breast cancer diagnosis system.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3wT8l6lfj--"
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      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqsQmOXGXXTe"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwJ9zLukg3Q_"
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      "source": [
        "Data Collection & Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6bMZMKUgz7L"
      },
      "source": [
        "# loading the data from sklearn\n",
        "breast_cancer_dataset = pd.read_csv('/content/cancer.csv')"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
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        "outputId": "55faffab-13c3-4a3a-a350-3db018df0f9a"
      },
      "source": [
        "print(breast_cancer_dataset)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
            "0           M        17.99         10.38          122.80     1001.0   \n",
            "1           M        20.57         17.77          132.90     1326.0   \n",
            "2           M        19.69         21.25          130.00     1203.0   \n",
            "3           M        11.42         20.38           77.58      386.1   \n",
            "4           M        20.29         14.34          135.10     1297.0   \n",
            "..        ...          ...           ...             ...        ...   \n",
            "564         M        21.56         22.39          142.00     1479.0   \n",
            "565         M        20.13         28.25          131.20     1261.0   \n",
            "566         M        16.60         28.08          108.30      858.1   \n",
            "567         M        20.60         29.33          140.10     1265.0   \n",
            "568         B         7.76         24.54           47.92      181.0   \n",
            "\n",
            "     smoothness_mean  compactness_mean  concavity_mean  concavepoints_mean  \\\n",
            "0            0.11840           0.27760         0.30010             0.14710   \n",
            "1            0.08474           0.07864         0.08690             0.07017   \n",
            "2            0.10960           0.15990         0.19740             0.12790   \n",
            "3            0.14250           0.28390         0.24140             0.10520   \n",
            "4            0.10030           0.13280         0.19800             0.10430   \n",
            "..               ...               ...             ...                 ...   \n",
            "564          0.11100           0.11590         0.24390             0.13890   \n",
            "565          0.09780           0.10340         0.14400             0.09791   \n",
            "566          0.08455           0.10230         0.09251             0.05302   \n",
            "567          0.11780           0.27700         0.35140             0.15200   \n",
            "568          0.05263           0.04362         0.00000             0.00000   \n",
            "\n",
            "     symmetry_mean  fractaldimension_mean  \n",
            "0           0.2419                0.07871  \n",
            "1           0.1812                0.05667  \n",
            "2           0.2069                0.05999  \n",
            "3           0.2597                0.09744  \n",
            "4           0.1809                0.05883  \n",
            "..             ...                    ...  \n",
            "564         0.1726                0.05623  \n",
            "565         0.1752                0.05533  \n",
            "566         0.1590                0.05648  \n",
            "567         0.2397                0.07016  \n",
            "568         0.1587                0.05884  \n",
            "\n",
            "[569 rows x 11 columns]\n"
          ]
        }
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    },
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      "source": [
        "# print the first 5 rows of the dataframe\n",
        "breast_cancer_dataset.head()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0         M        17.99         10.38          122.80     1001.0   \n",
              "1         M        20.57         17.77          132.90     1326.0   \n",
              "2         M        19.69         21.25          130.00     1203.0   \n",
              "3         M        11.42         20.38           77.58      386.1   \n",
              "4         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concavepoints_mean  \\\n",
              "0          0.11840           0.27760          0.3001             0.14710   \n",
              "1          0.08474           0.07864          0.0869             0.07017   \n",
              "2          0.10960           0.15990          0.1974             0.12790   \n",
              "3          0.14250           0.28390          0.2414             0.10520   \n",
              "4          0.10030           0.13280          0.1980             0.10430   \n",
              "\n",
              "   symmetry_mean  fractaldimension_mean  \n",
              "0         0.2419                0.07871  \n",
              "1         0.1812                0.05667  \n",
              "2         0.2069                0.05999  \n",
              "3         0.2597                0.09744  \n",
              "4         0.1809                0.05883  "
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              "      <th>diagnosis</th>\n",
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              "      <td>17.99</td>\n",
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              "      <th>1</th>\n",
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              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
