Lung_cancer.ipynb

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        "<a href=\"https://colab.research.google.com/github/aayushi-0407/ML-Projects/blob/main/Lung_cancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/cancer patient data sets.csv\")\n",
        "data"
      ],
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          "data": {
            "text/plain": [
              "     index Patient Id  Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0        0         P1   33       1              2            4             5   \n",
              "1        1        P10   17       1              3            1             5   \n",
              "2        2       P100   35       1              4            5             6   \n",
              "3        3      P1000   37       1              7            7             7   \n",
              "4        4       P101   46       1              6            8             7   \n",
              "..     ...        ...  ...     ...            ...          ...           ...   \n",
              "995    995       P995   44       1              6            7             7   \n",
              "996    996       P996   37       2              6            8             7   \n",
              "997    997       P997   25       2              4            5             6   \n",
              "998    998       P998   18       2              6            8             7   \n",
              "999    999       P999   47       1              6            5             6   \n",
              "\n",
              "     OccuPational Hazards  Genetic Risk  chronic Lung Disease  ...  Fatigue  \\\n",
              "0                       4             3                     2  ...        3   \n",
              "1                       3             4                     2  ...        1   \n",
              "2                       5             5                     4  ...        8   \n",
              "3                       7             6                     7  ...        4   \n",
              "4                       7             7                     6  ...        3   \n",
              "..                    ...           ...                   ...  ...      ...   \n",
              "995                     7             7                     6  ...        5   \n",
              "996                     7             7                     6  ...        9   \n",
              "997                     5             5                     4  ...        8   \n",
              "998                     7             7                     6  ...        3   \n",
              "999                     5             5                     4  ...        8   \n",
              "\n",
              "     Weight Loss  Shortness of Breath  Wheezing  Swallowing Difficulty  \\\n",
              "0              4                    2         2                      3   \n",
              "1              3                    7         8                      6   \n",
              "2              7                    9         2                      1   \n",
              "3              2                    3         1                      4   \n",
              "4              2                    4         1                      4   \n",
              "..           ...                  ...       ...                    ...   \n",
              "995            3                    2         7                      8   \n",
              "996            6                    5         7                      2   \n",
              "997            7                    9         2                      1   \n",
              "998            2                    4         1                      4   \n",
              "999            7                    9         2                      1   \n",
              "\n",
              "     Clubbing of Finger Nails  Frequent Cold  Dry Cough  Snoring   Level  \n",
              "0                           1              2          3        4     Low  \n",
              "1                           2              1          7        2  Medium  \n",
              "2                           4              6          7        2    High  \n",
              "3                           5              6          7        5    High  \n",
              "4                           2              4          2        3    High  \n",
              "..                        ...            ...        ...      ...     ...  \n",
              "995                         2              4          5        3    High  \n",
              "996                         4              3          1        4    High  \n",
              "997                         4              6          7        2    High  \n",
              "998                         2              4          2        3    High  \n",
              "999                         4              6          7        2    High  \n",
              "\n",
              "[1000 rows x 26 columns]"
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Patient Id</th>\n",
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              "      <td>P100</td>\n",
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              "      <td>P1000</td>\n",
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              "    [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-ef8c5f5e-9182-4a1d-9a81-b62bdc037e20 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-ef8c5f5e-9182-4a1d-9a81-b62bdc037e20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKVI249X1Zuy",
        "outputId": "d0d2436e-1617-4b11-aa1f-5c0b92e6bd41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 26 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   index                     1000 non-null   int64 \n",
            " 1   Patient Id                1000 non-null   object\n",
            " 2   Age                       1000 non-null   int64 \n",
            " 3   Gender                    1000 non-null   int64 \n",
            " 4   Air Pollution             1000 non-null   int64 \n",
            " 5   Alcohol use               1000 non-null   int64 \n",
            " 6   Dust Allergy              1000 non-null   int64 \n",
            " 7   OccuPational Hazards      1000 non-null   int64 \n",
            " 8   Genetic Risk              1000 non-null   int64 \n",
            " 9   chronic Lung Disease      1000 non-null   int64 \n",
            " 10  Balanced Diet             1000 non-null   int64 \n",
            " 11  Obesity                   1000 non-null   int64 \n",
