ASD.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "Hs9bUJarhgCF"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler , StandardScaler,LabelEncoder\n",
        "from sklearn.metrics import mean_squared_log_error,mean_squared_error, r2_score,mean_absolute_error\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd = pd.read_csv(\"train_final_i_think.csv\")\n",
        "asd_t=pd.read_csv(\"test_final_i_think.csv\")\n",
        "print(\"Dataset loaded...\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9xram2bhzJ9",
        "outputId": "79be8ebc-d7bf-4272-ae99-852c37a58b14"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset loaded...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd=asd[0:2094]\n",
        "asd_t=asd_t[0:400]"
      ],
      "metadata": {
        "id": "ig56Zm_5qdmd"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "asd"
      ],
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        },
        "id": "ud8Hdp8zLT3u",
        "outputId": "4ab9ef5f-0a94-440a-ba24-58744d0e1200"
      },
      "execution_count": 4,
      "outputs": [
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              "      Unnamed: 0  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
              "0              0         1         0         1         0         1         0   \n",
              "1              1         0         0         0         0         0         0   \n",
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              "4              4         0         0         0         0         0         0   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "2089        2089         1         1         0         1         0         0   \n",
              "2090        2090         1         0         0         0         0         0   \n",
              "2091        2091         1         0         0         0         0         0   \n",
              "2092        2092         0         1         0         1         0         0   \n",
              "2093        2093         1         0         1         1         1         1   \n",
              "\n",
              "      A7_Score  A8_Score  A9_Score  ...  gender       ethnicity jaundice  \\\n",
              "0            1         0         1  ...       f          Unkown       no   \n",
              "1            0         0         0  ...       m          Unkown       no   \n",
              "2            1         1         1  ...       m  White-European       no   \n",
              "3            0         0         0  ...       f          Unkown       no   \n",
              "4            0         0         0  ...       m          Unkown       no   \n",
              "...        ...       ...       ...  ...     ...             ...      ...   \n",
              "2089         0         1         0  ...       m          Latino      yes   \n",
              "2090         0         1         0  ...       m        Hispanic       no   \n",
              "2091         0         1         0  ...       f           Asian       no   \n",
              "2092         0         1         0  ...       m  White-European       no   \n",
              "2093         1         1         1  ...       f  White-European       no   \n",
              "\n",
              "     austim   contry_of_res used_app_before result     age_desc  relation  \\\n",
              "0        no         Austria              no      6  18 and more      Self   \n",
              "1        no           India              no      0  18 and more      Self   \n",
              "2       yes   United States              no     10  18 and more      Self   \n",
              "3        no   United States              no      0  18 and more      Self   \n",
              "4        no    South Africa              no      0  18 and more      Self   \n",
              "...     ...             ...             ...    ...          ...       ...   \n",
              "2089    yes      Costa Rica              no      5  18 and more    Parent   \n",
              "2090     no   United States              no      3  18 and more      Self   \n",
              "2091     no  United Kingdom              no      2  18 and more      Self   \n",
              "2092    yes  United Kingdom              no      3  18 and more      Self   \n",
              "2093     no   United States              no      9  18 and more      Self   \n",
              "\n",
              "     Class/ASD  \n",
              "0            0  \n",
              "1            0  \n",
              "2            1  \n",
              "3            0  \n",
              "4            0  \n",
              "...        ...  \n",
              "2089         0  \n",
              "2090         0  \n",
              "2091         0  \n",
              "2092         0  \n",
              "2093         1  \n",
              "\n",
              "[2094 rows x 22 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>A1_Score</th>\n",
              "      <th>A2_Score</th>\n",
              "      <th>A3_Score</th>\n",
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              "      <td>Unkown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>United States</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Self</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>m</td>\n",
              "      <td>Unkown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Self</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2089</th>\n",
              "      <td>2089</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>m</td>\n",
              "      <td>Latino</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Parent</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2090</th>\n",
              "      <td>2090</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>m</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>United States</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Self</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2091</th>\n",
              "      <td>2091</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>f</td>\n",
              "      <td>Asian</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Self</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2092</th>\n",
              "      <td>2092</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>m</td>\n",
              "      <td>White-European</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Self</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2093</th>\n",
              "      <td>2093</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>f</td>\n",
              "      <td>White-European</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>United States</td>\n",
