lOGESTIC_R_on_hypothyroid_MS.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/hypothyroid.csv\")"
      ],
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      "outputs": []
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        "df"
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              "     age sex on thyroxine query on thyroxine on antithyroid medication sick  \\\n",
              "0     41   F            f                  f                         f    f   \n",
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              "4     70   F            f                  f                         f    f   \n",
              "...   ..  ..          ...                ...                       ...  ...   \n",
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              "3770  72   M            f                  f                         f    f   \n",
              "3771  64   F            f                  f                         f    f   \n",
              "\n",
              "     pregnant thyroid surgery I131 treatment query hypothyroid  ...  \\\n",
              "0           f               f              f                 f  ...   \n",
              "1           f               f              f                 f  ...   \n",
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              "...       ...             ...            ...               ...  ...   \n",
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              "3770        f               f              f                 f  ...   \n",
              "3771        f               f              f                 f  ...   \n",
              "\n",
              "     TT4 measured  TT4 T4U measured   T4U FTI measured  FTI TBG measured TBG  \\\n",
              "0               t  125            t  1.14            t  109            f   ?   \n",
              "1               t  102            f     ?            f    ?            f   ?   \n",
              "2               t  109            t  0.91            t  120            f   ?   \n",
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              "4               t   61            t  0.87            t   70            f   ?   \n",
              "...           ...  ...          ...   ...          ...  ...          ...  ..   \n",
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              "\n",
              "     referral source binaryClass  \n",
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              "4                SVI           P  \n",
              "...              ...         ...  \n",
              "3767           other           P  \n",
              "3768             SVI           P  \n",
              "3769           other           P  \n",
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              "3771           other           P  \n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_83c5cc07-9526-4226-9930-6320a4136931\">\n",
              "    <style>\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Za0S7a14gGzv",
        "outputId": "6b6c7b0c-cab7-4354-ca22-d3f3f9945537"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3772 entries, 0 to 3771\n",
            "Data columns (total 30 columns):\n",
            " #   Column                     Non-Null Count  Dtype \n",
            "---  ------                     --------------  ----- \n",
            " 0   age                        3772 non-null   object\n",
            " 1   sex                        3772 non-null   object\n",
            " 2   on thyroxine               3772 non-null   object\n",
            " 3   query on thyroxine         3772 non-null   object\n",
            " 4   on antithyroid medication  3772 non-null   object\n",
            " 5   sick                       3772 non-null   object\n",
            " 6   pregnant                   3772 non-null   object\n",
            " 7   thyroid surgery            3772 non-null   object\n",
            " 8   I131 treatment             3772 non-null   object\n",
            " 9   query hypothyroid          3772 non-null   object\n",
            " 10  query hyperthyroid         3772 non-null   object\n",
            " 11  lithium                    3772 non-null   object\n",
            " 12  goitre                     3772 non-null   object\n",
            " 13  tumor                      3772 non-null   object\n",
            " 14  hypopituitary              3772 non-null   object\n",
            " 15  psych                      3772 non-null   object\n",
            " 16  TSH measured               3772 non-null   object\n",
            " 17  TSH                        3772 non-null   object\n",
            " 18  T3 measured                3772 non-null   object\n",
            " 19  T3                         3772 non-null   object\n",
            " 20  TT4 measured               3772 non-null   object\n",
            " 21  TT4                        3772 non-null   object\n",
            " 22  T4U measured               3772 non-null   object\n",
            " 23  T4U                        3772 non-null   object\n",
            " 24  FTI measured               3772 non-null   object\n",
            " 25  FTI                        3772 non-null   object\n",
            " 26  TBG measured               3772 non-null   object\n",
            " 27  TBG                        3772 non-null   object\n",
            " 28  referral source            3772 non-null   object\n",
            " 29  binaryClass                3772 non-null   object\n",
            "dtypes: object(30)\n",
            "memory usage: 884.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qt1Rxd84gPkN",
        "outputId": "2f5227e9-3a58-4e3e-8a40-8b88deb3a0cf"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'on thyroxine', 'query on thyroxine',\n",
              "       'on antithyroid medication', 'sick', 'pregnant', 'thyroid surgery',\n",
              "       'I131 treatment', 'query hypothyroid', 'query hyperthyroid', 'lithium',\n",
              "       'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH measured', 'TSH',\n",
              "       'T3 measured', 'T3', 'TT4 measured', 'TT4', 'T4U measured', 'T4U',\n",
              "       'FTI measured', 'FTI', 'TBG measured', 'TBG', 'referral source',\n",
              "       'binaryClass'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFMN9HOPgSxO",
        "outputId": "8d7a1404-4b00-4c56-bb06-f98d65b04200"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                          object\n",
              "sex                          object\n",
              "on thyroxine                 object\n",
              "query on thyroxine           object\n",
              "on antithyroid medication    object\n",
              "sick                         object\n",
              "pregnant                     object\n",
              "thyroid surgery              object\n",
              "I131 treatment               object\n",
              "query hypothyroid            object\n",
              "query hyperthyroid           object\n",
              "lithium                      object\n",
              "goitre                       object\n",
              "tumor                        object\n",
              "hypopituitary                object\n",
              "psych                        object\n",
              "TSH measured                 object\n",
              "TSH                          object\n",
              "T3 measured                  object\n",
              "T3                           object\n",
              "TT4 measured                 object\n",
              "TT4                          object\n",
              "T4U measured                 object\n",
              "T4U                          object\n",
              "FTI measured                 object\n",
              "FTI                          object\n",
              "TBG measured                 object\n",
              "TBG                          object\n",
              "referral source              object\n",
              "binaryClass                  object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bgx1ADvUgXnI",
        "outputId": "27c4a8a2-0f3a-4f73-ee19-210e4ad6178e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                          0\n",
              "sex                          0\n",
              "on thyroxine                 0\n",
              "query on thyroxine           0\n",
              "on antithyroid medication    0\n",
              "sick                         0\n",
              "pregnant                     0\n",
              "thyroid surgery              0\n",
              "I131 treatment               0\n",
              "query hypothyroid            0\n",
              "query hyperthyroid           0\n",
              "lithium                      0\n",
              "goitre                       0\n",
              "tumor                        0\n",
              "hypopituitary                0\n",
              "psych                        0\n",
              "TSH measured                 0\n",
              "TSH                          0\n",
              "T3 measured                  0\n",
              "T3                           0\n",
              "TT4 measured                 0\n",
              "TT4                          0\n",
              "T4U measured                 0\n",
              "T4U                          0\n",
              "FTI measured                 0\n",
              "FTI                          0\n",
              "TBG measured                 0\n",
              "TBG                          0\n",
              "referral source              0\n",
              "binaryClass                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('TBG',axis=1)"
      ],
      "metadata": {
        "id": "aZULUzXKhpKT"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# outlier detection"
      ],
      "metadata": {
        "id": "6tZ_vhoZkpeZ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['sex'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "3pzWMgHkvlNH",
        "outputId": "fb387a7d-8721-4fab-ac5c-1bf128672c3c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='sex'>"
            ]
          },
          "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": [
        "sns.boxplot(df['age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "BEN3VC6Ck3Q-",
