Predicting_Heart_Disease_KNN.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Start"
      ],
      "metadata": {
        "id": "o_SL0UZlw8oi"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "First Import all you will use"
      ],
      "metadata": {
        "id": "_mGuo64AxLQz"
      }
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    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib-venn"
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        "outputId": "bc3549b9-e0d5-482c-9619-1ad91e3c8134"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from matplotlib-venn) (3.7.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from matplotlib-venn) (1.10.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (0.11.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (23.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (4.39.3)\n",
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            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (3.0.9)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->matplotlib-venn) (8.4.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib->matplotlib-venn) (3.15.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.7->matplotlib->matplotlib-venn) (1.16.0)\n"
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "import warnings\n",
        "\n",
        "from sklearn.linear_model import LogisticRegressionCV\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import (classification_report, confusion_matrix, accuracy_score,\n",
        "                             recall_score, precision_score, roc_curve, roc_auc_score)\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from pylab import rcParams"
      ],
      "metadata": {
        "id": "SC_NO3UCyDKZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rcParams\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "plt.rcParams[\"font.family\"] = \"Calibri\"\n",
        "plt.rcParams[\"font.size\"] = 12\n",
        "rcParams['figure.figsize'] = (5, 3)\n",
        "rcParams['legend.fontsize'] = 8\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "O-pITzGqzy2y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['font.family'] = 'sans-serif' # Change the font family here"
      ],
      "metadata": {
        "id": "vTKhY1kC9aor"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's import our data set"
      ],
      "metadata": {
        "id": "dwdZVDu47jvZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
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          "height": 73
        },
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        "outputId": "88fb8801-7b0d-41d9-b465-f92f2f509acb"
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      "execution_count": null,
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
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          "name": "stdout",
          "text": [
            "Saving heart_2020_cleaned.csv to heart_2020_cleaned.csv\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "The data for the dataset came from Personal Key Indicators in Kaggle, which includes a number of risk factors and significant markers for heart disease, including high BMI (obesity), diabetes, smoking, and alcohol usage."
      ],
      "metadata": {
        "id": "v9-QklQz6Jbj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease_df = pd.read_csv('heart_2020_cleaned.csv')\n",
        "heart_disease_df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
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        "id": "l6Yo_JU90BBj",
        "outputId": "f71ee53f-f8b6-41df-e191-1fae0b85ae1c"
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              "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
              "0           No  16.60     Yes              No     No             3.0   \n",
              "1           No  20.34      No              No    Yes             0.0   \n",
              "2           No  26.58     Yes              No     No            20.0   \n",
              "3           No  24.21      No              No     No             0.0   \n",
              "4           No  23.71      No              No     No            28.0   \n",
              "\n",
              "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
              "0          30.0          No  Female        55-59  White      Yes   \n",
              "1           0.0          No  Female  80 or older  White       No   \n",
              "2          30.0          No    Male        65-69  White      Yes   \n",
              "3           0.0          No  Female        75-79  White       No   \n",
              "4           0.0         Yes  Female        40-44  White       No   \n",
              "\n",
              "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
              "0              Yes  Very good        5.0    Yes            No        Yes  \n",
              "1              Yes  Very good        7.0     No            No         No  \n",
              "2              Yes       Fair        8.0    Yes            No         No  \n",
              "3               No       Good        6.0     No            No        Yes  \n",
              "4              Yes  Very good        8.0     No            No         No  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoking</th>\n",
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              "      <th>Race</th>\n",
              "      <th>Diabetic</th>\n",
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              "      <th>GenHealth</th>\n",
              "      <th>SleepTime</th>\n",
              "      <th>Asthma</th>\n",
              "      <th>KidneyDisease</th>\n",
              "      <th>SkinCancer</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>No</td>\n",
              "      <td>16.60</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>30.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>No</td>\n",
              "      <td>26.58</td>\n",
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              "      <td>24.21</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Transform Data\n",
        "\n"
      ],
      "metadata": {
        "id": "ER6_Nc6O8k4u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Choose only the pertinent columns to reduce the data to those that have a substantial impact on the likelihood of heart disease before beginning the training procedure for our model."
      ],
      "metadata": {
        "id": "_sy_lZ7R6vvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease_df_filtered = heart_disease_df[['HeartDisease', 'Sex', 'AgeCategory', 'Race', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke', 'Diabetic', 'PhysicalActivity', 'GenHealth']]\n",
        "heart_disease_df_filtered"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "gdmZXAmU0lZj",
        "outputId": "1b0f6d7d-3e50-47ee-9b11-09e6eeff8af5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       HeartDisease     Sex  AgeCategory      Race    BMI Smoking  \\\n",
              "0                No  Female        55-59     White  16.60     Yes   \n",
              "1                No  Female  80 or older     White  20.34      No   \n",
              "2                No    Male        65-69     White  26.58     Yes   \n",
              "3                No  Female        75-79     White  24.21      No   \n",
              "4                No  Female        40-44     White  23.71      No   \n",
              "...             ...     ...          ...       ...    ...     ...   \n",
              "319790          Yes    Male        60-64  Hispanic  27.41     Yes   \n",
              "319791           No    Male        35-39  Hispanic  29.84     Yes   \n",
