_6f1e6_row11_col4\" class=\"data row11 col4\" >0.0143</td>\n",
              "      <td id=\"T_6f1e6_row11_col5\" class=\"data row11 col5\" >0.0228</td>\n",
              "      <td id=\"T_6f1e6_row11_col6\" class=\"data row11 col6\" >0.0220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1d118278f6d49c7965598b3c4ef2cb5"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(rf)"
      ],
      "metadata": {
        "id": "EykYDCi6ClOL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bba60ed7-3b38-42ed-afa4-03e9b68ef014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
            "                     criterion='gini', max_depth=None, max_features='sqrt',\n",
            "                     max_leaf_nodes=None, max_samples=None,\n",
            "                     min_impurity_decrease=0.0, min_samples_leaf=1,\n",
            "                     min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                     monotonic_cst=None, n_estimators=100, n_jobs=-1,\n",
            "                     oob_score=False, random_state=7487, verbose=0,\n",
            "                     warm_start=False)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tuned_rf)"
      ],
      "metadata": {
        "id": "uUk-5_OcCou5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3fad368-f99b-4037-ac1f-c081d04e3130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
            "                     criterion='gini', max_depth=None, max_features='sqrt',\n",
            "                     max_leaf_nodes=None, max_samples=None,\n",
            "                     min_impurity_decrease=0.0, min_samples_leaf=1,\n",
            "                     min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                     monotonic_cst=None, n_estimators=100, n_jobs=-1,\n",
            "                     oob_score=False, random_state=7487, verbose=0,\n",
            "                     warm_start=False)\n"
          ]
        }
      ]
    }
  ]
}