        "outputId": "9472194c-9c53-414b-fca8-fbdbbf2ff7c4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='age'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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iWrVqTuvquggRERHXYeqwM2jQIBITE7l48aJjyaq0tBR/f3/HZz766CMOHTpEXl4eXl5ejiAUGRnptK62nouIiLgOUy9jPfzww+zatYvU1FQWL15MXFwcZWVlXHfddQDk5eXx888/8+ijj/L1118zZcoUTp48CcCNN97otK56dkRERFyHqWd2Vq5cibu7O5GRkQwbNoyYmBgaNGjgCDLu7u6UlJTw4osvcunSJdzc3AgPDyclJYWjR486nd2ZOHEi48ePdzzPyclR4BEREamiTB12vvzyS8rKykhMTATgyJEjABw/fpy33noLu91OQkICubm53HbbbdhsNrp3787LL79M8+bNndZVz46IiIjrMHXY+edrIf5Z27ZtiY+PZ+HChSxdupRt27Y5mpmPHj3Khg0bCAkJcVpXPTsiIiKuw9Q9O4Zh4O3tTWpqquMRHh5OUlISsbGxZGZmcvr0aUewWbFiBbfccgv9+vVzGpRAPTsiIiKuxNRhx8/PD29vb8LDwx0Pf39/cnNzgYoZnjNnzpCeng7AwIEDWb58OWfOnHGcuXM1OmdHRETEdZh6Gat27docPnyYiIgIbDYb7dq1o2PHjvz6669Axfk7hmHw3Xff0aVLF8f5OvXq1cPHx6eSR//fd/78ebKzsyt7GCKmcvr06Sv+KSIVAgMDCQsLq+xh/FeYOuw89NBDjB8/nvz8fC5evMinn35KSUkJ8fHxAOTn5zN06FAOHz4MQFBQEH369OHcuXP079/fad24uDimTZv2X/kd/lvOnz/PffcPpaTY/vsfFnFBM2bMqOwhiJiKp5eVD5d+4BKBx9Rhp7CwEH9/f4KCgigqKqJ69eqkpaWxadMmRowYgbu7O6dPn77iv9iys7P54YcfaNq0qdO6VXHreXZ2NiXFdgrr3kK5LbCyhyMiIibmVpQNJ7eQnZ2tsFPZtm3bRv/+/Vm4cKHjtaCgIA4cOACAzWbj559/dixjAcydO5cWLVpcs25V3npebguk3Nf5TjQRERFXY+oG5cLCQhYtWoTFYnE8srKyfnM6smEYjn/fvHnz79a12+3k5ORc8RAREZGqydRhp6CggMDAiiUZN7eKoXp7ezN79mwA0tLS2LdvH++++67jO+fOnSMzM/OadbX1XERExHWYOuycPXuW7Oxs3N3dCQkJoU2bNlitVtatWwfAu+++yw033MAnn3zi+M7s2bNZs2bNNetq67mIiIjrMHXYyc7OxsvLixo1auDr60u9evV4+OGHiYuLA+Dvf/87TZo04fPPPycpKQmAOXPmMGzYsEoctYiIiJiJqRuUi4qKKCkpcZyGnJSUhJubGzExMQDUr1+f1NRU7rrrLsd3Pv30U8aNG3fNulVx67mIiIhcnalndq677jo8PDxYunQpO3fu5M0336S8vJwGDRqwZs0aLl26xKBBg9iwYQNLly4FoGXLluzbt4+0tDSndbWMJSIi4jpMPbPTq1cvzp07x6RJk0hPTyciIoKaNWty/fXXs2nTJoqKili+fDnLly93fOett97irbfeYsqUKUydOvWqdavy1nMRERG5kqnDjtVqpaCgAE9PT2rWrEmrVq1ISEggMjKSAQMG8OOPP3LixAkMwyAkJITExEReeeUV+vXr51jquhrdei4iIuI6TL2MdeDAAR555BEaN27M+fPn+eSTT8jOziY6Oprw8HBGjBjBsmXLuPfee7lw4QIAL7zwAqWlpdesq63nIiIirsPUYccwDF5//XW2bNmCr68vPXr0wGazOe7CGjlyJL/88gsrVqzg/fffBypmabp27UpRUZHTuurZERERcR2mDjsNGzakXbt2lJWVkZ6eTkJCArGxsY5lJ8MwmDt3LpMnT6Zv377k5eUBFYcNfv75507rWq1WAgICrniIiIhI1WTqsLNo0SKOHz+Op6cnbm5uBAYGcvDgQWrWrAlUbEVPS0tjwoQJWCwW/Pz8gIpr67dv3+60rq6LEBERcR2mDjsXLlzg4sWL/O1vf2Pq1KmUlZVRVFREcXExJ06ccBwu2LdvX5YsWULt2rVp27YtHTp0uObWc/XsiIiIuA5Thx03Nzfc3NxYtWoV8fHxdOvWDYDFixfj5eXFrl27APjyyy958cUXuffee0lISMDT0/OaddWzIyIi4jpMvfU8MjKS7t27s2DBAgASExP54osvuHDhAlFRUaxatYp69eoREBDApUuXWLduHe7u7qSkpNCyZctKHr2IiIiYgalndjIyMli4cCEWiwWLxUKDBg2Af9yA/tJLL2GxWMjNzaW8vByr1crChQvZvn077dq1c1pXy1giIiKuw9RhZ9WqVbi7u/PMM89w1113OXZNPfzwwwC0atWKESNG4OPjw3PPPYe/vz/Z2dmUl5fTrFkzp3W1jCUiIuI6TL2M1aNHDz7//HMeeOABMjMzCQgIIDQ0lBdffBGoOGfHMAzCwsJ4+eWXyczMpKSkBIAzZ85w3XXXXbWurosQERFxHaae2XnnnXcYNmwYly5dwtfXl4KCAnr16oXFYnF8xmKxMG3aNJo1a0ZJSQlBQUEAju3pV6Ot5yIiIq7D1GFn7dq1FBYWsnTpUp5++mnKyspYtmwZu3fvBuDEiRPceeedWK1WNmzYAFT083Tq1Omay1jq2REREXEdpg47X3/9NQUFBdx33308//zzGIZBaWkpb7/9NgBeXl6cOXOG0tJSPDwqVuRKS0v59NNPr1lXPTsiIiKuw9RhxzAMDMPg1KlTWCwWnnzySby8vHjqqacACA4Opri4mNWrVzt6dQoLC/n2228rc9giIiJiIqYOO3Xq1MFisVCnTh0Mw2D27NkUFxc7ZnZGjRpFcXExo0aNwtfXF4Dy8nLsdvs162oZS0RExHWYOuzs2rWLpKQkatSowYABAxg4cCAArVu3Jj4+no8//hhPT0/i4+NZsmQJULGM9XvhRctYIiIirsPUW89DQ0PZu3cv6enpvPDCC7zzzjt4e3uzbds2ysvLKSkp4ciRI/Tt2/eK7z366KMcPXrUaV1tPRcREXEdpg47UHHWjmEYFBcX8+GHHxIeHk5xcTEvvfQShw8fxtPTk5kzZ+Lv70/z5s3x8vLi3XffvWZNu91+xVKXtp6LiIhUXaYOO7fddhuJiYmkp6dTUlKC3W4nIyOD9957j6KiIrZv3w5Ahw4dHN8pLi7m4sWL16wbFxfHtGnT/tKxi4iIiDmYumenvLycvLw87HY7ZWVleHt74+HhQUREBFFRUTz44IO0aNGCTz75hI0bNzq+V7t27WvWVc+OiIiI6zD1zM7lgwJPnz5N3bp1Wb58OQ8++CA//fQTNpuNxYsXc/DgQZo2bQpAixYtOH/+PIsXL6ZNmzZO66pnR0RExHWYemanrKyM5557jhYtWlBeXs7IkSPJycmhbdu2FBQUADB58mTHrej79u0jNTWVr7766pp1dV2EiIiI6zB12Hn55Zd56623yMrKAiAvLw8vLy++/fZbGjduTP369dm2bRvt2rXj7rvvJjg4GIBXX331mnV1zo6IiIjrMHXY2bZtG61btwZg5cqVPP7445SVlbFhwwY8PT1Zt24dNpuN3bt3s3r1ary8vPjggw+49957r1lXPTsiIiKuw9Rhp3379hw7doyjR49yzz33MHDgQAzDID8/H4AGDRrQpUsXx71YPj4+bNu2jYyMjMoctoiIiJiIqcMOVPTXNGrUCIvFQosWLQgLC7tit1XPnj0JCwsjKCiIs2fPEh8fT506dcjLy3NaU8tYIiIirsPUYWfu3Lnk5+czceJEXn31VWrUqMHZs2cJDAx0fMbd3Z2kpCTuuusudu/eTUJCAnl5efzwww9O62oZS0RExHWYeut5QUEBVquV2bNnExgYSOPGjUlPT2f16tW8/vrrAIwdO5aAgAAWLFiAm5sbTZs2JSQkhOTkZKd1tfVcRETEdZg67JSVlZGZmQnAhQsXuHDhAgAWiwWAgwcPkpKSgr+/P56enri5uRETE8PFixepWbOm07q6LkJERMR1mHoZq1GjRnh4eLB06VJ++uknmjVrhru7u+P282HDhgEVJy1PmDCBXr16cfz4cQDq1avntK56dkRERFyHqcNOr169qFatGpMmTeLmm2/m8OHDPProo7zwwgsAHDp0CKgIO3PmzGH//v1YrVbCw8P58MMPndZVz46IiIjrMHXYsVqtFBQUOC72vP3223nqqafw8vICoFWrVgDMmTOHoqIiXnrpJdzd3bnhhht+t2cnICDgioeIiIhUTaYOOzfddBOtW7emrKwMm83Gl19+Sf369dmyZQsAa9aswWq1Mnr0aKxWK0OHDqWgoIBffvmF6Ohop3V1XYSIiIjrMHXYWblyJd9//z1dunTh008/ZcOGDXh4eDjCzvPPP09YWBg+Pj4MHjzYsSX9/PnzPPzww07rqmdHRETEdZh6N9aiRYsAWL9+PevXr3e8vmPHDk6cOMFbb73FwYMH+fDDD3nllVfw9PTEYrHQpEmTazYoT5w4kfHjxzue5+TkKPCIiIhUUaae2WnSpAljx47lb3/7G6GhoTRr1gwfHx9uu+02x63nUBF+Zs+eTWFhIVarFT8/v0octYiIiJiJqcPOsWPHePvtt6lRowazZs0iNTWVgoICVq1a5bj1/I477iA/P5/bb7+dxo0bU1RUhL+//zXrahlLRETEdZg67BiGAcCCBQv4+9//TkFBAUFBQRw9ehRPT09HADpx4gRNmzYlJSUF4HfDi7aei4iIuA5Th53IyEiGDh1KRkYG1apV4/PPPyckJITc3FwATp8+TXFxMZcuXaKkpMRxG/rixYupU6eO07raei4iIuI6TB12br75Zo4ePcqYMWPo3bs3P//8M8ePH3fM+Nx///189dVX+Pj44O/v77jvqnXr1iQkJDitq63nIiIirsPUYWfcuHFs27aNhIQE2rRpw5w5cwAICQkBIDg4mBEjRmCz2di4cSMHDx4EYNeuXVc0MP8r9eyIiIi4DlOHnfDwcAICAvD19WXw4MHYbDa8vb2pXr06UHGoYEpKCjNnzuSmm26ibt26AHh5ebF7926nddWzIyIi4jpMHXZ2795NVlYWSUlJAKSkpFBYWMi+ffvw8PBgw4YNGIbByJEjcXd3x93dHahYppo/f35lDl1ERERMwtRhp2vXrkyaNAlfX18CAgLw8PDAzc2N6Oho9u3bx6RJk5gxYwaBgYGUl5dTXl4OwOjRo1mxYoXTulrGEhERcR2mDjunTp0iLi6OW2+9lW+++YZff/0Vm82Gj48PsbGxhIeHs3nzZvz8/K44Efn9998nLy/PaV0tY4mIiLgOU4edKVOmUF5ezldffUX79u1p0KABBQUF/Prrr3h4eHDixAk2btxIQkICjz32mON79evX5+2333ZaV1vPRUREXIep78Y6fPgw9913H+fPn2f37t3UqFGDwsJCOnbsyNNPP+3YceXm9o/MtnfvXp5++mnHktbV2O127Ha743lV2nruVphV2UMQERGTc7W/FaYOOydOnODYsWN4enri6emJ3W4nOTmZ8+fPExsbyy233ALAdddd5/jO0KFDOXjwIGvXrnVaNy4ujmnTpv3l468M3knfV/YQRERETMXUYccwDBo1asSaNWswDIP333+fmTNnsmfPHgAsFgsDBgwgLy+P7du3c+nSJfLz83n//fe5/fbbndatyreeF8Z0oty7WmUPQ0RETMytMMul/uPY1GGnVq1a3HzzzTRo0ACAGTNmMHfuXEpLSx2fqVmzJnPnzuXUqVPExMSwcuVKWrRocc26VqvVcdpyVVPuXY1y35DKHoaIiIhpmLpBuV27dnzzzTfExMTg7e1NWFgYhYWFREdHOz4THx+Pu7u740DBESNGsGPHjmvW1XURIiIirsPUMzvVq1fnzJkzeHh4UF5eTlZWFoZh0Lx5cwAGDx5M8+bNCQgIIC0tjQULFnDq1Cm6devG9u3biY2NvWrdqtyzIyIiIlcy9cxOcnIyXbt2JSYmBnd3d/z8/HB3d+fSpUsAjBw5khtuuIEXX3yRBQsWAHDx4kXy8vKYNWuW07o6Z0dERMR1mDrstG/fnsTERNauXUtxcTGbNm3Czc2N/Px8x2eGDRuGYRgYhkFmZiYAvr6+jktDRURExLWZOuxARX9No0aNsFgstGjRgrCwMGrXru14Pz4+nuuvvx6LxeK4IDQ+Pt5xM/rV6LoIERER12HqsDN37lzy8/OZOHEir776KjVq1ODs2bMEBgZy4sQJXnjhBY4ePUq3bt246667AAgNDWXSpEmkp6c7ratlLBEREddh6gblgoICrFYrs2fPJjAwkMaNG5Oens7q1at56qmn2LhxIwcPHiQ/P5/g4GAAfv75Z7p27crChQuZOHHiVetW5a3nIiIiciVTz+x4eXnRpk0bIiIiyM3N5dSpU473oqKi2LJlC5mZmdjtdlJSUgCIjo4mMTGRTZs2Oa2rreciIiKuw9Rhp06dOiQkJPDAAw+QkJCAp6cnAA0bNgQgPz+fxx57jLVr1zJv3jwA2rRpA8CTTz7ptK56dkRERFyHqcNOjRo1aNy4MYsXL+bWW28lOTmZevXqUaNGDQDc3d05e/YsI0eOdNx6npSURKtWrejZs6fTuurZERERcR2mDjudOnUiPz+fjh07UrNmTb744gtyc3Pp3bs3ADabjVWrVnHu3Dk2bNgAQGZmJuPGjavMYYuIiIiJmLpBuaioiNOnT3P69GkAevfuTUhICEOGDAFg1KhRbNy4kZSUFEfDsbe3N/fcc8816+oEZREREddh6pmdDz/8EIBx48bx2Wef8eKLL1JSUsL8+fMBaNmyJa+++iorV650LGOVlpZy6NAhxwGDV6NlLBEREddhMQzDqOxBOGOxWK76enh4OKmpqQBMnTr1qrM0ixcvZtiwYf/Wz8nJySEwMJDs7GwCAgL+8Hgr07Fjxxg5ciT51/XRreciInJNbvkX8T28hvj4eMemn/9F/+7fb1MvY9lsNsrLywkODsZms9GuXTsiIyNZvXq14zNTp05l6tSpDBkyhI0bN+Lr68uRI0fw8vJyWtdut2O32x3PtfVcRESk6jJ12AEoLi52zOIkJSUB0KxZM06dOkVMTMxVv/PFF1/Qv39/pzXVsyMiIuI6TN2zc/jwYYYPH06tWrWwWq2OLeddu3YlKiqK1NRUjh49yoQJE/D09KR+/fq4ubnRpUuXa9ZVz46IiIjrMHXYiYmJYf78+Zw9e5aioiIGDRqE1WrFy8sLd3d3wsPDadiwIbNmzaK4uBhfX18sFotjG7ozVquVgICAKx4iIiJSNZl6GSsuLo5Vq1Zx5MgRvL29ycrKwmKxEBERAeB0KWvw4MF4eHg4XcpSz46IiIjrMPXMTnx8PF27dmXlypX079+fkpISSktL6dOnDydPnuSjjz5i/fr17Nq1i169euHj44O3tze+vr706tXLaV1dFyEiIuI6TD2z06ZNGz744ANee+01LBYLYWFhnD9/nuTkZOrXr8+PP/7IG2+8QWZmJmVlZbRo0YKioiLatWuHn5+f07oTJ05k/Pjxjuc5OTkKPCIiIlWUqWd2VqxYQUpKCseOHaOkpISpU6cCUL16dSIiIli3bh3nz59n0aJFeHh4MGvWLH799Vcefvjhyh24iIiImIapww7AuXPnuPPOOzEMgzFjxuDj40NRUZHj/fPnzzNhwgTc3d3p1asXvr6+hIaGXrOmlrFERERch6nDzqVLl7j55ps5efIk9evXJzw8nPj4eIKCggAwDIOePXty/vx5XnjhBWw2G9dffz3dunUjPz/faV1tPRcREXEdpg47L7/8Mn5+fuTn55Obm8vWrVsZMmQI9erVA+D48ePs27eP8PBwQkNDsdvtfP755xQWFrJ8+XKndbX1XERExHWYOuzMmTOHQ4cOAZCWlkbdunWxWCyMGTMGwLF9/IcffmDcuHGUlJSwfft2rFYrW7dudVrXbreTk5NzxUNERESqJlOHnbKyMgDuueceVqxYwYgRIwDw9vYGoHHjxtSuXZs777yTS5cuAbBq1SrOnj3ruGLiatSzIyIi4jpMHXbKy8uBigAzcOBA5s+fD+C4CNTT05O4uDiOHTvm+M6BAwfo1asXbm7OfzX17IiIiLgOU4ed2rVr8/DDD2MYBna7neDgYPr06ePYjVVQUMCLL77IqlWryMrKAmDKlClkZGRQt27dShy5iIiImIWpw06NGjVYvnw5AQEBVKtWjYyMDOx2O9HR0QCMGjWK4uJiRo0a5bhC4quvvuLnn3+mb9++TutqGUtERMR1mDrsDBo0iKKiIoYPH851111HWFgYCQkJ9O3blzVr1rBy5UoMw2DKlCl89dVXACxYsIDOnTvTo0cPp3W1jCUiIuI6TH1dxPjx42nYsCFPPvkkx44do1atWvj4+BASEsKmTZsoLCwkKSmJRx999Irv/V54sVqtWK3Wv3LoIiIiYhKmntkBuOOOOxg0aBBhYWG8/PLLlJaW0q5dO5555hnatWtHx44d2bp1K7/88gsAXl5evPfee9esqa3nIiIirsPUYadOnTpYLBamTZvG+fPnue+++yguLubtt98mPDycdevWUVRURIcOHWjevDkA4eHhtG3b9pp11bMjIiLiOkwddnbt2sXSpUuBil6cgQMHAtC6dWsAhg8fzu7du3n44Yf57LPPgIrDBw8fPnzNuurZERERcR2m7tkJDQ3lvvvu47777gMqztDx9vZm27ZtdOzYkZUrVzJq1CjeffddoOKurG7dujF//nxatmzptK56dkRERFyHqcPOPysuLmb+/PkUFhaydetWBg8eDMDXX3+NxWK54rPnzp27Zi273e64agJQz46IiEgVZuplrIkTJ/L9999z6tQpJkyYQEFBAVBxTYSHR0VOS05O5oYbbmDJkiW0adMGgNGjR1+zrnp2REREXIepZ3bS09MZOnQoKSkplJSUEBAQQJ06dahdu7bjOghvb2+OHDnCo48+Sv369YmKirrmvVhQEaLGjx/veJ6Tk6PAIyIiUkX9RzM7iYmJJCQkUFhYCFT0zPyZFi5cyKlTp7jzzjsB+OCDDwgKCgKgZs2aAERHR+Pr64u3tzdlZWX4+vpy8uTJP3UcIiIi8r/rD4WdjIwMunXrRsOGDbn99tsdMykPP/wwTz755J82uHnz5lG7dm1Wr16NxWIhLi6OjIwMoGJbekREBEFBQdSqVYv8/HxOnDjB0aNH2bNnzzXrahlLRETEdfyhsDNu3Dg8PDxITk7Gx8fH8fq9997L+vXr/7TBWa1WcnJyCA4OZvjw4XTt2pVDhw6RkZGBxWKhf//+bN++naZNm7Jq1SoefPBB3NzcSExM5MSJE07rauu5iIiI6/hDPTvffPMNCQkJREZGXvF6gwYNOH369J8yMIDq1auTnZ0NwKJFi4CKpbIPP/yQ5cuX07JlS7p06cL333/P559/TvPmzfn666/p0aMHiYmJ1KtX76p1tfVcRETEdfyhmZ38/PwrZnQuy8zM/FNDxL59+2jatCk+Pj4EBATQuHFjoOIKiW+//ZadO3dSVlbGxYsXKSgoYM+ePcyYMQP4R0/P1ei6CBEREdfxh8JOx44d+eCDDxzPLRYL5eXlzJo1iy5duvxpg/vpp58YOHAghmGQnZ3N0aNH8fT0pHbt2o5QtXXrVvr27cv8+fO58cYb2bJlC0FBQTRr1sxpXfXsiIiIuI4/tIw1a9Ysunbtys8//0xxcTF///vfOXToEJmZmfz4449/2uDWr19PcXExAwcOJDs7mw8++IA33niD48ePU15eDkCtWrXYsGEDn3/+OVFRUfj7+5OTk0NpaanjLJ5/pa3nIiIiruMPzezExsZy7NgxOnToQN++fcnPz+eee+5h7969Tvtk/igvLy/q169Py5YteeyxxwAIDAx0LFPNnDmTjIwMioqKOH78OI0aNcLDw8Np0IGKnp2AgIArHiIiIlI1/eFDBQMDA5k0adKfOZZrKi8v54477gDg0KFDjq3nc+bMYcqUKaSkpODr60tmZqbjBnRndF2EiIiI6/hDYWf//v1Xfd1isWCz2a7oqflPTJw4kV69elG7dm2GDx/OsWPHgIrrIiwWCxMmTGDixIlMnDiRli1bMmzYMAzD4Pz585SVleHu7n7VunFxcUybNu0/Hp+IiIiY3x8KOy1atHBcvnn51OR/vozT09OTe++9l/feew+bzfaHB3f5uojk5GQMw+C6667Dw8OD2rVrAzB27FiKiop48803SUtLw9/fn/j4eEaOHMmpU6ecLqmpZ0dERMR1/KGendWrV9OgQQPi4+P55Zdf+OWXX4iPj6dRo0YsW7aMhQsXsmnTJiZPnvwfDW7BggXccccd2Gw2hg0bxqFDhxzXRVz26KOPUrt2bTp27Mjx48c5fPgwMTExCi8iIiIC/MGZnRkzZvD6669z2223OV67/vrriYyM5LnnnmPnzp34+vry5JNP8uqrr/7hwQUGBpKbmwvAkiVLWLJkCVBxXUVmZiYTJ07kgw8+oKioCIvFQo0aNahTpw7Lly93uoQFWsYSERFxJX9oZufAgQNER0f/5vXo6GgOHDgAVCx1/d7t47/nctD5V1arlZSUFH799VeKioqAfyynnTp1inbt2pGYmOi0rq6LEBERcR1/KOw0btyYl156ieLiYsdrJSUlvPTSS45Tjs+dO0dYWNh/NLjVq1cD4O7ujru7u6MvaPfu3bRo0YLNmzdjGIbj8cknn+Dl5YW3t/c1LwPV1nMRERHX8YeWsd5++2369OlDZGSk46TiAwcOUFZWxtq1awE4efIkjz766H80uM6dOzNy5Ei++uorLl68SElJCX5+fvTp04dnnnmG8vJynn32WdatW8fJkyfx9PTEYrFgGMYVW8v/lbaei4iIuI4/FHbat29PUlISH330kWM7eP/+/Rk8eDD+/v4A3H///f/x4N555x1WrlzJ+++/T1JSEk888QT5+fmcPXuW2NhYfvnlF1atWsXDDz9M48aNeeihh4CKWabbb7/daV317IiIiLiOP3yooL+/P506daJOnTqO5azNmzcD0KdPnz9lcNu2baNv37707t2b2267jdtvv52ffvqJtLQ0AEJDQ2nQoAGzZ88mIyMDLy8vOnXqxIYNGxy9PFejreciIiKu4w+FnZMnT3L33Xdz4MABx7LRP5+zU1ZW9qcMrn379sTHx7N582Y2btzI7Nmz2blzJ8899xwAERERfPzxx9x22200b96ctWvXsnXrVjZu3Ei1atX+lDGIiIjI/7Y/1KD8xBNPEBMTQ3p6Oj4+Phw8eJAtW7bQqlUrvvvuuz9tcIGBgRQUFHDrrbdSXl7OuHHj6NWrF0OGDAHg9ddfp1atWuzcuZNvv/2WrKwsnn76aQYNGnTNpmPdei4iIuI6/lDY2b59O9OnTyckJAQ3Nzfc3d3p0KEDcXFxPP7443/a4E6dOkV5eTnVqlWjX79+3Hnnnbz//vvMnDmTnJwc5syZg4+PD+PGjQNg8ODBFBcX89Zbb12zrraei4iIuI4/FHbKysocjcghISGkpKQAFefsHD169E8b3IoVKxg4cCBZWVnMnDmTNWvW4O3tzRtvvMGePXtITk7m/PnzjoMLN2/ezMGDB8nOzr5mXW09FxERcR1/qGfn8k6omJgY2rRpw6xZs/Dy8iI+Pp66dev+aYMrKCigadOmGIZBWVkZK1asoLi4GKvVSufOnTEMg5KSErp06cKPP/7I8ePHqV+//u/W1dZzERER1/GHZnYmT55MeXk5ANOnTycpKYmOHTuybt063njjjT9tcA0aNOCxxx7DYrHg4eHBkCFD8PLyYuDAgQAcOXKE2rVrs23bNgAee+wxDhw4QFpa2hUHHv4r9eyIiIi4jj8Udm677TbuueceAOrXr8+RI0e4ePEi6enp3HrrrX/a4MaPH0+vXr0IDQ3F09MTT09PCgsLuemmm8jPz+e2224jLS3NcVXE+vXradasGTVr1nQEoKtRz46IiIjr+MPn7Pyr6tWr/1mlHAYMGMCAAQOueM3T05M33ngDf39/zp49S3Z2Nnv27KFLly6cOnWKmJgYvvnmGzp37uy0rtVqxWq1/unjFREREfP5QzM7laGsrIxBgwZRWlrK2bNnsdvtWCwWzp496zh3JzY2FqiY4bkWu91OTk7OFQ8RERGpmkwddiZOnMjixYvx9fXFy8uLFStWANC8eXPatm2Lt7c3bdq0cdyO3qZNGwzDYMmSJY6eoqtRz46IiIjrMHXYSU9PZ+rUqY5m4xo1auDu7o6vry+hoaHcdNNN5OXl8csvvwDw7bffApCRkcGmTZuc1lXPjoiIiOv403p2/goLFy4E4IEHHqB69eq89tprBAUFsW/fPgDGjh3Lli1bOH36NL6+vlSrVo2wsDAuXrzI1q1b6datWyWOvnK4FV37jCERERFX+1th6rADFTeff/HFF7i7u/Puu+9SXFxMXl4eAC1btsTd3Z3mzZtTWFiIzWYjMzMTgNTUVKc1q+Kt54GBgXh6WeHklsoeioiI/A/w9LISGBhY2cP4rzB12Hn44YdZsmQJt956Kw8++CA//PAD7777Lg0aNABg9erV1KlTh4yMDAoKCigoKMBms+Hm5oabm/MVuqp463lYWBgfLv3gd0+PFnE1p0+fZsaMGUyaNIno6OjKHo6IaQQGBhIWFlbZw/ivMHXY+fbbbykvL2fjxo1s3LjRcbP6xo0b8fDwYPz48WRnZ5OTk0PdunV56KGH6N69O23atCEoKMhp3aq69TwsLMxl/h9X5P+v6OhoGjZsWNnDEJFKYOqwc3m56jLDMPDw8GDgwIHcd9999OzZ0/HeyZMnmTx5MpMnTwagX79+TuvquggRERHXYerdWFlZWVgsFh577DG++eYbZs2aRVlZGefPn6dbt26kpqby2muvsXbtWrZv307fvn0BaNiwIS1btnRaV1vPRUREXIfFuHzXggm5u7tjs9nIz893vBYZGUlZWZmjAfmZZ55hyZIlZGRkUF5ejs1mIyUl5ZpNV1eb2YmKiiI7O1s3oItUMceOHWPkyJHEx8drGUukisnJySEwMPB3/36bembHz8+P4uJiIiIiqFu3LkOGDGH06NFXNB+/9NJLnDlzhg4dOlBeXs5nn33mMt3lIiIi8vtMHXZuuOEG6tevz/r165k3bx5JSUm8/PLLREZGOj6zadMmoqKi2LKlYsv1P8/YOKNlLBEREddh6rDzyiuvkJiYyNq1a6lXrx4PPvggubm5NGvWDICSkhKeffZZCgoKHLurLl26RFpamuPU5avRCcoiIiKuw9Rhp3Xr1qxevZrly5cTGxvLnDlziI6OJiQkBIBz586xY8cOcnNzKSoqAuChhx6iZs2abNu2zWldq9VKQEDAFQ8RERGpmky99Rzgjjvu4I477gAqtqLXrl2bmjVrAlCnTh0Mw6Bz586EhISwcuVKVq9ezV133XXNmtp6LiIi4jpMPbPTvn17LBaL4+Hv709OTg6DBg0CoKioiPvuu48tW7bw1VdfARXb1X+PenZERERch6nDTk5ODh4eHnh6ehIeHk6fPn3Yvn07oaGhAIwbN44vv/yS0NBQvv/+ewBefvnl362rnh0RERHXYeplrL/97W98/vnnjlvO/1l2djYLFy5k2bJl/O1vf3O8fuTIEX766Sfatm3rtG5VvS5CREREfsvUMzu7du1i//79jos9Q0JCeP/99wHYvXs3JSUlrF+/nuDgYPz8/AAIDg5m+/bt16xrt9vJycm54iEiIiJVk6nDTuvWrXn66af54osvmD9/PjabjWHDhrFz507S0tJwc3MjISGBuLg44uPjAcjNzWX//v0kJyc7raueHREREddh6mWsqVOnXvG8X79+BAUF8c477zhOTB4+fDijRo1yfKa4uJglS5ZgGAZLliy5at2JEycyfvx4x/PL10WIiIhI1WPqmZ1/VlZWxvr167FYLHh6epKdnQ3AsGHDMAzD8XB3d6dv375Og46IiIi4FlOHnbi4OGJjY7FYLHh4eHD//ffj7e3N9ddf77j/au3atdx9992Ehobi5+dHWVkZHh7XnrDSMpaIiIjrMHXYiY+Pp1evXixatIipU6fi6elJQUEBTZo0wcfHB4vFwtixY7l48SJvvfUW9erVw93dnR9//JHy8nKndbX1XERExHWYOuy0adOGjz76iEceeYT58+fTo0cPAN58803Cw8MxDIOysjIOHjzIQw89RL169ahRowbnz59n06ZNTuvquggRERHXYeoG5Z9++onU1FSg4h6sc+fOAXDgwAFatmwJgGEYjlOTV69eDYC7uztbt26lW7duV62r6yJERERch6lndvr27cuqVavYuXMnGzdudNyJNWbMGAIDAwkLCwPg9ttv54svvqBFixbUqFGDsrIyR0i6GvXsiIiIuA5Tz+zk5eUxbtw4UlNTcXNzo6SkhLCwMJ588kkAGjZsiI+PD+vXr2fdunVYLBbuuecekpKScHNznuO09VxERMR1mDrsLFy4EID/+7//4/PPP6egoIDHH38ci8UCgJubG7m5uQQFBREaGkrPnj2ZMWMGdevWpW7duk7r6roIERER12HqsGMYBv369WPdunVYrVZycnIIDw93vD948GACAgJYvnw533//Pa+//jpLliwhKyuLPn36OK2rnh0RERHXYeqenTFjxpCQkED//v0dy0wWi4XCwkIARowYweTJk0lOTmbRokUMHz6crKwsfH19iYyMdFpXPTsiIiKuw9RhZ968eRQUFPDhhx9y6NAhAB566CE+/vhjAI4fP86JEyc4c+YM9957r2O7uYeHB8uXL3daV+fsiIiIuA5Th53LV0BMmTLFsXy1evVqhg0bBuBYitqxYwfFxcWOG9FtNhtbt26tlDGLiIiIuZg67ACUl5ezePFiHnjggSteP3HiBCtXrsTNzY369etjsVjo0KEDwO8eKqhlLBEREddh+rCzceNGkpOTeeihh6543cvLi82bN+Pj43PF6zfccAPAFY3M/0rLWCIiIq7D1LuxAHr06IFhGL95PSoqii1btjieZ2dnU1xczIwZMzh8+DCtWrVyWlNbz0VERFyHqWd26tSpg8VicTwA7r77bsaMGQNULGVdvvE8KiqK0aNHs3jxYoqLi7nrrruc1rXb7eTk5FzxEBERkarJ1DM7u3btIicnh6SkJAC6d+8OVCxVHTlyhN69exMSEsLLL79MzZo1efzxx8nJyaFnz56OS0OvJi4ujmnTpv1XfgcRERGpXKYOO6GhoRw6dMgRci4bMWIE3bp149SpU4waNYopU6Zw/vx5xxURjz/++DXr6roIERER12HqZSyAzp07YxgGdrud4OBgZsyYgWEYjmsjnnjiCc6cOcPx48cpLi7Gzc2NHTt2VPawRURExCRMH3a+//577rzzTsLDw8nIyHDssmrbti2+vr7YbDYsFgt16tTBMAzKy8uZNm0ar7zyitOa2nouIiLiOkwfdvLz82nevDm1a9cGoHr16kDFEtenn37qeB0qbkGPjo4GoF+/fk5rauu5iIiI6zB92OnVqxcjRozgwIEDv3mvR48enD59mk8++QSAL7/8krS0NOrXr/+7t54HBARc8RAREZGqyfRhB2Dx4sXUqFHD6fv9+/fHMAz279+P3W5n1KhR16ynreciIiKuw9S7seLi4li1ahW7d+/G29sbgHPnzjneX7x4MU2aNCE0NJRt27Y57sz656UtZ3W19VxERMQ1mHpmZ8uWLdxyyy0YhuG4xXzatGnk5+cDcPToUe666y6aNGnC+PHj8fCoyG5eXl7XrKueHREREddh6rCzfv16Xn31VQzDoE+fPgBcuHCB3bt3A/DSSy+RlpbGzp07sVgsFBcX/1t11bMjIiLiOkwdduAfW88jIiIcr13ekQVw3333ccMNN3DhwgXHa9OnT79mTfXsiIiIuA7Th52LFy8SHh7uOPG4Ro0alJaWkpycDFRcKREREYG3tzcvvfQSwBWnI1+NztkRERFxHaYPO9WrV2fBggVMmDABgPT0dG644Qaef/551qxZQ2pqKsHBwQA88sgjAPj5+V2zpnp2REREXIfpw07nzp0ZM2YMkZGRAKxevRrDMFiyZAmbNm0iNzeXAwcOUFhYSLVq1QC455576Ny5c+UNWkREREzD1GHnnXfeISQkhHfeeYdLly4BOJqTMzMzyc3NJSQkBE9PT0JCQujYsSMAUVFRLFiwwGldLWOJiIi4DlOHnbVr11JYWMjSpUtJSEgAKoLK7t27SUlJISsri0WLFvHrr7/y8ccfk5aWBkBycjKnT592WlfLWCIiIq7D1IcKfv3110DFjqvLysrKePvtt1m0aBErV650vF6vXj1mzJjBgAED8Pf3JzExka5du161rtVqxWq1/rWDFxEREVMwddgxDMPx72VlZY5DA0tKSgA4deoUMTExv/lebm4uJ0+edFrXbrdjt9sdz7X1XEREpOoy9TIWwI4dO/Dx8XGcily9enXKy8tJTk4mKCiI0aNHs3btWhISEggODsbX1xeLxcLf//53pzXVsyMiIuI6TD2zAxWzLoWFhY7nmZmZLFu2DE9PT+bNm8eJEyd44IEHyMzMxGq14uPjQ58+fRzb0a9m4sSJV5zFk5OTo8AjIiJSRZl+Zqd79+4MHTqUsWPHYhgG1apVIzY2liVLluDt7c1nn31Gw4YNufXWW/n222/JzMzk//7v/yp72CIiImISpg87U6ZMYfXq1axYscJx/1VZWRlQMSPTo0cPSkpKsFqtdOnSBYvFwhNPPOE4YflqtIwlIiLiOkwddsaMGcOcOXO4/fbbefrppwEoKCigcePGjqCTmZlJYmIikZGReHp68n//939MmjQJm83mtK62nouIiLgOi/HPW55Mplu3bnz77be/ed1isWCxWCgvL7/q95KSkqhTp86//XNycnIIDAwkOztbN6CLVDHHjh1j5MiRxMfH07Bhw8oejoj8if7dv9+mntlZvXo1Bw4ccDyg4jydIUOG8Msvv1BWVoafnx/Tp08nKCgIq9XKTTfdxL59+65ZV7eei4iIuA5Th53Zs2dz/fXXOx4AZ86cITg4mIiICEaMGEFeXh7PP/88ly5dom3btvTq1Yt77rmHLVu2OK2rnh0RERHXYeqwA9C0aVNSU1NJTU0FcBwimJKS4rgeon79+oSGhpKSksLBgwe54447ePfdd53WVM+OiIiI6zB92PHw8CA8PJzw8HAAXnrpJebOnUtsbCyrV6/Gw8ODBx54gPT0dGbMmMGXX35Jo0aNrrkby2q1EhAQcMVDREREqiZTh51du3axf/9+3NzccHOrGOqmTZsc7z/22GO4u7szdepUQkNDmTFjBj4+PiQmJhIdHe20rnp2REREXIepT1Bu3bo1TZo0ISoqiszMTKZPn86bb75J69atueWWW2jZsiVRUVFMmzaN+++/n3feeQeLxcKaNWt+t2dn2rRp/8XfRERERCqLqcPO1KlT+e677+jSpcsVrw8dOpQHHniAJUuWABAUFMSTTz5JcXExhmEwb948OnTo4LSurosQERFxHaZexgLo3LkzhmFQWlrK8uXLsVgsDB8+3BF0cnNz+eijj2jXrh1jxowhJiaGhx56qHIHLSIiIqZh6pkdgMWLFzNixAjHFRE2m82xDT0nJ4fatWuTnZ0NwPfff0/btm3Zu3cvbdq0cVpTy1giIiKuw9QzO0899RSpqakMGjSIMWPGAFBUVMSNN97ouC7Cz8+Pl19+mXfeeYe2bdvy66+/0qNHDy5cuOC0rraei4iIuA5Tz+ycPXuWZcuWkZGRQWhoKACNGjVi6dKlDBo0iB07dgA47s36Z/v376dr165XrWu1WrFarX/dwEVERMQ0TD2zs2LFClasWEGPHj0c92AVFhZit9sdvTzl5eU899xzhIeHY7PZsFgsWK1Wmjdv7rSutp6LiIi4DlPP7EycOJGgoCCio6Pp2rUr48aNIzk5mSFDhnDy5Ek+/vhjUlJSWLhwIWVlZRQXFwMVu7P8/Pyc1lXPjoiIiOswddhJT09n+fLlpKamEhgYCMC9995L9+7dSUlJ4fvvv+ebb74BoEaNGjRr1gybzcaaNWt4//33GTVq1FXrauu5iIiI6zB12Fm4cOEVzy0Wi+NurIiICN5++23q1avH3r17adGiheNz3t7e1ww7IiIi4jpM3bNTVlbGc889R0xMDN7e3gD8+OOPGIYB4LgINC4ujoiICHx8fOjZsycWi+WafTi69VxERMR1mDrsXN5SPn78eFauXAnA9u3befrpp0lOTqagoACAQ4cO8e6777J06VKSkpIoLCx0XBx6Ndp6LiIi4jpMvYy1bds22rRpw+OPP+54rbS0lFdeeYX09HTuvfdeAFJSUujfvz/BwcG0atWKkydPOmZ/rkZbz0VERFyHqWd22rdvz+HDhzl69KgjvAQGBvLhhx+yZMkSatWqBcCoUaOw2+2kpKTw4YcfUlpaSmlpqdO62nouIiLiOkwddux2O6dPn6ZRo0ZYLBagYmanadOmJCcn06RJE8cJyjabDT8/P2rWrAmAl5eX07rq2REREXEdpg47hw4dwt3d/YrX8vPzueGGG3j++ef5+eefMQwDX19f7HY7+fn5WCwWOnTogIeH8xU69eyIiIi4DlOHnW+++Ybw8HAMw3A8XnjhBRo1asSSJUsYN24c48aNIzc3l6ysLNLT08nLy6O4uJi6des6rWu1WgkICLjiISIiIlWTqcNOYWEhKSkpuLm54e7uTlRUFKdOnaK8vJz09HR27NhBUFAQ4eHhjn/edNNN7Nq1i759+zqtq54dERER12HqsBMQEICPjw8TJ05k6tSpXLp0iYULF9K9e3dOnjwJVFwCWlBQwPTp07n55pvZtWsXgYGB9OjRw2ld9eyIiIi4DlOHnVOnTjF8+HA+/PBDZs6cSVBQEFBxQvLli0HLysrw9PRk+vTpnD59murVq5OVlcVPP/3ktK56dkRERFyHqcOOv78/c+fO5fTp0xQWFrJ582YAsrKyHLuuDMPgxIkTFBcXc/r0abp164aPjw/bt2+vzKGLiIiISZg67ADk5uYyduxYoqOjadSoEVBx1k6dOnWoVq0abm5uzJkzh5o1a+Lt7c26devw9fV1XCVxNVrGEhERcR2mDjtPPfUUffv2Zd26ddSuXRsPDw9sNhvz588nJSWFO+64g/LycmbPns2UKVO4//77KSgoIDs7+5qHCmoZS0RExHWYOuycOnWKzZs3k5iYyPbt2+nevTsHDx6kQYMGzJs3j2HDhgHg4eHBk08+ycGDB/nqq68oLi7mwoULTutq67mIiIjrMHXYWbRoEQDVq1fn119/pUOHDtSvX5/U1FS2bt1KSEgIAE2bNsVms7F//37mzJkDVGwvd0Zbz0VERFyHqcPOM888g7u7O5GRkezcuZN33nmHiIgIUlNTSU1NJT8/H4CdO3fy7LPP8u6777J9+/Zrnp4M6tkRERFxJaa+9XzevHkA/PLLL9x3332O18PDw3FzcyMvLw+ADh06MGPGDOx2O+3bt2fjxo1kZGQ4rTtx4kTGjx/veJ6Tk6PAIyIiUkWZOuxcvun8gQcewN/fn0mTJjFo0CBSUlKoW7cu6enpALzwwgt07NjR8T2bzUZxcbHTularFavV+tcOXkREREzB1GGnrKyM/v37s3btWtzd3fn6668pLS0lNTWVp556ynET+jPPPMPp06fJyMggOjoau91OtWrVnNa12+1X9PSoZ0dERKTqMnXPzrPPPsvnn3/O8OHD+eabbxg8eDDJycnYbDYefPBBLBYLFouFn376idGjR7N69WpsNhsAbm7OfzX17IiIiLgOU4edb7/9FsMwmDdvHp06deLFF18EKg4a9Pb2JiwsDMMwaNeuHW+++SZ33XUXISEheHp6OpqXr0bn7IiIiLgOU4ed3r17ExERwZdffsmBAwf47LPP8PDw4Oabb2bfvn20atUKi8VCWloau3fvprCwkPvvv5+SkhLuueeeyh6+iIiImICpe3amTJlCYmIid955p+O1GjVq0KpVK2JjYwEYPnw4y5YtIzIyEovFgmEYhISEMHr0aKd14+LimDZt2l8+fhEREal8pp7Z+eSTT0hISOCuu+7itddeA+DixYscPnzY8ZmYmBg8PT0du6s8PDzIyclh48aNTutqGUtERMR1mDrsTJgwgWnTprF69WrGjh0LwL333ktycjIAhYWFTJkyhblz5xISEsKBAweoVasWN9xwA6+++qrTurouQkRExHWYOuzk5+fz1VdfERMTg7e3NwCHDh2irKwMgJKSEkpKSpg5cybVqlWjffv2JCcnc/DgwWs2KOu6CBEREddh6rBTp04dEhISeOCBB9iwYQMA+/fvJzo6GoCAgAAiIyM5duwYsbGxfPTRRwQGBlJYWHhFn8+/0tZzERER12HqsFOjRg0aN27M4sWL6d69OwC1atWiRo0aAOzevZsLFy4QHR3NDz/8QP/+/cnLy+POO+9kwoQJTuuqZ0dERMR1mDrsdOrUifz8fDZs2EBhYSEAeXl59O7dG4AtW7Zgt9tJTk4mJSWFoqIiSktLWbNmDTExMZU5dBERETEJU4edZ555hrvvvptGjRo5robw9PSkYcOGAPTs2ROouAtrwoQJjtkfwzB44YUXnNbVMpaIiIjrMHXY+eSTT3jvvfeoUaMGU6dOBSArK4uOHTuyc+dOx/1Xd911F+3atSM9PR13d3fCw8NZt26d07paxhIREXEdpj5U8KmnnqKoqIjCwkJH2CktLaW0tJThw4fz888/4+HhQWRkJI899hgJCQk0b94cf39/x/b0q9Gt5yIiIq7D1DM7BQUFGIbBxo0bMQwDwzCYOXMmNpuN6tWr4+XlRatWrfjggw+YMGECTZs2JTIyEqvV6tixdTXaei4iIuI6TB12+vTpg4eHB3fffTehoaFYLBamT5+O3W4nNTWVkpIScnJyOH/+PH//+98JCwsjNTWVgwcP8uijjzqtq54dERER12HqsPPmm29yyy23UFBQwMWLFwHw9vZm4MCBuLm5sW3bNhITE7nvvvsIDw8nKyuLkpISgoOD6dChg9O66tkRERFxHaYOO/7+/mzcuJHS0lLy8vIAWLRoEWVlZdStW5e9e/dSUlLC8uXLOXfuHGVlZRiGQUZGBpGRkU7r6roIERER12HqBuV/5uvrC1Scs5OQkMCsWbPo168f3bp1u+Jzbdq0oaCggE6dOjmtZbfbsdvtjufq2REREam6TB92EhISMAyDRo0aAfDcc8/RuHFjHnzwQTw9PQkODnZ8duvWrRQUFODt7e04Zflq4uLimDZt2l8+dhEREal8pl7GAsjOzmbMmDE0btwYgCZNmpCQkICnp+cVn7t06RI9e/bEw8OD8PDwa9ZUz46IiIjrMH3YGTBgACdOnHAsO40cOZLAwMArPlNSUkLz5s3x9vYmLS2N2rVrV8ZQRURExIRMH3a+//577rzzTiIiIgDYsWPHFe9/8sknhISEcObMGS5evPhvzdJo67mIiIjrMH3YuXjxIuHh4Tz11FMAnD9/nn379pGcnExJSQnTp08nNzeXUaNGOT5fXFxMWVmZ05paxhIREXEdFsMwjMoexLV89913dOnS5TevP/DAA0ydOtXp7eYWiwU3Nzfsdjvu7u7X/Bk5OTkEBgaSnZ2tbegiVcyxY8cYOXIk8fHxjkuERaRq+Hf/fpt+Zqdz586OqyIAVq9ejWEYLFmyhDp16nDp0iVGjhxJWFgYAGFhYURERDB48GD27dt31aCj6yJERERch+nDzu955513WLlyJTNnzgTgySef5Pz585w9e5bY2Nirfkc9OyIiIq7jfz7sbNu2jb59+3LrrbcC0L17d4KCgkhLS3P6HfXsiIiIuI7/+bDTvn17vv32W06ePAnA0aNHcXNz47nnnqvkkYmIiIgZ/M+HHbvdzunTp+natSsAAwcOpLy8nCFDhjj9jpaxREREXIfpw05eXh779u1j3759ACQlJTm2ngPs3bsXd3d3Hn30UQCGDRuG3W7njTfecFpTy1giIiKu43966/mSJUvw9vamqKjoN+8HBwdz8eLFf+tnaOu5SNWlreciVde/+/fb9BeBXt56fi1eXl4EBwdjs9lo164dkZGRrF692unndeu5iIiI6zDNMtZLL72ExWJh7Nixjtc6d+6MxWK54vHII49c8b2ioiKKi4tJTU0lKSmJZcuW8corr3D77bc7/Vnq2REREXEdpgg7u3bt4r333qNZs2a/eW/EiBGkpqY6HrNmzfrNZ7p3706tWrWwWq1ERkbi6elJkyZNnP489eyIiIi4jkoPO3l5eQwZMoT58+cTFBT0m/d9fHwIDw93PK62Jvfoo49y9uxZioqKOHPmDM2aNePUqVNOf6bVaiUgIOCKh4iIiFRNlR52xowZQ+/evenWrdtV3//oo48ICQkhNjaWiRMnUlBQ8JvPDBgwAIvFgqenJ82bN+fo0aPUrFnT6c/UdREiIiKuo1IblFesWMGePXvYtWvXVd8fPHgw0dHRREREsH//fp5++mmOHj3KqlWrHJ+pVq0avXv3pn379nz33Xd8+umnWCwW+vTp4/TnxsXFMW3atD/99xERERHzqbSwc+bMGZ544gk2bNiAzWa76mdGjhzp+Pfrr7+emjVr0rVrV06cOEG9evUAuO2229i0aROffvopoaGhNGzYkGPHjpGcnEydOnWuWnfixImMHz/e8TwnJ0dNyiIiIlVUpS1j7d69m/T0dG688UY8PDzw8PBgy5YtvPHGG3h4eFBWVvab77Rp0waAxMREx2srVqwgJSUFu93O2bNneeqppwDw8/P77/wiIiIiYmqVFna6du3KgQMHHKcj79u3j1atWjFkyBD27duHu7s7ALm5uYwdO5bo6GiqV68OQEZGhqPOqlWr6NGjB8HBwVgsFmbMmIG7uzs33nij05+treciIiKuo9KWsfz9/YmNjb3iNV9fX4KDg4mNjeXEiRMsW7aMH374gVOnTvHII48wb948LBYLo0eP5pZbbmHPnj189dVXNGjQgM6dOzNp0iROnz59xVk9V6NlLBEREddh2hOUvby8SEhI4Mcff8TDw4NFixYxcOBAJk+eTJcuXZg3bx4dOnRgz549JCYmUlhYCFTs7po9e/Y1a1utVqxW63/j1xAREZFKVulbz//Zd999x9y5cwGIiori66+/BmD9+vUcP36cWbNmERAQgLe3N1u3bqVnz57s2bOHfv364eFRkdsWLFhA8+bN+fnnn53+HG09FxERcR2mCjv/yt/fn3bt2vHCCy+QkpJCWVkZH374Idu3byc1NRWA4cOH88EHH9C2bVsA5s+fz6RJk/D29nZaVz07IiIirsPUYQdg6dKlGIbhuA7ijTfeYNCgQbi5VQx90aJFGIbBli1bABg6dCiDBg1yenYP6LoIERERV2Lanp3L6tWrx5YtW8jPzycnJ4eaNWty7733UrduXQCaNGnCbbfdxtGjR/n6669p1KgRTz75JMOGDXNaUz07IiIirsP0MzuX+fr6UrNmTS5dukRCQgJ9+/YF4OTJk8ybN49Lly4BcOLECUaNGvWb29H/mXp2REREXIfpw05CQgLr168nKSmJDRs20KVLFxo3bsyDDz4IQHl5OY0aNaK0tBSA2bNn07RpU9577z1Onjx51Zrq2REREXEdpg872dnZjBkzhsaNGzN06FA6dOhAQkICnp6eAAQEBLB//37H7qsnnniCgwcPApCSknLVmurZERERcR2m79kZMGAAAwYMcPp+jx49OHPmDD/88AMAhmEQGxtLUlISN910039rmCIiImJSpp/Z+efrIry9vWnfvv0VO618fX3ZunUrXl5eBAYGUr16dQ4fPszLL7+Ml5fXVWtqGUtERMR1mD7sDB8+nA0bNrB06VIOHDhAjx496NatG+fOnQOgS5cuvPDCC8TExFBQUEBWVhZWq5WBAwc6rallLBEREddh6rBTWFjIypUrmTVrFp06daJ+/fpMnTqV+vXrM2/ePAAGDx7M5MmTOXr0KJ999hlQsdtq//79TutarVYCAgKueIiIiEjVZOqwU1paSllZGYsWLbpiGau0tJStW7dSUlLC008/zfXXX4+vry+jRo2icePG+Pv707x5c6d1tfVcRETEdZg67Pj7+xMcHMz69et57bXX2LdvH+Hh4ezfv5/k5GQKCgrYs2cPt99+O4ZhcP78eY4cOUKtWrUICQlxWlc9OyIiIq7D1GGnsLCQrKwsYmJi6NevH02bNuXs2bNUr16dnJwcAgMD2bBhA88//zz79+9n8uTJABw5coQ9e/Y4raueHREREddh6q3nl5exXn/9ddq2beu4LiI0NJTy8nLH53x9falfvz4vvPACt9xyC927d+eLL77gxhtvrMTRi6sqKioiOTm5soch/8/p06ev+KdUvtq1a2Oz2Sp7GOJCTB12/vnW82XLlhEWFsZ7773HxYsXCQsL+83ni4qKePrpp/Hz88NisTitGxcXx7Rp0/7KoYsLS05OZuTIkZU9DPkXM2bMqOwhyP8THx9Pw4YNK3sY4kIshmEYlT2Ia1m8eDFz5szh4MGDuLm5YbVa8fHxISQkhN27dzNjxgz69OlDSEgIDz30EIcOHSIvL489e/bQtGnTq9a02+3Y7XbH85ycHKKiosjOztbOLPmPaWZH5No0syN/lsstLb/399vUMztQsURVUFCAp6cn1apVY8CAAaSkpGC323F3d+fIkSMsWbKEtLQ03N3d6datG9OnT3cadEC3nstfy2az6b9aRURMxPQzO//q0qVLxMTEMGvWLEaOHElJSQkDBgzg+PHjbN68mdDQ0P/fNf/dZCgiIiLmUWVmdhISEjAMg0aNGpGYmMiECRMct56XlJTwt7/9jT179rB27VrKyspIS0sDoHr16k6vixARERHXYfqwk52dzcSJEx1bzvv168eMGTPw9PTk1KlTrFmzBoAWLVpc8b3NmzfTuXPn//6ARURExFT+55ax/gpaxhIREfnf8+/+/Tb1oYIiIiIi/ymFHREREanSFHZERESkSlPYERERkSpNYUdERESqNIUdERERqdIUdkRERKRKU9gRERGRKk1hR0RERKo0018X8d9w+RDpnJycSh6JiIiI/Lsu/93+vcsgFHaA3NxcAKKioip5JCIiIvL/V25uLoGBgU7f191YQHl5OSkpKfj7+2OxWCp7OCLyJ8rJySEqKoozZ87o7juRKsYwDHJzc4mIiMDNzXlnjsKOiFRpuuhXRNSgLCIiIlWawo6IiIhUaQo7IlKlWa1WpkyZgtVqreyhiEglUc+OiIiIVGma2REREZEqTWFHREREqjSFHREREanSFHZERESkSlPYERERkSpNYUdERESqNIUdERERqdIUdkRERKRK+/8A3+wJYgYHuFAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['binaryClass'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "STL9BrjYlYsq",