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              "      <th>2</th>\n",
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              "      <td>19.69</td>\n",
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              "      <th>3</th>\n",
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              "      <td>11.42</td>\n",
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              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
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      "source": [
        "# adding the 'target' column to the data frame\n",
        "data_frame['label'] = breast_cancer_dataset.diagnosis"
      ],
      "execution_count": 85,
      "outputs": []
    },
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      "source": [
        "# print last 5 rows of the dataframe\n",
        "breast_cancer_dataset.tail()"
      ],
      "execution_count": 86,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "564         M        21.56         22.39          142.00     1479.0   \n",
              "565         M        20.13         28.25          131.20     1261.0   \n",
              "566         M        16.60         28.08          108.30      858.1   \n",
              "567         M        20.60         29.33          140.10     1265.0   \n",
              "568         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concavepoints_mean  \\\n",
              "564          0.11100           0.11590         0.24390             0.13890   \n",
              "565          0.09780           0.10340         0.14400             0.09791   \n",
              "566          0.08455           0.10230         0.09251             0.05302   \n",
              "567          0.11780           0.27700         0.35140             0.15200   \n",
              "568          0.05263           0.04362         0.00000             0.00000   \n",
              "\n",
              "     symmetry_mean  fractaldimension_mean  \n",
              "564         0.1726                0.05623  \n",
              "565         0.1752                0.05533  \n",
              "566         0.1590                0.05648  \n",
              "567         0.2397                0.07016  \n",
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              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
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              "      <td>858.1</td>\n",
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              "      <td>7.76</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57596d88-4fcc-4ff6-a8bf-821c292c3ed4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sw3wjdK6iwK4",
        "outputId": "8401f5fb-8335-4035-fe5d-93e7b90b7c46"
      },
      "source": [
        "# number of rows and columns in the dataset\n",
        "breast_cancer_dataset.shape"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AWOjMuyBi77M",
        "outputId": "639d14da-f84a-41a5-9707-d00b2cdb7022"
      },
      "source": [
        "# getting some information about the data\n",
        "breast_cancer_dataset.info()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 11 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   diagnosis              569 non-null    object \n",
            " 1   radius_mean            569 non-null    float64\n",
            " 2   texture_mean           569 non-null    float64\n",
            " 3   perimeter_mean         569 non-null    float64\n",
            " 4   area_mean              569 non-null    float64\n",
            " 5   smoothness_mean        569 non-null    float64\n",
            " 6   compactness_mean       569 non-null    float64\n",
            " 7   concavity_mean         569 non-null    float64\n",
            " 8   concavepoints_mean     569 non-null    float64\n",
            " 9   symmetry_mean          569 non-null    float64\n",
            " 10  fractaldimension_mean  569 non-null    float64\n",
            "dtypes: float64(10), object(1)\n",
            "memory usage: 49.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RuoVIUTYjLpk",
        "outputId": "dab7d896-1549-4647-ecd3-e6ded4227b5c"
      },
      "source": [
        "# checking for missing values\n",
        "breast_cancer_dataset.isnull().sum()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diagnosis                0\n",
              "radius_mean              0\n",
              "texture_mean             0\n",
              "perimeter_mean           0\n",
              "area_mean                0\n",
              "smoothness_mean          0\n",
              "compactness_mean         0\n",
              "concavity_mean           0\n",
              "concavepoints_mean       0\n",
              "symmetry_mean            0\n",
              "fractaldimension_mean    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "oLMuXI33jlkq",
        "outputId": "bbf62c10-bd60-44aa-a9d9-7083f80e79a4"
      },
      "source": [
        "# statistical measures about the data\n",
        "breast_cancer_dataset.describe()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       smoothness_mean  compactness_mean  concavity_mean  concavepoints_mean  \\\n",
              "count       569.000000        569.000000      569.000000          569.000000   \n",
              "mean          0.096360          0.104341        0.088799            0.048919   \n",
              "std           0.014064          0.052813        0.079720            0.038803   \n",
              "min           0.052630          0.019380        0.000000            0.000000   \n",
              "25%           0.086370          0.064920        0.029560            0.020310   \n",
              "50%           0.095870          0.092630        0.061540            0.033500   \n",
