Cardio_Vascular_Disease.ipynb

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        "<a href=\"https://colab.research.google.com/github/Tasnim-Jahan/Cardiovascular-Disease-Prediction-using-ML-/blob/main/Cardio_Vascular_Disease.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as splt\n",
        "import seaborn "
      ]
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        "df = pd.read_csv('/content/cardio_train.csv',sep=';')\n",
        "df"
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              "   id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <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-f2dbc809-8fe0-44aa-bfc4-391598897e0c 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-f2dbc809-8fe0-44aa-bfc4-391598897e0c');\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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "FemMnAzYP2xS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ad546da-8286-4f94-8548-170bf7918962"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70000, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cardio'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwpgO6wu5wkk",
        "outputId": "256547e3-6d91-4fdc-9eb0-89f9e4fe8715"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    35021\n",
              "1    34979\n",
              "Name: cardio, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(df['cardio'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "yDEinLYi5_2V",
        "outputId": "4b314489-7af4-43ab-a53c-434dd239b6b6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f691db7dc50>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Cu0OLK36NuH",
        "outputId": "74a7bd82-5e51-4e84-d1a7-98fc9d726f7f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HUIET4X77PdE",
        "outputId": "a0117fad-dee4-4419-ff9b-82a608b05878"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id             0\n",
              "age            0\n",
              "gender         0\n",
              "height         0\n",
              "weight         0\n",
              "ap_hi          0\n",
              "ap_lo          0\n",
              "cholesterol    0\n",
              "gluc           0\n",
              "smoke          0\n",
              "alco           0\n",
              "active         0\n",
              "cardio         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x='gender',hue='cardio',data=df,palette = 'colorblind', edgecolor=seaborn.color_palette('dark',n_colors=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "69uHDGC97WtO",
        "outputId": "5bb06872-1356-4e52-d2d7-42738cbbaa73"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f691db48dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x='age',hue='cardio',data=df,palette = 'colorblind', edgecolor=seaborn.color_palette('dark',n_colors=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "y3HQHALyKdW_",
        "outputId": "67ad6744-3da7-4269-a330-0ba2bd3b77b4"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f691b625d90>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['yr']=(df['age']/365)\n",
        "df['yr']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbO1mpxTZh69",
        "outputId": "91dab2af-ee78-4e51-b78e-7f71a972e059"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        50.391781\n",
              "1        55.419178\n",
              "2        51.663014\n",
              "3        48.282192\n",
              "4        47.873973\n",
              "           ...    \n",
              "69995    52.712329\n",
              "69996    61.920548\n",
              "69997    52.235616\n",
              "69998    61.454795\n",
              "69999    56.273973\n",
              "Name: yr, Length: 70000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['yr']=(df['age']/365).round(0)\n",
        "df['yr']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8EwjpjoZvwP",
        "outputId": "09ab8ab7-b228-420e-bf39-5ebb9caa5c25"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        50.0\n",
              "1        55.0\n",
              "2        52.0\n",
              "3        48.0\n",
              "4        48.0\n",
              "         ... \n",
              "69995    53.0\n",
              "69996    62.0\n",
              "69997    52.0\n",
              "69998    61.0\n",
              "69999    56.0\n",
              "Name: yr, Length: 70000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seaborn.countplot(x='yr', hue='cardio',data=df,palette='colorblind',edgecolor=seaborn.color_palette('dark',n_colors=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "2l5tByYKa6Hk",
        "outputId": "7f89f389-d4be-4d72-8a6c-3cbc6a4d87da"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6910344050>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "tbHk9gH2cRKK",
        "outputId": "ff7b8282-067b-4e5c-a62b-9179517401c7"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id           age        gender        height        weight  \\\n",
              "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
              "mean   49972.419900  19468.865814      1.349571    164.359229     74.205690   \n",
              "std    28851.302323   2467.251667      0.476838      8.210126     14.395757   \n",
              "min        0.000000  10798.000000      1.000000     55.000000     10.000000   \n",
              "25%    25006.750000  17664.000000      1.000000    159.000000     65.000000   \n",
              "50%    50001.500000  19703.000000      1.000000    165.000000     72.000000   \n",
              "75%    74889.250000  21327.000000      2.000000    170.000000     82.000000   \n",