              "      <td>no</td>\n",
              "      <td>9</td>\n",
              "      <td>18 and more</td>\n",
              "      <td>Self</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2094 rows × 22 columns</p>\n",
              "</div>\n",
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              "    async function quickchart(key) {\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd = asd.sample(frac=1)"
      ],
      "metadata": {
        "id": "fWnbavUtWOIc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "asd.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "uyb2QcBuqyLc",
        "outputId": "2476b56b-104a-4ddd-dc51-c7bb58c4ec70"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
              "377          377         1         1         1         1         1         0   \n",
              "1434        1434         0         1         0         0         0         0   \n",
              "132          132         0         1         0         0         0         0   \n",
              "357          357         0         0         0         0         0         0   \n",
              "977          977         1         1         1         1         1         1   \n",
              "\n",
              "      A7_Score  A8_Score  A9_Score  ...  gender        ethnicity jaundice  \\\n",
              "377          1         0         1  ...       f  Middle Eastern        no   \n",
              "1434         0         1         1  ...       m           Unkown       no   \n",
              "132          0         0         0  ...       m  Middle Eastern        no   \n",
              "357          0         0         0  ...       m           Unkown       no   \n",
              "977          1         1         1  ...       m   White-European       no   \n",
              "\n",
              "     austim   contry_of_res used_app_before result     age_desc  relation  \\\n",
              "377      no   United States              no      8  18 and more      Self   \n",
              "1434     no          Russia              no      3  18 and more    Unkown   \n",
              "132      no           India              no      2  18 and more      Self   \n",
              "357      no     New Zealand              no      0  18 and more      Self   \n",
              "977      no  United Kingdom              no     10   4-11 years    Parent   \n",
              "\n",
              "     Class/ASD  \n",
              "377          1  \n",
              "1434         0  \n",
              "132          0  \n",
              "357          0  \n",
              "977          1  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "      <th></th>\n",
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              "      <th>A1_Score</th>\n",
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              "      <td>10</td>\n",
              "      <td>4-11 years</td>\n",
              "      <td>Parent</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 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-7de479d9-ccca-43a5-a853-162bd8db78f0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "      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",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7de479d9-ccca-43a5-a853-162bd8db78f0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0b702077-ed1f-4f5f-a4a6-85293f5ca86c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "QfWCgQKKq20r",
        "outputId": "5eeecf1f-ffef-4fb0-94ca-7be4e7e3e6e0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0     A1_Score     A2_Score     A3_Score     A4_Score  \\\n",
              "count  2094.000000  2094.000000  2094.000000  2094.000000  2094.000000   \n",
              "mean   1046.500000     0.645177     0.494269     0.503820     0.482808   \n",
              "std     604.630052     0.478574     0.500087     0.500105     0.499824   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%     523.250000     0.000000     0.000000     0.000000     0.000000   \n",
              "50%    1046.500000     1.000000     0.000000     1.000000     0.000000   \n",
              "75%    1569.750000     1.000000     1.000000     1.000000     1.000000   \n",
              "max    2093.000000     1.000000     1.000000     1.000000     1.000000   \n",
              "\n",
              "          A5_Score     A6_Score     A7_Score     A8_Score     A9_Score  \\\n",
              "count  2094.000000  2094.000000  2094.000000  2094.000000  2094.000000   \n",
              "mean      0.506686     0.379656     0.440306     0.577364     0.436008   \n",
              "std       0.500075     0.485417     0.496542     0.494097     0.496007   \n",
              "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
              "50%       1.000000     0.000000     0.000000     1.000000     0.000000   \n",
              "75%       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
              "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
              "\n",
              "         A10_Score       result    Class/ASD  \n",
              "count  2094.000000  2094.000000  2094.000000  \n",
              "mean      0.618434     5.084527     0.289398  \n",
              "std       0.485887     2.952081     0.453591  \n",
              "min       0.000000     0.000000     0.000000  \n",
              "25%       0.000000     3.000000     0.000000  \n",
              "50%       1.000000     5.000000     0.000000  \n",
              "75%       1.000000     8.000000     1.000000  \n",
              "max       1.000000    10.000000     1.000000  "
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            "text/html": [
              "\n",
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              "      <th>Unnamed: 0</th>\n",
              "      <th>A1_Score</th>\n",
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              "      <th>A8_Score</th>\n",
              "      <th>A9_Score</th>\n",
              "      <th>A10_Score</th>\n",
              "      <th>result</th>\n",
              "      <th>Class/ASD</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
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              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
              "      <td>2094.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.645177</td>\n",
              "      <td>0.494269</td>\n",
              "      <td>0.503820</td>\n",
              "      <td>0.482808</td>\n",
              "      <td>0.506686</td>\n",
              "      <td>0.379656</td>\n",
              "      <td>0.440306</td>\n",
              "      <td>0.577364</td>\n",
              "      <td>0.436008</td>\n",
              "      <td>0.618434</td>\n",