              "75%           0.105300          0.130400        0.130700            0.074000   \n",
              "max           0.163400          0.345400        0.426800            0.201200   \n",
              "\n",
              "       symmetry_mean  fractaldimension_mean  \n",
              "count     569.000000             569.000000  \n",
              "mean        0.181162               0.062798  \n",
              "std         0.027414               0.007060  \n",
              "min         0.106000               0.049960  \n",
              "25%         0.161900               0.057700  \n",
              "50%         0.179200               0.061540  \n",
              "75%         0.195700               0.066120  \n",
              "max         0.304000               0.097440  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66be743d-6156-4b4f-b155-c9866036b39d\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concavepoints_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractaldimension_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-66be743d-6156-4b4f-b155-c9866036b39d 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-66be743d-6156-4b4f-b155-c9866036b39d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tC8Yii4Yjzer",
        "outputId": "81ce4da4-fba9-4ee6-b5c0-9fad9f697b59"
      },
      "source": [
        "# checking the distribution of Target Varibale\n",
        "breast_cancer_dataset['diagnosis'].value_counts()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbbkzWeFkjqc"
      },
      "source": [
        "1 --> Benign\n",
        "\n",
        "0 --> Malignant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "YGWHjrVSkN5c",
        "outputId": "36747143-4beb-468d-eab3-ffcfb8d18ee5"
      },
      "source": [
        "breast_cancer_dataset.groupby('diagnosis').mean()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           radius_mean  texture_mean  perimeter_mean   area_mean  \\\n",
              "diagnosis                                                          \n",
              "B            12.146524     17.914762       78.075406  462.790196   \n",
              "M            17.462830     21.604906      115.365377  978.376415   \n",
              "\n",
              "           smoothness_mean  compactness_mean  concavity_mean  \\\n",
              "diagnosis                                                      \n",
              "B                 0.092478          0.080085        0.046058   \n",
              "M                 0.102898          0.145188        0.160775   \n",
              "\n",
              "           concavepoints_mean  symmetry_mean  fractaldimension_mean  \n",
              "diagnosis                                                            \n",
              "B                    0.025717       0.174186               0.062867  \n",
              "M                    0.087990       0.192909               0.062680  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concavepoints_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractaldimension_mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>12.146524</td>\n",
              "      <td>17.914762</td>\n",
              "      <td>78.075406</td>\n",
              "      <td>462.790196</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.080085</td>\n",
              "      <td>0.046058</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.174186</td>\n",
              "      <td>0.062867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
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          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUPYps4DlVFR"
      },
      "source": [
        "Separating the features and target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5pD8rP5kzKD"
      },
      "source": [
        "X = breast_cancer_dataset.drop(columns='diagnosis', axis=1)\n",
        "Y = breast_cancer_dataset['diagnosis']"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-GWJHpAlpWJ",
        "outputId": "9000d0de-de34-480b-bb7c-dc38a737e016"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
            "0          17.99         10.38          122.80     1001.0          0.11840   \n",
            "1          20.57         17.77          132.90     1326.0          0.08474   \n",
            "2          19.69         21.25          130.00     1203.0          0.10960   \n",
            "3          11.42         20.38           77.58      386.1          0.14250   \n",
            "4          20.29         14.34          135.10     1297.0          0.10030   \n",
            "..           ...           ...             ...        ...              ...   \n",
            "564        21.56         22.39          142.00     1479.0          0.11100   \n",
            "565        20.13         28.25          131.20     1261.0          0.09780   \n",
            "566        16.60         28.08          108.30      858.1          0.08455   \n",
            "567        20.60         29.33          140.10     1265.0          0.11780   \n",
            "568         7.76         24.54           47.92      181.0          0.05263   \n",
            "\n",
            "     compactness_mean  concavity_mean  concavepoints_mean  symmetry_mean  \\\n",
            "0             0.27760         0.30010             0.14710         0.2419   \n",
            "1             0.07864         0.08690             0.07017         0.1812   \n",
            "2             0.15990         0.19740             0.12790         0.2069   \n",
            "3             0.28390         0.24140             0.10520         0.2597   \n",
            "4             0.13280         0.19800             0.10430         0.1809   \n",
