Lung_Cancer_Report1.ipynb

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      "authorship_tag": "ABX9TyMgi7OuLWyM+1r/+ML65177",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/aayushi-0407/ML-Projects/blob/main/Lung_Cancer_Report1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "##We are having data  set of lung cancer disease we are going to work on it to understand what all age groups are prone to lung cancer and are train categorical models. Initially we have imported all the necessary modules"
      ],
      "metadata": {
        "id": "Y37D97N3t4AD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "mn28uyZssc_o"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import seaborn as sns\n",
        "from sklearn import svm\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the data"
      ],
      "metadata": {
        "id": "Lucnji-wuRvg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/cancer patient data sets.csv\")\n",
        "data.info()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYb65dZxsk4c",
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      "execution_count": 31,
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          "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": "markdown",
      "source": [
        "##cleaning data: we are removing unnecessary columns"
      ],
      "metadata": {
        "id": "2tcbyz5yuZAX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data=data.drop(['index','Patient Id'],axis=1)\n",
        "numeric_col=new_data.describe().columns\n",
        "new_data"
      ],
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      "execution_count": 12,
      "outputs": [
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              "     Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0     33       1              2            4             5   \n",
              "1     17       1              3            1             5   \n",
              "2     35       1              4            5             6   \n",
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              "4     46       1              6            8             7   \n",
              "..   ...     ...            ...          ...           ...   \n",
              "995   44       1              6            7             7   \n",
              "996   37       2              6            8             7   \n",
              "997   25       2              4            5             6   \n",
              "998   18       2              6            8             7   \n",
              "999   47       1              6            5             6   \n",
              "\n",
              "     OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
              "0                       4             3                     2              2   \n",
              "1                       3             4                     2              2   \n",
              "2                       5             5                     4              6   \n",
              "3                       7             6                     7              7   \n",
              "4                       7             7                     6              7   \n",
              "..                    ...           ...                   ...            ...   \n",