              "319792           No  Female        45-49  Hispanic  24.24      No   \n",
              "319793           No  Female        25-29  Hispanic  32.81      No   \n",
              "319794           No  Female  80 or older  Hispanic  46.56      No   \n",
              "\n",
              "       AlcoholDrinking Stroke Diabetic PhysicalActivity  GenHealth  \n",
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              "4                   No     No       No              Yes  Very good  \n",
              "...                ...    ...      ...              ...        ...  \n",
              "319790              No     No      Yes               No       Fair  \n",
              "319791              No     No       No              Yes  Very good  \n",
              "319792              No     No       No              Yes       Good  \n",
              "319793              No     No       No               No       Good  \n",
              "319794              No     No       No              Yes       Good  \n",
              "\n",
              "[319795 rows x 11 columns]"
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-67513e20-72cb-49fc-8d8b-b96f444d340f');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in the filtered DataFrame\n",
        "missing_values = heart_disease_df_filtered.isna().sum()\n",
        "print (missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLfU2SKM1kRL",
        "outputId": "ecdb752c-acd9-4818-a695-f594871f713a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HeartDisease        0\n",
            "Sex                 0\n",
            "AgeCategory         0\n",
            "Race                0\n",
            "BMI                 0\n",
            "Smoking             0\n",
            "AlcoholDrinking     0\n",
            "Stroke              0\n",
            "Diabetic            0\n",
            "PhysicalActivity    0\n",
            "GenHealth           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There were no missing values"
      ],
      "metadata": {
        "id": "DX5fNap-8Njv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease_df_filtered.groupby('HeartDisease').describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "2qZWgH6M1zaK",
        "outputId": "bf468932-1c31-4219-a7ff-dcfb1b1eae19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   BMI                                                        \n",
              "                 count       mean       std    min    25%    50%    75%    max\n",
              "HeartDisease                                                                  \n",
              "No            292422.0  28.224658  6.325660  12.02  23.89  27.26  31.32  94.85\n",
              "Yes            27373.0  29.401592  6.577151  12.21  25.06  28.34  32.69  83.33"
            ],
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              "      <th>No</th>\n",
              "      <td>292422.0</td>\n",
              "      <td>28.224658</td>\n",
              "      <td>6.325660</td>\n",
              "      <td>12.02</td>\n",
              "      <td>23.89</td>\n",
              "      <td>27.26</td>\n",
              "      <td>31.32</td>\n",
              "      <td>94.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>27373.0</td>\n",
              "      <td>29.401592</td>\n",
              "      <td>6.577151</td>\n",
              "      <td>12.21</td>\n",
              "      <td>25.06</td>\n",
              "      <td>28.34</td>\n",
              "      <td>32.69</td>\n",
              "      <td>83.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9c82b65-f9d0-401c-8dec-2b71b033e6f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "    }\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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f9c82b65-f9d0-401c-8dec-2b71b033e6f8 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-f9c82b65-f9d0-401c-8dec-2b71b033e6f8');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verifying a Binary Target Variable\n",
        "The HeartDisease column is the target variable, and it should only contain two distinct values, Yes and No."
      ],
      "metadata": {
        "id": "f1Kx20W08cWs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis\n",
        "We will analyse our filtered variables with various plots"
      ],
      "metadata": {
        "id": "epLI0USoPyjT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sb.countplot(x='HeartDisease', data=heart_disease_df_filtered)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "mG1rG0IS8b1c",
        "outputId": "3bc3b95f-9ec3-4e8b-bab6-a649245a41ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='HeartDisease', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of heart disease records is not spread out evenly, which shows that people with and without the disease have different levels of knowledge. This might inject bias into the trained model's predictions of heart disease. We will apply SMOTE (Synthetic Minority Oversampling Method) to balance the class distribution in order to solve this problem. To achieve the best results, all categorical data must be transformed into binary using dummy variables before applying SMOTE. Starting with the nearest minority class data items, the SMOTE technique creates new combinations. This will be done in the next section"
      ],
      "metadata": {
        "id": "ztgeeJem-zyD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Investigative Study of Feature Variables\n",
        "See BMI summary statistics belwo"
      ],
      "metadata": {
        "id": "LwAz9YOs-1nt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "heart_disease_df_filtered.groupby('HeartDisease').describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "0u-PO0AJ_Ani",
        "outputId": "fb420f24-9be1-4afd-b763-7e13dcaf037e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   BMI                                                        \n",
              "                 count       mean       std    min    25%    50%    75%    max\n",
              "HeartDisease                                                                  \n",
              "No            292422.0  28.224658  6.325660  12.02  23.89  27.26  31.32  94.85\n",
              "Yes            27373.0  29.401592  6.577151  12.21  25.06  28.34  32.69  83.33"
            ],
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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-9362af1f-d7d8-4cde-a5af-d312246b330a 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-9362af1f-d7d8-4cde-a5af-d312246b330a');\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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution according to sex"
      ],
      "metadata": {
        "id": "OU4xk2wJ-Wvp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data distribution of cases with Yes/No heart disease according to sex\n",
        "sb.countplot(x=heart_disease_df_filtered['Sex'], hue='HeartDisease', data=heart_disease_df_filtered)\n",
        "# Set the axis labels\n",
        "ax.set_title('Distribution of Heart Disease Cases by Sex', fontsize=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "H5l0B_Qn_e0T",
        "outputId": "bdcf7204-8c40-40fc-edf9-dc62d3f40b8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of Heart Disease Cases by Sex')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the data distribution of cases with Yes/No heart disease according to age category"
      ],
      "metadata": {
        "id": "XD9ELPZvC8F8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "an error just occured. I failed to define sns, so i will do that and re run the code"
      ],
      "metadata": {