            "..                ...             ...                 ...            ...   \n",
            "564           0.11590         0.24390             0.13890         0.1726   \n",
            "565           0.10340         0.14400             0.09791         0.1752   \n",
            "566           0.10230         0.09251             0.05302         0.1590   \n",
            "567           0.27700         0.35140             0.15200         0.2397   \n",
            "568           0.04362         0.00000             0.00000         0.1587   \n",
            "\n",
            "     fractaldimension_mean  \n",
            "0                  0.07871  \n",
            "1                  0.05667  \n",
            "2                  0.05999  \n",
            "3                  0.09744  \n",
            "4                  0.05883  \n",
            "..                     ...  \n",
            "564                0.05623  \n",
            "565                0.05533  \n",
            "566                0.05648  \n",
            "567                0.07016  \n",
            "568                0.05884  \n",
            "\n",
            "[569 rows x 10 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5rs8pColqsn",
        "outputId": "37804762-54b3-4e1a-9eff-5e80901e882a"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      M\n",
            "1      M\n",
            "2      M\n",
            "3      M\n",
            "4      M\n",
            "      ..\n",
            "564    M\n",
            "565    M\n",
            "566    M\n",
            "567    M\n",
            "568    B\n",
            "Name: diagnosis, Length: 569, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5yk9d-Nl4VV"
      },
      "source": [
        "Splitting the data into training data & Testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZtU30bPluG_"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=5)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWaeuX3amqYH",
        "outputId": "7aebc488-f0f3-4ecb-c3af-1b6103a5aadf"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 10) (455, 10) (114, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttUvP8-im6z3"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPPOng9Em8eb"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhBPDKZmm0dk"
      },
      "source": [
        "classifier = svm.SVC(kernel='linear')"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkQlXVftnKOm",
        "outputId": "b2c2bac3-1103-49d5-8500-d76eaba7c97b"
      },
      "source": [
        "# training the Logistic Regression model using Training data\n",
        "classifier.fit(X_train, Y_train)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Wvez7R5nj5P"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Um1zCUo1nmI1"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xy_Ob3Zcnh32"
      },
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = classifier.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsx-3RlfoMzN",
        "outputId": "38293164-b5d6-4357-8e95-d2b7d44d36b3"
      },
      "source": [
        "print('Accuracy on training data = ', training_data_accuracy)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data =  0.9098901098901099\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QYDU6YjoTFl"
      },
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = classifier.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7HbERDa8orMw",
        "outputId": "bd6af754-cf3f-4533-d440-4f06e66ee1ed"
      },
      "source": [
        "print('Accuracy on test data = ', test_data_accuracy)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data =  0.9473684210526315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDV18UAJpB2B"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rf_x3hPpouQh",
        "outputId": "796b0027-d773-4b5b-fbb5-c7151df2d9a8"
      },
      "source": [
        "input_data = (13.54,14.36,87.46,566.3,0.09779,0.08129,0.06664,0.04781,0.1885,0.05766)\n",
        "\n",
        "# change the input data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the numpy array as we are predicting for one datapoint\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = classifier.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0] == 0):\n",
        "  print('The Breast cancer is Malignant')\n",
        "\n",
        "else:\n",
        "  print('The Breast Cancer is Benign')\n",
        "\n"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['B']\n",
            "The Breast Cancer is Benign\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving the training model"
      ],
      "metadata": {
        "id": "nR_j4Tw_5OzM"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZVUPp5hqJn1"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'cancer_model.sav'\n",
        "pickle.dump(classifier, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "CHqxgPcv5iSs"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the saved model\n",
        "loaded_model = pickle.load(open('cancer_model.sav', 'rb'))"
      ],
      "metadata": {
        "id": "FtsupnSQ5tTl"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in X.columns:\n",
        "  print(column)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sLQ4jYH528J",
        "outputId": "0834e4b5-5d5e-4a94-e90e-d37cbdf54a41"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "radius_mean\n",
            "texture_mean\n",
            "perimeter_mean\n",
            "area_mean\n",
            "smoothness_mean\n",
            "compactness_mean\n",
            "concavity_mean\n",
            "concavepoints_mean\n",
            "symmetry_mean\n",
            "fractaldimension_mean\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AI4pf1Gj6Cag"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}