              "995                     7             7                     6              7   \n",
              "996                     7             7                     6              7   \n",
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              "998                     7             7                     6              7   \n",
              "999                     5             5                     4              6   \n",
              "\n",
              "     Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
              "0          4  ...        3            4                    2         2   \n",
              "1          2  ...        1            3                    7         8   \n",
              "2          7  ...        8            7                    9         2   \n",
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              "998        7  ...        3            2                    4         1   \n",
              "999        7  ...        8            7                    9         2   \n",
              "\n",
              "     Swallowing Difficulty  Clubbing of Finger Nails  Frequent Cold  \\\n",
              "0                        3                         1              2   \n",
              "1                        6                         2              1   \n",
              "2                        1                         4              6   \n",
              "3                        4                         5              6   \n",
              "4                        4                         2              4   \n",
              "..                     ...                       ...            ...   \n",
              "995                      8                         2              4   \n",
              "996                      2                         4              3   \n",
              "997                      1                         4              6   \n",
              "998                      4                         2              4   \n",
              "999                      1                         4              6   \n",
              "\n",
              "     Dry Cough  Snoring   Level  \n",
              "0            3        4     Low  \n",
              "1            7        2  Medium  \n",
              "2            7        2    High  \n",
              "3            7        5    High  \n",
              "4            2        3    High  \n",
              "..         ...      ...     ...  \n",
              "995          5        3    High  \n",
              "996          1        4    High  \n",
              "997          7        2    High  \n",
              "998          2        3    High  \n",
              "999          7        2    High  \n",
              "\n",
              "[1000 rows x 24 columns]"
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </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-87c74e5c-6a66-4d74-a63a-3fa24c2bd6fe 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-f3e62da4-3e6d-4b2b-b78a-2e83486e05b0 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-f3e62da4-3e6d-4b2b-b78a-2e83486e05b0');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##No outliers present however there can be unbalanced data"
      ],
      "metadata": {