        "id": "OfaTknPg-1UZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "gXK-SPmh-9Fj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a larger figure\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Plot the data distribution of cases with Yes/No heart disease according to age category\n",
        "sns.countplot(x='AgeCategory', hue='HeartDisease', data=heart_disease_df_filtered, ax=ax)\n",
        "\n",
        "# Set the axis labels\n",
        "ax.set_xlabel('Age', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "ax.set_title('Distribution of Heart Disease Cases by Age', fontsize=16)\n",
        "\n",
        "# Set the font size of the x-axis tick labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=8)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "-LAblzy6_2bT",
        "outputId": "754180da-ae28-47af-c5e4-94dafa40833c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Race-based data distribution of cases with heart disease: Yes/No. Our definition above solved the error"
      ],
      "metadata": {
        "id": "MlPo1UWmDcq0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a larger figure\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Plot the data distribution of cases with Yes/No heart disease according to age category\n",
        "sns.countplot(x='Race', hue='HeartDisease', data=heart_disease_df_filtered, ax=ax)\n",
        "\n",
        "# Set the axis labels\n",
        "ax.set_xlabel('Race', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "ax.set_title('Distribution of Heart Disease Cases by Race', fontsize=16)\n",
        "\n",
        "# Set the font size of the x-axis tick labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=6)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "CnfQ9uroDhc8",
        "outputId": "4e9d5a5e-18dd-4bc8-d366-d9669a27f57b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution of instances with heart disease, Yes/No, based on smoking"
      ],
      "metadata": {
        "id": "x9pZcDffEr1d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a larger figure\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Plot the data distribution of cases with Yes/No heart disease according to age category\n",
        "sns.countplot(x='Smoking', hue='HeartDisease', data=heart_disease_df_filtered, ax=ax)\n",
        "\n",
        "# Set the axis labels\n",
        "ax.set_xlabel('Smoking', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "ax.set_title('Distribution of Heart Disease Cases by Smoking', fontsize=16)\n",
        "\n",
        "# Set the font size of the x-axis tick labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=6)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "cyI1aogqEtPj",
        "outputId": "7debd914-49c3-48af-84e0-964debf5a387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution of instances with heart disease, Yes/No, based on been Diabetic"
      ],
      "metadata": {
        "id": "8qRoClGXE5xM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a larger figure\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Plot the data distribution of cases with Yes/No heart disease according to age category\n",
        "sns.countplot(x='Diabetic', hue='HeartDisease', data=heart_disease_df_filtered, ax=ax)\n",
        "\n",
        "# Set the axis labels\n",
        "ax.set_xlabel('Diabetic', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "ax.set_title('Distribution of Heart Disease Cases by Diabetic', fontsize=16)\n",
        "\n",
        "# Set the font size of the x-axis tick labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=6)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "_6_KiLfBFBrk",
        "outputId": "5fa38ac2-089e-49c4-9ba6-19a16743e31d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution of instances with heart disease, Yes/No, based on Physical Activity"
      ],
      "metadata": {
        "id": "JO1uajo5FQYd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a larger figure\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Plot the data distribution of cases with Yes/No heart disease according to age category\n",
        "sns.countplot(x='PhysicalActivity', hue='HeartDisease', data=heart_disease_df_filtered, ax=ax)\n",
        "\n",
        "# Set the axis labels\n",
        "ax.set_xlabel('PhysicalActivity', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "ax.set_title('Distribution of Heart Disease Cases by Physical Activity', fontsize=16)\n",
        "\n",
        "# Set the font size of the x-axis tick labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=6)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "lxwxpXS5FgQM",
        "outputId": "ed4d3e4e-ccf8-434f-85d8-7f633002f9e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution of instances with heart disease, Yes/No, based on General Health"
      ],
      "metadata": {
        "id": "0PpPxM0fGGD2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a larger figure\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Plot the data distribution of cases with Yes/No heart disease according to age category\n",
        "sns.countplot(x='GenHealth', hue='HeartDisease', data=heart_disease_df_filtered, ax=ax)\n",
        "\n",
        "# Set the axis labels\n",
        "ax.set_xlabel('General Health', fontsize=14)\n",
        "ax.set_ylabel('Count', fontsize=14)\n",
        "ax.set_title('Distribution of Heart Disease Cases by General Health', fontsize=16)\n",
        "\n",
        "# Set the font size of the x-axis tick labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), fontsize=6)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "iPrv0DLaGLzm",
        "outputId": "66a7e5da-0f80-46bb-fee5-7a40821dd496"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations:\n",
        "\n",
        "According to the data, heart disease becomes more common as people get older, which is consistent with the notion that growing older is a major risk factor for heart disease.\n",
        "The statistics also indicate that in the sample group, people who identified as white had a greater prevalence of heart disease. This would suggest that in order to increase the analysis's representation of other racial groups, the sample group's racial mix needs to be more diverse.\n",
        "The analysis also says that there isn't enough proof that drinking alcohol is a reliable way to predict heart disease. To reach any conclusions in this area, additional data collection and analysis may be required."
      ],
      "metadata": {
        "id": "iZ5Tx0WgHlYs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Categorical Features to Dummy Indicator Conversion"
      ],
      "metadata": {
        "id": "sKF0jzpbICDi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find categorical columns in the filtered heart disease DataFrame\n",
        "categorical_columns = [col for col in heart_disease_df_filtered.columns if heart_disease_df_filtered[col].dtype == 'O']\n",
        "\n",
        "# Print the number and names of the categorical columns\n",
        "num_categorical_columns = len(categorical_columns)\n",
        "print(f\"There are {num_categorical_columns} categorical features:\")\n",
        "print(\", \".join(categorical_columns))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApcVWUtaIcx8",
        "outputId": "218fdfa3-3b28-4adf-92fc-1489e534cfc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 10 categorical features:\n",
            "HeartDisease, Sex, AgeCategory, Race, Smoking, AlcoholDrinking, Stroke, Diabetic, PhysicalActivity, GenHealth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create binary dummy variables with values of 0 (indicating No) or 1 for all categorical attributes (representing Yes)."