        "outputId": "2eebc8a7-cf8b-4d89-f12a-3ac28e1c95e7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='binaryClass'>"
            ]
          },
          "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": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "R0gdk3rslx8S",
        "outputId": "604d410d-d6c4-49e6-b42d-dc94b1d01c6c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age sex on thyroxine query on thyroxine on antithyroid medication sick  \\\n",
              "0  41   F            f                  f                         f    f   \n",
              "1  23   F            f                  f                         f    f   \n",
              "2  46   M            f                  f                         f    f   \n",
              "3  70   F            t                  f                         f    f   \n",
              "4  70   F            f                  f                         f    f   \n",
              "\n",
              "  pregnant thyroid surgery I131 treatment query hypothyroid  ...   T3  \\\n",
              "0        f               f              f                 f  ...  2.5   \n",
              "1        f               f              f                 f  ...    2   \n",
              "2        f               f              f                 f  ...    ?   \n",
              "3        f               f              f                 f  ...  1.9   \n",
              "4        f               f              f                 f  ...  1.2   \n",
              "\n",
              "  TT4 measured  TT4 T4U measured   T4U FTI measured  FTI TBG measured  \\\n",
              "0            t  125            t  1.14            t  109            f   \n",
              "1            t  102            f     ?            f    ?            f   \n",
              "2            t  109            t  0.91            t  120            f   \n",
              "3            t  175            f     ?            f    ?            f   \n",
              "4            t   61            t  0.87            t   70            f   \n",
              "\n",
              "  referral source binaryClass  \n",
              "0            SVHC           P  \n",
              "1           other           P  \n",
              "2           other           P  \n",
              "3           other           P  \n",
              "4             SVI           P  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ffc2c0be-3556-48c4-97c9-89fb7f92a701\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on thyroxine</th>\n",
              "      <th>query on thyroxine</th>\n",
              "      <th>on antithyroid medication</th>\n",
              "      <th>sick</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>thyroid surgery</th>\n",
              "      <th>I131 treatment</th>\n",
              "      <th>query hypothyroid</th>\n",
              "      <th>...</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4 measured</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U measured</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI measured</th>\n",
              "      <th>FTI</th>\n",
              "      <th>TBG measured</th>\n",
              "      <th>referral source</th>\n",
              "      <th>binaryClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</td>\n",
              "      <td>F</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>...</td>\n",
              "      <td>2.5</td>\n",
              "      <td>t</td>\n",
              "      <td>125</td>\n",
              "      <td>t</td>\n",
              "      <td>1.14</td>\n",
              "      <td>t</td>\n",
              "      <td>109</td>\n",
              "      <td>f</td>\n",
              "      <td>SVHC</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>t</td>\n",
              "      <td>102</td>\n",
              "      <td>f</td>\n",
              "      <td>?</td>\n",
              "      <td>f</td>\n",
              "      <td>?</td>\n",
              "      <td>f</td>\n",
              "      <td>other</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
              "      <td>M</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>t</td>\n",
              "      <td>109</td>\n",
              "      <td>t</td>\n",
              "      <td>0.91</td>\n",
              "      <td>t</td>\n",
              "      <td>120</td>\n",
              "      <td>f</td>\n",
              "      <td>other</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>F</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>...</td>\n",
              "      <td>1.9</td>\n",
              "      <td>t</td>\n",
              "      <td>175</td>\n",
              "      <td>f</td>\n",
              "      <td>?</td>\n",
              "      <td>f</td>\n",
              "      <td>?</td>\n",
              "      <td>f</td>\n",
              "      <td>other</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</td>\n",
              "      <td>F</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>...</td>\n",
              "      <td>1.2</td>\n",
              "      <td>t</td>\n",
              "      <td>61</td>\n",
              "      <td>t</td>\n",
              "      <td>0.87</td>\n",
              "      <td>t</td>\n",
              "      <td>70</td>\n",
              "      <td>f</td>\n",
              "      <td>SVI</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 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-ffc2c0be-3556-48c4-97c9-89fb7f92a701')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ffc2c0be-3556-48c4-97c9-89fb7f92a701 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-ffc2c0be-3556-48c4-97c9-89fb7f92a701');\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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              "  </div>\n",
              "\n",
              "\n",
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              "            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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1da2a7df-3b95-4564-8a79-552883368908 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['TT4'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8gyKczCmAFR",
        "outputId": "3ec4690f-9f68-43e7-a281-bcc157df5088"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encoding"
      ],
      "metadata": {
        "id": "NDeTQmhTmIpb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le =LabelEncoder()"
      ],
      "metadata": {
        "id": "jqYQzX5BmK53"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCdIlkrwnL8H",
        "outputId": "d15311a2-996d-4a4c-912c-0eea45b81295"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'on thyroxine', 'query on thyroxine',\n",
              "       'on antithyroid medication', 'sick', 'pregnant', 'thyroid surgery',\n",
              "       'I131 treatment', 'query hypothyroid', 'query hyperthyroid', 'lithium',\n",
              "       'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH measured', 'TSH',\n",
              "       'T3 measured', 'T3', 'TT4 measured', 'TT4', 'T4U measured', 'T4U',\n",
              "       'FTI measured', 'FTI', 'TBG measured', 'referral source',\n",
              "       'binaryClass'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['age', 'sex', 'on thyroxine', 'query on thyroxine',\n",
        "       'on antithyroid medication', 'sick', 'pregnant', 'thyroid surgery',\n",
        "       'I131 treatment', 'query hypothyroid', 'query hyperthyroid', 'lithium',\n",
        "       'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH measured', 'TSH',\n",
        "       'T3 measured', 'T3', 'TT4 measured', 'TT4', 'T4U measured', 'T4U',\n",
        "       'FTI measured', 'FTI', 'TBG measured', 'referral source',\n",
        "       'binaryClass']:\n",
        "       print(i,df[i].nunique())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXzmgTE-mkR1",
        "outputId": "78accf2c-e4fc-490b-fb07-20cae683b472"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age 94\n",
            "sex 3\n",
            "on thyroxine 2\n",
            "query on thyroxine 2\n",
            "on antithyroid medication 2\n",
            "sick 2\n",
            "pregnant 2\n",
            "thyroid surgery 2\n",
            "I131 treatment 2\n",
            "query hypothyroid 2\n",
            "query hyperthyroid 2\n",
            "lithium 2\n",
            "goitre 2\n",
            "tumor 2\n",
            "hypopituitary 2\n",
            "psych 2\n",
            "TSH measured 2\n",
            "TSH 288\n",
            "T3 measured 2\n",
            "T3 70\n",
            "TT4 measured 2\n",
            "TT4 242\n",
            "T4U measured 2\n",
            "T4U 147\n",
            "FTI measured 2\n",
            "FTI 235\n",
            "TBG measured 1\n",
            "referral source 5\n",
            "binaryClass 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['sex', 'on thyroxine', 'query on thyroxine',\n",
        "          'on antithyroid medication', 'sick', 'pregnant', 'thyroid surgery',\n",
        "          'I131 treatment', 'query hypothyroid', 'query hyperthyroid', 'lithium',\n",
        "          'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH measured',\n",
        "          'T3 measured', 'TT4 measured', 'T4U measured',\n",
        "          'FTI measured', 'TBG measured', 'referral source','binaryClass']:\n",
        "          df[i] = le.fit_transform(df[i])\n"
      ],
      "metadata": {
        "id": "5NqByucLoyqr"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "NlAQ44NCsNjL",
        "outputId": "092d341e-0d36-4e9d-a61c-d180bfa4222b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  on thyroxine  query on thyroxine  on antithyroid medication  \\\n",
              "0     41    1             0                   0                          0   \n",
              "1     23    1             0                   0                          0   \n",
              "2     46    2             0                   0                          0   \n",