        "id": "Zr08mXGKvSFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in numeric_col:\n",
        "  plt.figure(figsize=(5,5))\n",
        "  plt.boxplot(data[column])\n",
        "  plt.title(column)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2CvYsl71twib",
        "outputId": "659ca55e-df0d-4846-e160-a53bef446d2d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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sbKwxn5kGAAgty7JUWFiohISE835uZMhDdeDAATmdzlAvCwAw0P79+8/7SfwhD1Xp70Hav39/mV8dAAAID263W06ns1K/Gy/koTr7s8UIFQCEt8pcAuLNFAAAoxEqAIDRCBUAwGiECgBgNEIFADAaoQIAGI1QAQCMRqgAAEYjVAAAoxEqAIDR/A5VYWGhJk2apBYtWig6OlpXXnml1q9fH4zZAADwP1RjxozRihUrtGDBAm3dulVXX321+vXrp++++y4Y8wEAwpxfv4r+5MmTio2N1bJlyzRw4EDv9q5duyotLU2PPPLIeR/D7XbL4XDI5XLxobQAEKb8aYFfZ1SnT5/WmTNnFBUV5bM9Ojpaq1ev9n9SAADOw69f8xEbG6tu3brp4YcfVkpKii666CK9+uqrWrt2rVq3bl3uMR6PRx6Px3vf7XZXbWLAEMXFxdq+fbvfx508eVIFBQVKTExUdHR0QGsnJyerXr16AR0L1DZ+/z6qBQsW6LbbbtPFF1+siIgIpaamatiwYdq4cWO5+2dlZWnmzJlVHhQwzfbt29W1a9caWXvjxo1KTU2tkbWBUPPrGtXZTpw4IbfbraZNm+rmm29WUVGR3nvvvTL7lXdG5XQ6uUaFWi/QM6pt27YpPT1dCxcuVEpKSkBrc0aF2s6fa1QB/4bfmJgYxcTE6NixY1q+fLlmz55d7n52u112uz3QZQBj1atXr0pnNSkpKZwVAZXgd6iWL18uy7LUrl075efna+rUqUpOTtaoUaOCMR8AIMz5/e+oXC6XMjMzlZycrBEjRqhHjx5avny5LrjggmDMBwAIc36fUd1000266aabgjELAABl8Fl/AACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACj+RWqM2fO6IEHHlDLli0VHR2tVq1a6eGHH5ZlWcGaDwAQ5iL92XnWrFnKycnR/Pnz1aFDB23YsEGjRo2Sw+HQxIkTgzUjACCM+RWqNWvWaMiQIRo4cKAkKTExUa+++qq+/PLLoAwHAIBfL/1deeWV+uSTT7Rz505J0r/+9S+tXr1aaWlpQRkOAAC/zqimTZsmt9ut5ORkRURE6MyZM3r00Uc1fPjwCo/xeDzyeDze+263O/BpAQBhx68zqjfeeEOLFi3S4sWLtWnTJs2fP19PPPGE5s+fX+ExWVlZcjgc3pvT6azy0ACA8GGz/HjLntPp1LRp05SZmend9sgjj2jhwoXavn17uceUd0bldDrlcrkUFxdXhdGB2mnTpk3q2rWrNm7cqNTU1JoeB6gRbrdbDoejUi3w66W/4uJi1anjexIWERGhkpKSCo+x2+2y2+3+LAMAgJdfoRo8eLAeffRRNW/eXB06dNDmzZv11FNP6bbbbgvWfACAMOdXqJ577jk98MADuuOOO3T48GElJCRo3LhxevDBB4M1HwAgzPkVqtjYWD399NN6+umngzQOAAC++Kw/AIDRCBUAwGiECgBgNEIFADAaoQIAGI1QAQCMRqgAAEYjVAAAoxEqAIDRCBUAwGiECgBgNEIFADAaoQIAGI1QAQCMRqgAAEYjVAAAoxEqAIDRCBUAwGiECgBgNEIFADAaoQIAGI1QAQCMRqgAAEYjVAAAoxEqAIDRCBUAwGiECgBgNEIFADAaoQIAGI1QAQCMRqgAAEYjVAAAoxEqAIDR/ApVYmKibDZbmVtmZmaw5gMAhLlIf3Zev369zpw5473/9ddfq3///ho6dGi1DwYAgORnqOLj433uZ2dnq1WrVurVq1e1DgUAQCm/QnW2U6dOaeHChbrnnntks9kq3M/j8cjj8Xjvu93uQJcEAIShgN9MsXTpUh0/flwjR448535ZWVlyOBzem9PpDHRJAEAYCjhU8+bNU1pamhISEs653/Tp0+Vyuby3/fv3B7okACAMBfTS3969e/Xxxx/r7bffPu++drtddrs9kGUAAAjsjCo3N1eNGzfWwIEDq3seAAB8+B2qkpIS5ebmKiMjQ5GRAb8XAwCASvE7VB9//LH27dun2267LRjzAADgw+9ToquvvlqWZQVjFgAAyuCz/gAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARvM7VN99953S09PVqFEjRUdH67e//a02bNgQjNkAAFCkPzsfO3ZM3bt3V58+ffTBBx8oPj5eu3btUoMGDYI1HwAgzPkVqlmzZsnpdCo3N9e7rWXLltU+FAAApfx66e+dd97RpZdeqqFDh6px48bq0qWL5s6de85jPB6P3G63zw0AgMryK1TffvutcnJy1KZNGy1fvlzjx4/XxIkTNX/+/AqPycrKksPh8N6cTmeVhwYAhA+bZVlWZXeuW7euLr30Uq1Zs8a7beLEiVq/fr3Wrl1b7jEej0cej8d73+12y+l0yuVyKS4urgqjA7XTpk2b1LVrV23cuFGpqak1PQ5QI9xutxwOR6Va4NcZVdOmTdW+fXufbSkpKdq3b1+Fx9jtdsXFxfncAACoLL9C1b17d+3YscNn286dO9WiRYtqHQoAgFJ+heruu+/WunXr9Nhjjyk/P1+LFy/WnDlzlJmZGaz5AABhzq9QXXbZZVqyZIleffVVdezYUQ8//LCefvppDR8+PFjzAQDCnF//jkqSBg0apEGDBgVjFgAAyuCz/gAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIzmV6hmzJghm83mc0tOTg7WbAAAKNLfAzp06KCPP/74fw8Q6fdDAABQaX5XJjIyUk2aNAnGLAAAlOH3Napdu3YpISFBSUlJGj58uPbt2xeMuQAAkOTnGdUVV1yhvLw8tWvXTt9//71mzpypnj176uuvv1ZsbGy5x3g8Hnk8Hu99t9tdtYmBarZr1y4VFhaGbL1t27b5/G+oxMbGqk2bNiFdE6gONsuyrEAPPn78uFq0aKGnnnpKo0ePLnefGTNmaObMmWW2u1wuxcXFBbo0UC127dqltm3b1vQYIbNz505iBSO43W45HI5KtaBK74SoX7++2rZtq/z8/Ar3mT59uu655x6f4ZxOZ1WWBapN6ZnUwoULlZKSEpI1T548qYKCAiUmJio6Ojoka27btk3p6ekhPXMEqkuVQlVUVKTdu3fr1ltvrXAfu90uu91elWWAoEtJSVFqamrI1uvevXvI1gJqO7/eTDFlyhR9/vnnKigo0Jo1a3T99dcrIiJCw4YNC9Z8AIAw59cZ1X/+8x8NGzZMR44cUXx8vHr06KF169YpPj4+WPMBAMKcX6F67bXXgjUHAADl4rP+AABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGq1KosrOzZbPZNGnSpGoaBwAAXwGHav369XrxxRd1ySWXVOc8AAD4CChURUVFGj58uObOnasGDRpU90wAAHgFFKrMzEwNHDhQ/fr1q+55AADwEenvAa+99po2bdqk9evXV2p/j8cjj8fjve92u/1dEgga2+kf1aVJHUUf3ykd+PW+tyj6+E51aVJHttM/1vQogN/8CtX+/ft11113acWKFYqKiqrUMVlZWZo5c2ZAwwHBFlW0T5vGXSitGietqulpgidF0qZxF2pb0T5JV9b0OIBfbJZlWZXdeenSpbr++usVERHh3XbmzBnZbDbVqVNHHo/H52tS+WdUTqdTLpdLcXFx1fAUgMBt/nKNRg/pqUWLFiklObmmxwmabdu3a/jw4Zq37B/qcjmhQs1zu91yOByVaoFfZ1R9+/bV1q1bfbaNGjVKycnJ+vOf/1wmUpJkt9tlt9v9WQYIGSsySpsPluhk/bZSQueaHidoTh4s0eaDJbIiK/dKCGASv0IVGxurjh07+myLiYlRo0aNymwHAKA6/HqvHgMAfhX8ftffL61cubIaxgAAoHycUQEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjOZXqHJycnTJJZcoLi5OcXFx6tatmz744INgzQYAgH+hatasmbKzs7Vx40Zt2LBBV111lYYMGaJvvvkmWPMBAMJcpD87Dx482Of+o48+qpycHK1bt04dOnSo1sEAAJD8DNXZzpw5ozfffFMnTpxQt27dKtzP4/HI4/F477vd7kCXBACEIb/fTLF161ZdeOGFstvtuv3227VkyRK1b9++wv2zsrLkcDi8N6fTWaWBAQDhxe9QtWvXTlu2bNEXX3yh8ePHKyMjQ//+978r3H/69OlyuVze2/79+6s0MAAgvPj90l/dunXVunVrSVLXrl21fv16PfPMM3rxxRfL3d9ut8tut1dtSgBA2Kryv6MqKSnxuQYFAEB18uuMavr06UpLS1Pz5s1VWFioxYsXa+XKlVq+fHmw5gMAhDm/QnX48GGNGDFC33//vRwOhy655BItX75c/fv3D9Z8AIAw51eo5s2bF6w5AAAoF5/1BwAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwGqECABiNUAEAjEaoAABGI1QAAKMRKgCA0QgVAMBohAoAYDRCBQAwml+hysrK0mWXXabY2Fg1btxY1113nXbs2BGs2QAA8C9Un3/+uTIzM7Vu3TqtWLFCP/30k66++mqdOHEiWPMBAMJcpD87f/jhhz738/Ly1LhxY23cuFG///3vq3UwAAAkP0P1Sy6XS5LUsGHDCvfxeDzyeDze+263uypLAtWquLhYkrRp06aQrXny5EkVFBQoMTFR0dHRIVlz27ZtIVkHCIaAQ1VSUqJJkyape/fu6tixY4X7ZWVlaebMmYEuAwTV9u3bJUljx46t4UlCIzY2tqZHAPxmsyzLCuTA8ePH64MPPtDq1avVrFmzCvcr74zK6XTK5XIpLi4ukKWBavPDDz9o6dKlSk5OVr169UKy5rZt25Senq6FCxcqJSUlJGtKP0eqTZs2IVsPOBe32y2Hw1GpFgR0RnXnnXfq73//u1atWnXOSEmS3W6X3W4PZBkg6H7zm99ozJgxNbJ2SkqKUlNTa2RtoDbxK1SWZWnChAlasmSJVq5cqZYtWwZrLgAAJPkZqszMTC1evFjLli1TbGysDh48KElyOBwhuygMAAgvfv07qpycHLlcLvXu3VtNmzb13l5//fVgzQcACHN+v/QHAEAo8Vl/AACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARvM7VKtWrdLgwYOVkJAgm82mpUuXBmEsAAB+5neoTpw4oU6dOumFF14IxjwAAPiI9PeAtLQ0paWlBWMWAADK4BoVAMBofp9R+cvj8cjj8Xjvu93uYC8JhERxcbG2b9/u93Hbtm3z+d9AJCcnq169egEfD9QmQQ9VVlaWZs6cGexlgJDbvn27unbtGvDx6enpAR+7ceNGpaamBnw8UJvYLMuyAj7YZtOSJUt03XXXVbhPeWdUTqdTLpdLcXFxgS4N1LhAz6hOnjypgoICJSYmKjo6OqC1OaNCbed2u+VwOCrVgqCfUdntdtnt9mAvA4RcvXr1Aj6r6d69ezVPA/x6+R2qoqIi5efne+/v2bNHW7ZsUcOGDdW8efNqHQ4AAL9DtWHDBvXp08d7/5577pEkZWRkKC8vr9oGAwBACiBUvXv3VhUuawEA4Bf+HRUAwGiECgBgNEIFADAaoQIAGI1QAQCMRqgAAEYjVAAAoxEqAIDRCBUAwGiECgBgtKB/evovlX78Er9AEQDCV2kDKvORfCEPVWFhoSTJ6XSGemkAgGEKCwvlcDjOuU+VfnFiIEpKSnTgwAHFxsbKZrOFcmnACKW/PHT//v388lCELcuyVFhYqISEBNWpc+6rUCEPFRDu/PnNpgB4MwUAwHCECgBgNEIFhJjdbtdDDz0ku91e06MAtQLXqAAARuOMCgBgNEIFADAaoQIAGI1QAQCMRqiAEFm1apUGDx6shIQE2Ww2LV26tKZHAmoFQgWEyIkTJ9SpUye98MILNT0KUKuE/ENpgXCVlpamtLS0mh4DqHU4owIAGI1QAQCMRqgAAEYjVAAAoxEqAIDReNcfECJFRUXKz8/33t+zZ4+2bNmihg0bqnnz5jU4GWA2Pj0dCJGVK1eqT58+ZbZnZGQoLy8v9AMBtQShAgAYjWtUAACjESoAgNEIFQDAaIQKAGA0QgUAMBqhAgAYjVABAIxGqAAARiNUAACjESoAgNEIFQDAaIQKAGC0/wfybus07EhwMgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####to check where data is balanced or not\n",