      ],
      "metadata": {
        "id": "WDIavdaWH0qd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical attributes into binary dummy variables with 'No' represented by 0 and 'Yes' by 1.\n",
        "# Identify categorical columns to be dropped after creating dummies.\n",
        "categorical_drop_first = [name for name in categorical_columns if name not in ['AgeCategory', 'Race', 'GenHealth']]\n",
        "# Identify categorical columns that should not be dropped after creating dummies.\n",
        "categorical_no_drop = [name for name in categorical_columns if name in ['AgeCategory', 'Race', 'GenHealth']]\n",
        "\n",
        "# Create dummy variables using Pandas' `get_dummies` function.\n",
        "# Drop the first column for each categorical variable to avoid the dummy variable trap.\n",
        "heart_disease_df_dummy = pd.get_dummies(data=heart_disease_df_filtered, columns=categorical_drop_first, drop_first=True)\n",
        "heart_disease_df_dummy = pd.get_dummies(data=heart_disease_df_dummy, columns=categorical_no_drop)\n",
        "\n",
        "# Preview the transformed data.\n",
        "heart_disease_df_dummy.head(5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "t1jg-Q6QIph1",
        "outputId": "34409e38-a4fa-471e-9f93-6e27311b8235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     BMI  HeartDisease_Yes  Sex_Male  Smoking_Yes  AlcoholDrinking_Yes  \\\n",
              "0  16.60                 0         0            1                    0   \n",
              "1  20.34                 0         0            0                    0   \n",
              "2  26.58                 0         1            1                    0   \n",
              "3  24.21                 0         0            0                    0   \n",
              "4  23.71                 0         0            0                    0   \n",
              "\n",
              "   Stroke_Yes  Diabetic_No, borderline diabetes  Diabetic_Yes  \\\n",
              "0           0                                 0             1   \n",
              "1           1                                 0             0   \n",
              "2           0                                 0             1   \n",
              "3           0                                 0             0   \n",
              "4           0                                 0             0   \n",
              "\n",
              "   Diabetic_Yes (during pregnancy)  PhysicalActivity_Yes  ...  Race_Asian  \\\n",
              "0                                0                     1  ...           0   \n",
              "1                                0                     1  ...           0   \n",
              "2                                0                     1  ...           0   \n",
              "3                                0                     0  ...           0   \n",
              "4                                0                     1  ...           0   \n",
              "\n",
              "   Race_Black  Race_Hispanic  Race_Other  Race_White  GenHealth_Excellent  \\\n",
              "0           0              0           0           1                    0   \n",
              "1           0              0           0           1                    0   \n",
              "2           0              0           0           1                    0   \n",
              "3           0              0           0           1                    0   \n",
              "4           0              0           0           1                    0   \n",
              "\n",
              "   GenHealth_Fair  GenHealth_Good  GenHealth_Poor  GenHealth_Very good  \n",
              "0               0               0               0                    1  \n",
              "1               0               0               0                    1  \n",
              "2               1               0               0                    0  \n",
              "3               0               1               0                    0  \n",
              "4               0               0               0                    1  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>BMI</th>\n",
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              "      <th>0</th>\n",
              "      <td>16.60</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>20.34</td>\n",
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              "      <th>2</th>\n",
              "      <td>26.58</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>24.21</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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          "metadata": {},
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      ]
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    {
      "cell_type": "code",
      "source": [
        "#After the conversion, the target features have expanded\n",
        "#from the original 11 columns to a total of 34 columns.\n",
        "\n",
        "heart_disease_df_dummy.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FjkLNxIJZts",
        "outputId": "a20021cc-6f4c-4212-e30c-e2d3ec0f05b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 319795 entries, 0 to 319794\n",
            "Data columns (total 34 columns):\n",
            " #   Column                               Non-Null Count   Dtype  \n",
            "---  ------                               --------------   -----  \n",
            " 0   BMI                                  319795 non-null  float64\n",
            " 1   HeartDisease_Yes                     319795 non-null  uint8  \n",
            " 2   Sex_Male                             319795 non-null  uint8  \n",
            " 3   Smoking_Yes                          319795 non-null  uint8  \n",
            " 4   AlcoholDrinking_Yes                  319795 non-null  uint8  \n",
            " 5   Stroke_Yes                           319795 non-null  uint8  \n",
            " 6   Diabetic_No, borderline diabetes     319795 non-null  uint8  \n",
            " 7   Diabetic_Yes                         319795 non-null  uint8  \n",
            " 8   Diabetic_Yes (during pregnancy)      319795 non-null  uint8  \n",
            " 9   PhysicalActivity_Yes                 319795 non-null  uint8  \n",
            " 10  AgeCategory_18-24                    319795 non-null  uint8  \n",
            " 11  AgeCategory_25-29                    319795 non-null  uint8  \n",
            " 12  AgeCategory_30-34                    319795 non-null  uint8  \n",
            " 13  AgeCategory_35-39                    319795 non-null  uint8  \n",
            " 14  AgeCategory_40-44                    319795 non-null  uint8  \n",
            " 15  AgeCategory_45-49                    319795 non-null  uint8  \n",
            " 16  AgeCategory_50-54                    319795 non-null  uint8  \n",
            " 17  AgeCategory_55-59                    319795 non-null  uint8  \n",
            " 18  AgeCategory_60-64                    319795 non-null  uint8  \n",
            " 19  AgeCategory_65-69                    319795 non-null  uint8  \n",
            " 20  AgeCategory_70-74                    319795 non-null  uint8  \n",
            " 21  AgeCategory_75-79                    319795 non-null  uint8  \n",
            " 22  AgeCategory_80 or older              319795 non-null  uint8  \n",
            " 23  Race_American Indian/Alaskan Native  319795 non-null  uint8  \n",
            " 24  Race_Asian                           319795 non-null  uint8  \n",
            " 25  Race_Black                           319795 non-null  uint8  \n",
            " 26  Race_Hispanic                        319795 non-null  uint8  \n",
            " 27  Race_Other                           319795 non-null  uint8  \n",
            " 28  Race_White                           319795 non-null  uint8  \n",
            " 29  GenHealth_Excellent                  319795 non-null  uint8  \n",
            " 30  GenHealth_Fair                       319795 non-null  uint8  \n",
            " 31  GenHealth_Good                       319795 non-null  uint8  \n",
            " 32  GenHealth_Poor                       319795 non-null  uint8  \n",
            " 33  GenHealth_Very good                  319795 non-null  uint8  \n",
            "dtypes: float64(1), uint8(33)\n",
            "memory usage: 12.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K-Nearest Neighbor Value"
      ],
      "metadata": {
        "id": "5SCKhUu6JzSm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you create synthetic samples, choose the best k value to use for SMOTE's k-nearest neighbours."
      ],
      "metadata": {
        "id": "rJc4PjaNJ4N2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the features and target variables from the heart disease DataFrame\n",
        "X = heart_disease_df_dummy.drop(['HeartDisease_Yes'], axis=1)\n",
        "y = heart_disease_df_dummy['HeartDisease_Yes']\n",
        "\n",
        "# Define a range of values for the number of neighbors parameter in KNN\n",
        "param_grid = {'n_neighbors': [55, 60]}\n",
        "\n",
        "# Create a KNN classifier object\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "# Use grid search to find the best value of k\n",
        "grid_search = GridSearchCV(knn, param_grid, cv=5)\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "# Print the best parameter and score found by grid search\n",
        "print(f\"Best parameter: {grid_search.best_params_}\")\n",
        "print(f\"Best score: {round(grid_search.best_score_, 2)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ysNH5QyyKBxE",
        "outputId": "21248b64-7da6-4487-c55c-f3d80dfe63a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameter: {'n_neighbors': 55}\n",
            "Best score: 0.92\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using SMOTE to Produce Synthetic Data"
      ],
      "metadata": {
        "id": "Lc5x6xCIO3SQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As previously mentioned, synthetic data will be produced using SMOTE in order to obtain a balanced distribution of data across all classes. The number to be utilised for k neighbors is 55 because it produced the best results for k-nearest neighbours during the hyperparameter tuning procedure."