            " 12  Smoking                   1000 non-null   int64 \n",
            " 13  Passive Smoker            1000 non-null   int64 \n",
            " 14  Chest Pain                1000 non-null   int64 \n",
            " 15  Coughing of Blood         1000 non-null   int64 \n",
            " 16  Fatigue                   1000 non-null   int64 \n",
            " 17  Weight Loss               1000 non-null   int64 \n",
            " 18  Shortness of Breath       1000 non-null   int64 \n",
            " 19  Wheezing                  1000 non-null   int64 \n",
            " 20  Swallowing Difficulty     1000 non-null   int64 \n",
            " 21  Clubbing of Finger Nails  1000 non-null   int64 \n",
            " 22  Frequent Cold             1000 non-null   int64 \n",
            " 23  Dry Cough                 1000 non-null   int64 \n",
            " 24  Snoring                   1000 non-null   int64 \n",
            " 25  Level                     1000 non-null   object\n",
            "dtypes: int64(24), object(2)\n",
            "memory usage: 203.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_col=data.describe().columns"
      ],
      "metadata": {
        "id": "B1ZgXrkD1bou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#No outliers because we have a categorical data"
      ],
      "metadata": {
        "id": "E1ze3ekl-4J0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in numeric_col:\n",
        "  plt.figure(figsize=(10,10))\n",
        "  plt.boxplot(data[column])\n",
        "  plt.title(column)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oIt7gLgB1sAX",
        "outputId": "a2fcdadb-4809-4478-f9fe-77fb3ddf16ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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UVo+WN998M2pra1v7sAAAQFIrV66MAw44YKs/b/Voqaqqioi/DVZdXd3ahwcAAJKor6+P2traxkbYmlaPlvcvCauurhYtAADANm8bcSM+AACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEit7GhZvXp1XHHFFdGnT5+orKyMY489NpYsWdISswEAAJQfLZ///Ofjsccei+9///vx/PPPx4gRI+Lkk0+ON954oyXmAwAA2rmKoiiK7V28fv36qKqqigceeCBGjRrVuH3o0KExcuTIuPbaa7f5GfX19VFTUxN1dXVRXV3dvKkBAIBd3va2QcdyPvS9996LjRs3xh577NFke2VlZTz55JNb3KehoSEaGhqaDAZAHuvWrYvly5e36jHXr18fK1asiL59+0ZlZWWrHnvAgAHRuXPnVj0mADumrGipqqqKY445Jq655poYOHBg7LvvvnHvvffGU089Ff3799/iPtOnT4+pU6fulGEB2PmWL18eQ4cObesxWs0zzzwTQ4YMaesxAChDWZeHRUS8+uqrMWHChFi4cGF06NAhhgwZEoccckg888wzsWzZss3Wb+lMS21trcvDAJJoizMty5Yti7Fjx8asWbNi4MCBrXpsZ1oA8miRy8MiIg466KB44oknYu3atVFfXx89e/aM8847Lw488MAtri+VSlEqlco9DACtpHPnzm125mHgwIHOegCwTc3+PS1dunSJnj17xrvvvhvz58+P008/fWfOBQAAEBHNONMyf/78KIoiDj300HjllVdi8uTJMWDAgBg/fnxLzAcAALRzZZ9pqauri0mTJsWAAQPis5/9bBx33HExf/782H333VtiPgAAoJ0r+0zLueeeG+eee25LzAIAALCZZt/TAgAA0BpECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAamVFy8aNG+PrX/969OvXLyorK+Oggw6Ka665JoqiaKn5AACAdq5jOYtvuOGGuP322+Oee+6JQYMGxdNPPx3jx4+PmpqauPzyy1tqRgAAoB0rK1oWLVoUp59+eowaNSoiIvr27Rv33ntv/O///m+LDAcAAFDW5WHHHnts/OxnP4uXXnopIiKee+65ePLJJ2PkyJEtMhwAAEBZZ1q++tWvRn19fQwYMCA6dOgQGzdujGnTpsWYMWO2uk9DQ0M0NDQ0vq+vr2/+tAAAQLtT1pmWH//4x/GDH/wgZs+eHUuXLo177rknvvWtb8U999yz1X2mT58eNTU1ja/a2todHhoAAGg/KooyHv1VW1sbX/3qV2PSpEmN26699tqYNWtWLF++fIv7bOlMS21tbdTV1UV1dfUOjA7Armrp0qUxdOjQeOaZZ2LIkCFtPQ4AbaS+vj5qamq22QZlXR62bt262G23pidnOnToEJs2bdrqPqVSKUqlUjmHAQAAaFRWtIwePTqmTZsWvXv3jkGDBsUvf/nL+Pa3vx0TJkxoqfkAAIB2rqxoueWWW+LrX/96TJw4Md56663o1atXXHzxxfGNb3yjpeYDAADaubKipaqqKm6++ea4+eabW2gcAACApsp6ehgAAEBrEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAILWyoqVv375RUVGx2WvSpEktNR8AANDOdSxn8ZIlS2Ljxo2N71944YU45ZRT4pxzztnpgwEAAESUGS3du3dv8v7666+Pgw46KD7+8Y/v1KEAAADeV1a0/L0NGzbErFmz4sorr4yKioqtrmtoaIiGhobG9/X19c09JAAA0A41+0b8efPmxapVq+LCCy/8wHXTp0+PmpqaxldtbW1zDwkAALRDzY6Wu+66K0aOHBm9evX6wHVTpkyJurq6xtfKlSube0gAAKAdatblYb/97W9jwYIFcf/9929zbalUilKp1JzDAAAANO9My8yZM6NHjx4xatSonT0PAABAE2VHy6ZNm2LmzJkxbty46Nix2ffxAwAAbJeyo2XBggXx+uuvx4QJE1piHgAAgCbKPlUyYsSIKIqiJWYBAADYTLOfHgYAANAaRAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGplR8sbb7wRY8eOjW7dukVlZWV85CMfiaeffrolZgMAAIiO5Sx+9913Y9iwYXHiiSfGI488Et27d4+XX3459tprr5aaDwAAaOfKipYbbrghamtrY+bMmY3b+vXrt9OHAgAAeF9Zl4c9+OCDceSRR8Y555wTPXr0iI9+9KNx5513ttRsAAAA5Z1p+c1vfhO33357XHnllfFv//ZvsWTJkrj88sujU6dOMW7cuC3u09DQEA0NDY3v6+vrd2xigA+5l19+OVavXt3WY7SoZcuWNfnfD7Oqqqo4+OCD23oMgF1aRVEUxfYu7tSpUxx55JGxaNGixm2XX355LFmyJJ566qkt7nP11VfH1KlTN9teV1cX1dXVzRgZ4MPr5ZdfjkMOOaStx2Ane+mll4QLwBbU19dHTU3NNtugrDMtPXv2jMMOO6zJtoEDB8acOXO2us+UKVPiyiuvbDJYbW1tOYcFaDfeP8Mya9asGDhwYBtP03LWr18fK1asiL59+0ZlZWVbj9Nili1bFmPHjv3QnzkDaGllRcuwYcPixRdfbLLtpZdeij59+mx1n1KpFKVSqXnTAbRTAwcOjCFDhrT1GC1q2LBhbT0CALuIsm7E//KXvxyLFy+O6667Ll555ZWYPXt23HHHHTFp0qSWmg8AAGjnyoqWo446KubOnRv33ntvHH744XHNNdfEzTffHGPGjGmp+QAAgHaurMvDIiJOO+20OO2001piFgAAgM2UdaYFAACgtYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJBaWdFy9dVXR0VFRZPXgAEDWmo2AACA6FjuDoMGDYoFCxb8/w/oWPZHAAAAbLeyi6Njx46x3377tcQsAAAAmyn7npaXX345evXqFQceeGCMGTMmXn/99Q9c39DQEPX19U1eAAAA26usaDn66KPj7rvvjkcffTRuv/32eO2112L48OGxevXqre4zffr0qKmpaXzV1tbu8NAAAED7UVa0jBw5Ms4555wYPHhwnHrqqfHwww/HqlWr4sc//vFW95kyZUrU1dU1vlauXLnDQwMAAO3HDt1Fv+eee8YhhxwSr7zyylbXlEqlKJVKO3IYAACgHduh39OyZs2aePXVV6Nnz547ax4AAIAmyoqWf/3Xf40nnngiVqxYEYsWLYozzzwzOnToEOeff35LzQcAALRzZV0e9rvf/S7OP//8eOedd6J79+5x3HHHxeLFi6N79+4tNR8AANDOlRUtP/zhD1tqDgAAgC3aoXtaAAAAWppoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABIbYei5frrr4+Kioq44oordtI4AAAATTU7WpYsWRIzZsyIwYMH78x5AAAAmmhWtKxZsybGjBkTd955Z+y11147eyYAAIBGzYqWSZMmxahRo+Lkk0/e2fMAAAA00bHcHX74wx/G0qVLY8mSJdu1vqGhIRoaGhrf19fXl3tIgHaj4r2/xEf32y0qV70U8aZnpezqKle9FB/db7eoeO8vbT0KwC6trGhZuXJlfOlLX4rHHnss9thjj+3aZ/r06TF16tRmDQfQ3uyx5vVYenHXiIUXRyxs62nYUQMjYunFXWPZmtcj4ti2Hgdgl1VRFEWxvYvnzZsXZ555ZnTo0KFx28aNG6OioiJ22223aGhoaPKziC2faamtrY26urqorq7eCX8EgA+PX/7vovjc6cPjBz/4QQwcMKCtx2EHLVu+PMaMGRN3PfA/8dGPiRaAf1RfXx81NTXbbIOyzrScdNJJ8fzzzzfZNn78+BgwYEB85Stf2SxYIiJKpVKUSqVyDgPQbhUd94hf/mFTrN/zkIhe/9TW47CD1v9hU/zyD5ui6Lh9VycAsGVlRUtVVVUcfvjhTbZ16dIlunXrttl2AACAncFdngAAQGplPz3sHz3++OM7YQwAAIAtc6YFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNTKipbbb789Bg8eHNXV1VFdXR3HHHNMPPLIIy01GwAAQHnRcsABB8T1118fzzzzTDz99NPxiU98Ik4//fT49a9/3VLzAQAA7VzHchaPHj26yftp06bF7bffHosXL45Bgwbt1MEAAAAiyoyWv7dx48b4yU9+EmvXro1jjjlmq+saGhqioaGh8X19fX1zDwkAALRDZd+I//zzz0fXrl2jVCrFJZdcEnPnzo3DDjtsq+unT58eNTU1ja/a2todGhgAAGhfyo6WQw89NJ599tn4xS9+EV/84hdj3Lhx8X//939bXT9lypSoq6trfK1cuXKHBgYAANqXsi8P69SpU/Tv3z8iIoYOHRpLliyJ7373uzFjxowtri+VSlEqlXZsSgAAoN3a4d/TsmnTpib3rAAAAOxMZZ1pmTJlSowcOTJ69+4dq1evjtmzZ8fjjz8e8+fPb6n5AACAdq6saHnrrbfis5/9bPz+97+PmpqaGDx4cMyfPz9OOeWUlpoPAABo58qKlrvuuqul5gAAANiiHb6nBQAAoCWJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1MqKlunTp8dRRx0VVVVV0aNHjzjjjDPixRdfbKnZAAAAyouWJ554IiZNmhSLFy+Oxx57LP7617/GiBEjYu3atS01HwAA0M51LGfxo48+2uT93XffHT169Ihnnnkmjj/++J06GAAAQMQO3tNSV1cXERF77733ThkGAADgH5V1puXvbdq0Ka644ooYNmxYHH744Vtd19DQEA0NDY3v6+vrm3tIgA+9devWRUTE0qVL23iSlrV+/fpYsWJF9O3bNyorK9t6nBazbNmyth4B4EOh2dEyadKkeOGFF+LJJ5/8wHXTp0+PqVOnNvcwAO3K8uXLIyLioosuauNJ2JmqqqraegSAXVpFURRFuTtdeuml8cADD8TChQujX79+H7h2S2daamtro66uLqqrq8ufGOBD7O2334558+bFgAEDonPnzm09TotZtmxZjB07NmbNmhUDBw5s63FaVFVVVRx88MFtPQZASvX19VFTU7PNNijrTEtRFHHZZZfF3Llz4/HHH99msERElEqlKJVK5RwGoN3aZ5994vOf/3xbj9FqBg4cGEOGDGnrMQBIrqxomTRpUsyePTseeOCBqKqqij/84Q8REVFTU/OhviYZAABoO2U9Pez222+Purq6OOGEE6Jnz56Nrx/96EctNR8AANDOlX15GAAAQGvaod/TAgAA0NJECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABIrexoWbhwYYwePTp69eoVFRUVMW/evBYYCwAA4G/Kjpa1a9fGEUccEbfeemtLzAMAANBEx3J3GDlyZIwcObIlZgEAANhM2dFSroaGhmhoaGh8X19f39KHBKAM69ati+XLl7fqMZctW9bkf1vTgAEDonPnzq1+XACar8WjZfr06TF16tSWPgwAzbR8+fIYOnRomxx77NixrX7MZ555JoYMGdLqxwWg+SqKoiiavXNFRcydOzfOOOOMra7Z0pmW2traqKuri+rq6uYeGoCdpC3OtKxfvz5WrFgRffv2jcrKylY9tjMtAHnU19dHTU3NNtugxc+0lEqlKJVKLX0YAJqpc+fObXLmYdiwYa1+TAB2TX5PCwAAkFrZZ1rWrFkTr7zySuP71157LZ599tnYe++9o3fv3jt1OAAAgLKj5emnn44TTzyx8f2VV14ZERHjxo2Lu+++e6cNBgAAENGMaDnhhBNiB+7dBwAAKIt7WgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUuvY2gcsiiIiIurr61v70AAAQCLvN8H7jbA1rR4tq1evjoiI2tra1j40AACQ0OrVq6OmpmarP68otpU1O9mmTZvizTffjKqqqqioqGjNQwOQRH19fdTW1sbKlSujurq6rccBoI0URRGrV6+OXr16xW67bf3OlVaPFgCor6+PmpqaqKurEy0AbJMb8QEAgNRECwAAkJpoAaDVlUql+OY3vxmlUqmtRwFgF+CeFgAAIDVnWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFgFazcOHCGD16dPTq1SsqKipi3rx5bT0SALsA0QJAq1m7dm0cccQRceutt7b1KADsQjq29QAAtB8jR46MkSNHtvUYAOxinGkBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDVPDwOg1axZsyZeeeWVxvevvfZaPPvss7H33ntH796923AyADKrKIqiaOshAGgfHn/88TjxxBM32z5u3Li4++67W38gAHYJogUAAEjNPS0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAILX/B/ybkactdKaoAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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ZW9u2beOee+6J73znO3HhhRdGt27dSleCPhy569O2bduYMmVKjBkzJr797W/H+++/H5MnTy5FS8QHLxG75ppr4gtf+EJ07969Wb9fgNZWUVzf6x0AoAVNnTo1jjvuuHjiiSdKL1Hj482dOzc++9nPxn/+53/Gqaee2trjADQr97QA0KJWr17dZLuhoSGuvvrqqK6ujv79+7fSVFuf66+/Pjp27Bhf+tKXWnsUgGbn5WEAtKizzz47Vq9eHYMGDYr6+vq4++67Y8aMGTF+/Pho165da4+X3q9//et44YUX4rrrrovvfve7pZv2AT7NvDwMgBZ16623xs9+9rNYsGBBvPvuu7HXXnvFmWeeGd/97ndbe7StQu/evePNN9+Mo446Km666aZm/SBNgCxECwAAkJp7WgAAgNRECwAAkFqL34jf2NgYS5YsiU6dOm30h4gBAACfPsViMZYvXx49evSIysoNX09p8WhZsmRJ1NbWtvRpAQCApBYtWhS77bbbBr/e4tGy9l1OFi1aFNXV1S19egAAIIm6urqora39xHdCbPFoWfuSsOrqatECAAB84m0jbsQHAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmVFS0NDQ1x4YUXxu677x7t2rWLPffcMy655JIoFovNNR8AALCNa1PO4ssvvzwmTZoUU6ZMiX79+sXTTz8dp59+etTU1MQ555zTXDMCAADbsLKiZcaMGXHsscfG0UcfHRERvXv3jttuuy3+8Ic/NMtwAAAAZUXL5z//+bjuuuvipZdein322Sfmzp0bTzzxREycOHGDx9TX10d9fX1pu66ubtOnBWCLW7VqVbz44ostes7Vq1fHwoULo3fv3tGuXbsWPXefPn2iffv2LXpOADZPWdHyox/9KOrq6qJPnz5RVVUVDQ0Ncemll8bJJ5+8wWMmTJgQ48aN2+xBAWgeL774YgwYMKC1x2gxs2fPjv79+7f2GACUoaJYxl30t99+e1xwwQVxxRVXRL9+/eKZZ56Jc889NyZOnBijR49e7zHru9JSW1sby5Yti+rq6s3/DgDYLK1xpWXevHlxyimnxM033xx9+/Zt0XO70gKQR11dXdTU1HxiG5R1peWCCy6IH/3oR3HiiSdGRMT+++8fr732WkyYMGGD0VIoFKJQKJRzGgBaUPv27VvtykPfvn1d9QDgE5X1lserVq2Kysqmh1RVVUVjY+MWHQoAAGCtsq60jBw5Mi699NLo2bNn9OvXL/74xz/GxIkT42tf+1pzzQcAAGzjyoqWq6++Oi688ML4zne+E2+99Vb06NEjvvWtb8VPfvKT5poPAADYxpUVLZ06dYqrrroqrrrqqmYaBwAAoKmy7mkBAABoaaIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAamVFS+/evaOiomKdx1lnndVc8wEAANu4NuUsnjVrVjQ0NJS2n3vuuTjiiCPihBNO2OKDAQAARJQZLV27dm2yfdlll8Wee+4Zw4YN26JDAQAArFVWtHzYmjVr4uabb47zzjsvKioqNriuvr4+6uvrS9t1dXWbekoAAGAbtMk34k+dOjWWLl0aX/3qVz923YQJE6Kmpqb0qK2t3dRTAgAA26BNjpYbbrghRowYET169PjYdWPGjIlly5aVHosWLdrUUwIAANugTXp52GuvvRaPPPJI3H333Z+4tlAoRKFQ2JTTAAAAbNqVlsmTJ8fOO+8cRx999JaeBwAAoImyo6WxsTEmT54co0ePjjZtNvk+fgAAgI1SdrQ88sgj8frrr8fXvva15pgHAACgibIvlRx55JFRLBabYxYAAIB1bPK7hwEAALQE0QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACC1sqNl8eLFccopp8SOO+4Y7dq1i/333z+efvrp5pgNAAAg2pSz+G9/+1sMHjw4hg8fHg888EB07do15s+fH126dGmu+QAAgG1cWdFy+eWXR21tbUyePLm0b/fdd9/iQwEAAKxV1svD7r333jj44IPjhBNOiJ133jkOOuiguP766z/2mPr6+qirq2vyAAAA2FhlRcsrr7wSkyZNir333jsefPDBOPPMM+Occ86JKVOmbPCYCRMmRE1NTelRW1u72UMDAADbjopisVjc2MXbbbddHHzwwTFjxozSvnPOOSdmzZoVv//979d7TH19fdTX15e26+rqora2NpYtWxbV1dWbMToAW6s5c+bEgAEDYvbs2dG/f//WHgeAVlJXVxc1NTWf2AZlXWnp3r17fOYzn2myr2/fvvH6669v8JhCoRDV1dVNHgAAABurrGgZPHhw/PnPf26y76WXXopevXpt0aEAAADWKitavv/978fMmTNj/PjxsWDBgrj11lvjuuuui7POOqu55gMAALZxZUXLwIED45577onbbrst9ttvv7jkkkviqquuipNPPrm55gMAALZxZX1OS0TEMcccE8ccc0xzzAIAALCOsq60AAAAtDTRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAILWyouWiiy6KioqKJo8+ffo012wAAADRptwD+vXrF4888sj/f4I2ZT8FAADARiu7ONq0aRPdunVrjlkAAADWUXa0zJ8/P3r06BHbb799DBo0KCZMmBA9e/bc4Pr6+vqor68vbdfV1W3apADbiPnz58fy5ctbe4xmNW/evCb//DTr1KlT7L333q09BsBWraJYLBY3dvEDDzwQK1asiH333TfeeOONGDduXCxevDiee+656NSp03qPueiii2LcuHHr7F+2bFlUV1dv+uQAn0Lz58+PffbZp7XHYAt76aWXhAvAetTV1UVNTc0ntkFZ0fJRS5cujV69esXEiRPj61//+nrXrO9KS21trWgBWI85c+bEgAED4uabb46+ffu29jjNZvXq1bFw4cLo3bt3tGvXrrXHaTbz5s2LU045JWbPnh39+/dv7XEA0tnYaNmsu+g7d+4c++yzTyxYsGCDawqFQhQKhc05DcA2p2/fvp/6H3IHDx7c2iMAsJXYrM9pWbFiRbz88svRvXv3LTUPAABAE2VFy/nnnx+PPfZYLFy4MGbMmBHHHXdcVFVVxUknndRc8wEAANu4sl4e9r//+79x0kknxV//+tfo2rVrDBkyJGbOnBldu3ZtrvkAAIBtXFnRcvvttzfXHAAAAOu1Wfe0AAAANDfRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAILXNipbLLrssKioq4txzz91C4wAAADS1ydEya9as+OUvfxkHHHDAlpwHAACgiU2KlhUrVsTJJ58c119/fXTp0mVLzwQAAFDSZlMOOuuss+Loo4+Ov//7v49/+Zd/+di19fX1UV9fX9quq6vblFMCbBMq3n83DupWGe2WvhSxxG2HW7t2S1+Kg7pVRsX777b2KABbtbKj5fbbb485c+bErFmzNmr9hAkTYty4cWUPBrAt2n7F6zHnWx0jHv9WxOOtPQ2bq29EzPlWx5i34vWI+HxrjwOw1SorWhYtWhTf+9734uGHH47tt99+o44ZM2ZMnHfeeaXturq6qK2tLW9KgG3Eux17Rv9frohbbrkl+vbp09rjsJnmvfhinHzyyXHDP/Rs7VEAtmplRcvs2bPjrbfeiv79+5f2NTQ0xOOPPx7XXHNN1NfXR1VVVZNjCoVCFAqFLTMtwKdcsc328ce/NMbqzvtE9Phsa4/DZlr9l8b4418ao9hm4/5HHwDrV1a0fOELX4hnn322yb7TTz89+vTpE//0T/+0TrAAAABsrrKipVOnTrHffvs12dehQ4fYcccd19kPAACwJXhrGgAAILVNesvjD5s2bdoWGAMAAGD9XGkBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSKytaJk2aFAcccEBUV1dHdXV1DBo0KB544IHmmg0AAKC8aNltt93isssui9mzZ8fTTz8dhx9+eBx77LHx/PPPN9d8AADANq5NOYtHjhzZZPvSSy+NSZMmxcyZM6Nfv35bdDAAAICIMqPlwxoaGuLOO++MlStXxqBBgza4rr6+Purr60vbdXV1m3pKAABgG1T2jfjPPvtsdOzYMQqFQnz729+Oe+65Jz7zmc9scP2ECROipqam9Kitrd2sgQEAgG1L2dGy7777xjPPPBNPPfVUnHnmmTF69Oh44YUXNrh+zJgxsWzZstJj0aJFmzUwAACwbSn75WHbbbdd7LXXXhERMWDAgJg1a1b8/Oc/j1/+8pfrXV8oFKJQKGzelAAAwDZrsz+npbGxsck9KwAAAFtSWVdaxowZEyNGjIiePXvG8uXL49Zbb41p06bFgw8+2FzzAQAA27iyouWtt96K0047Ld54442oqamJAw44IB588ME44ogjmms+AABgG1dWtNxwww3NNQcAAMB6bfY9LQAAAM1JtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEitrGiZMGFCDBw4MDp16hQ777xzjBo1Kv785z8312wAAADlRctjjz0WZ511VsycOTMefvjheO+99+LII4+MlStXNtd8AADANq5NOYt/+9vfNtm+8cYbY+edd