        "here we observe that data is quite **balanced**"
      ],
      "metadata": {
        "id": "Fc09kla82wGa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data[\"Level\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7Y6wfL82vSy",
        "outputId": "be329517-f009-497f-a860-c65a0508c97b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "High      365\n",
              "Medium    332\n",
              "Low       303\n",
              "Name: Level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Visualization of Cancer Prevalence Among Different Age Groups\n",
        "\n",
        "Objective:\n",
        "The objective of this data analysis is to visualize the distribution of cancer cases among different age groups based on the data available in the DataFrame. The data contains information about various health parameters of 1000 patients, including their age and the presence or absence of cancer (indicated by 'High' or 'Low' in the 'Level' column)."
      ],
      "metadata": {
        "id": "Sem_MPPpysQN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Assuming you have already loaded the DataFrame into a variable called 'df'\n",
        "\n",
        "# Create bins for age groups\n",
        "bins = [0, 20, 40, 60, 80, 100]\n",
        "age_groups = ['0-19', '20-39', '40-59', '60-79', '80+']\n",
        "\n",
        "# Use cut function to create age groups based on the 'Age' column\n",
        "new_data['Age Group'] = pd.cut(new_data['Age'], bins=bins, labels=age_groups)\n",
        "\n",
        "# Count the number of cancer cases in each age group\n",
        "cancer_counts = new_data[new_data['Level'] == 'High']['Age Group'].value_counts().reindex(age_groups, fill_value=0)\n",
        "\n",
        "# Plot the bar chart\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.bar(cancer_counts.index, cancer_counts.values)\n",
        "plt.xlabel('Age Groups')\n",
        "plt.ylabel('Number of Cancer Cases')\n",
        "plt.title('Cancer Cases among Different Age Groups')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "KhFO4-rbuyn7",
        "outputId": "9d56ad14-97e3-4ee0-b686-ee6fd1542d84"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###The analysis and visualization of the data revealed the following findings:\n",
        "\n",
        "The DataFrame contains data for patients with different ages, ranging from young to elderly individuals.\n",
        "The bar plot shows the distribution of cancer cases among different age groups.\n",
        "The prevalence of cancer varies across age groups, with higher incidences observed in certain age brackets.\n",
        "We observe that age group 20-39 have higher chances of lung cancer"
      ],
      "metadata": {
        "id": "G_e2qffxCprV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Encoding Cancer Levels for Data Analysis\n",
        "\n",
        "Objective:\n",
        "The objective of this data analysis is to encode the 'Level' column in the 'new_data' DataFrame, representing the presence or absence of cancer, into numeric labels. The encoded labels will be stored in a new column called 'level_encoded' using the LabelEncoder from scikit-learn."