      ],
      "metadata": {
        "id": "RI-fr3RkO1iX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Define the oversampling method\n",
        "oversample = SMOTE(k_neighbors=55, random_state=0)\n",
        "\n",
        "# Print information about the oversampling method\n",
        "print(f\"Oversampling method: {oversample}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IgveVy7PPl0",
        "outputId": "4aa42a7d-9a54-478a-b6d3-ad9e754dabad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Oversampling method: SMOTE(k_neighbors=55, random_state=0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Balance the dataset using SMOTE\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE()\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
        "\n",
        "# Convert y_resampled to a Pandas Series for easier handling\n",
        "y_resampled = pd.Series(y_resampled)\n",
        "\n",
        "# Display the number of records before and after SMOTE\n",
        "print(f'Number of records before SMOTE:\\n{y.value_counts()}\\n')\n",
        "print(f'Number of records after SMOTE:\\n{y_resampled.value_counts()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-pvqP_ZQLCi",
        "outputId": "cd2b3628-cb98-4437-d5b3-d843968427f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records before SMOTE:\n",
            "0    292422\n",
            "1     27373\n",
            "Name: HeartDisease_Yes, dtype: int64\n",
            "\n",
            "Number of records after SMOTE:\n",
            "0    292422\n",
            "1    292422\n",
            "Name: HeartDisease_Yes, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test And Train"
      ],
      "metadata": {
        "id": "3M83NGJrGJNJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split Data Into Training And Test Sets"
      ],
      "metadata": {
        "id": "xHEJSK-R2_53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Assume that X and y are the original features and target variable\n",
        "smote = SMOTE(random_state=0)\n",
        "X_features_smoted, y_target_smoted = smote.fit_resample(X, y)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_features_smoted, y_target_smoted, test_size=0.3, random_state=0)\n",
        "\n",
        "# Print the number of training and test datasets\n",
        "num_train = X_train.shape[0]\n",
        "num_test = X_test.shape[0]\n",
        "\n",
        "print(\"Data Split:\")\n",
        "print(\"-----------\")\n",
        "print(f\"Number of training datasets: {num_train}\")\n",
        "print(f\"Number of test datasets: {num_test}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZCFlaFJQoql",
        "outputId": "b18c6432-bf41-4d77-d1f5-0916cb50a46a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data Split:\n",
            "-----------\n",
            "Number of training datasets: 409390\n",
            "Number of test datasets: 175454\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's Build A Model"
      ],
      "metadata": {
        "id": "LlqQGNhiShtY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset is split into two sections, with 80% used to train a heart disease prediction algorithm. Logistic regression with built-in cross-validation is used to build the model."
      ],
      "metadata": {
        "id": "ETmA5u1sSsI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a logistic regression model with cross-validation\n",
        "logreg_model = LogisticRegressionCV(solver='lbfgs')\n",
        "\n",
        "# Fit the model on the training data\n",
        "logreg_model.fit(X_train, y_train)\n",
        "\n",
        "# Print the type of the trained model\n",
        "print(f\"The trained model is a {type(logreg_model).__name__}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21ULhkhlS-n-",
        "outputId": "e653dfde-490e-4976-c292-4cdd50e57514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The trained model is a LogisticRegressionCV.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both the training set's and the test set's accuracy scores are fairly similar, at 0.8341 and 0.8325 respectively. Overfitting is therefore not supported by the data."
      ],
      "metadata": {
        "id": "vzQtye9gUUG9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Model"
      ],
      "metadata": {
        "id": "sySSR9N4UPtA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "# Define the sequential model\n",
        "model = Sequential([\n",
        "    Dense(64, activation='relu', input_shape=(33,)), # input_shape is now (33,)\n",
        "    Dense(10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "u2tAq3VuWYoV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n",
        "\n",
        "# Evaluate model on training dataset\n",
        "train_accuracy = accuracy_score(y_train, model.predict(X_train).argmax(axis=-1))\n",
        "print(\"Training Accuracy:\", round(train_accuracy, 4))\n",
        "\n",
        "# Evaluate model on testing dataset\n",
        "test_accuracy = accuracy_score(y_test, model.predict(X_test).argmax(axis=-1))\n",
        "print(\"Testing Accuracy:\", round(test_accuracy, 4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w6kXaA0fWeE1",
        "outputId": "345e0971-6e05-4a50-ba5a-a831a51a9b36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "12794/12794 [==============================] - 34s 3ms/step - loss: 0.4753 - accuracy: 0.7751 - val_loss: 0.4469 - val_accuracy: 0.7853\n",
            "Epoch 2/10\n",
            "12794/12794 [==============================] - 36s 3ms/step - loss: 0.4418 - accuracy: 0.7878 - val_loss: 0.4400 - val_accuracy: 0.7891\n",
            "Epoch 3/10\n",
            "12794/12794 [==============================] - 36s 3ms/step - loss: 0.4363 - accuracy: 0.7902 - val_loss: 0.4489 - val_accuracy: 0.7810\n",
            "Epoch 4/10\n",
            "12794/12794 [==============================] - 35s 3ms/step - loss: 0.4330 - accuracy: 0.7920 - val_loss: 0.4304 - val_accuracy: 0.7936\n",
            "Epoch 5/10\n",
            "12794/12794 [==============================] - 36s 3ms/step - loss: 0.4312 - accuracy: 0.7926 - val_loss: 0.4349 - val_accuracy: 0.7894\n",
            "Epoch 6/10\n",
            "12794/12794 [==============================] - 32s 3ms/step - loss: 0.4292 - accuracy: 0.7939 - val_loss: 0.4366 - val_accuracy: 0.7888\n",
            "Epoch 7/10\n",
            "12794/12794 [==============================] - 33s 3ms/step - loss: 0.4278 - accuracy: 0.7942 - val_loss: 0.4271 - val_accuracy: 0.7947\n",
            "Epoch 8/10\n",
            "12794/12794 [==============================] - 32s 3ms/step - loss: 0.4267 - accuracy: 0.7948 - val_loss: 0.4282 - val_accuracy: 0.7940\n",
            "Epoch 9/10\n",
            "12794/12794 [==============================] - 35s 3ms/step - loss: 0.4256 - accuracy: 0.7947 - val_loss: 0.4252 - val_accuracy: 0.7955\n",
            "Epoch 10/10\n",
            "12794/12794 [==============================] - 33s 3ms/step - loss: 0.4248 - accuracy: 0.7954 - val_loss: 0.4267 - val_accuracy: 0.7946\n",
            "12794/12794 [==============================] - 18s 1ms/step\n",
            "Training Accuracy: 0.7954\n",
            "5483/5483 [==============================] - 10s 2ms/step\n",
            "Testing Accuracy: 0.7946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating the Model"
      ],
      "metadata": {
        "id": "wJjJhyjUYjCL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To view the summary of prediction results and get a clear sense of the performance of the classification model, we will utilise the classification report and confusion matrix."
