c_pia_de_project_10_heart_disease_prediction.ipynb

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      "source": [
        "# importação de bibliotecas\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egMd5zeurTMR"
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      "source": [
        "Data Collection and Processing"
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      "source": [
        "#@title\n",
        "# loading the csv data to a Pandas DataFrame\n",
        "heart_data = pd.read_csv('heart.csv')"
      ],
      "execution_count": 23,
      "outputs": []
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      "source": [
        "# print first 5 rows of the dataset\n",
        "heart_data.head()"
      ],
      "execution_count": 24,
      "outputs": [
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
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              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
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        "# print last 5 rows of the dataset\n",
        "heart_data.tail()"
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      "execution_count": 43,
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              "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
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              "      slope  ca  thal  target  \n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nX1tIzbrz0u",
        "outputId": "c04ee5fa-766d-444a-a52e-f79ae33314e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# number of rows and columns in the dataset\n",
        "heart_data.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1025, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_xTcw1Sr6aJ",
        "outputId": "5f72f778-19ef-4ee1-f822-7e9af20f492c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# getting some info about the data\n",
        "heart_data.info()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1025 entries, 0 to 1024\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1025 non-null   int64  \n",
            " 1   sex       1025 non-null   int64  \n",
            " 2   cp        1025 non-null   int64  \n",
            " 3   trestbps  1025 non-null   int64  \n",
            " 4   chol      1025 non-null   int64  \n",
            " 5   fbs       1025 non-null   int64  \n",
            " 6   restecg   1025 non-null   int64  \n",
            " 7   thalach   1025 non-null   int64  \n",
            " 8   exang     1025 non-null   int64  \n",
            " 9   oldpeak   1025 non-null   float64\n",
            " 10  slope     1025 non-null   int64  \n",
            " 11  ca        1025 non-null   int64  \n",
            " 12  thal      1025 non-null   int64  \n",
            " 13  target    1025 non-null   int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 112.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjHtW31rsGlb",
        "outputId": "c9e0b755-6968-4820-eaea-e58f441aa142",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# checking for missing values\n",
        "heart_data.isnull().sum()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHmcP7DJsSEP",
        "outputId": "9eee1235-0ae6-4103-a5d8-8428191c96af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "# statistical measures about the data\n",
        "heart_data.describe()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age          sex           cp     trestbps        chol  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
              "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
              "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
              "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
              "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
              "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
              "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
              "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
              "\n",
              "               fbs      restecg      thalach        exang      oldpeak  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
              "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
              "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
              "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
              "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
              "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
              "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
              "\n",
              "             slope           ca         thal       target  \n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
              "mean      1.385366     0.754146     2.323902     0.513171  \n",
              "std       0.617755     1.030798     0.620660     0.500070  \n",
              "min       0.000000     0.000000     0.000000     0.000000  \n",
              "25%       1.000000     0.000000     2.000000     0.000000  \n",
              "50%       1.000000     0.000000     2.000000     1.000000  \n",
              "75%       2.000000     1.000000     3.000000     1.000000  \n",
              "max       2.000000     4.000000     3.000000     1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4e37a32-c542-4b7e-99f0-c8bee0f047d4\">\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",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.00000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.434146</td>\n",
              "      <td>0.695610</td>\n",
              "      <td>0.942439</td>\n",
              "      <td>131.611707</td>\n",
              "      <td>246.00000</td>\n",
              "      <td>0.149268</td>\n",
              "      <td>0.529756</td>\n",
              "      <td>149.114146</td>\n",
              "      <td>0.336585</td>\n",
              "      <td>1.071512</td>\n",
              "      <td>1.385366</td>\n",
              "      <td>0.754146</td>\n",
              "      <td>2.323902</td>\n",
              "      <td>0.513171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.072290</td>\n",