              "      <td>5.084527</td>\n",
              "      <td>0.289398</td>\n",
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              "      <td>604.630052</td>\n",
              "      <td>0.478574</td>\n",
              "      <td>0.500087</td>\n",
              "      <td>0.500105</td>\n",
              "      <td>0.499824</td>\n",
              "      <td>0.500075</td>\n",
              "      <td>0.485417</td>\n",
              "      <td>0.496542</td>\n",
              "      <td>0.494097</td>\n",
              "      <td>0.496007</td>\n",
              "      <td>0.485887</td>\n",
              "      <td>2.952081</td>\n",
              "      <td>0.453591</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>523.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1046.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e028b789-8869-446c-bcb4-436207602dc1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZQJhfM4rEmw",
        "outputId": "b08f1853-8894-4545-fccc-29754b7e1d0e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'A1_Score', 'A2_Score', 'A3_Score', 'A4_Score',\n",
              "       'A5_Score', 'A6_Score', 'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score',\n",
              "       'age', 'gender', 'ethnicity', 'jaundice', 'austim', 'contry_of_res',\n",
              "       'used_app_before', 'result', 'age_desc', 'relation', 'Class/ASD'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd.drop(['age_desc', 'used_app_before','Unnamed: 0'], axis = 1, inplace = True)\n",
        "asd_t.drop(['age_desc', 'used_app_before','Unnamed: 0'], axis = 1, inplace = True)\n",
        "asd.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sW5j9j-XrGXH",
        "outputId": "09e25024-94ac-45c3-e0c7-8ee793a4cfd5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A1_Score', 'A2_Score', 'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score',\n",
              "       'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score', 'age', 'gender',\n",
              "       'ethnicity', 'jaundice', 'austim', 'contry_of_res', 'result',\n",
              "       'relation', 'Class/ASD'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFwltQ40rvqv",
        "outputId": "f5e9f345-79b8-4b57-e301-2d5022c9a324"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A1_Score          int64\n",
              "A2_Score          int64\n",
              "A3_Score          int64\n",
              "A4_Score          int64\n",
              "A5_Score          int64\n",
              "A6_Score          int64\n",
              "A7_Score          int64\n",
              "A8_Score          int64\n",
              "A9_Score          int64\n",
              "A10_Score         int64\n",
              "age              object\n",
              "gender           object\n",
              "ethnicity        object\n",
              "jaundice         object\n",
              "austim           object\n",
              "contry_of_res    object\n",
              "result            int64\n",
              "relation         object\n",
              "Class/ASD         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Class/ASD' , data=asd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "AYtUOxdprxSy",
        "outputId": "f2c218b2-344d-4cb0-d3d6-bcc3fe399f9c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Class/ASD', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = asd.corr()\n",
        "plt.figure(figsize = (15,15))\n",
        "sns.heatmap(data = corr, annot = True, square = True, cbar = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "025__qAEMUkj",
        "outputId": "13494f33-f76a-4d2d-ba91-2a4b579e97ae"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='ethnicity',hue='gender',data=asd)\n",
        "plt.xticks(rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "MInwTikRMezg",
        "outputId": "ca9aa91f-d672-4d71-e50b-5df8f2a74afd"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
              " [Text(0, 0, 'Middle Eastern '),\n",
              "  Text(1, 0, 'Unkown'),\n",
              "  Text(2, 0, 'White-European'),\n",
              "  Text(3, 0, 'Asian'),\n",
              "  Text(4, 0, 'Latino'),\n",
              "  Text(5, 0, 'Others'),\n",
              "  Text(6, 0, 'Black'),\n",
              "  Text(7, 0, 'Pasifika'),\n",
              "  Text(8, 0, 'South Asian'),\n",
              "  Text(9, 0, 'Hispanic'),\n",
              "  Text(10, 0, 'Turkish'),\n",
              "  Text(11, 0, 'others')])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (16,8))\n",
        "sns.countplot(x = 'ethnicity', data = asd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "hXqGxZV4MtXw",
        "outputId": "8aa40b97-0c3f-4ef0-a89f-5041d2d71a1d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='ethnicity', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85GXhz9OM3wD",
        "outputId": "1b22e1dd-4486-4282-c1f2-d3841f73b57d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['A1_Score', 'A2_Score', 'A3_Score', 'A4_Score', 'A5_Score', 'A6_Score',\n",
              "       'A7_Score', 'A8_Score', 'A9_Score', 'A10_Score', 'age', 'gender',\n",
              "       'ethnicity', 'jaundice', 'austim', 'contry_of_res', 'result',\n",
              "       'relation', 'Class/ASD'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asd.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gK-xvZ8VNHDA",
        "outputId": "95b4c4c9-b7d2-4e08-ba1f-d68908b13757"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2094 entries, 377 to 407\n",
            "Data columns (total 19 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   A1_Score       2094 non-null   int64 \n",
            " 1   A2_Score       2094 non-null   int64 \n",
            " 2   A3_Score       2094 non-null   int64 \n",
            " 3   A4_Score       2094 non-null   int64 \n",
            " 4   A5_Score       2094 non-null   int64 \n",
            " 5   A6_Score       2094 non-null   int64 \n",
            " 6   A7_Score       2094 non-null   int64 \n",
            " 7   A8_Score       2094 non-null   int64 \n",
            " 8   A9_Score       2094 non-null   int64 \n",
            " 9   A10_Score      2094 non-null   int64 \n",
            " 10  age            2094 non-null   object\n",
            " 11  gender         2094 non-null   object\n",
            " 12  ethnicity      2094 non-null   object\n",
            " 13  jaundice       2094 non-null   object\n",
            " 14  austim         2094 non-null   object\n",
            " 15  contry_of_res  2094 non-null   object\n",
            " 16  result         2094 non-null   int64 \n",
            " 17  relation       2094 non-null   object\n",
            " 18  Class/ASD      2094 non-null   int64 \n",
            "dtypes: int64(12), object(7)\n",