      ],
      "metadata": {
        "id": "zxFMcIRT3ec_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()\n",
        "new_data['level_encoded'] = encoder.fit_transform(new_data['Level'])"
      ],
      "metadata": {
        "id": "f1FuA93fzgvC"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = new_data.drop(['Level' , 'level_encoded' ,'Age','Age Group'] , axis = 1)\n",
        "y = new_data['level_encoded']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=143)"
      ],
      "metadata": {
        "id": "-Fpz4qFt3llO"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creating model"
      ],
      "metadata": {
        "id": "XNc2fWGXDT6M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SVM Model for Cancer Prediction and Evaluation\n",
        "\n",
        "Objective:\n",
        "The objective of this data analysis is to build a Support Vector Machine (SVM) model to predict cancer presence based on the health parameters provided in the dataset. The data has been split into training and testing sets, and the SVM model has been trained on the training data and evaluated on the testing data."
      ],
      "metadata": {
        "id": "0cmDtx6SDkR5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1=svm.SVC()\n",
        "model1.fit(X_train ,y_train)\n",
        "pred1= model1.predict(X_test)\n",
        "accuracy_score(pred1 , y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APcHEOwh3sMy",
        "outputId": "e636830c-a102-459e-e135-98ce2b86f90b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Results:\n",
        "The analysis and evaluation of the SVM model resulted in the following findings:\n",
        "\n",
        "The SVM model achieved an accuracy of 100% on the testing data, indicating that it correctly classified all instances in the testing set.\n",
        "\n",
        "Recommendations:\n",
        "An accuracy of 100% raises the possibility of overfitting, where the model might have learned the training data too well and is unable to generalize to new, unseen data.\n",
        "\n",
        "Conclusion:\n",
        "The SVM model achieved a high accuracy of 100% on the testing data, indicating its capability to classify cancer presence effectively. However, further evaluation is necessary to confirm the model's generalization and robustness."
      ],
      "metadata": {
        "id": "R4BHe8hkDmdf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###A confusion matrix is a table that is often used to evaluate the performance of a classification model. It summarizes the results of a classification task by comparing the predicted labels with the actual labels of a dataset. The confusion matrix provides valuable insights into the model's ability to make correct and incorrect predictions for each class."
      ],
      "metadata": {
        "id": "UXRMfP0rEdgY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(pred1 , y_test)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Zo1ANT-5vpz",
        "outputId": "eb960361-2b71-4f41-e879-bcfc4cbdcca4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[114,   0,   0],\n",
              "       [  0,  91,   0],\n",
              "       [  0,   0,  95]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###After checking cross validation"
      ],
      "metadata": {
        "id": "OS-KJs_8Fh4R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Assuming you have already loaded the SVM model (model1) and data (X_train, y_train) into variables\n",
        "\n",
        "# Perform cross-validation with 5 folds\n",
        "cv_scores = cross_val_score(model1, X_train, y_train, cv=10)\n",
        "\n",
        "# Calculate the average cross-validation accuracy\n",
        "cv_accuracy = cv_scores.mean() * 100\n",
        "\n",
        "print(\"Cross-Validation Accuracy:\")\n",
        "print(cv_accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_HXc7JRFUkr",
        "outputId": "29710960-208d-4b3f-ee34-2735bbbf9b8c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Accuracy:\n",
            "100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(pred1 , y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_PQ_yEA6_eZ",
        "outputId": "b65a7df0-f06c-452a-b53e-820f128304e8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       114\n",
            "           1       1.00      1.00      1.00        91\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           1.00       300\n",
            "   macro avg       1.00      1.00      1.00       300\n",
            "weighted avg       1.00      1.00      1.00       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluation of SGDClassifier Model for Cancer Prediction\n",
        "\n",
        "Objective:\n",
        "The objective of this data analysis is to evaluate the performance of an SGDClassifier model, trained using the 'StandardScaler'-scaled training data, in predicting cancer presence based on various health parameters. The model uses the 'squared_hinge' loss function and is assessed using accuracy, classification report, and confusion matrix on the test data.\n",
        "\n",
        "Results:\n",
        "The analysis and evaluation of the SGDClassifier model resulted in the following findings:\n",
        "\n",
        "The model achieved an accuracy of 100%, indicating that it correctly classified all instances in the test set.\n",
        "The classification report reveals high precision, recall, and F1-score for both classes ('High' and 'Low'), indicating that the model effectively identifies positive and negative cancer cases.\n",
        "The confusion matrix shows no false positives or false negatives, supporting the perfect accuracy score\n",
        "\n",
        "Recommendations:\n",
        "An accuracy of 100% raises the possibility of overfitting, where the model might have learned the training data too well and is unable to generalize to new, unseen data."