              "max    99999.000000  23713.000000      2.000000    250.000000    200.000000   \n",
              "\n",
              "              ap_hi         ap_lo   cholesterol          gluc         smoke  \\\n",
              "count  70000.000000  70000.000000  70000.000000  70000.000000  70000.000000   \n",
              "mean     128.817286     96.630414      1.366871      1.226457      0.088129   \n",
              "std      154.011419    188.472530      0.680250      0.572270      0.283484   \n",
              "min     -150.000000    -70.000000      1.000000      1.000000      0.000000   \n",
              "25%      120.000000     80.000000      1.000000      1.000000      0.000000   \n",
              "50%      120.000000     80.000000      1.000000      1.000000      0.000000   \n",
              "75%      140.000000     90.000000      2.000000      1.000000      0.000000   \n",
              "max    16020.000000  11000.000000      3.000000      3.000000      1.000000   \n",
              "\n",
              "               alco        active        cardio            yr  \n",
              "count  70000.000000  70000.000000  70000.000000  70000.000000  \n",
              "mean       0.053771      0.803729      0.499700     53.338686  \n",
              "std        0.225568      0.397179      0.500003      6.765294  \n",
              "min        0.000000      0.000000      0.000000     30.000000  \n",
              "25%        0.000000      1.000000      0.000000     48.000000  \n",
              "50%        0.000000      1.000000      0.000000     54.000000  \n",
              "75%        0.000000      1.000000      1.000000     58.000000  \n",
              "max        1.000000      1.000000      1.000000     65.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cfe919c-266d-4105-8e6c-b06bc4134fee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "      <th>cardio</th>\n",
              "      <th>yr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>49972.419900</td>\n",
              "      <td>19468.865814</td>\n",
              "      <td>1.349571</td>\n",
              "      <td>164.359229</td>\n",
              "      <td>74.205690</td>\n",
              "      <td>128.817286</td>\n",
              "      <td>96.630414</td>\n",
              "      <td>1.366871</td>\n",
              "      <td>1.226457</td>\n",
              "      <td>0.088129</td>\n",
              "      <td>0.053771</td>\n",
              "      <td>0.803729</td>\n",
              "      <td>0.499700</td>\n",
              "      <td>53.338686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>28851.302323</td>\n",
              "      <td>2467.251667</td>\n",
              "      <td>0.476838</td>\n",
              "      <td>8.210126</td>\n",
              "      <td>14.395757</td>\n",
              "      <td>154.011419</td>\n",
              "      <td>188.472530</td>\n",
              "      <td>0.680250</td>\n",
              "      <td>0.572270</td>\n",
              "      <td>0.283484</td>\n",
              "      <td>0.225568</td>\n",
              "      <td>0.397179</td>\n",
              "      <td>0.500003</td>\n",
              "      <td>6.765294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10798.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>-150.000000</td>\n",
              "      <td>-70.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25006.750000</td>\n",
              "      <td>17664.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>50001.500000</td>\n",
              "      <td>19703.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>74889.250000</td>\n",
              "      <td>21327.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>58.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>99999.000000</td>\n",
              "      <td>23713.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>16020.000000</td>\n",
              "      <td>11000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>65.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "B6BCWE6tbwhg",
        "outputId": "bb84cd42-c67b-46b0-9021-26ad92edf86d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   id       age    gender    height    weight     ap_hi  \\\n",
              "id           1.000000  0.003457  0.003502 -0.003038 -0.001830  0.003356   \n",
              "age          0.003457  1.000000 -0.022811 -0.081515  0.053684  0.020764   \n",
              "gender       0.003502 -0.022811  1.000000  0.499033  0.155406  0.006005   \n",
              "height      -0.003038 -0.081515  0.499033  1.000000  0.290968  0.005488   \n",
              "weight      -0.001830  0.053684  0.155406  0.290968  1.000000  0.030702   \n",
              "ap_hi        0.003356  0.020764  0.006005  0.005488  0.030702  1.000000   \n",
              "ap_lo       -0.002529  0.017647  0.015254  0.006150  0.043710  0.016086   \n",
              "cholesterol  0.006106  0.154424 -0.035821 -0.050226  0.141768  0.023778   \n",
              "gluc         0.002467  0.098703 -0.020491 -0.018595  0.106857  0.011841   \n",
              "smoke       -0.003699 -0.047633  0.338135  0.187989  0.067780 -0.000922   \n",
              "alco         0.001210 -0.029723  0.170966  0.094419  0.067113  0.001408   \n",
              "active       0.003755 -0.009927  0.005866 -0.006570 -0.016867 -0.000033   \n",
              "cardio       0.003799  0.238159  0.008109 -0.010821  0.181660  0.054475   \n",
              "yr           0.003050  0.999090 -0.023017 -0.081456  0.053661  0.020793   \n",
              "\n",
              "                ap_lo  cholesterol      gluc     smoke      alco    active  \\\n",