            "memory usage: 327.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "columns = ['ethnicity','contry_of_res', 'relation', 'Class/ASD', 'age', 'jaundice','gender','austim']\n",
        "columns_t = ['ethnicity','contry_of_res', 'relation', 'age', 'jaundice','gender','austim']\n",
        "for col in columns:\n",
        "    asd[col] = le.fit_transform(asd[col])\n",
        "for col in columns_t:\n",
        "    asd_t[col] = le.fit_transform(asd_t[col])\n",
        "asd.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MHegsq8NIb4",
        "outputId": "deaf37d9-d81c-4efb-cc1b-978e7dc136f1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A1_Score         int64\n",
              "A2_Score         int64\n",
              "A3_Score         int64\n",
              "A4_Score         int64\n",
              "A5_Score         int64\n",
              "A6_Score         int64\n",
              "A7_Score         int64\n",
              "A8_Score         int64\n",
              "A9_Score         int64\n",
              "A10_Score        int64\n",
              "age              int64\n",
              "gender           int64\n",
              "ethnicity        int64\n",
              "jaundice         int64\n",
              "austim           int64\n",
              "contry_of_res    int64\n",
              "result           int64\n",
              "relation         int64\n",
              "Class/ASD        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= asd.drop(['Class/ASD'], axis = 1)\n",
        "Y= asd['Class/ASD']\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.20, random_state = 7)\n",
        "print('Processed...')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffSHVqn1O_Z5",
        "outputId": "125e2ccf-a1f9-44ba-9bed-0d278aa86eaa"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(x_train, y_train)\n",
        "preds = logreg.predict(x_test)\n",
        "logreg.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yETyqFlwNkVK",
        "outputId": "b652bec6-72c3-44b1-c825-7e8f21e3795b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9050746268656716"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "pred1=logreg.predict(x_train)\n",
        "accuracy_score(y_train, pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUjPSvxZPaVp",
        "outputId": "5c84cc5f-b00e-4576-fbd9-f5e80938d470"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9050746268656716"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "error_rate = []\n",
        "for i in range(1,40):\n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(x_train, y_train)\n",
        "    pred = knn.predict(x_test)\n",
        "    error_rate.append(np.mean(y_test != pred))"
      ],
      "metadata": {
        "id": "22FEXVkNPigI"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=27)\n",
        "knn.fit(x_train, y_train)\n",
        "pred = knn.predict(x_test)\n",
        "pred1=knn.predict(x_train)\n",
        "accuracy_score(y_train, pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ngmu5-OTPlCa",
        "outputId": "15d26788-a57c-4a80-9583-11ebe3678b2c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8286567164179105"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EZJQxX_PryT",
        "outputId": "9c38a5ae-fe7c-4ef3-ec2c-29b821cbdbd2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[293,  22],\n",
              "       [ 61,  43]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NB = GaussianNB()\n",
        "NB.fit(x_train, y_train)\n",
        "pred = NB.predict(x_test)\n",
        "pred1=NB.predict(x_train)\n",
        "accuracy_score(y_train, pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlezqqlq-cZN",
        "outputId": "f24f19fe-5bb1-4739-cad0-2548143dcb46"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9170149253731343"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC()\n",
        "model.fit(x_train, y_train)\n",
        "pred = model.predict(x_test)\n",
        "pred1=model.predict(x_train)\n",
        "accuracy_score(y_train, pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QK2yhri9Ps9C",
        "outputId": "7ba52ff9-0875-4b1e-f8dc-3ffa4faab815"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8871641791044776"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TopciHpPuPJ",
        "outputId": "9482844b-4352-4dfd-fc8d-e168e2065bbc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[287,  28],\n",
              "       [ 33,  71]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = []\n",
        "models.append(('Logistic Regression:', LogisticRegression()))\n",
        "models.append(('Naive Bayes        :', GaussianNB()))\n",
        "models.append(('SVM                :', SVC()))\n",
        "models.append(('KNN                :', KNeighborsClassifier()))\n",
        "\n",
        "\n",
        "for name, model in models:\n",
        "    model.fit(x_train, y_train)\n",
        "    pred = model.predict(x_test).astype(int)\n",
        "    print(name, accuracy_score(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6rU3RO1Pvvp",
        "outputId": "840258f1-51ab-43fb-8cea-0a1068e674fd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression: 0.8926014319809069\n",
            "Naive Bayes        : 0.8949880668257757\n",
            "SVM                : 0.8544152744630071\n",
            "KNN                : 0.8448687350835322\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "model_filename = 'logistic_regression_model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(logreg, file)"
      ],
      "metadata": {
        "id": "7yn7nHwdPxx2"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_filename = 'knn_model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(knn, file)"
      ],
      "metadata": {
        "id": "9UpUy87c930c"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_filename = 'SVM_model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(model, file)"
      ],
      "metadata": {
        "id": "cgjG7IhA9_RV"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_filename = 'NB_model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(logreg, file)"
      ],
      "metadata": {
        "id": "xksRJcea_COl"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1WbtByK_FGP",
        "outputId": "de61f4d9-35fc-4dcb-c489-74be3b9ba841"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "t4vlzRGtahi_",
        "outputId": "db946af3-d78c-4967-9401-f1d0060ef90d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
              "1423         0         1         0         0         0         0         0   \n",
              "1192         1         0         1         0         0         0         0   \n",
              "1962         1         0         0         1         1         0         0   \n",