47Zs2fHoYceukUHAwAAiCgzWj5q2bJlERGxww47bHBNfX191NfXl7br6uo255QAn2qrVq2KiIg5c+a08iTNa/Xq1bFw4cLo3bt3tGvXrrXHaTbz5s1r7REAPhU2OVoaGxvj3HPPjcGDB8d+++23wXUTJkyIcePGbeppALYpL774YkREnHHGGa08CVtSp06dWnsEgK1aRbFYLG7KgWeeeWY88MAD8cQTT8Ruu+22wXXru9JSW1sby5Yti+rq6k05NcCn1v/93//F1KlTo0+fPtG+ffvWHqfZzJs3L0455ZS4+eabo2/fvq09TrPq1KlT7L333q09BkBKdXV1UVNT84ltsElXWr773e/Gb37zm3j88cc/NlgiIgqFQhQKhU05DcA2Z6eddopvfOMbrT1Gi+nbt2/079+/tccAILmyoqVYLMbZZ58d99xzT0ybNi1233335poLAAAgIsqMlrPOOituvfXW+J//+Z/o1KlT/OUvf4mIiJqamk/1jZQAAEDrKetzWiZNmhTLli2Lww47LLp371563HHHHc01HwAAsI0r++VhAAAALamsKy0AAAAtTbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABIrexoefzxx2PkyJHRo0ePqKioiKlTpzbDWAAAAB8oO1pWrlwZBx54YFx77bXNMQ8AAEATbco9YMSIETFixIjmmAUAAGAdZUdLuerr66O+vr60XVdX19ynBKAMq1atihdffLFFzzlv3rwm/2xJffr0ifbt27f4eQHYdM0eLRMmTIhx48Y192kA2EQvvvhiDBgwoFXOfcopp7T4OWfPnh39+/dv8fMCsOkqisVicZMPrqiIe+65J0aNGrXBNeu70lJbWxvLli2L6urqTT01AFtIa1xpWb16dSxcuDB69+4d7dq1a9Fzu9ICkEddXV3U1NR8Yhs0+5WWQqEQhUKhuU8DwCZq3759q1x5GDx4cIufE4Ctk89pAQAAUiv7SsuKFStiwYIFpe1XX301nnnmmdhhhx2iZ8+eW3Q4AACAsqPl6aefjuHDh5e2zzvvvIiIGD16dNx4441bbDAAAICITYiWww47LDbj3n0AAICyuKcFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACC1Ni19wmKxGBERdXV1LX1qAAAgkbVNsLYRNqTFo2X58uUREVFbW9vSpwYAABJavnx51NTUbPDrFcVPypotrLGxMZYsWRKdOnWKioqKljw1AEnU1dVFbW1tLFq0KKqrq1t7HABaSbFYjOXLl0ePHj2isnLDd660eLQAQF1dXdTU1MSyZctECwCfyI34AABAaqIFAABITbQA0OIKhUKMHTs2CoVCa48CwFbAPS0AAEBqrrQAAACpiRYAACA10QIAAKQmWgAAgNRECwAt5vHHH4+RI0dGjx49oqKiIqZOndraIwGwFRAtALSYlStXxoEHHhjXXntta48CwFakTWsPAMC2Y8SIETFixIjWHgOArYwrLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACk5t3DAGgxK1asiAULFpS2X3311XjmmWdihx12iJ49e7biZABkVlEsFoutPQQA24Zp06bF8OHD19k/evTouPHGG1t+IAC2CqIFAABIzT0tAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACC1/wdO9KVJ6vB/5AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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uuST69OkT5513XvTq1SvOPPPMko5RU1MTp512Wvz2t7+NWbNmRXl5eQwZMiR+8pOfxGc+85m2fDsA7aas2NZ3ZwKwzVm/fn306tUr/v7v/z6mTp3a2eN0iIsuuihuvfXWWLly5SZv6AcgB/e0AHQxf/7zn1t8KMAPfvCDeOutt9r8ZvMs1qxZ0+z5m2++GbNmzYpDDjlEsABsA5xpAehi5s6dG1/+8pfj5JNPjl133TUWLlwY06ZNi9ra2liwYEGzX075YXHAAQfEYYcdFrW1tfHaa6/FtGnT4uWXX47f/va38YlPfKKzxwNgM9zTAtDFDBw4MGpqauI73/lOvPXWW7HLLrvE2LFjY/LkyR/KYIn4y6ee/exnP4spU6ZEWVlZDBs2LKZNmyZYALYRzrQAAACpuacFAABITbQAAACpdfg9LevXr4+XX345Kisr2+x3DgAAANueYrEYK1asiH79+kW3bps+n9Lh0fLyyy9HTU1NRx8WAABI6sUXX4zddtttk1/v8GiprKyMiL8MVlVV1dGHBwAAkmhoaIiampqmRtiUDo+WDZeEVVVViRYAAGCzt424ER8AAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApFZytKxYsSIuuuiiGDBgQFRUVMTBBx8c8+fPb4/ZAAAASo+Ws88+O+67776YNWtWPPHEE3HMMcfEUUcdFS+99FJ7zAcAAHRxZcVisbili9esWROVlZVx1113xXHHHde0ffjw4TFq1Ki4+uqrN/saDQ0NUV1dHfX19VFVVdW6qQEAgG3elrZBeSkvunbt2li3bl306NGj2faKiop44IEHNrpPY2NjNDY2NhsMgDxWr14dixcv7tBjrlmzJpYtWxYDBw6MioqKDj32kCFDYocddujQYwKwdUqKlsrKyjjooIPiqquuitra2vjIRz4SP/zhD+Ohhx6KPfbYY6P7TJo0KSZOnNgmwwLQ9hYvXhzDhw/v7DE6zIIFC2LYsGGdPQYAJSjp8rCIiKVLl8aZZ54Z8+bNi+7du8ewYcNir732igULFsSiRYtarN/YmZaamhqXhwEk0RlnWhYtWhRjxoyJ2267LWprazv02M60AOTRLpeHRUQMHjw4fv/738eqVauioaEh+vbtG5/73Odi0KBBG11fKBSiUCiUehgAOsgOO+zQaWceamtrnfUAYLNa/XtaevbsGX379o2333475syZEyeeeGJbzgUAABARrTjTMmfOnCgWi7H33nvHkiVL4tJLL40hQ4bEGWec0R7zAQAAXVzJZ1rq6+tj/PjxMWTIkBg7dmwccsghMWfOnNhuu+3aYz4AAKCLK/lMyymnnBKnnHJKe8wCAADQQqvvaQEAAOgIogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAILWSomXdunVx+eWXx+677x4VFRUxePDguOqqq6JYLLbXfAAAQBdXXsri6667Lurq6mLmzJkxdOjQePTRR+OMM86I6urquPDCC9trRgAAoAsrKVoefPDBOPHEE+O4446LiIiBAwfGD3/4w3jkkUfaZTgAAICSLg87+OCD47e//W0888wzERHxX//1X/HAAw/EqFGj2mU4AACAks60XHbZZdHQ0BBDhgyJ7t27x7p16+Kaa66J0aNHb3KfxsbGaGxsbHre0NDQ+mkBAIAup6QzLT/5yU/i9ttvjzvuuCMWLlwYM2fOjG9961sxc+bMTe4zadKkqK6ubnrU1NRs9dAAAEDXUVYs4aO/ampq4rLLLovx48c3bbv66qvjtttui8WLF290n42daampqYn6+vqoqqraitEB2FYtXLgwhg8fHgsWLIhhw4Z19jgAdJKGhoaorq7ebBuUdHnY6tWro1u35idnunfvHuvXr9/kPoVCIQqFQimHAQAAaFJStJxwwglxzTXXRP/+/WPo0KHx2GOPxQ033BBnnnlme80HAAB0cSVFy8033xyXX355fPGLX4zXX389+vXrF+eee2584xvfaK/5AACALq6kaKmsrIwbb7wxbrzxxnYaBwAAoLmSPj0MAACgo4kWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJBaSdEycODAKCsra/EYP358e80HAAB0ceWlLJ4/f36sW7eu6fmTTz4ZRx99dJx88sltPhgAAEBEidHSq1evZs8nT54cgwcPjpEjR7bpUAAAABuUFC3v9c4778Rtt90WX/nKV6KsrGyT6xobG6OxsbHpeUNDQ2sPCQAAdEGtvhH/zjvvjOXLl8fpp5/+gesmTZoU1dXVTY+amprWHhIAAOiCWh0t06ZNi1GjRkW/fv0+cN2ECROivr6+6fHiiy+29pAAAEAX1KrLw55//vn4zW9+Ez//+c83u7ZQKEShUGjNYQAAAFp3pmX69OnRu3fvOO6449p6HgAAgGZKjpb169fH9OnTY9y4cVFe3ur7+AEAALZIydHym9/8Jl544YU488wz22MeAACAZko+VXLMMcdEsVhsj1kAAABaaPWnhwEAAHQE0QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkFrJ0fLSSy/FmDFjYtddd42Kior4m7/5m3j00UfbYzYAAIAoL2Xx22+/HSNGjIjDDz887r777ujVq1c8++yzsfPOO7fXfAAAQBdXUrRcd911UVNTE9OnT2/atvvuu7f5UAAAABuUdHnYL37xi/joRz8aJ598cvTu3TsOPPDAmDp1anvNBgAAUFq0/OlPf4q6urrYc889Y86cOXHeeefFhRdeGDNnztzkPo2NjdHQ0NDsAQAAsKVKujxs/fr18dGPfjSuvfbaiIg48MAD48knn4x/+7d/i3Hjxm10n0mTJsXEiRO3flIAAKBLKulMS9++fWOfffZptq22tjZeeOGFTe4zYcKEqK+vb3q8+OKLrZsUAADokko60zJixIh4+umnm2175plnYsCAAZvcp1AoRKFQaN10AABAl1fSmZYvf/nL8fDDD8e1114bS5YsiTvuuCOmTJkS48ePb6/5AACALq6kaPnYxz4Ws2fPjh/+8Iex7777xlVXXRU33nhjjB49ur3mAwAAuriSLg+LiDj++OPj+OOPb49ZAAAAWijpTAsAAEBHEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAILWSouXKK6+MsrKyZo8hQ4a012wAAABRXuoOQ4cOjd/85jf//wLlJb8EAADAFiu5OMrLy6NPnz7tMQsAAEALJUfLs88+G/369YsePXrEQQcdFJMmTYr+/ftvcn1jY2M0NjY2PW9oaGjdpABdxLPPPhsrVqzo7DHa1aJFi5r988OssrIy9txzz84eA2CbVlYsFotbuvjuu++OlStXxt577x2vvPJKTJw4MV566aV48skno7KycqP7XHnllTFx4sQW2+vr66Oqqqr1kwN8CD377LOx1157dfYYtLFnnnlGuABsRENDQ1RXV2+2DUqKlvdbvnx5DBgwIG644YY466yzNrpmY2daampqRAvARixcuDCGDx8et912W9TW1nb2OO1mzZo1sWzZshg4cGBUVFR09jjtZtGiRTFmzJhYsGBBDBs2rLPHAUhnS6Nlq+6i32mnnWKvvfaKJUuWbHJNoVCIQqGwNYcB6HJqa2s/9D/kjhgxorNHAGAbsVW/p2XlypWxdOnS6Nu3b1vNAwAA0ExJ0XLJJZfE73//+1i2bFk8+OCD8elPfzq6d+8ep512WnvNBwAAdHElXR72P//zP3HaaafFm2++Gb169YpDDjkkHn744ejVq1d7zQcAAHRxJUXLj370o/aaAwAAYKO26p4WAACA9iZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABS26pomTx5cpSVlcVFF13URuMAAAA01+pomT9/ftx6662x3377teU8AAAAzbQqWlauXBmjR4+OqVOnxs4779zWMwEAADRpVbSMHz8+jjvuuDjqqKPaeh4AAIBmykvd4Uc/+lEsXLgw5s+fv0XrGxsbo7Gxsel5Q0NDqYcE6DLK1v45DuzTLSqWPxPxss9K2dZVLH8mDuzTLcrW/rmzRwHYppUULS+++GJ86Utfivvuuy969OixRftMmjQpJk6c2KrhALqaHitfiIXn7hgx79yIeZ09DVurNiIWnrtjLFr5QkQc3NnjAGyzyorFYnFLF995553x6U9/Orp37960bd26dVFWVhbdunWLxsbGZl+L2PiZlpqamqivr4+qqqo2eAsAHx6PPfJgnHXioXH77bdH7ZAhnT0OW2nR4sUxevTomHbXf8SBHxctAO/X0NAQ1dXVm22Dks60HHnkkfHEE08023bGGWfEkCFD4p/+6Z9aBEtERKFQiEKhUMphALqsYnmPeOzV9bFmp70i+h3Q2eOwlda8uj4ee3V9FMu37OoEADaupGiprKyMfffdt9m2nj17xq677tpiOwAAQFtwlycAAJBayZ8e9n5z585tgzEAAAA2zpkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFIrKVrq6upiv/32i6qqqqiqqoqDDjoo7r777vaaDQAAoLRo2W233WLy5MmxYMGCePTRR+OII46IE088MZ566qn2mg8AAOjiyktZfMIJJzR7fs0110RdXV08/PDDMXTo0