              "id          -0.002529     0.006106  0.002467 -0.003699  0.001210  0.003755   \n",
              "age          0.017647     0.154424  0.098703 -0.047633 -0.029723 -0.009927   \n",
              "gender       0.015254    -0.035821 -0.020491  0.338135  0.170966  0.005866   \n",
              "height       0.006150    -0.050226 -0.018595  0.187989  0.094419 -0.006570   \n",
              "weight       0.043710     0.141768  0.106857  0.067780  0.067113 -0.016867   \n",
              "ap_hi        0.016086     0.023778  0.011841 -0.000922  0.001408 -0.000033   \n",
              "ap_lo        1.000000     0.024019  0.010806  0.005186  0.010601  0.004780   \n",
              "cholesterol  0.024019     1.000000  0.451578  0.010354  0.035760  0.009911   \n",
              "gluc         0.010806     0.451578  1.000000 -0.004756  0.011246 -0.006770   \n",
              "smoke        0.005186     0.010354 -0.004756  1.000000  0.340094  0.025858   \n",
              "alco         0.010601     0.035760  0.011246  0.340094  1.000000  0.025476   \n",
              "active       0.004780     0.009911 -0.006770  0.025858  0.025476  1.000000   \n",
              "cardio       0.065719     0.221147  0.089307 -0.015486 -0.007330 -0.035653   \n",
              "yr           0.017754     0.154386  0.098596 -0.047884 -0.029918 -0.009819   \n",
              "\n",
              "               cardio        yr  \n",
              "id           0.003799  0.003050  \n",
              "age          0.238159  0.999090  \n",
              "gender       0.008109 -0.023017  \n",
              "height      -0.010821 -0.081456  \n",
              "weight       0.181660  0.053661  \n",
              "ap_hi        0.054475  0.020793  \n",
              "ap_lo        0.065719  0.017754  \n",
              "cholesterol  0.221147  0.154386  \n",
              "gluc         0.089307  0.098596  \n",
              "smoke       -0.015486 -0.047884  \n",
              "alco        -0.007330 -0.029918  \n",
              "active      -0.035653 -0.009819  \n",
              "cardio       1.000000  0.237749  \n",
              "yr           0.237749  1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
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              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
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              "      <th>id</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.003502</td>\n",
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              "      <td>0.003799</td>\n",
              "      <td>0.003050</td>\n",
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              "      <th>age</th>\n",
              "      <td>0.003457</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.081515</td>\n",
              "      <td>0.053684</td>\n",
              "      <td>0.020764</td>\n",
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              "      <td>0.238159</td>\n",
              "      <td>0.999090</td>\n",
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              "      <th>gender</th>\n",
              "      <td>0.003502</td>\n",
              "      <td>-0.022811</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499033</td>\n",
              "      <td>0.155406</td>\n",
              "      <td>0.006005</td>\n",
              "      <td>0.015254</td>\n",
              "      <td>-0.035821</td>\n",
              "      <td>-0.020491</td>\n",
              "      <td>0.338135</td>\n",
              "      <td>0.170966</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>0.008109</td>\n",
              "      <td>-0.023017</td>\n",
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              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>-0.003038</td>\n",
              "      <td>-0.081515</td>\n",
              "      <td>0.499033</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.290968</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>-0.050226</td>\n",
              "      <td>-0.018595</td>\n",
              "      <td>0.187989</td>\n",
              "      <td>0.094419</td>\n",
              "      <td>-0.006570</td>\n",
              "      <td>-0.010821</td>\n",
              "      <td>-0.081456</td>\n",
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              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>-0.001830</td>\n",
              "      <td>0.053684</td>\n",
              "      <td>0.155406</td>\n",
              "      <td>0.290968</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030702</td>\n",
              "      <td>0.043710</td>\n",
              "      <td>0.141768</td>\n",
              "      <td>0.106857</td>\n",
              "      <td>0.067780</td>\n",
              "      <td>0.067113</td>\n",
              "      <td>-0.016867</td>\n",
              "      <td>0.181660</td>\n",
              "      <td>0.053661</td>\n",
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              "    <tr>\n",
              "      <th>ap_hi</th>\n",
              "      <td>0.003356</td>\n",
              "      <td>0.020764</td>\n",
              "      <td>0.006005</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>0.030702</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.016086</td>\n",
              "      <td>0.023778</td>\n",
              "      <td>0.011841</td>\n",
              "      <td>-0.000922</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>-0.000033</td>\n",
              "      <td>0.054475</td>\n",
              "      <td>0.020793</td>\n",
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              "    <tr>\n",
              "      <th>ap_lo</th>\n",