              "1883         1         1         1         0         1         1         1   \n",
              "399          1         1         1         1         1         1         1   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "1221         0         1         1         0         0         0         0   \n",
              "1761         0         1         0         0         0         0         0   \n",
              "1211         1         1         0         1         1         0         0   \n",
              "1557         0         0         0         0         0         1         0   \n",
              "1488         0         1         1         1         0         0         0   \n",
              "\n",
              "      A8_Score  A9_Score  A10_Score  age  gender  ethnicity  jaundice  austim  \\\n",
              "1423         0         0          0   18       1         10         0       0   \n",
              "1192         1         0          1    6       1          0         0       0   \n",
              "1962         1         1          0    9       1         10         0       0   \n",
              "1883         1         1          1   18       1         10         0       0   \n",
              "399          1         1          1   52       1         10         1       0   \n",
              "...        ...       ...        ...  ...     ...        ...       ...     ...   \n",
              "1221         1         0          0   14       0          9         1       0   \n",
              "1761         1         0          0   18       0          8         0       0   \n",
              "1211         1         0          1    8       1          4         0       1   \n",
              "1557         1         0          0    9       0          9         0       0   \n",
              "1488         1         0          1   17       1         10         0       1   \n",
              "\n",
              "      contry_of_res  result  relation  \n",
              "1423             86       1         4  \n",
              "1192             40       4         2  \n",
              "1962              8       5         2  \n",
              "1883             86       9         4  \n",
              "399              86      10         4  \n",
              "...             ...     ...       ...  \n",
              "1221              0       3         5  \n",
              "1761             81       2         4  \n",
              "1211             59       6         2  \n",
              "1557             83       2         5  \n",
              "1488             85       5         4  \n",
              "\n",
              "[1675 rows x 18 columns]"
            ],
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              "      <th></th>\n",
              "      <th>A1_Score</th>\n",
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              "      <th>A9_Score</th>\n",
              "      <th>A10_Score</th>\n",
              "      <th>age</th>\n",
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              "      <th>jaundice</th>\n",
              "      <th>austim</th>\n",
              "      <th>contry_of_res</th>\n",
              "      <th>result</th>\n",
              "      <th>relation</th>\n",
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              "  </thead>\n",
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              "      <th>1423</th>\n",
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              "      <th>1192</th>\n",
              "      <td>1</td>\n",
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              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
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              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
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              "      <th>1883</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>86</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>86</td>\n",
              "      <td>10</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1221</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>14</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <th>1761</th>\n",
              "      <td>0</td>\n",
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              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <th>1211</th>\n",
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              "      <td>6</td>\n",
              "      <td>2</td>\n",
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              "<p>1675 rows × 18 columns</p>\n",
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              "      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-5aea36c9-ff07-4383-9783-796861e9a17d 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-5aea36c9-ff07-4383-9783-796861e9a17d');\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",
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              "\n",
              "\n",
              "<div id=\"df-326c91e7-8734-465b-94eb-cf1addaf25a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-326c91e7-8734-465b-94eb-cf1addaf25a0')\"\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",
              "    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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-326c91e7-8734-465b-94eb-cf1addaf25a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "clf=setup(data=x_train,target=y_train)\n",
        "best=compare_models()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2d12f97a1e474c98ac661a523a3fbbd7",
            "a9501689aac54299ab7b4f228d8ca2fc",
            "c8129da71d40452090db03d774dfbbf5",
            "5ae268c87f6c4e2f9e6b3e2e9e871c9d",
            "b3d68465f29d419a82d04f270c2416e6",
            "7f58728fde034d77ad0d5c6d6c8677bb",
            "8f1afdcdf5014d6e9726e0b39051a816",
            "f37984d49caf4e23a3c5c66824cef355",
            "08a6e8e0fb304a3b98942c50ab860b42",
            "bd18d6d51be548f2a1b859cca8c5df37",
            "ea7cabd8fa1a4c089e00391a2d455822"
          ]
        },
        "id": "h4ehvrb0ZpRt",
        "outputId": "cc84c776-1641-4e7d-a06f-9b9b5b5bb493"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x79763bf9be20>"