DYdDAAAIKLEaHmvdevWxU9/+tNYtWpVHHTQQZtc19jYGI2NjU3PGxoaWntIAACgCyr5RvwnnngidtxxxygUCvGFL3whZs+eHfvss88m10+aNCmqq6ubHjU1NVs1MAAA0LWUHC177713PP744/GHP/whzjvvvBg3blz893//9ybXT5gwIerr65seL7744lYNDAAAdC0lXx62/fbbxx577BEREcOHD4/58+fHTTfdFLfeeutG1xcKhSgUCls3JQAA0GVt9e9pWb9+fbN7VgAAANpSSWdaJkyYEKNGjYr+/fvHihUr4o477oi5c+fGnDlz2ms+AACgiyspWl5//fUYO3ZsvPLKK1FdXR377bdfzJkzJ44++uj2mg8AAOjiSoqWadOmtdccAAAAG7XV97QAAAC0J9ECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaaAEAAFITLQAAQGqiBQAASE20AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJBaSdEyadKk+NjHPhaVlZXRu3fvOOmkk+Lpp59ur9kAAABKi5bf//73MX78+Hj44Yfjvvvui3fffTeOOeaYWLVqVXvNBwAAdHHlpSy+5557mj2fMWNG9O7dOxYsWBCf+MQn2nQwAACAiK28p6W+vj4iInbZZZc2GQYAAOD9SjrT8l7r16+Piy66KEaMGBH77rvvJtc1NjZGY2Nj0/OGhobWHhLgQ2/16tUREbFw4cJOnqR9rVmzJpYtWxYDBw6MioqKzh6n3SxatKizRwD4UGh1tIwfPz6efPLJeOCBBz5w3aRJk2LixImtPQxAl7J48eKIiDjnnHM6eRLaUmVlZWePALBNKysWi8VSdzr//PPjrrvuinnz5sXuu+/+gWs3dqalpqYm6uvro6qqqvSJAT7E3njjjbjzzjtjyJAhscMOO3T2OO1m0aJFMWbMmLjtttuitra2s8dpV5WVlbHnnnt29hgAKTU0NER1dfVm26CkMy3FYjEuuOCCmD17dsydO3ezwRIRUSgUolAolHIYgC7rr/7qr+Lss8/u7DE6TG1tbQwbNqyzxwAguZKiZfz48XHHHXfEXXfdFZWVlfHqq69GRER1dfWH+ppkAACg85T06WF1dXVRX18fhx12WPTt27fp8eMf/7i95gMAALq4ki8PAwAA6Ehb9XtaAAAA2ptoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACplRwt8+bNixNOOCH69esXZWVlceedd7bDWAAAAH9RcrSsWrUq9t9//7jlllvaYx4AAIBmykvdYdSoUTFq1Kj2mAUAAKCFkqOlVI2NjdHY2Nj0vKGhob0PCUAJVq9eHYsXL+7QYy5atKjZPzvSkCFDYocddujw4wLQeu0eLZMmTYqJEye292EAaKXFixfH8OHDO+XYY8aM6fBjLliwIIYNG9bhxwWg9cqKxWKx1TuXlcXs2bPjpJNO2uSajZ1pqampifr6+qiqqmrtoQFoI51xpmXNmjWxbNmyGDhwYFRUVHTosZ1pAcijoaEhqqurN9sG7X6mpVAoRKFQaO/DANBKO+ywQ6eceRgxYkSHHxOAbZPf0wIAAKRW8pmWlStXxpIlS5qeP/fcc/H444/HLrvsEv3792/T4QAAAEqOlkcffTQOP/zwpudf+cpXIiJi3LhxMWPGjDYbDAAAIKIV0XLYYYfFVty7DwAAUBL3tAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApCZaAACA1EQLAACQmmgBAABSEy0AAEBqogUAAEhNtAAAAKmJFgAAIDXRAgAApFbe0QcsFosREdHQ0NDRhwYAABLZ0AQbGmFTOjxaVqxYERERNTU1HX1oAAAgoRUrVkR1dfUmv15W3FzWtLH169fHyy+/HJWVlVFWVtaRhwYgiYaGhqipqYkXX3wxqqqqOnscADpJsViMFStWRL9+/aJbt03fudLh0QIADQ0NUV1dHfX19aIFgM1yIz4AAJCaaAEAAFITLQB0uEKhEFdccUUUCoXOHgWAbYB7WgAAgNScaQEAAFITLQAAQGqiBQAASE20AAAAqYkWADrMvHnz4oQTToh+/fpFWVlZ3HnnnZ09EgDbANECQIdZtWpV7L///nHLLbd09igAbEPKO3sAALqOUaNGxahRozp7DAC2Mc60AAAAqYkWAAAgNdECAACkJloAAIDURAsAAJCaTw8DoMOsXLkylixZ0vT8ueeei8cffzx22WWX6N+/fydOBkBmZcVisdjZQwDQNcydOzcOP/zwFtvHjRsXM2bM6PiBANgmiBYAACA197QAAACpiRYAACA10QIAAKQmWgAAgNRECwAAkJpoAQAAUhMtAABAaqIFAABITbQAAACpiRYAACA10QIAAKQmWgAAgNT+Dz9Y/ArSArI6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Creating model without blancing data"
      ],
      "metadata": {
        "id": "4lSyCLwP_FlK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data=data.drop([\"Patient Id\",\"index\",\"Level\"],axis=1)\n",
        "new_data[\"chronic Lung Disease\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ip0-YwOv_OtW",
        "outputId": "137ef08e-09f4-44b5-9ec2-4fcc3c1151d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6    308\n",
              "2    173\n",
              "4    141\n",
              "3    141\n",
              "7    107\n",
              "5     80\n",
              "1     50\n",
              "Name: chronic Lung Disease, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_data.drop('chronic Lung Disease', axis=1)\n",
        "y = new_data['chronic Lung Disease']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=143)\n",
        "\n",
        "# Check the shapes of the resulting dataframes to ensure correct splitting\n",
        "class_model = svm.SVC()\n",
        "class_model.fit(X_train ,y_train )\n",
        "pred=class_model.predict(X_test)\n",
        "accuracy_score(pred , y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrCr8gQK_CZs",
        "outputId": "3bd3a100-caaf-4924-f0eb-451567e82531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.0"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F7xLqFms20YX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_count = new_data['chronic Lung Disease'].value_counts().max()\n",
        "max_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUJ4VSMZ2TYK",
        "outputId": "b4767eb2-2cc2-40c7-cfb0-7bd66d548e8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "308"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#After balancing our data"
      ],
      "metadata": {
        "id": "EYMhc_lw_rDP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df = new_data.groupby('chronic Lung Disease')\n",
        "balanced_df = grouped_df.apply(lambda x: x.sample(max_count, replace=True)).reset_index(drop=True)\n",
        "balanced_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "ljf-6fpL8ULk",
        "outputId": "c3e4529d-c765-4250-ff1c-8fd4bea36aee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0      61       1              2            3             4   \n",
              "1      44       2              3            2             2   \n",
              "2      18       2              3            2             1   \n",
              "3      35       2              2            1             2   \n",
              "4      24       2              3            2             2   \n",
              "...   ...     ...            ...          ...           ...   \n",
              "2151   36       1              6            7             7   \n",
              "2152   36       1              6            7             7   \n",
              "2153   33       1              6            7             7   \n",
              "2154   28       1              6            7             8   \n",
              "2155   53       1              6            8             7   \n",
              "\n",
              "      OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
              "0                        2             1                     1              2   \n",
              "1                        1             1                     1              1   \n",
              "2                        3             2                     1              3   \n",
              "3                        1             2                     1              2   \n",
              "4                        1             1                     1              1   \n",
              "...                    ...           ...                   ...            ...   \n",
              "2151                     7             7                     7              6   \n",
              "2152                     7             6                     7              7   \n",
              "2153                     7             7                     7              6   \n",