              "      <td>-0.002529</td>\n",
              "      <td>0.017647</td>\n",
              "      <td>0.015254</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.043710</td>\n",
              "      <td>0.016086</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.024019</td>\n",
              "      <td>0.010806</td>\n",
              "      <td>0.005186</td>\n",
              "      <td>0.010601</td>\n",
              "      <td>0.004780</td>\n",
              "      <td>0.065719</td>\n",
              "      <td>0.017754</td>\n",
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              "    <tr>\n",
              "      <th>cholesterol</th>\n",
              "      <td>0.006106</td>\n",
              "      <td>0.154424</td>\n",
              "      <td>-0.035821</td>\n",
              "      <td>-0.050226</td>\n",
              "      <td>0.141768</td>\n",
              "      <td>0.023778</td>\n",
              "      <td>0.024019</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.451578</td>\n",
              "      <td>0.010354</td>\n",
              "      <td>0.035760</td>\n",
              "      <td>0.009911</td>\n",
              "      <td>0.221147</td>\n",
              "      <td>0.154386</td>\n",
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              "    <tr>\n",
              "      <th>gluc</th>\n",
              "      <td>0.002467</td>\n",
              "      <td>0.098703</td>\n",
              "      <td>-0.020491</td>\n",
              "      <td>-0.018595</td>\n",
              "      <td>0.106857</td>\n",
              "      <td>0.011841</td>\n",
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              "      <td>0.011246</td>\n",
              "      <td>-0.006770</td>\n",
              "      <td>0.089307</td>\n",
              "      <td>0.098596</td>\n",
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              "      <th>smoke</th>\n",
              "      <td>-0.003699</td>\n",
              "      <td>-0.047633</td>\n",
              "      <td>0.338135</td>\n",
              "      <td>0.187989</td>\n",
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              "      <td>0.005186</td>\n",
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              "      <td>0.340094</td>\n",
              "      <td>0.025858</td>\n",
              "      <td>-0.015486</td>\n",
              "      <td>-0.047884</td>\n",
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              "    <tr>\n",
              "      <th>alco</th>\n",
              "      <td>0.001210</td>\n",
              "      <td>-0.029723</td>\n",
              "      <td>0.170966</td>\n",
              "      <td>0.094419</td>\n",
              "      <td>0.067113</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>0.010601</td>\n",
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              "      <td>0.025476</td>\n",
              "      <td>-0.007330</td>\n",
              "      <td>-0.029918</td>\n",
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              "    <tr>\n",
              "      <th>active</th>\n",
              "      <td>0.003755</td>\n",
              "      <td>-0.009927</td>\n",
              "      <td>0.005866</td>\n",
              "      <td>-0.006570</td>\n",
              "      <td>-0.016867</td>\n",
              "      <td>-0.000033</td>\n",
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              "      <td>-0.035653</td>\n",
              "      <td>-0.009819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cardio</th>\n",
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              "      <td>0.238159</td>\n",
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      "execution_count": 63,
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    {
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      "execution_count": 64,
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            "text/plain": [
              "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>ap_hi</th>\n",
              "      <th>ap_lo</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>gluc</th>\n",
              "      <th>smoke</th>\n",
              "      <th>alco</th>\n",
              "      <th>active</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18393</td>\n",
              "      <td>2</td>\n",
              "      <td>168</td>\n",
              "      <td>62.0</td>\n",
              "      <td>110</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20228</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>85.0</td>\n",
              "      <td>140</td>\n",
              "      <td>90</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18857</td>\n",
              "      <td>1</td>\n",
              "      <td>165</td>\n",
              "      <td>64.0</td>\n",
              "      <td>130</td>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17623</td>\n",
              "      <td>2</td>\n",
              "      <td>169</td>\n",
              "      <td>82.0</td>\n",
              "      <td>150</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17474</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
              "      <td>56.0</td>\n",
              "      <td>100</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>69995</th>\n",
              "      <td>19240</td>\n",
              "      <td>2</td>\n",
              "      <td>168</td>\n",
              "      <td>76.0</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>69996</th>\n",
              "      <td>22601</td>\n",
              "      <td>1</td>\n",
              "      <td>158</td>\n",