      ],
      "metadata": {
        "id": "EmFuUapWZDGw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the test loss and accuracy\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "# Predict on test data\n",
        "y_pred_test = model.predict(X_test)\n",
        "\n",
        "if y_pred_test.shape[1] > 1:\n",
        "    y_pred_test_binary = y_pred_test.argmax(axis=1)\n",
        "else:\n",
        "    y_pred_test_binary = (y_pred_test > 0.5).astype(int)\n",
        "\n",
        "print(\"Classification Report of Test Dataset\\n\")\n",
        "print(classification_report(y_test, y_pred_test_binary))\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_test_binary)\n",
        "TN, FP, FN, TP = cm.ravel()\n",
        "\n",
        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], index=['Predict Positive:1', 'Predict Negative:0'])\n",
        "sb.heatmap(cm_matrix, annot=True, fmt='d')\n",
        "plt.title('Confusion Matrix', fontsize=14, fontweight='bold')\n",
        "\n",
        "print(\"Test Loss:\", test_loss)\n",
        "print(\"Test Accuracy:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "Rd1ZjW1lOhve",
        "outputId": "4ec3ffef-d081-4915-ea58-e9ea3ab39553"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5483/5483 [==============================] - 8s 2ms/step\n",
            "Classification Report of Test Dataset\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.73      0.78     87481\n",
            "           1       0.76      0.86      0.81     87973\n",
            "\n",
            "    accuracy                           0.79    175454\n",
            "   macro avg       0.80      0.79      0.79    175454\n",
            "weighted avg       0.80      0.79      0.79    175454\n",
            "\n",
            "Test Loss: 0.42666399478912354\n",
            "Test Accuracy: 0.7945501208305359\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score of the developed model is 0.79, which represents the proportion of accurate predictions to overall predictions."
      ],
      "metadata": {
        "id": "Ux_76a6_afAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_samples_s = TP + TN + FP + FN\n",
        "accuracy_score_s = (TP + TN) / total_samples_s\n",
        "print(f\"Accuracy score: {round(accuracy_score_s, 2)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2PMdhBlaxLu",
        "outputId": "38e7958f-b437-46cb-f6b8-4a5b0fa6982c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 0.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_s = TP / float(TP + FN)\n",
        "recall_score_s = round(recall_s, 2)\n",
        "print(f'Recall score: {recall_score_s}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jih_K131bBbW",
        "outputId": "45f999a9-5d9c-416a-95e3-223d4210b3e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall score: 0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recall score, which measures the percentage of actual positive instances that the model accurately detected out of all positive cases of heart disease, is 0.85."
      ],
      "metadata": {
        "id": "JPiU7tFubFgu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC curve"
      ],
      "metadata": {
        "id": "ZEvSSpX4bdQw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict outputs for test data using the trained model\n",
        "y_pred_test = model.predict(X_test)\n",
        "\n",
        "# Extract probabilities of positive class\n",
        "y_prob_test = y_pred_test[:,1]\n",
        "\n",
        "# Compute ROC curve values for test data\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_prob_test)\n",
        "\n",
        "# Compute AUC (Area Under the Curve) for ROC curve\n",
        "auc = round(roc_auc_score(y_test, y_prob_test), 2)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve (auc=\" + str(auc) + \")\")\n",
        "plt.plot([0,1], [0,1], 'k--' )\n",
        "plt.title('ROC curve for Predicting Heart Disease', fontsize=14, fontweight='bold')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "jB3LMm28bg8d",
        "outputId": "ca157c7b-87e2-4eaf-b759-89edcf48f746"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5483/5483 [==============================] - 7s 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The well-balanced ROC (Receiver Operating Characteristics) curve shown indicates that the model does a decent job of differentiating between true positive and false positive heart disease instances. This finding is further backed up by the Area Under the Curve (AUC) score of 0.88. The ROC AUC score is higher because the ROC curve's placement near the top-left corner suggests a wider area under it. AUC of 1 would be attained by a perfect classifier, it is important to note."
      ],
      "metadata": {
        "id": "gx1H5KpFckF1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predicting test results"
      ],
      "metadata": {
        "id": "EsaD4DvQcvxK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the patient's medical history, which is provided below, we will assess our model by using it to predict the probability that a patient would acquire heart disease."