      ],
      "metadata": {
        "id": "rYQKFolrE3dh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Create and train the SGDClassifier model\n",
        "sgd_clf = SGDClassifier(loss='squared_hinge', random_state=42)\n",
        "sgd_clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict using the model\n",
        "y_pred = sgd_clf.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n"
      ],
      "metadata": {
        "id": "wUsDSQiG7kqh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e2c7d4b-167d-4add-f8f2-612fd5c06caa"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 100.00\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       114\n",
            "           1       1.00      1.00      1.00        91\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           1.00       300\n",
            "   macro avg       1.00      1.00      1.00       300\n",
            "weighted avg       1.00      1.00      1.00       300\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[114   0   0]\n",
            " [  0  91   0]\n",
            " [  0   0  95]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluation of SGDClassifier Model for Cancer Prediction\n",
        "\n",
        "Objective:\n",
        "The objective of this report is to evaluate the performance of an SGDClassifier model for predicting cancer presence based on various health parameters. The data has been preprocessed, including feature scaling using 'StandardScaler', and the model has been trained and tested on the respective datasets. The evaluation involves measuring accuracy, generating a classification report, and visualizing the confusion matrix.\n",
        "\n",
        "Results:\n",
        "The analysis and evaluation of the SGDClassifier model resulted in the following findings:\n",
        "\n",
        "The model achieved an accuracy of 97%, indicating its ability to correctly classify the majority of instances in the test set.\n",
        "\n",
        "The classification report provides a comprehensive evaluation of the model's precision, recall, and F1-score for both classes ('High' and 'Low'). The model demonstrates high precision, recall, and F1-score for both classes, indicating its effectiveness in identifying positive and negative cancer cases.\n",
        "\n",
        "The confusion matrix visually represents the true positive, true negative, false positive, and false negative predictions. The high accuracy is further supported by the small number of misclassifications."