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_49e8b_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_49e8b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_49e8b_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_49e8b_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_49e8b_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_49e8b_row0_col1\" class=\"data row0 col1\" >7998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_49e8b_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_49e8b_row1_col1\" class=\"data row1 col1\" >Class/ASD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_49e8b_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_49e8b_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_49e8b_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_49e8b_row3_col1\" class=\"data row3 col1\" >(1675, 19)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_49e8b_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_49e8b_row4_col1\" class=\"data row4 col1\" >(1675, 19)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_49e8b_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_49e8b_row5_col1\" class=\"data row5 col1\" >(1172, 19)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_49e8b_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_49e8b_row6_col1\" class=\"data row6 col1\" >(503, 19)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_49e8b_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_49e8b_row7_col1\" class=\"data row7 col1\" >18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_49e8b_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_49e8b_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_49e8b_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_49e8b_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_49e8b_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_49e8b_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_49e8b_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_49e8b_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_49e8b_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_49e8b_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_49e8b_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_49e8b_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_49e8b_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_49e8b_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_49e8b_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_49e8b_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_49e8b_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_49e8b_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_49e8b_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_49e8b_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_49e8b_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_49e8b_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_49e8b_row18_col1\" class=\"data row18 col1\" >1f10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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          "data": {
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              "#T_358fc th {\n",
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              "#T_358fc_row0_col0, #T_358fc_row0_col2, #T_358fc_row0_col4, #T_358fc_row1_col0, #T_358fc_row1_col1, #T_358fc_row1_col3, #T_358fc_row1_col5, #T_358fc_row1_col6, #T_358fc_row1_col7, #T_358fc_row2_col0, #T_358fc_row2_col1, #T_358fc_row2_col2, #T_358fc_row2_col4, #T_358fc_row2_col5, #T_358fc_row2_col6, #T_358fc_row2_col7, #T_358fc_row3_col0, #T_358fc_row3_col1, #T_358fc_row3_col2, #T_358fc_row3_col3, #T_358fc_row3_col4, #T_358fc_row3_col5, #T_358fc_row3_col6, #T_358fc_row3_col7, #T_358fc_row4_col0, #T_358fc_row4_col1, #T_358fc_row4_col2, #T_358fc_row4_col3, #T_358fc_row4_col4, #T_358fc_row4_col5, #T_358fc_row4_col6, #T_358fc_row4_col7, #T_358fc_row5_col0, #T_358fc_row5_col1, #T_358fc_row5_col2, #T_358fc_row5_col3, #T_358fc_row5_col4, #T_358fc_row5_col5, #T_358fc_row5_col6, #T_358fc_row5_col7, #T_358fc_row6_col0, #T_358fc_row6_col1, #T_358fc_row6_col2, #T_358fc_row6_col3, #T_358fc_row6_col4, #T_358fc_row6_col5, #T_358fc_row6_col6, #T_358fc_row6_col7, #T_358fc_row7_col0, #T_358fc_row7_col1, #T_358fc_row7_col2, #T_358fc_row7_col3, #T_358fc_row7_col4, #T_358fc_row7_col5, #T_358fc_row7_col6, #T_358fc_row7_col7, #T_358fc_row8_col0, #T_358fc_row8_col1, #T_358fc_row8_col2, #T_358fc_row8_col3, #T_358fc_row8_col4, #T_358fc_row8_col5, #T_358fc_row8_col6, #T_358fc_row8_col7, #T_358fc_row9_col0, #T_358fc_row9_col1, #T_358fc_row9_col2, #T_358fc_row9_col3, #T_358fc_row9_col4, #T_358fc_row9_col5, #T_358fc_row9_col6, #T_358fc_row9_col7, #T_358fc_row10_col0, #T_358fc_row10_col1, #T_358fc_row10_col2, #T_358fc_row10_col3, #T_358fc_row10_col4, #T_358fc_row10_col5, #T_358fc_row10_col6, #T_358fc_row10_col7, #T_358fc_row11_col0, #T_358fc_row11_col1, #T_358fc_row11_col2, #T_358fc_row11_col3, #T_358fc_row11_col4, #T_358fc_row11_col5, #T_358fc_row11_col6, #T_358fc_row11_col7, #T_358fc_row12_col0, #T_358fc_row12_col1, #T_358fc_row12_col2, #T_358fc_row12_col3, #T_358fc_row12_col4, #T_358fc_row12_col5, #T_358fc_row12_col6, #T_358fc_row12_col7, #T_358fc_row13_col0, #T_358fc_row13_col1, #T_358fc_row13_col2, #T_358fc_row13_col3, #T_358fc_row13_col4, #T_358fc_row13_col5, #T_358fc_row13_col6, #T_358fc_row13_col7, #T_358fc_row14_col0, #T_358fc_row14_col1, #T_358fc_row14_col2, #T_358fc_row14_col3, #T_358fc_row14_col4, #T_358fc_row14_col5, #T_358fc_row14_col6, #T_358fc_row14_col7 {\n",
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              "#T_358fc_row0_col1, #T_358fc_row0_col3, #T_358fc_row0_col5, #T_358fc_row0_col6, #T_358fc_row0_col7, #T_358fc_row1_col2, #T_358fc_row1_col4, #T_358fc_row2_col3 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_358fc_row0_col8, #T_358fc_row1_col8, #T_358fc_row2_col8, #T_358fc_row3_col8, #T_358fc_row4_col8, #T_358fc_row5_col8, #T_358fc_row6_col8, #T_358fc_row7_col8, #T_358fc_row9_col8, #T_358fc_row10_col8, #T_358fc_row12_col8, #T_358fc_row13_col8, #T_358fc_row14_col8 {\n",
              "  text-align: left;\n",
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              "}\n",
              "#T_358fc_row8_col8, #T_358fc_row11_col8 {\n",
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              "  background-color: yellow;\n",
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              "</style>\n",
              "<table id=\"T_358fc\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_358fc_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_358fc_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_358fc_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_358fc_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_358fc_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_358fc_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_358fc_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_358fc_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_358fc_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
              "      <td id=\"T_358fc_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_358fc_row0_col1\" class=\"data row0 col1\" >0.9326</td>\n",
              "      <td id=\"T_358fc_row0_col2\" class=\"data row0 col2\" >0.9689</td>\n",