      ],
      "metadata": {
        "id": "BVqbl3Kvc4Y3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from enum import Enum\n",
        "\n",
        "class Feature(Enum):\n",
        "\n",
        "    BMI = 0\n",
        "    Sex = 1\n",
        "    Smoking = 2\n",
        "    AlcoholDrinking = 3\n",
        "    Stroke = 4\n",
        "    DiabeticBorderline = 5\n",
        "    Diabetic = 6\n",
        "    DiabeticPregnancy= 7\n",
        "    PhysicalActivity = 8\n",
        "    AgeCategory_18_24 = 9\n",
        "    AgeCategory_25_29 = 10\n",
        "    AgeCategory_30_34 = 11\n",
        "    AgeCategory_35_39 = 12\n",
        "    AgeCategory_40_44 = 13\n",
        "    AgeCategory_45_49 = 14\n",
        "    AgeCategory_50_54 = 15\n",
        "    AgeCategory_55_59 = 16\n",
        "    AgeCategory_60_64 = 17\n",
        "    AgeCategory_65_69 = 18\n",
        "    AgeCategory_70_74 = 19\n",
        "    AgeCategory_75_79 = 20\n",
        "    AgeCategory_80_older = 21\n",
        "    Race_American_Indian = 22\n",
        "    Race_Asian = 23\n",
        "    Race_Black = 24\n",
        "    Race_Hispanic = 25\n",
        "    Race_Other = 26\n",
        "    Race_White = 27\n",
        "    GenHealth_Excellent = 28\n",
        "    GenHealth_Fair = 29\n",
        "    GenHealth_Good = 30\n",
        "    GenHealth_Poor = 31\n",
        "    GenHealth_Very_Good = 32\n",
        "\n",
        "\n",
        "def getPrediction(input_data):\n",
        "    df = np.array(input_data).reshape(1,-1)\n",
        "    return round(model.predict_proba(df)[0][1] * 100, 2)\n",
        "\n",
        "\n",
        "def testPrediction(input_data):\n",
        "\n",
        "    print(\"-----------------------------------------------------------\")\n",
        "    print(\"                   PATIENT'S RECORD                        \")\n",
        "    print(\"-----------------------------------------------------------\")\n",
        "    for key, value in input_data.items():\n",
        "        print(f\"{key}: {value}\")\n",
        "    print(\"-----------------------------------------------------------\")\n",
        "\n",
        "    upd_input_data = [0] * (heart_disease_df_dummy.columns.size - 1)\n",
        "\n",
        "    upd_input_data[Feature.BMI.value] = input_data['BMI']\n",
        "    upd_input_data[Feature.Sex.value] = 1 if input_data['Sex'].lower()  == 'male' else 0\n",
        "    upd_input_data[Feature.AlcoholDrinking.value] = 1 if input_data['Alcohol Drinking'].lower() == 'yes' else 0\n",
        "    upd_input_data[Feature.PhysicalActivity.value] = 1 if input_data['Physical Activity'].lower()  == 'yes' else 0\n",
        "    upd_input_data[Feature.Smoking.value] = 1 if input_data['Smoking'].lower()  == 'yes' else 0\n",
        "    upd_input_data[Feature.Stroke.value] = 1 if input_data['Stroke'].lower()  == 'yes' else 0\n",
        "\n",
        "\n",
        "    race = input_data['Race'].lower()\n",
        "    if race in ['american indian', 'alaskan native']:\n",
        "        upd_input_data[Feature.Race_American_Indian.value] = 1\n",
        "    elif race == 'asian':\n",
        "        upd_input_data[Feature.Race_Asian.value] = 1\n",
        "    elif race == 'black':\n",
        "        upd_input_data[Feature.Race_Black.value] = 1\n",
        "    elif race == 'hispanic':\n",
        "        upd_input_data[Feature.Race_Hispanic.value] = 1\n",
        "    elif race == 'white':\n",
        "        upd_input_data[Feature.Race_White.value] = 1\n",
        "    elif race == 'other':\n",
        "        upd_input_data[Feature.Race_Other.value] = 1\n",
        "\n",
        "\n",
        "    age = input_data['Age']\n",
        "    if 18 >= age <= 24:\n",
        "        upd_input_data[Feature.AgeCategory_18_24.value] = 1\n",
        "    elif 25 >= age <= 29:\n",
        "        upd_input_data[Feature.AgeCategory_25_29.value] = 1\n",
        "    elif 30 >= age <= 34:\n",
        "        upd_input_data[Feature.AgeCategory_30_34.value] = 1\n",
        "    elif 35 >= age <= 39:\n",
        "        upd_input_data[Feature.AgeCategory_35_39.value] = 1\n",
        "    elif 40 >= age <= 44:\n",
        "        upd_input_data[Feature.AgeCategory_40_44.value] = 1\n",
        "    elif 45 >= age <= 49:\n",
        "        upd_input_data[Feature.AgeCategory_45_49.value] = 1\n",
        "    elif 50 >= age <= 54:\n",
        "        upd_input_data[Feature.AgeCategory_50_54.value] = 1\n",
        "    elif 55 >= age <= 59:\n",
        "        upd_input_data[Feature.AgeCategory_55_59.value] = 1\n",
        "    elif 60 >= age <= 64:\n",
        "        upd_input_data[Feature.AgeCategory_60_64.value] = 1\n",
        "    elif 65 >= age <= 69:\n",
        "        upd_input_data[Feature.AgeCategory_65_69.value] = 1\n",
        "    elif 70 >= age <= 74:\n",
        "        upd_input_data[Feature.AgeCategory_70_74.value] = 1\n",
        "    elif 75 >= age <= 79:\n",
        "        upd_input_data[Feature.AgeCategory_75_79.value] = 1\n",
        "    elif age >= 80:\n",
        "        upd_input_data[Feature.AgeCategory_80_older.value] = 1\n",
        "\n",
        "\n",
        "    diabetic = input_data['Diabetic'].lower()\n",
        "    if diabetic  == 'yes':\n",
        "        upd_input_data[Feature.Diabetic.value] = 1\n",
        "    elif diabetic.endswith(\"borderline\"):\n",
        "        upd_input_data[Feature.DiabeticBorderline.value] = 1\n",
        "    elif diabetic.endswith(\"during pregnancy\"):\n",
        "        upd_input_data[Feature.DiabeticPregnancy.value] = 1\n",
        "\n",
        "\n",
        "    general_health = input_data['General Health'].lower()\n",
        "    if general_health == 'poor':\n",
        "        upd_input_data[Feature.GenHealth_Poor.value] = 1\n",
        "    elif general_health == 'fair':\n",
        "        upd_input_data[Feature.GenHealth_Fair.value] = 1\n",
        "    elif general_health == 'good':\n",
        "        upd_input_data[Feature.GenHealth_Good.value] = 1\n",
        "    elif general_health == 'very good':\n",
        "        upd_input_data[Feature.GenHealth_Very_Good.value] = 1\n",
        "    elif general_health == 'excellent':\n",
        "        upd_input_data[Feature.GenHealth_Excellent.value] = 1\n",
        "\n",
        "\n",
        "    prediction = getPrediction(upd_input_data)\n",
        "    print(f\"\\nThe probability of {input_data['Name']} developing heart disease is {prediction}%\")"
      ],
      "metadata": {