              "2154                     7             6                     7              7   \n",
              "2155                     7             6                     7              7   \n",
              "\n",
              "      Obesity  ...  Coughing of Blood  Fatigue  Weight Loss  \\\n",
              "0           4  ...                  5        2            1   \n",
              "1           1  ...                  6        2            1   \n",
              "2           2  ...                  2        2            2   \n",
              "3           3  ...                  1        3            4   \n",
              "4           1  ...                  6        2            1   \n",
              "...       ...  ...                ...      ...          ...   \n",
              "2151        7  ...                  7        8            5   \n",
              "2152        7  ...                  8        4            2   \n",
              "2153        7  ...                  7        8            5   \n",
              "2154        2  ...                  8        2            3   \n",
              "2155        3  ...                  6        5            2   \n",
              "\n",
              "      Shortness of Breath  Wheezing  Swallowing Difficulty  \\\n",
              "0                       3         2                      1   \n",
              "1                       2         3                      4   \n",
              "2                       1         3                      4   \n",
              "3                       5         1                      3   \n",
              "4                       2         3                      4   \n",
              "...                   ...       ...                    ...   \n",
              "2151                    7         6                      7   \n",
              "2152                    3         1                      4   \n",
              "2153                    7         6                      7   \n",
              "2154                    6         4                      2   \n",
              "2155                    5         2                      3   \n",
              "\n",
              "      Clubbing of Finger Nails  Frequent Cold  Dry Cough  Snoring  \n",
              "0                            3              2          1        2  \n",
              "1                            2              1          1        1  \n",
              "2                            4              1          4        1  \n",
              "3                            2              1          2        2  \n",
              "4                            2              1          1        1  \n",
              "...                        ...            ...        ...      ...  \n",
              "2151                         8              7          6        2  \n",
              "2152                         5              6          7        5  \n",
              "2153                         8              7          6        2  \n",
              "2154                         3              1          2        1  \n",
              "2155                         2              1          7        6  \n",
              "\n",
              "[2156 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-619c542a-6ab4-403d-84a9-5cc1c64287cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Air Pollution</th>\n",
              "      <th>Alcohol use</th>\n",
              "      <th>Dust Allergy</th>\n",
              "      <th>OccuPational Hazards</th>\n",
              "      <th>Genetic Risk</th>\n",
              "      <th>chronic Lung Disease</th>\n",
              "      <th>Balanced Diet</th>\n",
              "      <th>Obesity</th>\n",
              "      <th>...</th>\n",
              "      <th>Coughing of Blood</th>\n",
              "      <th>Fatigue</th>\n",
              "      <th>Weight Loss</th>\n",
              "      <th>Shortness of Breath</th>\n",
              "      <th>Wheezing</th>\n",
              "      <th>Swallowing Difficulty</th>\n",
              "      <th>Clubbing of Finger Nails</th>\n",
              "      <th>Frequent Cold</th>\n",
              "      <th>Dry Cough</th>\n",
              "      <th>Snoring</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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>2151</th>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2153</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2154</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2155</th>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2156 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-619c542a-6ab4-403d-84a9-5cc1c64287cd')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3e99be17-cdbf-4d2c-baed-9f167f983245\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e99be17-cdbf-4d2c-baed-9f167f983245')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3e99be17-cdbf-4d2c-baed-9f167f983245 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-619c542a-6ab4-403d-84a9-5cc1c64287cd 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-619c542a-6ab4-403d-84a9-5cc1c64287cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "balanced_df['chronic Lung Disease'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spAfwhkZ89Mc",
        "outputId": "d9ff2767-bca8-43f2-9df9-9a8bbab03d61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    308\n",
              "2    308\n",
              "3    308\n",
              "4    308\n",
              "5    308\n",
              "6    308\n",
              "7    308\n",
              "Name: chronic Lung Disease, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G6yKlo_aCft8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features (X) and target variable (y)\n"
      ],
      "metadata": {
        "id": "F3gPxM1W9Lw_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X = balanced_df.drop('chronic Lung Disease', axis=1)\n",
        "y = balanced_df['chronic Lung Disease']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1432)\n",
        "class_model = svm.SVC()\n",
        "class_model.fit(X_train ,y_train )\n",
        "pred=class_model.predict(X_test)\n",
        "accuracy_score(pred , y_test)*100\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaVJjqR49f9N",
        "outputId": "2eedc2b8-7bcd-4bf0-a756-61965215832a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92.58400926998841"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoded = pd.get_dummies(balanced_df['chronic Lung Disease'])\n",