              "3     70    1             1                   0                          0   \n",
              "4     70    1             0                   0                          0   \n",
              "...   ..  ...           ...                 ...                        ...   \n",
              "3767  30    1             0                   0                          0   \n",
              "3768  68    1             0                   0                          0   \n",
              "3769  74    1             0                   0                          0   \n",
              "3770  72    2             0                   0                          0   \n",
              "3771  64    1             0                   0                          0   \n",
              "\n",
              "      sick  pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...  \\\n",
              "0        0         0                0               0                  0  ...   \n",
              "1        0         0                0               0                  0  ...   \n",
              "2        0         0                0               0                  0  ...   \n",
              "3        0         0                0               0                  0  ...   \n",
              "4        0         0                0               0                  0  ...   \n",
              "...    ...       ...              ...             ...                ...  ...   \n",
              "3767     0         0                0               0                  0  ...   \n",
              "3768     0         0                0               0                  0  ...   \n",
              "3769     0         0                0               0                  0  ...   \n",
              "3770     0         0                0               0                  0  ...   \n",
              "3771     0         0                0               0                  0  ...   \n",
              "\n",
              "       T3  TT4 measured  TT4  T4U measured   T4U  FTI measured  FTI  \\\n",
              "0     2.5             1  125             1  1.14             1  109   \n",
              "1       2             1  102             0     ?             0    ?   \n",
              "2       ?             1  109             1  0.91             1  120   \n",
              "3     1.9             1  175             0     ?             0    ?   \n",
              "4     1.2             1   61             1  0.87             1   70   \n",
              "...   ...           ...  ...           ...   ...           ...  ...   \n",
              "3767    ?             0    ?             0     ?             0    ?   \n",
              "3768  2.1             1  124             1  1.08             1  114   \n",
              "3769  1.8             1  112             1  1.07             1  105   \n",
              "3770    2             1   82             1  0.94             1   87   \n",
              "3771  2.2             1   99             1  1.07             1   92   \n",
              "\n",
              "     TBG measured  referral source binaryClass  \n",
              "0               0                1           1  \n",
              "1               0                4           1  \n",
              "2               0                4           1  \n",
              "3               0                4           1  \n",
              "4               0                3           1  \n",
              "...           ...              ...         ...  \n",
              "3767            0                4           1  \n",
              "3768            0                3           1  \n",
              "3769            0                4           1  \n",
              "3770            0                3           1  \n",
              "3771            0                4           1  \n",
              "\n",
              "[3772 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc7bbcc8-5fc6-4b46-abf1-3b058ecbf864\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on thyroxine</th>\n",
              "      <th>query on thyroxine</th>\n",
              "      <th>on antithyroid medication</th>\n",
              "      <th>sick</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>thyroid surgery</th>\n",
              "      <th>I131 treatment</th>\n",
              "      <th>query hypothyroid</th>\n",
              "      <th>...</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4 measured</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U measured</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI measured</th>\n",
              "      <th>FTI</th>\n",
              "      <th>TBG measured</th>\n",
              "      <th>referral source</th>\n",
              "      <th>binaryClass</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</td>\n",
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              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>125</td>\n",
              "      <td>1</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>102</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
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              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>1</td>\n",
              "      <td>0.91</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</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>3767</th>\n",
              "      <td>30</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3768</th>\n",
              "      <td>68</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1</td>\n",
              "      <td>124</td>\n",
              "      <td>1</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1</td>\n",
              "      <td>114</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3769</th>\n",
              "      <td>74</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>112</td>\n",
              "      <td>1</td>\n",
              "      <td>1.07</td>\n",
              "      <td>1</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3770</th>\n",
              "      <td>72</td>\n",
              "      <td>2</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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>0.94</td>\n",
              "      <td>1</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3771</th>\n",
              "      <td>64</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "      <td>1.07</td>\n",
              "      <td>1</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3772 rows × 29 columns</p>\n",
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              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='sex',y='age',data=df,width=0.8,hue='tumor')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "BdRc3RwutEdi",
        "outputId": "67ed70a4-91bc-4e86-90d9-b4b094b76c0b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='age'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": [
        "df.sex.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cvbYTWFt33B",
        "outputId": "33278d08-b643-48d6-d941-e6ebc24d7fda"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "oFvFTdGfvt0R",
        "outputId": "9aba56aa-00b7-47e9-be93-ba9bd5650acd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age  sex  on thyroxine  query on thyroxine  on antithyroid medication  sick  \\\n",
              "0  41    1             0                   0                          0     0   \n",
              "1  23    1             0                   0                          0     0   \n",
              "2  46    2             0                   0                          0     0   \n",
              "3  70    1             1                   0                          0     0   \n",
              "4  70    1             0                   0                          0     0   \n",
              "\n",
              "   pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...   T3  \\\n",
              "0         0                0               0                  0  ...  2.5   \n",
              "1         0                0               0                  0  ...    2   \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n1= df.drop(['TSH','TT4','T4U','FTI','T3'],axis=1)"
      ],
      "metadata": {
        "id": "LfsBfo8fv6O8"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df.drop(['age','sex', 'on thyroxine', 'query on thyroxine',\n",
        "          'on antithyroid medication', 'sick', 'pregnant', 'thyroid surgery',\n",
        "          'I131 treatment', 'query hypothyroid', 'query hyperthyroid', 'lithium',\n",
        "          'goitre', 'tumor', 'hypopituitary', 'psych', 'TSH measured',\n",
        "          'T3 measured', 'TT4 measured', 'T4U measured',\n",
        "          'FTI measured', 'TBG measured', 'referral source','binaryClass'],axis=1)"
      ],
      "metadata": {
        "id": "w8defg-10fCV"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "xsm9KpwI1Z03",
        "outputId": "3cdeeec2-aeb1-4c51-b6e9-c0b98ab85baf"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age  sex  on thyroxine  query on thyroxine  on antithyroid medication  sick  \\\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
              "      <td>2</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 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-1cb944a5-ecc9-41f8-8252-30f0cbac99b4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1cb944a5-ecc9-41f8-8252-30f0cbac99b4 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-1cb944a5-ecc9-41f8-8252-30f0cbac99b4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-45758896-9c95-4f84-84a4-af6d066f6c32\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45758896-9c95-4f84-84a4-af6d066f6c32')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45758896-9c95-4f84-84a4-af6d066f6c32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "n1"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UtmfSE101eGk",
        "outputId": "dff3c9dc-f76a-43ab-9780-40cac48b0d90"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    TSH   T3  TT4   T4U  FTI\n",
              "0   1.3  2.5  125  1.14  109\n",
              "1   4.1    2  102     ?    ?\n",
              "2  0.98    ?  109  0.91  120\n",
              "3  0.16  1.9  175     ?    ?\n",
              "4  0.72  1.2   61  0.87   70"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d44c20a-664c-4032-8041-df2ace6536a7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>125</td>\n",
              "      <td>1.14</td>\n",
              "      <td>109</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.1</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.98</td>\n",
              "      <td>?</td>\n",
              "      <td>109</td>\n",
              "      <td>0.91</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.16</td>\n",
              "      <td>1.9</td>\n",
              "      <td>175</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.72</td>\n",
              "      <td>1.2</td>\n",
              "      <td>61</td>\n",
              "      <td>0.87</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d44c20a-664c-4032-8041-df2ace6536a7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4d44c20a-664c-4032-8041-df2ace6536a7 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-4d44c20a-664c-4032-8041-df2ace6536a7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8abf269-920e-4bb9-a297-ea4bde83ff72')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b8abf269-920e-4bb9-a297-ea4bde83ff72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y",