              "      <td id=\"T_358fc_row0_col3\" class=\"data row0 col3\" >0.9458</td>\n",
              "      <td id=\"T_358fc_row0_col4\" class=\"data row0 col4\" >0.8497</td>\n",
              "      <td id=\"T_358fc_row0_col5\" class=\"data row0 col5\" >0.8943</td>\n",
              "      <td id=\"T_358fc_row0_col6\" class=\"data row0 col6\" >0.8451</td>\n",
              "      <td id=\"T_358fc_row0_col7\" class=\"data row0 col7\" >0.8486</td>\n",
              "      <td id=\"T_358fc_row0_col8\" class=\"data row0 col8\" >0.3500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
              "      <td id=\"T_358fc_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_358fc_row1_col1\" class=\"data row1 col1\" >0.9283</td>\n",
              "      <td id=\"T_358fc_row1_col2\" class=\"data row1 col2\" >0.9698</td>\n",
              "      <td id=\"T_358fc_row1_col3\" class=\"data row1 col3\" >0.9230</td>\n",
              "      <td id=\"T_358fc_row1_col4\" class=\"data row1 col4\" >0.8521</td>\n",
              "      <td id=\"T_358fc_row1_col5\" class=\"data row1 col5\" >0.8855</td>\n",
              "      <td id=\"T_358fc_row1_col6\" class=\"data row1 col6\" >0.8335</td>\n",
              "      <td id=\"T_358fc_row1_col7\" class=\"data row1 col7\" >0.8356</td>\n",
              "      <td id=\"T_358fc_row1_col8\" class=\"data row1 col8\" >0.4740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row2\" class=\"row_heading level0 row2\" >ada</th>\n",
              "      <td id=\"T_358fc_row2_col0\" class=\"data row2 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_358fc_row2_col1\" class=\"data row2 col1\" >0.9275</td>\n",
              "      <td id=\"T_358fc_row2_col2\" class=\"data row2 col2\" >0.9642</td>\n",
              "      <td id=\"T_358fc_row2_col3\" class=\"data row2 col3\" >0.9458</td>\n",
              "      <td id=\"T_358fc_row2_col4\" class=\"data row2 col4\" >0.8372</td>\n",
              "      <td id=\"T_358fc_row2_col5\" class=\"data row2 col5\" >0.8871</td>\n",
              "      <td id=\"T_358fc_row2_col6\" class=\"data row2 col6\" >0.8341</td>\n",
              "      <td id=\"T_358fc_row2_col7\" class=\"data row2 col7\" >0.8386</td>\n",
              "      <td id=\"T_358fc_row2_col8\" class=\"data row2 col8\" >0.2930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
              "      <td id=\"T_358fc_row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_358fc_row3_col1\" class=\"data row3 col1\" >0.9249</td>\n",
              "      <td id=\"T_358fc_row3_col2\" class=\"data row3 col2\" >0.9671</td>\n",
              "      <td id=\"T_358fc_row3_col3\" class=\"data row3 col3\" >0.9174</td>\n",
              "      <td id=\"T_358fc_row3_col4\" class=\"data row3 col4\" >0.8469</td>\n",
              "      <td id=\"T_358fc_row3_col5\" class=\"data row3 col5\" >0.8800</td>\n",
              "      <td id=\"T_358fc_row3_col6\" class=\"data row3 col6\" >0.8256</td>\n",
              "      <td id=\"T_358fc_row3_col7\" class=\"data row3 col7\" >0.8277</td>\n",
              "      <td id=\"T_358fc_row3_col8\" class=\"data row3 col8\" >0.7480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row4\" class=\"row_heading level0 row4\" >nb</th>\n",
              "      <td id=\"T_358fc_row4_col0\" class=\"data row4 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_358fc_row4_col1\" class=\"data row4 col1\" >0.9206</td>\n",
              "      <td id=\"T_358fc_row4_col2\" class=\"data row4 col2\" >0.9548</td>\n",
              "      <td id=\"T_358fc_row4_col3\" class=\"data row4 col3\" >0.9089</td>\n",
              "      <td id=\"T_358fc_row4_col4\" class=\"data row4 col4\" >0.8427</td>\n",
              "      <td id=\"T_358fc_row4_col5\" class=\"data row4 col5\" >0.8734</td>\n",
              "      <td id=\"T_358fc_row4_col6\" class=\"data row4 col6\" >0.8158</td>\n",
              "      <td id=\"T_358fc_row4_col7\" class=\"data row4 col7\" >0.8183</td>\n",
              "      <td id=\"T_358fc_row4_col8\" class=\"data row4 col8\" >0.0640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row5\" class=\"row_heading level0 row5\" >xgboost</th>\n",
              "      <td id=\"T_358fc_row5_col0\" class=\"data row5 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_358fc_row5_col1\" class=\"data row5 col1\" >0.9198</td>\n",
              "      <td id=\"T_358fc_row5_col2\" class=\"data row5 col2\" >0.9661</td>\n",
              "      <td id=\"T_358fc_row5_col3\" class=\"data row5 col3\" >0.8946</td>\n",
              "      <td id=\"T_358fc_row5_col4\" class=\"data row5 col4\" >0.8483</td>\n",
              "      <td id=\"T_358fc_row5_col5\" class=\"data row5 col5\" >0.8700</td>\n",
              "      <td id=\"T_358fc_row5_col6\" class=\"data row5 col6\" >0.8121</td>\n",
              "      <td id=\"T_358fc_row5_col7\" class=\"data row5 col7\" >0.8136</td>\n",
              "      <td id=\"T_358fc_row5_col8\" class=\"data row5 col8\" >0.2980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row6\" class=\"row_heading level0 row6\" >lr</th>\n",
              "      <td id=\"T_358fc_row6_col0\" class=\"data row6 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_358fc_row6_col1\" class=\"data row6 col1\" >0.9087</td>\n",
              "      <td id=\"T_358fc_row6_col2\" class=\"data row6 col2\" >0.9582</td>\n",
              "      <td id=\"T_358fc_row6_col3\" class=\"data row6 col3\" >0.8605</td>\n",
              "      <td id=\"T_358fc_row6_col4\" class=\"data row6 col4\" >0.8425</td>\n",
              "      <td id=\"T_358fc_row6_col5\" class=\"data row6 col5\" >0.8494</td>\n",
              "      <td id=\"T_358fc_row6_col6\" class=\"data row6 col6\" >0.7840</td>\n",
              "      <td id=\"T_358fc_row6_col7\" class=\"data row6 col7\" >0.7861</td>\n",
              "      <td id=\"T_358fc_row6_col8\" class=\"data row6 col8\" >0.6570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row7\" class=\"row_heading level0 row7\" >et</th>\n",
              "      <td id=\"T_358fc_row7_col0\" class=\"data row7 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_358fc_row7_col1\" class=\"data row7 col1\" >0.9087</td>\n",
              "      <td id=\"T_358fc_row7_col2\" class=\"data row7 col2\" >0.9545</td>\n",
              "      <td id=\"T_358fc_row7_col3\" class=\"data row7 col3\" >0.8490</td>\n",
              "      <td id=\"T_358fc_row7_col4\" class=\"data row7 col4\" >0.8489</td>\n",
              "      <td id=\"T_358fc_row7_col5\" class=\"data row7 col5\" >0.8479</td>\n",
              "      <td id=\"T_358fc_row7_col6\" class=\"data row7 col6\" >0.7827</td>\n",
              "      <td id=\"T_358fc_row7_col7\" class=\"data row7 col7\" >0.7837</td>\n",