        "df_one_hot = pd.concat([balanced_df, one_hot_encoded], axis=1)\n",
        "df_one_hot\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "FENgXgJ59xhn",
        "outputId": "8220a82f-1cb5-40e7-efde-78a96bf9e125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0      61       1              2            3             4   \n",
              "1      44       2              3            2             2   \n",
              "2      18       2              3            2             1   \n",
              "3      35       2              2            1             2   \n",
              "4      24       2              3            2             2   \n",
              "...   ...     ...            ...          ...           ...   \n",
              "2151   36       1              6            7             7   \n",
              "2152   36       1              6            7             7   \n",
              "2153   33       1              6            7             7   \n",
              "2154   28       1              6            7             8   \n",
              "2155   53       1              6            8             7   \n",
              "\n",
              "      OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
              "0                        2             1                     1              2   \n",
              "1                        1             1                     1              1   \n",
              "2                        3             2                     1              3   \n",
              "3                        1             2                     1              2   \n",
              "4                        1             1                     1              1   \n",
              "...                    ...           ...                   ...            ...   \n",
              "2151                     7             7                     7              6   \n",
              "2152                     7             6                     7              7   \n",
              "2153                     7             7                     7              6   \n",
              "2154                     7             6                     7              7   \n",
              "2155                     7             6                     7              7   \n",
              "\n",
              "      Obesity  ...  Frequent Cold  Dry Cough  Snoring  1  2  3  4  5  6  7  \n",
              "0           4  ...              2          1        2  1  0  0  0  0  0  0  \n",
              "1           1  ...              1          1        1  1  0  0  0  0  0  0  \n",
              "2           2  ...              1          4        1  1  0  0  0  0  0  0  \n",
              "3           3  ...              1          2        2  1  0  0  0  0  0  0  \n",
              "4           1  ...              1          1        1  1  0  0  0  0  0  0  \n",
              "...       ...  ...            ...        ...      ... .. .. .. .. .. .. ..  \n",
              "2151        7  ...              7          6        2  0  0  0  0  0  0  1  \n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5045758-b165-486a-a8b6-5c24b5521df9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoded"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "a8IbfHUp-uWU",
        "outputId": "e854aac7-33dc-420d-d611-f3dad5ee144e"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>2151</th>\n",
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              "      <td>0</td>\n",
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              "      <th>2153</th>\n",
              "      <td>0</td>\n",
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              "      <th>2154</th>\n",
              "      <td>0</td>\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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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "balanced_df['chronic Lung Disease encoded version'] = label_encoder.fit_transform(balanced_df['chronic Lung Disease'])\n",
        "balanced_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "T85rVH7dDKCy",
        "outputId": "11c86dcb-677e-428f-b5cd-4e6ae97f84a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0      61       1              2            3             4   \n",
              "1      44       2              3            2             2   \n",
              "2      18       2              3            2             1   \n",
              "3      35       2              2            1             2   \n",
              "4      24       2              3            2             2   \n",
              "...   ...     ...            ...          ...           ...   \n",
              "2151   36       1              6            7             7   \n",
              "2152   36       1              6            7             7   \n",
              "2153   33       1              6            7             7   \n",
              "2154   28       1              6            7             8   \n",
              "2155   53       1              6            8             7   \n",
              "\n",
              "      OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
              "0                        2             1                     1              2   \n",
              "1                        1             1                     1              1   \n",
              "2                        3             2                     1              3   \n",
              "3                        1             2                     1              2   \n",
              "4                        1             1                     1              1   \n",
              "...                    ...           ...                   ...            ...   \n",
              "2151                     7             7                     7              6   \n",
              "2152                     7             6                     7              7   \n",
              "2153                     7             7                     7              6   \n",
              "2154                     7             6                     7              7   \n",
              "2155                     7             6                     7              7   \n",
              "\n",
              "      Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
              "0           4  ...        2            1                    3         2   \n",
              "1           1  ...        2            1                    2         3   \n",