              "summary": "{\n  \"name\": \"y\",\n  \"rows\": 3772,\n  \"fields\": [\n    {\n      \"column\": \"TSH\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 288,\n        \"samples\": [\n          \"19\",\n          \"188\",\n          \"472\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"1\",\n          \"2.5\",\n          \"3.9\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TT4\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 242,\n        \"samples\": [\n          \"129\",\n          \"72\",\n          \"48\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T4U\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"0.31\",\n          \"1.63\",\n          \"1.7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTI\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 235,\n        \"samples\": [\n          \"85\",\n          \"49\",\n          \"349\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.where(y=='?',np.nan,y)"
      ],
      "metadata": {
        "id": "70Lvw4nz3jZ0"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = pd.DataFrame(y,columns=['TSH','T3','TT4','T4U','FTI'])"
      ],
      "metadata": {
        "id": "G3udUDp13sh1"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TOfzgWOz4BgY",
        "outputId": "07cbda2e-34af-4ec1-c801-229178e75b9b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    TSH   T3  TT4   T4U  FTI\n",
              "0   1.3  2.5  125  1.14  109\n",
              "1   4.1    2  102   NaN  NaN\n",
              "2  0.98  NaN  109  0.91  120\n",
              "3  0.16  1.9  175   NaN  NaN\n",
              "4  0.72  1.2   61  0.87   70"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa7c4a44-bc20-40ac-af4a-2268e608e9c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>125</td>\n",
              "      <td>1.14</td>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.1</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>109</td>\n",
              "      <td>0.91</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.16</td>\n",
              "      <td>1.9</td>\n",
              "      <td>175</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.72</td>\n",
              "      <td>1.2</td>\n",
              "      <td>61</td>\n",
              "      <td>0.87</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa7c4a44-bc20-40ac-af4a-2268e608e9c7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-aa7c4a44-bc20-40ac-af4a-2268e608e9c7 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-aa7c4a44-bc20-40ac-af4a-2268e608e9c7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05f7598a-8391-480f-9f5f-18cd19f45162\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05f7598a-8391-480f-9f5f-18cd19f45162')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05f7598a-8391-480f-9f5f-18cd19f45162 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y",
              "summary": "{\n  \"name\": \"y\",\n  \"rows\": 3772,\n  \"fields\": [\n    {\n      \"column\": \"TSH\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 287,\n        \"samples\": [\n          \"1.1\",\n          \"472\",\n          \"7.2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 69,\n        \"samples\": [\n          \"2.8\",\n          \"2.5\",\n          \"5.3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TT4\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 241,\n        \"samples\": [\n          \"129\",\n          \"72\",\n          \"17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T4U\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 146,\n        \"samples\": [\n          \"1.33\",\n          \"1.28\",\n          \"0.56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTI\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 234,\n        \"samples\": [\n          \"157\",\n          \"49\",\n          \"52\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-Fx2M0T4Gw2",
        "outputId": "cf01516f-8994-4887-cda3-63f832f514cc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TSH    369\n",
              "T3     769\n",
              "TT4    231\n",
              "T4U    387\n",
              "FTI    385\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkAzxybz4Oin",
        "outputId": "7a560e1d-ab52-4491-f99c-016512c6f0d7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TSH    object\n",
              "T3     object\n",
              "TT4    object\n",
              "T4U    object\n",
              "FTI    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['TSH','T3','TT4','T4U','FTI']:\n",
        "  y[i]=y[i].fillna(y[i].mode()[0])\n",
        "y[i]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2fEpdW54eGH",
        "outputId": "d59b945b-e394-4428-d792-d7749267f275"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       109\n",
              "1       100\n",
              "2       120\n",
              "3       100\n",
              "4        70\n",
              "       ... \n",
              "3767    100\n",
              "3768    114\n",
              "3769    105\n",
              "3770     87\n",
              "3771     92\n",
              "Name: FTI, Length: 3772, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fqniKUG95dN1",
        "outputId": "f396cfa4-4eaa-4f40-c938-ed0c2aa3a04d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    TSH   T3  TT4   T4U  FTI\n",
              "0   1.3  2.5  125  1.14  109\n",
              "1   4.1    2  102  0.99  100\n",
              "2  0.98    2  109  0.91  120\n",
              "3  0.16  1.9  175  0.99  100\n",
              "4  0.72  1.2   61  0.87   70"
            ],
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              "\n",
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              "      <th>TSH</th>\n",
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              "      <th>FTI</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>125</td>\n",
              "      <td>1.14</td>\n",
              "      <td>109</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.1</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
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              "      <td>0.16</td>\n",
              "      <td>1.9</td>\n",
              "      <td>175</td>\n",
              "      <td>0.99</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.72</td>\n",
              "      <td>1.2</td>\n",
              "      <td>61</td>\n",
              "      <td>0.87</td>\n",
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              "        document.querySelector('#df-ae3ee64d-255d-4532-af31-77c34cb8abea 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-ae3ee64d-255d-4532-af31-77c34cb8abea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y",
              "summary": "{\n  \"name\": \"y\",\n  \"rows\": 3772,\n  \"fields\": [\n    {\n      \"column\": \"TSH\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 287,\n        \"samples\": [\n          \"2.4\",\n          \"472\",\n          \"7.2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 69,\n        \"samples\": [\n          \"2.8\",\n          \"2.5\",\n          \"5.3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TT4\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 241,\n        \"samples\": [\n          \"129\",\n          \"72\",\n          \"17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T4U\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 146,\n        \"samples\": [\n          \"1.33\",\n          \"1.28\",\n          \"0.56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTI\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 234,\n        \"samples\": [\n          \"157\",\n          \"49\",\n          \"52\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "minmax= MinMaxScaler()"
      ],
      "metadata": {
        "id": "qnjHU_Pb1sWW"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = minmax.fit_transform(y)"
      ],
      "metadata": {
        "id": "dj71m9-j15ZR"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(x1,columns=['TSH','T3','TT4','T4U','FTI'])"
      ],
      "metadata": {
        "id": "Qaenqcrv5t44"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HNaiyarB6O2G",
        "outputId": "9446a56f-e051-420a-a685-b96781acde16"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TSH        T3       TT4       T4U       FTI\n",
              "0  0.002443  0.232227  0.287383  0.429952  0.272265\n",
              "1  0.007726  0.184834  0.233645  0.357488  0.249364\n",
              "2  0.001840  0.184834  0.250000  0.318841  0.300254\n",
              "3  0.000292  0.175355  0.404206  0.357488  0.249364\n",
              "4  0.001349  0.109005  0.137850  0.299517  0.173028"
            ],
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.007726</td>\n",
              "      <td>0.184834</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.001840</td>\n",
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              "      <th>4</th>\n",
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              "      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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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-d74ff20c-5a83-4b48-8cf6-8d72c507764a 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-d74ff20c-5a83-4b48-8cf6-8d72c507764a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d3ca422c-fdaf-40cc-89bb-27b0bb5dd6df')\"\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",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d3ca422c-fdaf-40cc-89bb-27b0bb5dd6df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 3772,\n  \"fields\": [\n    {\n      \"column\": \"TSH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04403072948786336,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          0.00451891055575996,\n          0.890565005330239,\n          0.013575599769809151\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06997934427196395,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          0.26066350710900477,\n          0.23222748815165878,\n          0.4976303317535545\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TT4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08070358728702061,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 241,\n        \"samples\": [\n          0.29672897196261677,\n          0.16355140186915887,\n          0.035046728971962614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"T4U\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0894505441086331,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          0.5217391304347827,\n          0.49758454106280203,\n          0.14975845410628022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FTI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08019063792396719,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 234,\n        \"samples\": [\n          0.3944020356234097,\n          0.11959287531806616,\n          0.12722646310432573\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "oBCzUhl86piV",
        "outputId": "e569a2da-60c0-42a1-c6dd-f11e88889912"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age  sex  on thyroxine  query on thyroxine  on antithyroid medication  sick  \\\n",
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              "\n",