        "id": "DHCwfZZsdQ4m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getPrediction(input_data):\n",
        "    df = np.array(input_data).reshape(1,-1)\n",
        "    return round(model.predict(df)[0][0] * 100, 2)"
      ],
      "metadata": {
        "id": "yEJ3yCllhZ6H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Data #1"
      ],
      "metadata": {
        "id": "YwNchAOBqM4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = {\n",
        "    'Name' : 'Janet Calvin',\n",
        "    'Sex' : 'Female',\n",
        "    'Age' : 35,\n",
        "    'Race' : 'White',\n",
        "    'BMI' : 21.6,\n",
        "    'Smoking' : 'No',\n",
        "    'Alcohol Drinking' : 'No',\n",
        "    'Stroke' : 'No',\n",
        "    'Diabetic' : 'No',\n",
        "    'Physical Activity' : 'Yes',\n",
        "    'General Health' : 'Good'\n",
        "}\n",
        "\n",
        "testPrediction(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIub8lnTrL5o",
        "outputId": "17741978-e1b6-4944-e2b8-00aa59882417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------------------\n",
            "                   PATIENT'S RECORD                        \n",
            "-----------------------------------------------------------\n",
            "Name: Janet Calvin\n",
            "Sex: Female\n",
            "Age: 35\n",
            "Race: White\n",
            "BMI: 21.6\n",
            "Smoking: No\n",
            "Alcohol Drinking: No\n",
            "Stroke: No\n",
            "Diabetic: No\n",
            "Physical Activity: Yes\n",
            "General Health: Good\n",
            "-----------------------------------------------------------\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "\n",
            "The probability of Janet Calvin developing heart disease is 89.96%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Data #2"
      ],
      "metadata": {
        "id": "zHoEhgSls7bi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = {\n",
        "    'Name' : 'Gabriel Lockes',\n",
        "    'Sex' : 'Male',\n",
        "    'Age' : 60,\n",
        "    'Race' : 'Hispanic',\n",
        "    'BMI' : 40.6,\n",
        "    'Smoking' : 'Yes',\n",
        "    'Alcohol Drinking' : 'Yes',\n",
        "    'Stroke' : 'No',\n",
        "    'Diabetic' : 'No',\n",
        "    'Physical Activity' : 'Yes',\n",
        "    'General Health' : 'Good'\n",
        "}\n",
        "\n",
        "testPrediction(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPog2YyKs_CZ",
        "outputId": "ff85b726-d619-4469-e4d9-e5d8b72143d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------------------\n",
            "                   PATIENT'S RECORD                        \n",
            "-----------------------------------------------------------\n",
            "Name: Gabriel Lockes\n",
            "Sex: Male\n",
            "Age: 60\n",
            "Race: Hispanic\n",
            "BMI: 40.6\n",
            "Smoking: Yes\n",
            "Alcohol Drinking: Yes\n",
            "Stroke: No\n",
            "Diabetic: No\n",
            "Physical Activity: Yes\n",
            "General Health: Good\n",
            "-----------------------------------------------------------\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "\n",
            "The probability of Gabriel Lockes developing heart disease is 94.14%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Data #3"
      ],
      "metadata": {
        "id": "5GIw_KDUvLpJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = {\n",
        "    'Name' : 'Justina Bervely',\n",
        "    'Sex' : 'Female',\n",
        "    'Age' : 80,\n",
        "    'Race' : 'American_Indian',\n",
        "    'BMI' : 94.6,\n",
        "    'Smoking' : 'Yes',\n",
        "    'Alcohol Drinking' : 'Yes',\n",
        "    'Stroke' : 'Yes',\n",
        "    'Diabetic' : 'Yes',\n",
        "    'Physical Activity' : 'No',\n",
        "    'General Health' : 'Poor'\n",
        "}\n",
        "\n",
        "testPrediction(input_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pskzv2AyvTQA",
        "outputId": "02e496c8-5442-46ef-d8e5-93e5d785c5c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------------------\n",
            "                   PATIENT'S RECORD                        \n",
            "-----------------------------------------------------------\n",
            "Name: Justina Bervely\n",
            "Sex: Female\n",
            "Age: 80\n",
            "Race: American_Indian\n",
            "BMI: 94.6\n",
            "Smoking: Yes\n",
            "Alcohol Drinking: Yes\n",
            "Stroke: Yes\n",
            "Diabetic: Yes\n",
            "Physical Activity: No\n",
            "General Health: Poor\n",
            "-----------------------------------------------------------\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "\n",
            "The probability of Justina Bervely developing heart disease is 0.01%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Data #4"
      ],
      "metadata": {
        "id": "EMK6PI1VwGPz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = {\n",
        "    'Name' : 'Alfred Gomez',\n",
        "    'Sex' : 'Male',\n",
        "    'Age' : 70,\n",
        "    'Race' : 'Other',\n",
        "    'BMI' : 94.6,\n",
        "    'Smoking' : 'Yes',\n",
        "    'Alcohol Drinking' : 'Yes',\n",
        "    'Stroke' : 'No',\n",
        "    'Diabetic' : 'Yes',\n",
        "    'Physical Activity' : 'No',\n",
        "    'General Health' : 'Poor'\n",
        "}\n",
        "\n",
        "testPrediction(input_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6n2n2GewKVY",
        "outputId": "a02881ed-ba2f-4725-d1a5-9a30ee2417ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------------------\n",
            "                   PATIENT'S RECORD                        \n",
            "-----------------------------------------------------------\n",
            "Name: Alfred Gomez\n",
            "Sex: Male\n",
            "Age: 70\n",
            "Race: Other\n",
            "BMI: 94.6\n",
            "Smoking: Yes\n",
            "Alcohol Drinking: Yes\n",
            "Stroke: No\n",
            "Diabetic: Yes\n",
            "Physical Activity: No\n",
            "General Health: Poor\n",
            "-----------------------------------------------------------\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "\n",
            "The probability of Alfred Gomez developing heart disease is 92.81%\n"
          ]
        }
      ]
    }
  ]
}