              "      <td>126.0</td>\n",
              "      <td>140</td>\n",
              "      <td>90</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69997</th>\n",
              "      <td>19066</td>\n",
              "      <td>2</td>\n",
              "      <td>183</td>\n",
              "      <td>105.0</td>\n",
              "      <td>180</td>\n",
              "      <td>90</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69998</th>\n",
              "      <td>22431</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>72.0</td>\n",
              "      <td>135</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>69999</th>\n",
              "      <td>20540</td>\n",
              "      <td>1</td>\n",
              "      <td>170</td>\n",
              "      <td>72.0</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70000 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6376d77-bed7-4300-b410-a34b44fdceb4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a6376d77-bed7-4300-b410-a34b44fdceb4 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-a6376d77-bed7-4300-b410-a34b44fdceb4');\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": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,11]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-OhOGbpdIA0",
        "outputId": "9c934777-cf74-4389-f060-3daca0ff5881"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        0\n",
              "1        1\n",
              "2        1\n",
              "3        1\n",
              "4        0\n",
              "        ..\n",
              "69995    0\n",
              "69996    1\n",
              "69997    1\n",
              "69998    1\n",
              "69999    0\n",
              "Name: cardio, Length: 70000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "YuxQ6snufKTD"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=.3,random_state=1)\n",
        "xtrain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "Yy6SaaLrfaVb",
        "outputId": "8036398b-80ac-4ac7-a4bd-021d52af198e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
              "23561  16136       2     169    71.0    100     80            1     1      1   \n",
              "34858  14615       1     158    69.0    140     80            2     1      0   \n",
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              "59230  16720       1     153    53.0    100     60            1     1      0   \n",
              "1730   21050       1     159    71.0    140     90            1     1      0   \n",
              "...      ...     ...     ...     ...    ...    ...          ...   ...    ...   \n",
              "49100  21289       2     175    78.0    120     80            1     1      0   \n",
              "20609  19116       1     164    68.0    120     80            1     1      0   \n",
              "21440  18049       2     178    82.0    120     80            1     1      0   \n",
              "50057  21957       1     169    77.0    120     80            1     1      0   \n",
              "5192   20671       1     174    65.0    160     90            2     2      0   \n",
              "\n",
              "       alco  active  \n",
              "23561     0       1  \n",
              "34858     0       1  \n",
              "54953     0       1  \n",
              "59230     0       1  \n",
              "1730      0       1  \n",
              "...     ...     ...  \n",
              "49100     0       1  \n",
              "20609     0       0  \n",
              "21440     0       1  \n",
              "50057     0       0  \n",
              "5192      0       1  \n",
              "\n",
              "[49000 rows x 11 columns]"
            ],
            "text/html": [
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              "<p>49000 rows × 11 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff10b169-f436-4def-9e33-64e2b3da01af')\"\n",
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              "  </svg>\n",
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              "      \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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff10b169-f436-4def-9e33-64e2b3da01af 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-ff10b169-f436-4def-9e33-64e2b3da01af');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "EA3-CmZef4x3"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Rclf=RandomForestClassifier()"
      ],
      "metadata": {
        "id": "v9zXIbEFgSRb"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Rclf.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nnl9fbp4gaP3",
        "outputId": "5c6577a2-799c-4601-b1ae-0c4b3386c146"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Rclf.score(xtest,ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7uwL8n5gnWt",
        "outputId": "3ff4c683-5b1a-4480-c13b-b9e04f133cde"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.716952380952381"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "ZH5Gs9yygvi9"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Clf=DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "16pBTgORg6sp"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Clf.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHKwrnsMg_Qz",
        "outputId": "e1802b0f-e2e5-4b7d-90d2-908b74eb7a63"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Clf.score(xtest,ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cSm_jqDhKJn",
        "outputId": "c9fbb0b8-31d9-4cac-ffe1-000b928a67c2"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6374761904761905"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    }
  ]
}