              "      <td id=\"T_358fc_row7_col8\" class=\"data row7 col8\" >0.6890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row8\" class=\"row_heading level0 row8\" >ridge</th>\n",
              "      <td id=\"T_358fc_row8_col0\" class=\"data row8 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_358fc_row8_col1\" class=\"data row8 col1\" >0.9002</td>\n",
              "      <td id=\"T_358fc_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row8_col3\" class=\"data row8 col3\" >0.8804</td>\n",
              "      <td id=\"T_358fc_row8_col4\" class=\"data row8 col4\" >0.8097</td>\n",
              "      <td id=\"T_358fc_row8_col5\" class=\"data row8 col5\" >0.8414</td>\n",
              "      <td id=\"T_358fc_row8_col6\" class=\"data row8 col6\" >0.7690</td>\n",
              "      <td id=\"T_358fc_row8_col7\" class=\"data row8 col7\" >0.7726</td>\n",
              "      <td id=\"T_358fc_row8_col8\" class=\"data row8 col8\" >0.0520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row9\" class=\"row_heading level0 row9\" >lda</th>\n",
              "      <td id=\"T_358fc_row9_col0\" class=\"data row9 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_358fc_row9_col1\" class=\"data row9 col1\" >0.8968</td>\n",
              "      <td id=\"T_358fc_row9_col2\" class=\"data row9 col2\" >0.9529</td>\n",
              "      <td id=\"T_358fc_row9_col3\" class=\"data row9 col3\" >0.8918</td>\n",
              "      <td id=\"T_358fc_row9_col4\" class=\"data row9 col4\" >0.7958</td>\n",
              "      <td id=\"T_358fc_row9_col5\" class=\"data row9 col5\" >0.8389</td>\n",
              "      <td id=\"T_358fc_row9_col6\" class=\"data row9 col6\" >0.7636</td>\n",
              "      <td id=\"T_358fc_row9_col7\" class=\"data row9 col7\" >0.7685</td>\n",
              "      <td id=\"T_358fc_row9_col8\" class=\"data row9 col8\" >0.0960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row10\" class=\"row_heading level0 row10\" >dt</th>\n",
              "      <td id=\"T_358fc_row10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_358fc_row10_col1\" class=\"data row10 col1\" >0.8959</td>\n",
              "      <td id=\"T_358fc_row10_col2\" class=\"data row10 col2\" >0.8743</td>\n",
              "      <td id=\"T_358fc_row10_col3\" class=\"data row10 col3\" >0.8205</td>\n",
              "      <td id=\"T_358fc_row10_col4\" class=\"data row10 col4\" >0.8352</td>\n",
              "      <td id=\"T_358fc_row10_col5\" class=\"data row10 col5\" >0.8242</td>\n",
              "      <td id=\"T_358fc_row10_col6\" class=\"data row10 col6\" >0.7505</td>\n",
              "      <td id=\"T_358fc_row10_col7\" class=\"data row10 col7\" >0.7538</td>\n",
              "      <td id=\"T_358fc_row10_col8\" class=\"data row10 col8\" >0.0630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row11\" class=\"row_heading level0 row11\" >svm</th>\n",
              "      <td id=\"T_358fc_row11_col0\" class=\"data row11 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_358fc_row11_col1\" class=\"data row11 col1\" >0.8464</td>\n",
              "      <td id=\"T_358fc_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row11_col3\" class=\"data row11 col3\" >0.7644</td>\n",
              "      <td id=\"T_358fc_row11_col4\" class=\"data row11 col4\" >0.7783</td>\n",
              "      <td id=\"T_358fc_row11_col5\" class=\"data row11 col5\" >0.7340</td>\n",
              "      <td id=\"T_358fc_row11_col6\" class=\"data row11 col6\" >0.6320</td>\n",
              "      <td id=\"T_358fc_row11_col7\" class=\"data row11 col7\" >0.6602</td>\n",
              "      <td id=\"T_358fc_row11_col8\" class=\"data row11 col8\" >0.0520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
              "      <td id=\"T_358fc_row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_358fc_row12_col1\" class=\"data row12 col1\" >0.8430</td>\n",
              "      <td id=\"T_358fc_row12_col2\" class=\"data row12 col2\" >0.8945</td>\n",
              "      <td id=\"T_358fc_row12_col3\" class=\"data row12 col3\" >0.6835</td>\n",
              "      <td id=\"T_358fc_row12_col4\" class=\"data row12 col4\" >0.7691</td>\n",
              "      <td id=\"T_358fc_row12_col5\" class=\"data row12 col5\" >0.7225</td>\n",
              "      <td id=\"T_358fc_row12_col6\" class=\"data row12 col6\" >0.6137</td>\n",
              "      <td id=\"T_358fc_row12_col7\" class=\"data row12 col7\" >0.6167</td>\n",
              "      <td id=\"T_358fc_row12_col8\" class=\"data row12 col8\" >0.0750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
              "      <td id=\"T_358fc_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_358fc_row13_col1\" class=\"data row13 col1\" >0.7252</td>\n",
              "      <td id=\"T_358fc_row13_col2\" class=\"data row13 col2\" >0.6430</td>\n",
              "      <td id=\"T_358fc_row13_col3\" class=\"data row13 col3\" >0.3791</td>\n",
              "      <td id=\"T_358fc_row13_col4\" class=\"data row13 col4\" >0.6609</td>\n",
              "      <td id=\"T_358fc_row13_col5\" class=\"data row13 col5\" >0.4003</td>\n",
              "      <td id=\"T_358fc_row13_col6\" class=\"data row13 col6\" >0.2642</td>\n",
              "      <td id=\"T_358fc_row13_col7\" class=\"data row13 col7\" >0.3122</td>\n",
              "      <td id=\"T_358fc_row13_col8\" class=\"data row13 col8\" >0.0690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_358fc_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_358fc_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_358fc_row14_col1\" class=\"data row14 col1\" >0.7005</td>\n",
              "      <td id=\"T_358fc_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_358fc_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_358fc_row14_col8\" class=\"data row14 col8\" >0.1270</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,
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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/b3e629b1971e1542/manager.min.js"
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVRH6kjiaXOZ",
        "outputId": "d5bb2bd7-14f8-413e-9ee6-b5ba89d6cae5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
            "                           learning_rate=0.1, loss='log_loss', max_depth=3,\n",
            "                           max_features=None, max_leaf_nodes=None,\n",
            "                           min_impurity_decrease=0.0, min_samples_leaf=1,\n",
            "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                           n_estimators=100, n_iter_no_change=None,\n",
            "                           random_state=7998, subsample=1.0, tol=0.0001,\n",
            "                           validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lu4ehFHmb1nA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}