              "      <td>0.460373</td>\n",
              "      <td>1.029641</td>\n",
              "      <td>17.516718</td>\n",
              "      <td>51.59251</td>\n",
              "      <td>0.356527</td>\n",
              "      <td>0.527878</td>\n",
              "      <td>23.005724</td>\n",
              "      <td>0.472772</td>\n",
              "      <td>1.175053</td>\n",
              "      <td>0.617755</td>\n",
              "      <td>1.030798</td>\n",
              "      <td>0.620660</td>\n",
              "      <td>0.500070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>275.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4e37a32-c542-4b7e-99f0-c8bee0f047d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4e37a32-c542-4b7e-99f0-c8bee0f047d4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4InaOSIUsfWP",
        "outputId": "f3bfc305-45ce-4517-cbce-25d452cedb97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# checking the distribution of Target Variable\n",
        "heart_data['target'].value_counts()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    526\n",
              "0    499\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSOBu4qDtJy5"
      },
      "source": [
        "1 --> Defective Heart\n",
        "\n",
        "0 --> Healthy Heart"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tW8i4igjtPRC"
      },
      "source": [
        "Splitting the Features and Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6yfbswrs7m3"
      },
      "source": [
        "X = heart_data.drop(columns='target', axis=1)\n",
        "Y = heart_data['target']"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJoCp4ZKtpZy",
        "outputId": "1103d6ac-0eaa-4db8-dfc9-b9257bf3a8a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
            "0      52    1   0       125   212    0        1      168      0      1.0   \n",
            "1      53    1   0       140   203    1        0      155      1      3.1   \n",
            "2      70    1   0       145   174    0        1      125      1      2.6   \n",
            "3      61    1   0       148   203    0        1      161      0      0.0   \n",
            "4      62    0   0       138   294    1        1      106      0      1.9   \n",
            "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
            "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
            "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
            "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
            "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
            "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
            "\n",
            "      slope  ca  thal  \n",
            "0         2   2     3  \n",
            "1         0   0     3  \n",
            "2         0   0     3  \n",
            "3         2   1     3  \n",
            "4         1   3     2  \n",
            "...     ...  ..   ...  \n",
            "1020      2   0     2  \n",
            "1021      1   1     3  \n",
            "1022      1   1     2  \n",
            "1023      2   0     2  \n",
            "1024      1   1     3  \n",
            "\n",
            "[1025 rows x 13 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nukuj-YItq1w",
        "outputId": "658374ed-fb99-40c1-e09b-2c5093f4855a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       0\n",
            "1       0\n",
            "2       0\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "1020    1\n",
            "1021    0\n",
            "1022    0\n",
            "1023    1\n",
            "1024    0\n",
            "Name: target, Length: 1025, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EcjSE3Et18n"
      },
      "source": [
        "Splitting the Data into Training data & Test Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-UUfRUxtuga"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7PrjC6zuf6X",
        "outputId": "32e9493c-791b-4b3f-f36b-26eb79d2a611",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1025, 13) (820, 13) (205, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beSkZmpVuvn9"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gi2NOWZjuxzw"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-Md74FYuqNL"
      },
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCdHYxGUu7XD",
        "outputId": "55e79cf3-b83f-4d9e-d0a0-b52ffa4699be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# training the LogisticRegression model with Training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYIw8Gi9vXfU"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmxAekfZvZa9"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g19JaUTMvPKy"
      },
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQBZvBh8v7R_",
        "outputId": "c0d2d1c9-97cd-4de0-c7c2-82afdb3e7adb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('Accuracy on Training data : ', training_data_accuracy)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training data :  0.8524390243902439\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDONDJdlwBIO"
      },
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MBS-OqdwYpf",
        "outputId": "9fe426f4-fa89-42f4-ca28-e46df756376c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('Accuracy on Test data : ', test_data_accuracy)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Test data :  0.8048780487804879\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIruVh3Qwq0e"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ercruC9wb4C",
        "outputId": "6b4fb3ca-3045-4309-8c55-52fec636bbb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)\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 only on instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0] == 0):\n",
        "  print('The Person does not have a Heart Disease')\n",
        "else:\n",
        "  print('The Person has Heart Disease')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "The Person does not have a Heart Disease\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 LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}