              "   pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...  \\\n",
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              "\n",
              "   T4U measured  FTI measured  TBG measured  referral source  binaryClass  \n",
              "0             1             1             0                1            1  \n",
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              "\n",
              "[5 rows x 24 columns]"
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              "      <th>sex</th>\n",
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              "      <th>query on thyroxine</th>\n",
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              "<p>5 rows × 24 columns</p>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d47e8a0e-b6a5-465b-9f3a-79d40a306a03')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d47e8a0e-b6a5-465b-9f3a-79d40a306a03');\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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              "  </div>\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14f5803f-ca7e-4c88-a61e-6080ba610df9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "n1"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([n1,x],axis=1)"
      ],
      "metadata": {
        "id": "Ny8M7oWH5-RN"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "Wl8iN5ks64Tn",
        "outputId": "d20d8011-81a7-4108-a1cc-3aa43fa12cd8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  on thyroxine  query on thyroxine  on antithyroid medication  \\\n",
              "0     41    1             0                   0                          0   \n",
              "1     23    1             0                   0                          0   \n",
              "2     46    2             0                   0                          0   \n",
              "3     70    1             1                   0                          0   \n",
              "4     70    1             0                   0                          0   \n",
              "...   ..  ...           ...                 ...                        ...   \n",
              "3767  30    1             0                   0                          0   \n",
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              "3769  74    1             0                   0                          0   \n",
              "3770  72    2             0                   0                          0   \n",
              "3771  64    1             0                   0                          0   \n",
              "\n",
              "      sick  pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...  \\\n",
              "0        0         0                0               0                  0  ...   \n",
              "1        0         0                0               0                  0  ...   \n",
              "2        0         0                0               0                  0  ...   \n",
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              "3770     0         0                0               0                  0  ...   \n",
              "3771     0         0                0               0                  0  ...   \n",
              "\n",
              "      T4U measured  FTI measured  TBG measured  referral source  binaryClass  \\\n",
              "0                1             1             0                1            1   \n",
              "1                0             0             0                4            1   \n",
              "2                1             1             0                4            1   \n",
              "3                0             0             0                4            1   \n",
              "4                1             1             0                3            1   \n",
              "...            ...           ...           ...              ...          ...   \n",
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              "3770             1             1             0                3            1   \n",
              "3771             1             1             0                4            1   \n",
              "\n",
              "           TSH        T3       TT4       T4U       FTI  \n",
              "0     0.002443  0.232227  0.287383  0.429952  0.272265  \n",
              "1     0.007726  0.184834  0.233645  0.357488  0.249364  \n",
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              "...        ...       ...       ...       ...       ...  \n",
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              "3770  0.001311  0.184834  0.186916  0.333333  0.216285  \n",
              "3771  0.001877  0.203791  0.226636  0.396135  0.229008  \n",
              "\n",
              "[3772 rows x 29 columns]"
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              "<p>3772 rows × 29 columns</p>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f8e48f33-480c-41f1-810c-b9b23940a6b8 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-f8e48f33-480c-41f1-810c-b9b23940a6b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b1aaf49e-310d-434c-b8ea-f43244791cda\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b1aaf49e-310d-434c-b8ea-f43244791cda')\"\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b1aaf49e-310d-434c-b8ea-f43244791cda button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_aedde283-a2fd-4131-a930-58dd15d0df40\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_aedde283-a2fd-4131-a930-58dd15d0df40 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A=df['TSH'].sum()\n",
        "B=df['T3'].sum()\n",
        "C=df['TT4'].sum()\n",
        "D=df['T4U'].sum()\n",
        "E=df['FTI'].sum()"
      ],
      "metadata": {
        "id": "6xYJfV7a7ODn"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(A)\n",
        "print(B)\n",
        "print(C)\n",
        "print(D)\n",
        "print(E)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oMYqIUJ8TAS",
        "outputId": "a471c655-d666-4293-b100-7679bbfe60cf"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32.76484683817772\n",
            "701.0369668246447\n",
            "933.0509345794392\n",
            "1356.6202898550725\n",
            "1030.8312977099238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels ='TSH','T3','TT4','T4U','FTI'\n",
        "sizes =(A,B,C,D,E)\n",
        "explode=(0,0,0.1,0,0.2)\n",
        "colors =('blue','green','aqua','red','yellow')\n",
        "plt.pie(sizes,explode=explode,labels=labels,autopct='%1.1f%%',colors=colors)\n",
        "plt.axis=('equal')\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "hzmAzcuv8e5F",
        "outputId": "5b1fb086-54ac-40d3-dd4f-04e500bef55d"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "block : bool, optional\n",
              "    Whether to wait for all figures to be closed before returning.\n",
              "\n",
              "    If `True` block and run the GUI main loop until all figure windows\n",
              "    are closed.\n",
              "\n",
              "    If `False` ensure that all figure windows are displayed and return\n",
              "    immediately.  In this case, you are responsible for ensuring\n",
              "    that the event loop is running to have responsive figures.\n",
              "\n",
              "    Defaults to True in non-interactive mode and to False in interactive\n",
              "    mode (see `.pyplot.isinteractive`).\n",
              "\n",
              "See Also\n",
              "--------\n",
              "ion : Enable interactive mode, which shows / updates the figure after\n",
              "      every plotting command, so that calling ``show()`` is not necessary.\n",
              "ioff : Disable interactive mode.\n",
              "savefig : Save the figure to an image file instead of showing it on screen.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "**Saving figures to file and showing a window at the same time**\n",
              "\n",
              "If you want an image file as well as a user interface window, use\n",
              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
              "limitation of command order does not apply if the show is non-blocking or\n",
              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
              "\n",
              "**Auto-show in jupyter notebooks**\n",
              "\n",
              "The jupyter backends (activated via ``%matplotlib inline``,\n",
              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
              "explicitly there.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
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              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# logistic regression"
      ],
      "metadata": {
        "id": "NEGCxf7f7FnG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "t2dUwASeBKVS",
        "outputId": "a6f29fda-19be-43f4-b019-0e17428765f2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age  sex  on thyroxine  query on thyroxine  on antithyroid medication  sick  \\\n",
              "0  41    1             0                   0                          0     0   \n",
              "1  23    1             0                   0                          0     0   \n",
              "2  46    2             0                   0                          0     0   \n",
              "3  70    1             1                   0                          0     0   \n",
              "4  70    1             0                   0                          0     0   \n",
              "\n",
              "   pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...  \\\n",
              "0         0                0               0                  0  ...   \n",
              "1         0                0               0                  0  ...   \n",
              "2         0                0               0                  0  ...   \n",
              "3         0                0               0                  0  ...   \n",
              "4         0                0               0                  0  ...   \n",
              "\n",
              "   T4U measured  FTI measured  TBG measured  referral source  binaryClass  \\\n",
              "0             1             1             0                1            1   \n",
              "1             0             0             0                4            1   \n",
              "2             1             1             0                4            1   \n",
              "3             0             0             0                4            1   \n",
              "4             1             1             0                3            1   \n",
              "\n",
              "        TSH        T3       TT4       T4U       FTI  \n",
              "0  0.002443  0.232227  0.287383  0.429952  0.272265  \n",
              "1  0.007726  0.184834  0.233645  0.357488  0.249364  \n",
              "2  0.001840  0.184834  0.250000  0.318841  0.300254  \n",
              "3  0.000292  0.175355  0.404206  0.357488  0.249364  \n",
              "4  0.001349  0.109005  0.137850  0.299517  0.173028  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18eabaed-96f8-44d7-9942-38058633ae9a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on thyroxine</th>\n",
              "      <th>query on thyroxine</th>\n",
              "      <th>on antithyroid medication</th>\n",