              "2           2  ...        2            2                    1         3   \n",
              "3           3  ...        3            4                    5         1   \n",
              "4           1  ...        2            1                    2         3   \n",
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              "2154        2  ...        2            3                    6         4   \n",
              "2155        3  ...        5            2                    5         2   \n",
              "\n",
              "      Swallowing Difficulty  Clubbing of Finger Nails  Frequent Cold  \\\n",
              "0                         1                         3              2   \n",
              "1                         4                         2              1   \n",
              "2                         4                         4              1   \n",
              "3                         3                         2              1   \n",
              "4                         4                         2              1   \n",
              "...                     ...                       ...            ...   \n",
              "2151                      7                         8              7   \n",
              "2152                      4                         5              6   \n",
              "2153                      7                         8              7   \n",
              "2154                      2                         3              1   \n",
              "2155                      3                         2              1   \n",
              "\n",
              "      Dry Cough  Snoring  chronic Lung Disease encoded version  \n",
              "0             1        2                                     0  \n",
              "1             1        1                                     0  \n",
              "2             4        1                                     0  \n",
              "3             2        2                                     0  \n",
              "4             1        1                                     0  \n",
              "...         ...      ...                                   ...  \n",
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              "2152          7        5                                     6  \n",
              "2153          6        2                                     6  \n",
              "2154          2        1                                     6  \n",
              "2155          7        6                                     6  \n",
              "\n",
              "[2156 rows x 24 columns]"
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              "    <tr>\n",
              "      <th>2151</th>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2153</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2154</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2155</th>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2156 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59db1ded-0ae0-44aa-91c5-a34b35ee286c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ee22bb88-abf4-4eb0-b1e6-bc689d71e9d4\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee22bb88-abf4-4eb0-b1e6-bc689d71e9d4')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ee22bb88-abf4-4eb0-b1e6-bc689d71e9d4 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-59db1ded-0ae0-44aa-91c5-a34b35ee286c 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-59db1ded-0ae0-44aa-91c5-a34b35ee286c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eYVJVjaaGOkH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tirth's Work"
      ],
      "metadata": {
        "id": "tbMeLZaFKdBK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = data.drop([\"Patient Id\",\"index\"],axis=1)\n",
        "new_data[\"Level\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6G3JMjtkK01A",
        "outputId": "7a8ae1d7-42f4-4855-8a1e-070f5db2b50b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "High      365\n",
              "Medium    332\n",
              "Low       303\n",
              "Name: Level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "endocer = LabelEncoder()\n",
        "\n",
        "endoded_coloum = endocer.fit_transform(new_data[\"Level\"])\n",
        "\n",
        "new_data[\"Level_encoded\"] = endoded_coloum"
      ],
      "metadata": {
        "id": "EeJAvYIGLR4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_data.drop(['Level' , 'Level_encoded' ] , axis = 1)\n",
        "y = new_data['Level_encoded']"
      ],
      "metadata": {
        "id": "DjIzCTOELcsm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=143)"
      ],
      "metadata": {
        "id": "wVLNH15KLuww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = svm.SVC()\n",
        "model.fit(X_train ,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "AYvUNVw_MlKr",
        "outputId": "e1d2c1f4-d29b-4c0c-9657-dae205c1488c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "-ipqx1adMbBI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(pred , y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xJCQKSRMr3z",
        "outputId": "ad0161ef-c325-417e-bdc3-527c9b198afe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.98"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "cm = confusion_matrix(pred , y_test)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NgBmqRENf-S",
        "outputId": "e3e28e26-b342-44d9-b3a4-1131c1c4f3af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[79,  0,  2],\n",
              "       [ 0, 56,  0],\n",
              "       [ 0,  2, 61]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = ['High' , 'Medium' , 'Low']\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "AY-2aQxrOiDJ",
        "outputId": "ff3150a0-d731-4ea4-bfa4-46f4c138c7dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(pred , y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBtifzeUPDls",
        "outputId": "3f5e47c2-4a9e-4733-e18f-bd7498b61024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99        81\n",
            "           1       0.97      1.00      0.98        56\n",
            "           2       0.97      0.97      0.97        63\n",
            "\n",
            "    accuracy                           0.98       200\n",
            "   macro avg       0.98      0.98      0.98       200\n",
            "weighted avg       0.98      0.98      0.98       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bJAORXLvPcgH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}