      ],
      "metadata": {
        "id": "IFgEgExKF6nr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Create and train the SGDClassifier model\n",
        "sgd_clf = SGDClassifier(loss='hinge', random_state=42)\n",
        "sgd_clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict using the model\n",
        "y_pred = sgd_clf.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V96LDnzI_knz",
        "outputId": "18f6890e-3009-408a-a3ed-e164dc5df4a3"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 97.33\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       114\n",
            "           1       0.92      1.00      0.96        91\n",
            "           2       1.00      0.92      0.96        95\n",
            "\n",
            "    accuracy                           0.97       300\n",
            "   macro avg       0.97      0.97      0.97       300\n",
            "weighted avg       0.98      0.97      0.97       300\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[114   0   0]\n",
            " [  0  91   0]\n",
            " [  0   8  87]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation of Logistic Regression Model for Cancer Prediction\n",
        "\n",
        "Objective:\n",
        "The objective of this report is to evaluate the performance of a Logistic Regression model for predicting cancer presence based on various health parameters. The data has been preprocessed using feature scaling with 'StandardScaler', and the model has been trained and tested on the respective datasets. The evaluation includes measuring accuracy, generating a classification report, and visualizing the confusion matrix.\n",
        "\n",
        "Results:\n",
        "The analysis and evaluation of the Logistic Regression model resulted in the following findings:\n",
        "\n",
        "The model achieved a remarkable accuracy of 100%, indicating its ability to correctly classify all instances in the test set.\n",
        "\n",
        "The classification report provides a comprehensive evaluation of the model's precision, recall, and F1-score for both classes ('High' and 'Low'). The model demonstrates perfect precision, recall, and F1-score for both classes, indicating its effectiveness in identifying positive and negative cancer cases.\n",
        "\n",
        "The confusion matrix visually represents the true positive, true negative, false positive, and false negative predictions. In this case, the matrix confirms the perfect accuracy by showing zero misclassifications.\n",
        "\n",
        "Conclusion:\n",
        "The Logistic Regression model achieved an impressive accuracy of 100% on the test data, indicating its capability to classify cancer presence with perfection."
      ],
      "metadata": {
        "id": "NebEMjcxGT6b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Create and train the SGDClassifier model\n",
        "sgd_clf = SGDClassifier(loss='', random_state=42)\n",
        "sgd_clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict using the model\n",
        "y_pred = sgd_clf.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0iDhtjDAd_M",
        "outputId": "1b111e3f-5c23-4297-8418-44d387068750"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 100.00\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       114\n",
            "           1       1.00      1.00      1.00        91\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           1.00       300\n",
            "   macro avg       1.00      1.00      1.00       300\n",
            "weighted avg       1.00      1.00      1.00       300\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[114   0   0]\n",
            " [  0  91   0]\n",
            " [  0   0  95]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:163: FutureWarning: The loss 'log' was deprecated in v1.1 and will be removed in version 1.3. Use `loss='log_loss'` which is equivalent.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###The different accuracy scores obtained for the last three models with different loss functions indicate that the choice of loss function can significantly impact the model's performance and predictive capabilities. The loss function defines the objective that the model aims to minimize during training, and each loss function has its characteristics and assumptions. Here's what the varying accuracies might indicate for each loss function:\n",
        "\n",
        "1. Hinge Loss (loss='hinge'):\n",
        "   - The model achieved an accuracy of 97%.\n",
        "   - The hinge loss is commonly used for Support Vector Machine (SVM) models, which aim to maximize the margin between classes. The slightly lower accuracy might be due to the potential non-linearity of the data or noise in the dataset.\n",
        "   - This accuracy could still be considered good, but it may be worth exploring other loss functions or fine-tuning hyperparameters to improve the model's performance.\n",
        "\n",
        "2. Logistic Loss (loss='log'):\n",
        "   - The model achieved an accuracy of 100%.\n",
        "   - The logistic loss, commonly used in Logistic Regression models, is well-suited for binary classification problems. It estimates the probability that a given instance belongs to a particular class.\n",
        "   - The perfect accuracy might indicate that the data is well-separated, and the model is effectively learning to distinguish between the classes with clear decision boundaries.\n",
        "   - However, 100% accuracy could also be a sign of potential overfitting. It is essential to validate the model's performance on unseen data and conduct cross-validation to ensure its generalization.\n",
        "\n",
        "3. Squared Hinge Loss (loss='squared_hinge'):\n",
        "   - The model achieved an accuracy of 100%.\n",
        "   - The squared hinge loss is an extension of the hinge loss, which can provide smoother gradients and better numerical stability during optimization.\n",
        "   - Like the logistic loss, the perfect accuracy might indicate that the data is well-separated, and the model is effectively learning to distinguish between the classes with clear decision boundaries.\n",
        "   - Again, 100% accuracy could raise concerns about potential overfitting. It is crucial to conduct further evaluation, including cross-validation, to confirm the model's robustness.\n",
        "\n"
      ],
      "metadata": {
        "id": "wb-yXlvlHJZs"
      }
    }
  ]
}