              "      <th>sick</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>thyroid surgery</th>\n",
              "      <th>I131 treatment</th>\n",
              "      <th>query hypothyroid</th>\n",
              "      <th>...</th>\n",
              "      <th>T4U measured</th>\n",
              "      <th>FTI measured</th>\n",
              "      <th>TBG measured</th>\n",
              "      <th>referral source</th>\n",
              "      <th>binaryClass</th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.002443</td>\n",
              "      <td>0.232227</td>\n",
              "      <td>0.287383</td>\n",
              "      <td>0.429952</td>\n",
              "      <td>0.272265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.007726</td>\n",
              "      <td>0.184834</td>\n",
              "      <td>0.233645</td>\n",
              "      <td>0.357488</td>\n",
              "      <td>0.249364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001840</td>\n",
              "      <td>0.184834</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.318841</td>\n",
              "      <td>0.300254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000292</td>\n",
              "      <td>0.175355</td>\n",
              "      <td>0.404206</td>\n",
              "      <td>0.357488</td>\n",
              "      <td>0.249364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001349</td>\n",
              "      <td>0.109005</td>\n",
              "      <td>0.137850</td>\n",
              "      <td>0.299517</td>\n",
              "      <td>0.173028</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 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-18eabaed-96f8-44d7-9942-38058633ae9a')\"\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "\n",
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            ],
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              "type": "dataframe",
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          "metadata": {},
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    {
      "cell_type": "code",
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        "df.dtypes"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "hnVkgbeRBPy9",
        "outputId": "4a4169ed-34b6-4793-eb69-c72de6378773"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                           object\n",
              "sex                            int64\n",
              "on thyroxine                   int64\n",
              "query on thyroxine             int64\n",
              "on antithyroid medication      int64\n",
              "sick                           int64\n",
              "pregnant                       int64\n",
              "thyroid surgery                int64\n",
              "I131 treatment                 int64\n",
              "query hypothyroid              int64\n",
              "query hyperthyroid             int64\n",
              "lithium                        int64\n",
              "goitre                         int64\n",
              "tumor                          int64\n",
              "hypopituitary                  int64\n",
              "psych                          int64\n",
              "TSH measured                   int64\n",
              "T3 measured                    int64\n",
              "TT4 measured                   int64\n",
              "T4U measured                   int64\n",
              "FTI measured                   int64\n",
              "TBG measured                   int64\n",
              "referral source                int64\n",
              "binaryClass                    int64\n",
              "TSH                          float64\n",
              "T3                           float64\n",
              "TT4                          float64\n",
              "T4U                          float64\n",
              "FTI                          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['age'].dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLIEQN4yOSJd",
        "outputId": "631e3b00-49e8-4f2d-bfdd-78f6f133c131"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['age'] == '?'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "W4XZzJ60OXmu",
        "outputId": "a1fc838d-a8f2-40d9-dcdb-56f8537f93be"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  on thyroxine  query on thyroxine  on antithyroid medication  \\\n",
              "1985   ?    1             1                   0                          0   \n",
              "\n",
              "      sick  pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...  \\\n",
              "1985     0         0                0               1                  0  ...   \n",
              "\n",
              "      T4U measured  FTI measured  TBG measured  referral source  binaryClass  \\\n",
              "1985             1             1             0                4            1   \n",
              "\n",
              "           TSH        T3       TT4       T4U       FTI  \n",
              "1985  0.001123  0.137441  0.275701  0.275362  0.366412  \n",
              "\n",
              "[1 rows x 29 columns]"
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              "<p>1 rows × 29 columns</p>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
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    {
      "cell_type": "code",
      "source": [
        "df['age'] = df['age'].replace('?', np.nan)\n"
      ],
      "metadata": {
        "id": "BxTr0GYYOjF1"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['age'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ybatpVesOyuV",
        "outputId": "4eaf2ffb-3ac9-46d2-e5bb-1539b5be62f8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['age']=df['age'].fillna(df['age'].median())"
      ],
      "metadata": {
        "id": "M2OdEj4-OosA"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "IRLD-8kwPLKB",
        "outputId": "7b980fa7-3d4f-46f3-d044-3f9c099d34ce"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                           object\n",
              "sex                            int64\n",
              "on thyroxine                   int64\n",
              "query on thyroxine             int64\n",
              "on antithyroid medication      int64\n",
              "sick                           int64\n",
              "pregnant                       int64\n",
              "thyroid surgery                int64\n",
              "I131 treatment                 int64\n",
              "query hypothyroid              int64\n",
              "query hyperthyroid             int64\n",
              "lithium                        int64\n",
              "goitre                         int64\n",
              "tumor                          int64\n",
              "hypopituitary                  int64\n",
              "psych                          int64\n",
              "TSH measured                   int64\n",
              "T3 measured                    int64\n",
              "TT4 measured                   int64\n",
              "T4U measured                   int64\n",
              "FTI measured                   int64\n",
              "TBG measured                   int64\n",
              "referral source                int64\n",
              "binaryClass                    int64\n",
              "TSH                          float64\n",
              "T3                           float64\n",
              "TT4                          float64\n",
              "T4U                          float64\n",
              "FTI                          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('binaryClass',axis=1)\n",
        "x1=pd.DataFrame(x)\n",
        "y = df['binaryClass']\n",
        "y1 = pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "upwcgwE57LcE"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
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        "y1.head()"
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        "id": "OkcPo2QG-2pU",
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      "execution_count": 62,
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              "   binaryClass\n",
              "0            1\n",
              "1            1\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"y1\",\n  \"rows\": 3772,\n  \"fields\": [\n    {\n      \"column\": \"binaryClass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      "execution_count": 63,
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        {
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            "text/plain": [
              "  age  sex  on thyroxine  query on thyroxine  on antithyroid medication  sick  \\\n",
              "0  41    1             0                   0                          0     0   \n",
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              "\n",
              "   pregnant  thyroid surgery  I131 treatment  query hypothyroid  ...  \\\n",
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              "\n",
              "   TT4 measured  T4U measured  FTI measured  TBG measured  referral source  \\\n",
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              "        TSH        T3       TT4       T4U       FTI  \n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e46cc57-8d28-4e68-acb9-fe000383b41f 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-6e46cc57-8d28-4e68-acb9-fe000383b41f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7f50a46f-08c5-47b7-9d7e-3fb576d760db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f50a46f-08c5-47b7-9d7e-3fb576d760db')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7f50a46f-08c5-47b7-9d7e-3fb576d760db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x1"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test=train_test_split(x1,y1,test_size=0.20,random_state=42)"
      ],
      "metadata": {
        "id": "qU4nW6ta--kn"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr = LogisticRegression()"
      ],
      "metadata": {
        "id": "r5OCLYEF_Tm3"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=lr.fit(x1_train,y1_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mai2zqIw_iMd",
        "outputId": "e2331e23-7ef3-4b59-928b-931c1a34c8b9"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred=model.predict(x1_test)"
      ],
      "metadata": {
        "id": "obhMA87xPZ3I"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y1_test)\n",
        "print(y1_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4RphwkbPf8I",
        "outputId": "4b088ab6-c492-4e22-ea16-17eadcc2aec0"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      binaryClass\n",
            "270             1\n",
            "2071            0\n",
            "3660            1\n",
            "3207            1\n",
            "1675            1\n",
            "...           ...\n",
            "1027            1\n",
            "3376            1\n",
            "2812            1\n",
            "3540            1\n",
            "543             1\n",
            "\n",
            "[755 rows x 1 columns]\n",
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"the accuracy score is:\",accuracy_score(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncaRPB_RPvcS",
        "outputId": "7f4d6283-fdda-4631-acb9-6f37659e05d2"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the accuracy score is: 0.9417218543046357\n"
          ]
        }
      ]
    }
  ]
}